POL101-2

RESEARCH ARTICLE

Reconstruction of the socio-semantic

dynamics of political activist Twitter

networks—Method and application to the

2017 French presidential election

Noé Gaumont 1 , Maziyar Panahi

2 , David Chavalarias

1,2*

1 Centre d’Analyse et de Mathématique Sociales (CAMS), Centre National de la Recherche Scientifique

(CNRS) / École des Hautes Études (EHESS), Paris, France, 2 Institut des Systèmes Complexes Paris Île-de- France (ISC-PIF), CNRS, Paris, France

* [email protected]

Abstract

Background

Digital spaces, and in particular social networking sites, are becoming increasingly present

and influential in the functioning of our democracies. In this paper, we propose an integrated

methodology for the data collection, the reconstruction, the analysis and the visualization of

the development of a country’s political landscape from Twitter data.

Method

The proposed method relies solely on the interactions between Twitter accounts and is inde-

pendent of the characteristics of the shared contents such as the language of the tweets.

We validate our methodology on a case study on the 2017 French presidential election (60

million Twitter exchanges between more than 2.4 million users) via two independent meth-

ods: the comparison between our automated political categorization and a human categori-

zation based on the evaluation of a sample of 5000 profiles descriptions; the

correspondence between the reconfigurations detected in the reconstructed political land-

scape and key political events reported in the media. This latter validation demonstrated the

ability of our approach to accurately reflect the reconfigurations at play in the off-line political

scene.

Results

We built on this reconstruction to give insights into the opinion dynamics and the reconfigu-

rations of political communities at play during a presidential election. First, we propose a

quantitative description and analysis of the political engagement of members of political

communities. Second, we analyze the impact of political communities on information diffu-

sion and in particular on their role in the fake news phenomena. We measure a differential

echo chamber effect on the different types of political news (fake news, debunks, standard

PLOS ONE | https://doi.org/10.1371/journal.pone.0201879 September 19, 2018 1 / 38

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OPEN ACCESS

Citation: Gaumont N, Panahi M, Chavalarias D

(2018) Reconstruction of the socio-semantic

dynamics of political activist Twitter networks—

Method and application to the 2017 French

presidential election. PLoS ONE 13(9): e0201879.

https://doi.org/10.1371/journal.pone.0201879

Editor: Nuno Araujo, Universidade de Lisboa,

PORTUGAL

Received: November 3, 2017

Accepted: July 20, 2018

Published: September 19, 2018

Copyright: © 2018 Gaumont et al. This is an open access article distributed under the terms of the

Creative Commons Attribution License, which

permits unrestricted use, distribution, and

reproduction in any medium, provided the original

author and source are credited.

Data Availability Statement: Data was collected

according to Twitter’s usage terms (https://

developer.twitter.com/en/developer-terms/

agreement-and-policy#id34). In the paper,

Supporting Information files, and supporting files

available from the Harvard Dataverse (DOI:10.

7910/DVN/AOGUIA) we provide Tweet Ids and a

script to allow recreation of our data from Twitter

searches. We have also built an API based

infrastructure to facilitate the reuse of our data,

available at https://api.iscpif.fr/docs/#/datapol.

news) caused by the community structure and emphasize the importance of addressing the

meso-structures of political networks in understanding the fake news phenomena.

Conclusions

Giving access to an intermediate level, between sociological surveys in the field and large

statistical studies (such as those conducted by national or international organizations) we

demonstrate that social networks data make it possible to qualify and quantify the activity of

political communities in a multi-polar political environment; as well as their temporal evolu-

tion and reconfiguration, their structure, their alliance strategies and their semantic particu-

larities during a presidential campaign through the analysis of their digital traces. We

conclude this paper with a comment on the political and ethical implications of the use of

social networks data in politics. We stress the importance of developing social macroscopes

that will enable citizens to better understand how they collectively make society and propose

as example the “Politoscope”, a macroscope that delivers some of our results in an interac-

tive way.

Introduction

Digital spaces are becoming increasingly present and influential in the functioning of our

democracies, to the point where events transpiring in these spaces can no longer be ignored

when it comes to analyzing trends in the political environment. In the wake of each of the

major political upheavals that have shaken western democracies in recent months (e.g. Brexit,

the election of Trump), commentators have turned to the social networking sites in the search

for a better understanding of these events.

In the space of a few years, the use of social networking sites has become fully integrated

into the strategies of political parties. Barack Obama was qualified as a “game changer” in

2012, when he became the first presidential candidate to make massive use of social network-

ing sites for his campaign. The 2016 presidential campaign in the USA confirmed this trend,

with an estimated total budget of around 500M € devoted to the use of social networking sites [1].

France followed the same trend. Nowadays, the largest political parties call on the services

of specialized companies, in order to gain a better understanding of their potential voters,

through the analysis of very large volumes of data provided by social networking sites [2].

Among these, Twitter is very well ranked among accessible sources of information. Moreover,

it is the social networking site used by the majority of political figures, in response to current

affairs or to provide visibility for their opinions and public statements. This social networking

site has thus become an unavoidable medium for the political world, when it comes to deliver-

ing messages or establishing legitimacy.

With the objective of evaluating the potential of platforms like Twitter for research on social

dynamics, and particularly in political sciences, we propose a method in the framework of

complex networks analysis allowing the political communities and their evolution in a multi-

polar political environment to be reconstructed and analyzed.

As a case study, we implement and evaluate this method on data we have collected during

the 2017 French presidential elections that feature almost 60 million Twitter exchanges

between more than 2.4 million users (Politoscope project—https://politoscope.org). The

Reconstruction of the socio-semantic dynamics of political activist Twitter networks

PLOS ONE | https://doi.org/10.1371/journal.pone.0201879 September 19, 2018 2 / 38

Funding: This work has been supported by the

CNRS (http://cnrs.fr), as hosting institution and

financer of the PI David Chavalarias and Maziyar

Panahi; The Complex Systems Institute of Paris Ile-

de-France (https://iscpif.fr), that hosted the team

and provided computing infrastructure; the ANR

project Algodiv (ANR-15-CE38-0001, http://www.

agence-nationale-recherche.fr), workpackage

leader for CAMS David Chavalarias, that financed

Noé Gaumont’s Post-Doc; The Region Île-de-

France through the Domaine d’Intérêt Majeur "Problématique Transversales aux Systèmes Complexes" DIM (Field of major interest) from the

Île -de-France Region that co-funded the IT

infrastructure; and The Science en Poche project

funded by the City of Paris (Programme

Emergence(s), PI David Chavalarias, https://www.

paris.fr) that financed the Multivac software

development. The funders had no role in study

design, data collection and analysis, decision to

publish, or preparation of the manuscript.

Competing interests: The authors have declared

that no competing interests exist.

unusual hectic nature of these elections (see Text A in S1 File for a summary) represents a

worse case scenario for this kind of reconstruction and guaranties the robustness of our

approach. We then present some perspectives this type of reconstruction brings to social sci-

ences and discuss its relevance for political sciences.

Related work

A considerable volume of academic literature has been devoted to Twitter, as this platform

benefits from two different advantages. Its content is massively duplicated by the traditional

press or television and it is accessible to the general public, even though it may not be represen-

tative of the global or even France’s population [3]. Furthermore, data collection is relatively

straightforward, thanks to various APIs proposed by Twitter (https://dev.twitter.com/

overview/api).

Numerous methodologies can be used to access Twitter productions, in the context of the

study of political activities. Some specific methods [4–6] are designed to take advantage of the

content of tweets to infer the political orientation of a tweet or user. However, the constraints

associated with tweet formats appear to limit the efficiency of these methods. For example,

according to Cohen and Ruths [7], the hashtags used to discuss policy do not discriminate

against types of political activism for people with low levels of activism.

In an effort to avoid the problems associated with automatic language processing, research

has been channeled towards methods that analyze the structure, rather than the content, of

exchanges made on Twitter. They have thus been inspired by a pioneering study of political

blogs made in the USA [8], which has shown that there are more links between two blogs if

they express the same political views.

In the framework of Twitter, there are several possible definitions for the relationship

between two accounts. An account can follow or mention another account. It can answer, cite,

comment or simply relay (retweet) a tweet from another account. All of these notions of a

‘link’ are significant, and depend on the objectives of the study [9]. However, in order to rank

accounts according to their political orientation, it appears that the use of retweets is the most

commonly used definition for a link between two accounts. In particular, Garimella et al. [10] and Connover et al. [6] have observed that in order to study political debates on Twitter, the use of retweets is more relevant than Twitter mentions.

Connover et al. [6] were among the first to take advantage of retweets in a study of the structure of the political environment in the USA. They retrieved its bipartisan nature, and

showed that there are significantly less retweets between two accounts when they belong to dif-

ferent parties. Subsequently, numerous other studies have made use of this type of relationship.

Boutet et al. [11] (resp. Larsson and Moe [12]) applied the same method to study the structure of the political space in England (resp. Sweden), and to analyze differences in practice, in

terms of the manner in which political groups use Twitter mentions, hashtags and Internet

links.

Garimella et al. [10] and Morales et al. [13] used the notion of retweets to measure the exis- tence of polarization and controversies on Twitter. Amor et al. [14] used this to characterize the debate on care.data of the English National Health System and Guerrero et al. [15] also used this approach to study the Catalan Process Toward Independence.

These techniques have made it possible to retrieve bipolar political structures, although the

notion of retweets also makes it possible to retrieve multi-polar structures. This was the case

with Cherepnalkoski et al. [16, 17], who used retweets to determine the political affiliation of the Members of the European Parliament (MEP). However, these authors note that it is

Reconstruction of the socio-semantic dynamics of political activist Twitter networks

PLOS ONE | https://doi.org/10.1371/journal.pone.0201879 September 19, 2018 3 / 38

difficult to generalize this method, because in addition to their political connections, MEPs

also have national preferences.

The retweet is not the only usable relationship that can characterize the structure of a politi-

cal environment. The research of Barberá [18] made use of follower/followee relationships

between accounts. The originality of their research is that of assigning a position, between -1

and +1 on a left-right ideological axis, to each account. Thus, an account is not assigned to the

Democrats (resp. Republicans) party, but to a position close to other members of the Demo-

crats (resp. Republicans). This approach makes it possible to compare a set of accounts over an

ideological continuum. These results were successfully reproduced using Twitter data in

France by Briatte and Gallic [19].

Using their ideological estimation technique, Barberá et al. [20] have also shown that there is a strong degree of retweet polarization when a political subject is mentioned, i.e. that there is

a high number of retweets between ideologically close, and a low number between ideologi-

cally distant, accounts.

Finally, it is important to note that despite the publication of numerous studies evaluating

ideological alignments, one of the aims of political science, i.e. that of predicting voting inten- tions, remains problematic [21], in particular as a consequence of the imperfect representative-

ness of Twitter with respect to the real population.

In just a few years, Twitter has thus become a productive research domain for social sci-

ences. Although it has certain limitations in terms of the types of population that can be cor-

rectly studied, it is noteworthy that there have been a huge increase of Twitter use in the last

few years with more than 15M active users in France in 2017 [22] (France population in 2017

was 66.9M) and an estimated number of active users that has tripled worldwide since the first

studies in 2011 [23]. Consequently, it can be expected that some limitations that have been

noted in previous papers might now be mitigated and new results could be obtained.

In the present paper, we reappraise the aforementioned retweet analysis techniques and we

improve them to handle temporal and multipolar analyses, by applying them to a corpus gen-

erated by political activists over a period of nearly one year. We also combine two Twitter

scrapping techniques (following accounts and following keywords) that have usually been

used separately, leading to a Twitter corpora of very high quality.

The extend of the tweets collection and the prolonged period of study combined with an

improved methodology and new analytics have, for one of the first times, made it possible to

study the socio-semantic dynamics of a multi-polar political environment.

Materials and methods

Data

We used the CNRS Multivac platform (http://multivac.iscpif.fr), a platform dedicated to

stream data capture and analysis, to capture in real-time the political twittosphere activity in

France. For a period of 11 months prior to the presidential election, we continuously collected

almost 60 million tweets concerning more than three thousands of the key French political fig-

ures and few dozens political keywords, that were used by more than 2.4 million individual

accounts (see Text B in S1 File). We refer to this set of data as D. All data was collected according to Twitter’s terms of service and privacy conditions and

are made available according to the terms of use of Twitter [24]. All anonymized data derived

from the analysis of this database in the context of this paper are available for download [25].

We estimated the proportion of tweets in D sent by bots to be less than 5% (see Text C in S1 File), thus ensuring that our results remain representative of online political activism.

Reconstruction of the socio-semantic dynamics of political activist Twitter networks

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Retrospectively, given the unusual hectic nature of these elections (see Text A of S1 File for

a summary) that took place in a multi-polar political environment, this dataset was found to

be both of great interest to understand the French political landscape and of high value to chal-

lenge community reconstruction methods.

Identification of political communities

Definition of social groups. Social groups, whatever their definition, have blurry and

shifting boundaries. Our approach is not focused on finding the exact location of social groups

boundaries, but rather the way in which they shift with time, the forking they can reveal, the

subcorpus of tweets that may be established through the estimation of social groups affiliations

and the diffusion processes it allows to study.

The methodology we propose for the identification of political social groups does not

assume that these groups can be determined accurately or exhaustively. The method takes on

board qualification inaccuracies at the level of the individual (for example, a some errors in the

determination of some political affiliations), and the arbitrary nature of certain choices related

to the positioning of boundaries between these groups (for example the choice of parameters

values for reconstruction algorithms).

One definition of social group from a sociological perspective is particularly adapted to

Twitter studies. In 1890, Gabriel Tarde [26] defined a social group as “a collection of beings as they are imitating each other or as, without imitating each other at present, they resemble each other and their common traits are ancient copies of the same model” (“Une collection d’êtres en tant qu’ils sont en train de s’imiter entre eux ou en tant que, sans s’imiter actuellement, ils se ressemblent et que leurs traits communs sont des copies anciennes d’un même modèle”). The notion of imitation invoked by Tarde was relatively general, and covers a high proportion of

broader notions related to social learning or social influence (see [27, 28] for the conceptualiza- tion of these ideas) and in particular, all cases where an attitude, a way of doing or expressing

oneself, or an opinion is transmitted with few modifications between two people.

Twitter offers an ideal terrain for the adaptation of this definition to digital information

space, by analyzing the way people express themselves on this platform. In fact, Twitter com-

bines three key elements, allowing the propagation cultural traits to be monitored:

• Everyone can publish, without filtering, content that is visible to all other users,

• The follower/followee system allows anyone to subscribe to the publications of any other

user,

• The retweet system makes it possible to effortlessly copy the content produced by an other

person (retweet) by forwarding an identical copy of that content to all of his/her followers.

By concentrating on retweet events (relayed, unmodified information), we can establish

markers allowing to define, for a given period, a set of user groups within which the Twitter

contents tend to circulate whilst being affected by modifications that are less significant (exact

copies), than when they circulate between such groups (transitions during which tweets can be

distorted, ironically or humorously commented, contradicted or simply not copied). That

being said, we considered the following definition:

[Definition] Social groups in the political twittosphere: dense networks of accounts that recurrently relay content, without modification (retweet), between each other.

The retweet communities. In order to define user groups based on retweets, we selected

the retweets from our dataset D and defined a graph GT = (V, E) in which each node, u2 V, is a Twitter account and each (u, v, w) 2E is a weighted and undirected link between nodes u

Reconstruction of the socio-semantic dynamics of political activist Twitter networks

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and nodes v, w being proportional to the number of retweets having occurred between accounts u and v during a period of time T.

To detect political groups in GT, we adopt an approach similar to those proposed by [29– 31] by applying a non-overlapping community detection algorithm to GT. From the set of existing and regularly cited state-of-the-art algorithms [32–34], we apply the Louvain algo-

rithm developed by Blondel et al. [35] which, in view of the definition adopted for the notion of a link, computes node communities that correspond to our interpretation for the social

groups we are looking for: these are subgroups of accounts, which exchange much more infor-

mation without modification within the group than with the rest of the network.

Thereafter, we adopt the following definitions:

[Definition] Link w-strong: two accounts A and B are related by a link w-strong over the T period if they have retweeted each other at least w times, in either direction.

[Definition] w-communities over period T: communities detected with the Louvain algorithm on the retweet graphs, on which only those links at least as strong as w-strong have been retained.

The political communities found over a period T according to this definition are noted CTw ¼fC

T i g1�i�N . In the analyses and visualizations, they are labeled with the names of the

main political figures present in these groups, in an effort to provide a view of the development

of political communities with respect to the key personalities in the French presidential cam-

paign (cf. Fig 1 for an example).

In the following, we have taken labels from the list of presidential candidates, and added a

small number of political figures who, although they were not candidates, played an important

role in this campaign (e.g. outgoing President, former President, preferred personalities in the

polls, etc., see S 2.3 Text). The resolution used with the Louvain algorithm was its default

value.

A significant portion of accounts do not belong to any community with this approach.

They correspond to accounts that, although dealing with political issues, do not fit into the

informational sphere of a group of political activists. We will refer to this set of account as “the sea”.

Temporal and social granularity. It is possible to study the political environment

coarsely or finely, by selecting a shorter or longer list of representative accounts, and by adjust-

ing the resolution of the Louvain algorithm in order to identify distinct communities in the

neighbourhood of these accounts. We observed that the different political currents within a

party could be identified in this way. Such an analysis lies however beyond the scope of the

present study and we choose to keep the default Louvain algorithm settings that made it possi-

ble to identify the major political parties and currents accurately.

Socio-semantic networks evolve together: the actors are influenced by the content they are

exposed to, which leads them to develop their local network, and thus to modify the nature of

the content they will be exposed to in the future. As a result, it is common for individuals to

change their sphere of political influence during a presidential campaign, or to be influenced

by new political figures, which leads to a modification of the Twitter patterns.

In order to take this phenomenon into account, we consider sets of graphs G ¼fGTgT2T where T is a set of time intervals, T ¼fTig1�i�K , such that Ti ¼ ½t

i min; t

i max�, with t

i min and t

i max

being the positive integers representing dates in the Unix reference system (to which we refer

using the conventional date). The links in a graph GTi are defined only by retweets that occur within the time interval Ti.

Reconstruction of the socio-semantic dynamics of political activist Twitter networks

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The choice of T is important: if the intervals are too long (several months), it will not be possible to follow the social groups reconfigurations as a result of excessive temporal aggrega-

tion. On the other hand, if the intervals are too short (e.g. 1 day), the corresponding graphs

will be almost empty and have no statistical significance. It is thus important to find a suitable

compromise between the temporal adaptability of the political groups reconstruction method

and its stability.

As a consequence of variations in political opinion, resulting from the overlapping of sev-

eral dynamic processes with highly different time constants (e.g. reconfiguration of political

currents inside a party, reconfiguration of parties themselves, development of the European

political scene, etc.), it may be relevant to analyze the data using several different time scales.

For example, the analysis of the 5–communities over the 5 months period August 1st to

December 31 2016 (cf. Fig 1) depicts a quite accurate picture of the pre-electoral political land-

scape. Its relevance was confirmed by the subsequent alliances that took place in 2017 during

the campaign while respecting the personalities’ proximities within this map: Hamon with

Jadot, Macron with Bayrou, Sarkozy with Fillon, Le Pen with Dupont-Aignan.

For the present study, we chose a time scale matching timing of Twitter and the media in the

context of a political campaign. By analyzing the temporal distribution of retweets within our

corpus, we observed that both current political events and Twitter activity have a weekly period-

icity, and that on average the retweet period of an original tweet is of the order of one week,

with 98.13% of retweets being made within a period of seven days (see Text D in S1 File).

Fig 1. Graph of the 5–communities of the French pre-electoral political twittosphere calculated from August 1st to December

31th 2016. The multi-polar political landscape is clearly visible with a position on a left-right axis for most political parties. It should

be noted that the alliances observed during the political campaign took place between political personalities that were close on this

map: Hamon-Jadot, Macron-Bayrou, Le Pen—Dupont-Aignan. The right-wing (Republicans, blue) is divided into several currents which are reflected by different communities. The Juppé community is at the left of the right-wing while the Fillon and Sarkozy

communities are close from each other and much more at the right. The latter two merged after the right-wing primary while the

Juppé community gradually migrated to the Macron community. The Socialist Party was also made up of a “left-wing” (Hamon community, Montebourg and Pinel community) and a “right-wing” (Holland, Valls). This graph has been spatialized with the Gephi

software [36] using the ForceAtlas2 algorithm [37]. See File A1 at DOI: 10.7910/DVN/AOGUIA for the anonymized gexf version of

this graph.

https://doi.org/10.1371/journal.pone.0201879.g001

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We thus considered seven-days intervals to be a characteristic period for political activity

on Twitter, and used two-week intervals Ti in order to obtain overlapping periods in the tem- poral reconstruction (see below). In the following analyses, we consider the communities com-

puted every day over a 14 days period to assign a community to each tweet of that day. For the

analysis of the dynamics of the communities, we took the subset of the communities computed

every Monday, with G ¼fGTgT2T where T is a set of temporal intervals, such that 8k2 [2, K − 1], |Tk| = 14 days, |Tk−1 \ Tk| = |Tk \ Tk+1| = 7 days, when the intervals are arranged accord- ing to their starting date.

Each tweet in our dataset is thus taken into account in exactly two retweet graphs for the

analysis of community reconfigurations. As shown below, this choice allows transformations

within political communities and their semantic space to be followed in a reasonably continu-

ous manner.

To set the value of w in the community definition, we choose to retain on average the stron- gest 10% retweet relationships over T for the analysis, which led us to conduct our analysis on the 3–communities. The change in the number of links and accounts of these 3–communities

over time is shown in Fig 2.

Detailed analyses. Not all candidates have the same communication strategy and some

use relatively little Twitter. We respect this choice and we have decided to present detailed sta-

tistics only for the communities which leaders have a significant presence on Twitter.

Computing the number of tweets of each candidates on March 31, 2017, we observed a gap

between the six most active candidates that tweeted more than 500 times and the others. Con-

sequently, we retain these six communities for detailed investigations: Asselineau, Fillon,

Hamon, Le Pen, Macron and Mélenchon. Among them, Asselineau was ranked in the “small”

candidates (0.92% of the votes at the first round). The other five represent the main political

trends in France: very left (Melenchon), left (Hamon), center liberal (Macron), right (Fillon),

extreme right (Le Pen).

Fig 2. Variation in the number of nodes and links for the graphs of GTi .

https://doi.org/10.1371/journal.pone.0201879.g002

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All 5 other candidates received less than 1.5% of the votes at the first round except for

Dupont-Aignan who received 4.7%. The total of their weight at the first round was 7.82% of

the votes.

Remarks on the use of retweets. On Twitter, markers of social influence related to forms

of expression or opinions extend far beyond the framework of retweets. Individuals can for

example influence one another in their use of expressions or hashtags, without necessarily hav-

ing to retweet. Furthermore, the act of retweeting can be interpreted in several different ways,

with for example a negative interpretation being common when the retweet is delayed (see

Text J in S1 File). However, in the vast majority of cases, when retweeting is repeated in a polit-

ical context, the retweet contributes to the propagation of ideas and opinions across the net-

work [38–41], and can be considered as a form of agreement with the ideas of a candidate or a

political party [42].

Account following is another well-studied functionality on Twitter, which could also be

used as a proxy to identify some form of social influence. It is however rather uncommon for a

user to stop following an account, such that from this perspective the “follow” action is less

informative than the retweet, which is intrinsically temporal and indicates a user’s affinity with

comments made on another account, at a given point in time. Furthermore, the “follow” func-

tion can be used for the simple purpose of keeping up with statements and comments made on

another account, whilst at the same time maintaining a certain distance from that user.

Finally, retweeting has the advantage of quantifying the social influence present between

two accounts since, for a given period, there may be no, few or many retweets between two

accounts. In addition, these retweet relationships can be mono- or bi-directional, however this

information is not considered in the present study.

Characterization of the themes of the 2017 presidential campaign

To further characterize the political communities, that have been reconstructed solely from

social interactions, we have analyzed their semantic profile. Our objective is not to characterize

them in an exhaustive manner, but to provide a preliminary overview of how communities dif-

fer in their political agenda and framing of the societal issues.

For this purpose, we have carried out an analysis of the political discourse in order to

extract the main debates of this campaign. This was done using two types of sources: policy

measures reported in campaign programs, written in developed language, and candidate

tweets, written in frequently spoken language.

For the analysis of candidates’ programs we first contacted, on 1 March 2017, all the cam-

paign teams that were about to achieve to collect the 500 signatures from French mayors

required to be able to become an official candidate (the Constitutional Council published the

official list of candidates only on 20 March 2017). We invited them to provide us with their

program elements and key expressions that they would consider characteristic of their pro-

gram. Unsurprisingly, we had almost no response, only the team of a “small” candidate indi-

cated to us that given the size of the campaign team, they were not sure they could participate.

We used then the programs of the eleven candidates downloaded from their websites (between

February 14 and March 24, 2017) from which we manually extracted 980 policy measures (see

File A2 at DOI: 10.7910/DVN/AOGUIA for an editable version of these policy measures).

To complement this corpus, we extracted from the Politoscope database 25, 883 tweets pub- lished by the eleven candidates and few other key political figures between August 2016 and

January 2017 (see Text B in S1 File). This second corpus has the advantage of highlighting the

themes that emerged during the political debates, independently of the candidates’ program-

matic orientations.

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There are two categories of mainstream methods for the extraction of topics from unstruc-

tured text: co-word analysis [43] and topic modeling with LDA like methods [44]. In these

approaches, topics are defined as “bags of words”, inferred from the statistics of appearance of

a list of predefined keywords the documents. This list is itself obtained through more or less

advanced text-mining methods within the fields of natural language processing (NLP) and

machine learning.

It has been demonstrated that LDA has some limitations on analyzing short documents

[45] or corpora of small size [46], which are two constraints present in our Twitter corpora

(short text messages) and political measures corpora (less than 1000 documents).

Consequently, we analyzed these two corpora using the CNRS text-mining software Gar- gantext (https://gargantext.org; open source at https://github.com/ISCPIF/gargantext), that implements advanced NLP methods and co-word topic detection; as well as visual analytics

methods for the representation and interaction with the results.

In the first few steps, Gargantext uses a combination of lemmatization, post-tagging and statistical analysis like tf-idf [47] and genericity/specificity analysis [48] to identify from the

text-mining few thousand groups of keywords that are specific to the political discourse. These

keywords were further screened by the authors in order to select the most meaningful ones (i. e. stop words or poorly formed expressions that would have passed the text-mining steps have been removed, important hashtags or neologisms from Twitter like frexit have been added). Last, we carefully read all the political measures with the selected keywords highlighted in the

text in order to check that no important keyword was missing. This led to a vocabulary of

nearly 1600 groups of keywords qualifying the themes of the presidential campaign (see Text I

in S1 File for the list of keywords).

We used the confidence proximity measure to assess the thematic proximity between the selected terms. The confidence measure is the maximum between two conditional probabilities. If P(x|y) is the probability that a document mentions term x knowing that it already mentions term y, the confidence is defined by max(P(x|y), P(y|x)). It has been demonstrated to be one of the best choices to automatically induce general-specific noun relations from web corpora fre-

quency counts [49].

We applied the Louvain algorithm [35] to identify groups of terms delineating topics. Last,

we generated the topic map for each of these two corpora (cf. Fig 3 for the map from the 2017

presidential programs). All these processing steps are part of the Gargantext workflow. We relied on these maps to select eleven topics that we identified as particularly important

and representative of the debates.

Validation analysis

In order to validate our reconstruction method, we have manually verified the political catego-

rization on Monday 6 February (communities computed over the activity period Monday 23

January—Sunday 5 February 2017) for all active followed accounts (2,440) and a sample of

2,500 active random accounts that day. This period corresponds to the end of the primary of

the right, before any changes in the political landscape due to some alliances between candi-

dates (ecologists/Jadot with socialists/Hamon); center/Bayrou with En Marche/Macron, DLF/

Dupont-Aignan with FN/Le Pen).

For these two samples, we have extracted the Twitter meta-data of the accounts and we

have manually coded the accounts political affiliation each times there was a clear support or a

declaration of party membership in the account profile.

We associated all the main political leaders with a reference party using the correspondence

in Table 1.

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In the account descriptions, some account holders were referring to parties that hadn’t any

candidates running for this election. We discarded the mentions that correspond to parties

that had not clearly rallied any running candidate (PCF and Parti Progressiste among the most

mentionned). We considered as equivalent to France Insoumise (FI) all the parties that were in the former Mélenchon’s Front de Gauche coalition (before he decided to run for the presidency and created the France Insoumise). Given the official declarations of the UDI party [51] we also considered the UDI (Union des Démocrates Indépendants) as supporting Les Républicains (LR) as of Feb. 06 2018, although this position it was clearly not unanimous since few weeks later, a

dozen UDI elected officials rallied Macron [52].

This manual analysis resulted in two distinct lists of 885 followed accounts and 414 random

accounts respectively that were publicly supporting one of the 11 running candidates at the

Fig 3. Map of the themes covered in the 2017 presidential programs, drawn-up using Gargantext. The map has been built from policy measures extracted from the candidates’ programs. The nodes of the map are labels for groups of terms deemed equivalent in

political discourse. The link between a label A and a label B indicates that the probability that A and B are jointly mobilized in the same political measure is high. Gargantext applies the Louvain algorithm to identify clusters of labels with strong interaction between them and displays them in the same color. To improve readability, the map was edited in the Gephi software (https://gephi.org) to

set the size of nodes and labels according to a monotonous function of their PageRank [50]. File A3 at DOI: 10.7910/DVN/AOGUIA

provides an editable version of this map (gexf).

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time of the community reconstruction (other accounts in the samples did not give enough

information in their profile to identify political affiliation).

We then compared the automatic categorization made on that period with this manual cod-

ing to assess the precision and recall of our algorithm. We limited this comparison to accounts

that have made at least 10 tweets over the 14 days period of observation.

Although we could expect a selection bias due to the fact that the most politically engaged

people are both more likely to declare a political affiliation in their Twitter profile and are

more easily categorized through automated methods, we consider this as a reasonable limita-

tion to the precision of our estimations of our reconstruction accuracy. It should also be

emphasized that there is no ground truth regarding political affiliations since at any time, a sig-

nificant proportion of the population is undecided or is changing its opinion. There will thus

always be an intrinsinc uncertainty in the categorisation of accounts whatever the

methodology.

To complement this validation, we compared the key reconfigurations highlighted in the

landscape reconstruction with major news events (see the Results section).

Results

Precision and recall of the political landscape reconstruction

The comparison between the two categorizations revealed a very good fit between the auto-

matic categorization and the manual coding of political affiliations: a precision above 93% for

both samples and a recall above 63% for both samples.

Tables 2, 3, 4 and 5 show the recall, the precision and the confidence intervals for each polit-

ical party and sample.

The precision and recall are similar between political parties and samples with two

exceptions.

The FN (extreme right) has a higher recall for the followed accounts. This indicates that FN

supporters express online their political opinions more strongly than any other supporters.

Table 1. Mapping between political leaders and their party on Feb. 6 2017.

Leader Party

Nathalie Arthaud LO

François Asselineau UPR

François Bayrou MODEM

Jacques Cheminade S&P

Nicolas Dupont-Aignan DLF

Francois Fillon LR

Benoit Hamon PS

François Hollande PS

Yves Jadot EELV

Alain Juppe LR

Marine Le Pen FN

Emmanuel Macron EM

Jean-Luc Melenchon FI

Philippe Poutou NPA

Nicolas Sarkozy LR

Manuel Valls PS

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The EM party (Macon’s new party) has a very small precision for the followed sample

(7.14% vs. an average of 93.54). This means that these accounts did not mention their support

to EM in their profile but their on-line behavior could be analyzed as relaying pro-Macron

information. Indeed, the list of followed accounts was based on the political figures elected

before the creation of the En Marche movement. At the time of the sample capture (01/23-06/ 02 2018) Macron’s movement what at it’s beginning and was gaining momentum. It started

recruiting support from other parties supporters but apparently not to the point that these sup-

ports would openly express their opinion in their Twitter profile.

Evolution of political communities

Another external validation of our approach comes from its ability to follow with great tempo-

ral accuracy the changes in the French political landscape that have been reported in the

media.

Opinions and political communities vary together. Political campaigns involve reversals,

bombshells, and reconfigurations of political support. Commentators do their best to antici-

pate and analyze these changes, whereas the political parties strive to mask what is going on

behind the scenes, in order to catch their adversaries off guard. The main result of our study is

that the automatic analysis of retweet patterns is able to measure, sometimes before the media

can make any announcement, the changes taking place inside political communities in

response to these events and, in some cases, in response to anticipated events.

These changes can be of large magnitude in a very short period of time. For example, the

comparison between the 3–communities political landscape of the two weeks before the first

round (Fig 4) and the landscape of the two weeks between the first and second round (Fig 5)

shows a complete reorganization of the communities, some being torn apart in the process.

Table 2. Recall for the followed accounts that were active on the period January 23–February 6 2017 with at least 10 tweets.

Correctly retrieved Manually tagged Recall Confidence Interval

LR 162 265 61.13% [-5.99%, +5.67%]

PS 132 215 61.40% [-6.65%, +6.25%]

FN 47 50 94.00% [-10.22%, +3.94%]

FI 18 36 50.00% [-15.53%, +15.52%]

MODEM 2 3 66.67% [-45.90%, +27.18%]

EM 1 2 50.00% [-40.55%, +40.55%]

Total 362 571 63.40% [-4.03%, +3.85%]

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Table 3. Precision for the followed accounts that were active on the period January 23–February 6 2017 with at least 10 tweets.

Coherently tagged Auto. tagged Precision Confidence Interval

LR 164 165 99.40% [-2.75%, +0.50%]

PS 132 138 95.65% [-4.81%, +2.34%]

FN 47 47 100.00% [-7.56%, +0.00%]

FI 18 18 100.00% [-17.59%, +0.00%]

EM 1.0 14 7.14% 5.87%, +24.32%]

DLF 0 1 0.00% [0.00%, +79.35%]

EELV 0 4 0.00% [0.00%, +48.98%]

Total 362 387 93.54% [-2.90%, +2.04%]

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In an effort to monitor political community reconfigurations over periods of time, when

changes are perceptible, we compared the communities pairwise, over consecutive intervals of

time. This approach allows transformations in the political communities to be followed with-

out interruption.

Between each period, for each community, we computed the number of community-chang-

ing accounts, and generated the global structure synthesizing the development of the political

landscape. This structure can be visualized with an alluvial diagram (cf. Fig 6), a visualization

that summarizes the reconfigurations of the political space over a period of 11 months, with

sufficient resolution to provide an accurate narrative of this presidential election (Fig 7). It can

be seen that whereas most of the major political events can be interpreted on this view, it also

shows more subtle relationships between communities that are not the subject of official decla-

rations as is the case for rallies or victories in primaries. For example (see analysis below), we

can observe the distancing of the Sarkozy community from that of Fillon, shortly before the

Penelopegate affair, followed by the rallying of a considerable number of Sarkozy activists to

Dupont-Aignan. This last rally was relatively ephemeral, since most of these accounts ceased

to follow Dupont-Aignan when he himself rallied to Le Pen between the two rounds of

elections.

The many episodes that can be described by this alluvial visualization are all evidences that

an analysis of Twitter political tweets can successfully reconstruct the political landscape

Table 4. Recall for the random sample of accounts that were active on the period January 23–February 6 2017 with at least 10 tweets.

Correctly retrieved Manually tagged Recall Confidence Interval

LR 69 122 56.56% [-8.86%, +8.46%]

PS 33 60 55.00% [-12.51%, +11.91%]

FN 66 86 76.74% [-9.95%, +7.67%]

FI 37 52 71.15% [-13.43%, +10.52%]

EM 31 51 60.78% [-13.70%, +12.19%]

UPR 7 8 87.50% [-34.59%, +10.26%]

EELV 5 6 83.33% [-39.68%, +13.66%]

S&P 1 1 100.00% [-79.35%, +0.00%]

DLF 3 3 100.00% [-56.15%, +0.00%]

MODEM 0 3 0.00% [0.00%, +56.15%]

PP 0 1 0.00% [0.00%, +79.35%]

Total 252 393 64.12% [-4.86%, +4.58%]

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Table 5. Precision the random sample of accounts that were active on the period January 23–February 6 2017 with at least 10 tweets.

Coherently tagged Auto. tagged Precision Confidence Interval

LR 69 71 97.18% [-6.88%, +2.04%]

PS 33 39 84.62% [-14.34%, +8.14%]

FN 66 66 100.00% [-5.50%, +0.00%]

FI 37 39 94.87% [-11.76%, +3.71%]

EM 31 32 96.88% [-12.62%, +2.57%]

UPR 7 7 100.00% [-35.43%, +0.00%]

EELV 5 5 100.00% [-43.45%, +0.00%]

S&P 1 1 100.00% [-79.35%, +0.00%]

DLF 3 4 75.00% [-44.94%, +20.44%]

Total 252 264 94.55% [-3.23%, +1.93%]

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dynamics: key events of the presidential campaign correspond to key elements in the recon-

struction, and thus in the visualization (like merge or split of communities, abrupt change in

community size, etc.). Among others, the following events can easily be identified as merge

[M], split [S], emergence [E], fade-out [F] or abrupt size change [C] of some of the communi- ties and associated to press coverages (cf. Fig 7 where each of the following event has been

identified):

Fig 4. 3-communities in the French political environment on the day of the first round of elections (T = [9 April 2017–23 April

2017]). The environment is highly fragmentary and multi-polar, and the main political forces are represented (the percentages

shown correspond to the scores obtained in the first round): Mélenchon and France Insoumise (bright red—19.58%), Hamon and the Parti Socialiste (light red—6.36%), Macron and En Marche! (pink—24.01%), Fillon and Les Républicains (blue—20.01%), Le Pen and the Front National (brown—21.30%), Dupont-Aignan and Debout la France (lilac—4.7%). In the case of the “small” candidates, Asselineau (0.92%) and Arthaud (0.64%) are located inside the same community (purple), Poutou (1.09%) is positioned close to

Mélenchon, but does not have a real Twitter community. It can be noted that not only is Bayrou located inside Macron’s

community, which is not surprising in view of their unification on February 23, 2017, but also that Juppé, who was previously a

candidate for the Les Républicains primary election, is considerably closer to the community of Macron than to that of Fillon and Sarkozy. His “lieutenant” Edouard Philippe, was later to become Macron’s Prime Minister. There are in addition two significant

communities that are not labeled with a political leader. Colored orange, to the left of the figure and close to Mélenchon’s

community, one can discern a community identified as being associated with the Discorde Insoumise, a video-game community which played an important role in Mélenchon’s campaign. Coloured yellow, at the bottom-right of the figure, close to Le Pen’s

community, we identify a community of English-speaking populist and nationalist accounts which strongly supported Le Pen. This

community is itself made up from sub-communities, in particular English-speaking supporters of Le Pen, Trump supporters, and

UK pro-Brexit supporters. One can also observe significant differences with respect to the pre-presidential campaign of 2016 (Fig 1):

in addition to the “left-right” axis, which was dominant at that time, a new (“vertical” on the map) axis appears to be significant,

distinguishing between parties with a nationalist, patriotism or protectionist focus and those accepting or adhering to the

globalization of economic trade. Note that the fact that the “small” candidates are located at the center of the map should not be

interpreted as a form of centrality. Only topological adjacencies are significant on this type of graph. As the “small” candidate

communities are not well connected with the other communities, they are located at the center as a consequence of the spatialization

algorithm used to make the graph. File A4 at DOI: 10.7910/DVN/AOGUIA provides an editable version of this graph.

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1. 2016/11/27-2016/12/01 [53]: [M + F] the reconfiguration of the right after the primaries with the alliance after negotiations between Fillon and Sarkozy and the fade-out of Juppé’s

community,

2. 2016/12/01 [54]: [E] the leverage effect of Holland’s renunciation on the Macron’s campaign,

3. 2017/01/17 [55]: [S] Frictions among les Républicains after disputed legislative investitures, including that of Nathalie Kosciusko-Morizet (NKM), to whom Fillon offered an easy-to-

win district,

4. 2017/01/25 [56]: [M + S + E] Start of the Penelope Gate, some fillionnists joint the Sarkozy community, the Alain Juppé community reappears,

5. 2017/01/29 [57]: [C + M + S] Second round of the left-wing primary won by Hamon,

Fig 5. 3-communities in the French political environment between the two rounds of elections (T = [April 27 2017–May 07

2017]). With only two candidates left running for the presidency (Macron and Le Pen), the political patterns of Twitter political

activist changed radically with two poles corresponding to supporters of the two remaining candidates (that came from the

recomposition of former communities) and a third pole constituted by those who refused to take sides. These latter positioned

themselves to be the next opposition force to the future government. It should be noted that the party Les Republicains split during this between-two rounds, the tenors of this party being scattered between Macron and Le Pen informational spheres. File A5 at DOI:

10.7910/DVN/AOGUIA provides an editable version of this graph.

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6. 2017/02/23 [58]: [M] Bayrou rallies to Macron,

7. 2017/02/26 [59]: [M] Jadot rallies to Hamon,

8. 2017/03/05-06 [60]: [S + M + C] François Fillon organizes a rally in Trocadéro (Paris). He announces 200 000 attendees (including leading supports of Sarkozy) and presented this as

a support to the upholding of his candidacy, sarkozists support Juppé come back but this

latter finally publicly renounces all prospect of presidential candidacy. Confirmation of the

legitimacy of Fillon by the political bureau of les Républicains, which was originally con- vened to ask that he be dismissed [61],

9. 2017/03/23 [62]: [M] Dupont Aignan claims that “many sarkozists are joining my movement”,

10. 2017/04/23 [63]: First round of the presidential election lead by Macron and Le Pen.

Mélenchon refuses to give voting instruction to his supports, in contradiction to what he

did in 2002 (he called to vote against the Front national),

11. 2017/04/28 [64]: [M] Dupont-Aignan rallies to Le Pen,

We analyze these key events in more detail in Figs 8, 9, 10, 11 and 12.

The reconstruction method we propose reveals the deep reconfigurations of a political land-

scape during a presidential campaign in multi-polar political environment like the French one.

Fig 6. Representing communities evolution with an alluvial diagram.

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Fig 7. Reconfigurations of political communities between June 2016 and April 2017. Several major newspaper headlines have

been added to shed light on the observed bifurcations. The labels of communities that were still active during the second round have

been added in large characters to improve their readability. Each vertical bar corresponds to a political community on Twitter,

labeled with the accounts of candidates who were active in the community. The bar’s height is proportional to the number of Twitter

accounts belonging to this community. These are computed every Monday.

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Fig 8. Reconfiguration of political communities at the time of the primaries held by the right-wing party. These primaries were

marked by three debates and two votes. One can observe the progressive and regular reinforcement of the Fillon community, week

after week, although no-one was predicting his victory in the second round, since Juppé was tipped by the polls to win. As a result of

the first round, following the surprise victory of Fillon and the equally surprising elimination of Sarkozy (president of the Les Républicains party and ex-President of France), only a minute portion of the Sarkozy community moved to that of Fillon. The Sarkozy community was reluctant to join the Fillon community, and Sarkozy spent several days negotiating positions for his

members within the Fillon structure. The Juppé community, however, immediately distanced itself from the new political

orientations adopted by the party. This right-wing split was apparent in the pre-electoral environment (see Fig 1). Following the

failed return of Juppé in March 2017 (see Fig 11), part of his community moved to join Macron. The bifurcations observed here are

clearly summarized by an article published by BFMTV on 12 December 2016: “Life after the primary: Sarkozy relocates his close allies, Juppé is ‘down’”. Each vertical bar corresponds to a political community on Twitter, labelled with the accounts of candidates who were active in the community. The height of the bar is proportional to the number of Twitter accounts belonging to this community.

These are computed every Monday.

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By describing the behavior of a set of activists, corresponding to a wide spectrum of political

affiliations, it is positioned at an intermediate level between political leaders and their electoral

base.

It provides access to a level of observation that is rarely accessible in the framework of socio-

logical studies or in political sciences. In total, between Aug 1st 2016 and May 8 2017, nearly

190,000 individual accounts have been associated to a political community, with most commu-

nities involving several tens of thousands of activists (see Table C in S1 File). These communi-

ties are made up from a large number of opinion leaders and political outlets. From these

results, we can safely assume that the political tribulations of sub-populations within this set of

individuals are indicators, and in some instance the precursors, of political change and

reconfiguration.

In the following sections, we propose insights and open questions about what can be

learned from such reconstructions beside the accurate and synthetic narrative of a political

campaign. We explore two distinct feature of communities: their social structure and their

semantic profiles.

Fig 9. Reconfiguration of political parties when the incumbent president François Hollande abandons the race for reelection.

This declaration was made shortly after Macron had announced his own candidacy. The alluvial graph clearly shows that Hollande’s

announcement revitalized the Macron community, at the expense of Valls. Whereas the size of the Macron community had

remained stable at the time when he announced his candidacy (427 accounts the previous week, 561 the week of the announcement,

472 the following week), it jumped to 749 accounts during the week when Hollande renounced standing for a second term, and then

to 1479 accounts the following week. From that time onwards, this community continued to grow regularly until the elections. This

change coincides with a slowdown in the Valls-Hollande community, the right-wing section of the Parti Socialiste that was ideologically closest to Macron. However, the Hamon community, which is further to the left, remained relatively stable during this

period. Each vertical bar corresponds to a political community on Twitter, labeled with the accounts of candidates who were active

in the community. The height of the bar is proportional to the number of Twitter accounts belonging to this community. These are

computed every Monday.

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Fig 10. Reconfiguration of political parties at the time of the primaries of the left-wing party. Benoı̂t Hamon (36.03% in the first

round and 58.69% in the second round) and Manuel Valls (31.48% in the first round and 41.31% in the second round) were the two

favorites of the primaries and represented two different currents in the Parti Socialiste, referred to by Valls as the “irreconcilable

lefts” during his campaign. Although François Hollande did not officially state his preference for one particular candidate, Valls

declared that he had his support (see for example in Le Monde on 4 January, 2017: “According to Valls, he has Hollande’s support”). Indeed, in our reconstruction, during the full period of observation, the Hamon community remains quite separate from the Valls

community. For most of the time, the latter community is also the Hollande community, in particular at the time of the primaries,

thus revealing the ideological proximity between these two political figures. Following Hamon’s victory, a controversy broke out

concerning the commitment made by the primary candidates to align themselves with the winner, whereas some were already clearly

indicating their preference for Macron. This indecision is clearly visible on the alluvial graph, with significant movements of activists

from the Valls-Hollande community towards the Macron community immediately following the second round of the primary. From

the 2,075 accounts making up the Valls-Hollande community shortly before its fusion with the Hamon community on February 13

(Valls also joined the Hamon community), only 959 accounts remained. Significant movements of activists continued to take place

between these two communities, until the time of the first round of the presidential election. Although Valls finally gave his official

support to Macron on March 29, he was no longer present in the Hamon community after March 13. He started appearing in the

Macron community on April 17, and remained until the second round, thus indicating his active support. One can also note in this

zoom the rallying of the ecologist Jadot to Hamon on February 23, 2017, which is immediately revealed by the fusion of his

community with that of Hamon. Each vertical bar corresponds to a political community on Twitter, labeled with the accounts of

candidates who were active in the community. The height of the bar is proportional to the number of Twitter accounts belonging to

this community. These are computed every Monday.

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Characterization of communities structure and political engagement

Stability and composition of the communities. Political communities evolve by gaining

or losing members who were not previously included among politicized accounts on Twitter,

or by recruiting new members from other communities. Our reconstruction of political land-

scape describes the co-evolution between the structure of a community and its members’ opin-

ions. Communities govern the circulation of information, and the members of a given

community are exposed to specific content. However, this exposure can contribute to changes

in their opinion and eventually cause them to switch to another community, thus modifying

the overall structure of the communities.

An important challenge in understanding the evolution of a political landscape is to under-

stand what are the structural particularities of each community and how they might impact

their evolution.

Fig 11. Reconfiguration of political communities at the time of Penelopegate. Penelopegate arrived at a time when Fillon’s

following was weakening as a consequence of a controversy surrounding the investiture for parliamentary elections, which was badly

received by Sarkozy supporters. Some of these individuals had already started to move away from the Les Républicains community, as early as January 23, 2017, and had formed a separate community throughout the full duration of this controversy. Various

revelations made by the Canard Enchaîné led to major movements of activists between different communities, with the communities supporting Juppé (a potential alternative, i.e. a “plan B”, for Fillon’s candidacy) and Sarkozy being progressively reinforced. On

March 1st, the day when Fillon was summoned in the context of his investigation, a large proportion of Sarkozy supporters were in

support of Juppé’s return, in a final attempt to push Fillon to resign, which was revealed by an unusual flow of accounts from the

Sarkozy community towards that of Juppé. The public gathering that took place on March 5th at the Trocadéro Square (Paris)

brought an end to the perspectives of Plan B. Although the Fillon community gained a large number of new activists following this

takeover, some of the Sarkozy and Juppé followers kept their distance, and finally joined, respectively, Dupont-Aignan and Macron.

Sarkozy himself withdrew for several weeks, which led to his absence from most of the Fillon communities between January 16th and

April 10th. It was only from April 7th, that he actively gave his renewed support to Fillon’s candidacy, as was for example mentioned

in the Parisien headline on that day “Presidential elections: Sarkozy (finally) supports Fillon”, and could be seen by his constant presence within the Fillon community starting on April 10th 2017. As can be seen, the bifurcations observed in the Politoscope take place at the same time, or in some cases even anticipate comments in the press concerning the activity of political communities. Each

vertical bar corresponds to a political community on Twitter, labelled with the accounts of candidates who were active in the

community. The bar’s height is proportional to the number of Twitter accounts belonging to this community. These are computed

every Monday.

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Fig 12. Reconfiguration of political communities at the time of the televised “Le Grand Débat” on April 4th, 2017. Philippe Poutou created a buzz during this confrontation when he openly condemned the trickery and corruption of candidates Fillon and Le

Pen, by criticizing their anti-system attitudes: “When we are summoned by the police, we have no working-class immunity, we go to the police station.” His remarks were relayed by most of the media, and received more than 1 million online consultations on YouTube. Following this intervention his Twitter community, which until that time had been almost inexistent, and often overlapped with that

of Mélenchon, developed considerably (to more than 13,000 accounts) over a short period of time.

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We analyzed the stability and composition of the communities of the 6 candidates who

used Twitter most extensively in their campaign. A given candidate c may appear in communi- ties at different periods of time over a set of periods T . Since these 6 candidates have always had distinct communities, for sake of clarity, we will name “the community of c” the set of all communities c has belonged to over the set of time periods T (noted CTc ).

In addition, in order to ensure that the indicators calculated on the communities are com-

parable, we calculated them over the set T of periods covering February 1st to April 22th 2017. February 1st is the day all the names of the candidates running for the presidency have been

confirmed. April 22 2017 is the eve of the first round.

The analysis of the cumulative distribution of time spent in a given community (see Text E

in S1 File) shows that for most communities (except Asselineau who was a “small” candidate

with 0.92% of votes), about 15% of accounts were present for just 1 day, and more than 10% of

accounts were involved for more than 40 days.

There is thus a general political community structure on Twitter, comprising a core of

activists who remain for several weeks within the community, a considerably more volatile

peripheral membership, and an intermediate set of accounts covering a broad spectrum of

participation.

This however loosely characterizes the structure of a political community and the political

engagement of its members. To refine the analysis of the communities, we propose several

indicators that contribute to characterize quantitatively the types and degree of political

engagement of their members.

Indicators of political engagement. Let CTc be the community of a candidate c over the set of periods T . We can compute the number of tweets categorized in this community and the number of accounts having been involved at least one day in that community.

The days contribution of every account a to every community CTc is the number of days a has been involved in CTc over the set of periods T . Averaged over a community, we get the average number of days accounts contributing to CTc have been involved in C

T c (designed by < days >).

The cumulative distribution of days spent within a community is given by Fig F in S1 File.

Overall during T , within the Twitter communities of the 6 leaders that used the most inten- sively Twitter for their campaign (resp. all the Twitter communities), there were 18, 018, 417

tweets emitted (resp. 19, 483, 583) by 92, 762 distinct accounts (resp. 105, 418) with an average

number of tweets per account of 184.82 (resp. 194.24).

Let the commitment of an account a toward a candidate c be the percentage of a’s commu-

nity tweets that belong to CTc : coða;c; T Þ¼ NTtwða;cÞ NTtwðaÞ

, where NTtwða; cÞ is the number of tweets

issued by a within CTc and N T twðaÞ is the total number of tweets issued by a within any commu-

nity during the same period. This index indicates the extent to which a contributes to the cir- culation of ideas in the community of c when a is involved in interactions with political groups. The average commitment of a community CTc is then defined as the average of commit-

ments of accounts participating to CTc : < coðC T c Þ >¼

S a2CTc

coða;c;T Þ

jfa2CTc gj .

Several other indicators can be computed at the account level. Since one account can be

involved in several political communities over a set of periods (that could reflect the fact that

the owner of the account is undecided), we generalized these indicators at the community level

by weighting them by the commitments of the accounts (see Tables 6 and 7):

• the activity of an account a over T is the number of political tweets emitted for that set of periods (i.e. number of tweets captured by the Politoscope). The activity of a community CTc is then defined as the average of weighted activity of accounts participating to CTc .

Reconstruction of the socio-semantic dynamics of political activist Twitter networks

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• the politicization of an account a over a set of periods T is the percentage of political tweets emitted by an account (i.e. number of tweets captured by the Politoscope during T over the total number of estimated tweets during the same period). Since we don’t follow most of the

accounts that interact with the Politoscope, we estimate for every account the total number of tweets emitted during T as the difference between the total number of tweets for that account mentioned in the meta-data of the last observed tweet and the same quantity men-

tioned in first observed. This estimate can be biased in two ways. First we might not capture

all political tweets. Second, users can delete tweets. This decreases the number of tweets men-

tioned in the meta-data but not the number of tweets seen in the Politoscope, leading politici- zation to be more than 1 for some users. Consequently, we set the maximum of politicization of an account to 1. As these two biases should apply equally to all communities, we consider

this estimate as a good indicator for comparing the degree of politicization of communities.

The politicization of CTc is then defined as the weighted average of politicization of accounts participating to CTc .

• the political integration of an account a over T is the percentage of tweets emitted within any community among the total number of political tweets captured with a as author. An account that tweets mostly in interaction with a community will have a high political integra- tion index. On the contrary, an account that mostly tweets political content not in relation with any structured group on Twitter (i.e. this account if often categorized as belonging to the “sea”) will have a low political integration index. The average integration of CTc is then defined as the weighted average of political integration of accounts participating to CTc .

These indicators makes it possible to characterize different types of political engagement

that could be specific to some communities. In turn, the identification of some particularities

Table 6. Detailed statistics for communities of the 6 candidates with the most intensive use of Twitter in their campaign (Tk 2 [Feb. 1st 2017–April 22 2017]): Aver- age time spent per account inside the community (< days >), total number of tweets from that community (|Tweets|), number of accounts present at least once in the community (|accounts|) and number of accounts present at least once in the community weighted by the commitment (|weighted accounts|). Outliers are bolded for greater readability.

Name < days > |Tweets| |accounts| |weighted accounts| Jean-Luc Mélenchon 12.11 3,110,517 22,118 17,375.96

Benoit Hamon 11.69 1,828,418 18,950 14,340.52

Emmanuel Macron 13.92 3,643,822 24,803 18,717.05

François Fillon 18.49 5,032,109 18,264 14,428.71

Marine Le Pen 17.18 3,693,650 18,438 14,622.39

François Asslineau 8.95 709,901 6,597 3,713.01

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Table 7. Detailed statistics for communities of the 6 candidates with the most intensive use of Twitter in their campaign (Tk 2 [Feb. 1st 2017–April 22 2017]): Aver- age commitment of accounts in the community (< commitment >); quantities weighted by the commitment: Average number of tweets per account (< activity>), political integration (< integration >) and politicization (< politicization >). Outliers are bolded for greater readability.

Name < commitment > < activity> < integration > < politicization> Jean-Luc Mélenchon 93.88% 140.63 54.19% 2.62%

Benoit Hamon 91.96% 96.49 57.16% 6.16%

Emmanuel Macron 91.93% 146.91 54.51% 2.91%

François Fillon 93.24% 275.52 63.40% 5.42%

Marine Le Pen 95.74% 200.33 60.72% 3.63%

François Asslineau 85.43% 107.61 56.05% 2.80%

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at the level of the communities could point toward specificities in the mode of recruitment of

new members.

The Asselineau community is clearly an outlier in this set of political groups with by far the

smallest community size, lowest commitment level and a politicization and an integration

among the lowest.

Excluding this atypical community and ranking the other five along a left-right axis, we

observe a U shape for the commitment which indicates that people with extreme political

views tend to be the most committed to their ideology. This is to be compared with the obser-

vation of Preotçiuc-Pietro et al. [65] in the US that the people whose political ideas are most extreme have the highest level of political engagement.

Le Pen and Fillon community members are most active on Twitter with an activity of

200.33 (resp. 275.52). They are also the most long-lasting ones inside communities (resp. 17.18

and 18.49 days) and the ones with the highest integration (resp. 63.40 and 60.72).

While the average number of days spent within a community is directly linked to the activ-

ity level, high integration means that more than for other communities, when members of Fil-

lon and Le Pen communities express their political views on Twitter, it is to rally some

particular political group. These two communities are however distinct with respect to the

commitment of their members, Le Pen community members having the highest commitment

score of all communities (95, 74%). Combined together, high commitment and high integra-

tion of Le Pen community members means that they are the most inflexible of all political

activists in their opinions and the most committed to a leader. Given the fact that Le Pen com-

munity is also one of the most active on Twitter, it is the one with the most stable community

core. This probably explains why right-wing leaders are unable to attract far-right voters

despite their stated will to do so; and why, as reported in [42], instead of fighting Le Pen who

was in good position to pass the first round of the election, most leaders preferred to fight

among themselves to win the opportunity to compete against her in the second round of the

election.

The Macron and Melenchon communities are by far the ones with the largest numbers of

accounts (24k and 22.1k respectively) but also the ones with the smallest integration and politi- cization and quite low averages of days spent inside the community. This reflects the highly

dynamics character of these both candidacies which final scores were unanticipated at the

beginning of the campaign and that got momentum when getting closer to the end of the cam-

paign. This also suggests that they were successful in recruiting a low politicization audience.

Hamon community is the one with the highest politicization (6.16%) with moderate levels

of commitment and integration which tends to indicate that it was a community with a strong

political background that was more focused on ideas than on the support to a particular leader.

This is in line with the analysis of [42] that demonstrated from the analysis of the leaders men-

tions in the Politoscope data that the Hamon community was the one that expressed its political ideas in the most detached way from any reference to a candidate or a party. The low average

number of days spent in the community reflects the useful voting phenomenon (vote utile): at some point (around March), many people from the Hamon community joined either the

Melenchon or the Macron communities when the collective belief ended on the idea that

Hamon would not qualify for the second round. And indeed, he ended up far behind all the

other candidates.

The decomposition of the online engagement of political communities members into activ- ity, politicization, political integration and commitment allows to discriminate different types of political groups. As a perspective, we conjecture that the analysis of their evolution through

time would be a good predictor of the destinies of communities.

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Propagation of political information

Social systems are among the most sophisticated complex systems in terms of entanglement of

levels and multi-scale dynamics. We should consequently expect their multi-level structure to

be a key factor in the diffusion phenomena, social networks being a privileged place to observe

their role.

In this section, we look at how communities act on the dissemination and circulation of

political information, defending certain themes or highlighting certain topics with a specific

framing. Particular attention is paid to the phenomenon of fake news which can be studied

here in all its community dimension.

Semantic profiles and the echo chamber effect. Given our reconstruction method, a first

intuitive results is that political communities generate strong echo chamber effects [66]: the analysis of retweet propagation (see Text F in S1 File) shows that the vast majority of tweets are

retweeted within their original community.

To go beyond the echo chambers effect observation, we have selected 11 of the main topics of discussion for this election (see Text G in S1 File) and analyzed the evolution of the semantic

profiles of the tweets circulating within each community with respect to these topics. This

reveals a high heterogeneity among communities both in terms of topics, framing and agenda.

More precisely, we analyzed the specificity of the political discourse, as a function of theme,

at several levels:

• Diversity of the political parties dealing with each theme,

• Diversity of the themes discussed by a given political party,

• Diversity of the expressions used by each political community.

For each theme, the computed value of entropy, based on the number of communities

addressing this theme, shows that all of the latter are covered by an average of approximately

six communities, with a lesser degree of diversity for the themes of homeland security and immigration and multiculturalism, which are strongly promoted by the extreme right (see Table D in S1 File).

For each political community, the computation of the diversity of the addressed themes

clearly reveals communities heterogeneity (see Table 8) and a differentiated positioning of

themes on the political agenda, at different times during the campaign (see the analysis of the

dynamics of themes by community in Text G.2 in S1 File). This heterogeneity can also be

found at the level of the vocabulary used by the members of each political community (see

Table 9). However, as this analysis does not search for all possible themes, the present study

cannot be used to qualify the candidates with respect to the diversity of the themes they

address.

The Politoscope, the companion platform of this paper (https://presidentielle2017. politoscope.org/dashboard), makes it possible to explore, for each of the main candidates and

each of the eleven themes, the changes in the 15 most significant keywords for each of the

respective candidates. One can also observe, in addition to the semantic specificity of the com-

munities with respect to these themes, a variation as a function of time for the different candi-

dates, with the usage of certain keywords appearing, strengthening or disappearing over time

(see Text G.3 in S1 File for details of the method and examples).

Fake news circulation within the communities. Fake news is an ill-defined problem as it

seems to cover propaganda, disinformation and false allegation in the media. Nevertheless

studying the effectiveness of these phenomena is of paramount importance. Fake news are

currently discussed in the United State of America, particularly in the context of the 2016

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presidential election [67, 68] and is now the subject of an adaptation of legislation in France

since the attempts to manipulate public opinion during the French 2017 presidential elections

[69].

The question of how comparatively true and false information spreads on a social network-

ing site like Twitter and which contexts influence this spread is an important issue for the “sci-

ence of fake news” that should help to solve the post-true crisis [70, 71]. To assess the

importance of the reconstruction of political communities in the understanding of this phe-

nomenon, we used the Decodex database, an initiative from Le Monde, one of the major French newspaper (center-left oriented), that has collected, throughout the year 2017, URLs of web

pages and Facebook posts that include fake news [72]. URLs referring to the same fake news

(e.g. “Jean-Luc Mélenchon has a Rolex” or “More than 30% of Emmanuel Macron’s campaign was financed by Saudi Arabia”) have been grouped together in stories and associated to a debunk, a blog post in which Le Monde journalists unveil and explain why this news is a fake. There are 179 stories and 179 debunks in total (see Files A6 and A7 at DOI: 10.7910/DVN/

AOGUIA for a json version).

The Decodex database is a good entry point to identify the stakes of the fake news phenome- non through the lens of our political landscape reconstruction.

Differential echo chamber effet for fake news. After cleaning and normalizing the Deco- dex URLs; and having double checked that the tweets mentioning a link to a fake news or a debunk have really for purpose to broadcast them (see Text H in S1 File), we have studied their

diffusion. We observed only 4 888 shares of fake news links inside our dataset of 60 million

tweets (0.081%). This indicates that either fake news are not heavily shared in France by people

interested in politics on Twitter or that the Decodex failed to identify a significant proportion of fake news references on Twitter. Likewise, the debunks were shared only 1 275 times.

Table 8. Entropy of the themes addressed by each candidate, and its 3–community. The two dominant themes as well as their associated percentages are also shown.

The maximum entropy is 3.46, if the 11 themes are discussed in a uniform manner. With respect to the selected themes, Benoı̂t Hamon is the candidate who uniformly

addressed the greatest number of themes, whereas François Asselineau is the candidate who was the most focused on a subset of themes (including foreign policy and

democracy). The variations in attention paid by the candidates to these themes are plotted in Figs I to N in S1 File.

Name Entropy # themes Dominant themes %

François Asselineau 2.63 6.2 foreign policy, democracy 57.7

François Fillon 3.06 8.3 immigration and multiculturalism, democracy 33.5

Benoı̂t Hamon 3.19 9.1 employment, foreign policy 39.3

Marine Le Pen 2.78 6.9 immigration and multiculturalism, foreign policy 49.0

Emmanuel Macron 3.03 8.2 foreign policy, democracy 44.2

Jean-Luc Mélenchon 3.10 8.6 democracy, foreign policy 37.3

All candidates 3.08 8.4 foreign policy, democracy 36.9

https://doi.org/10.1371/journal.pone.0201879.t008

Table 9. Entropy of the keywords used by each candidate and its 3-community. The 10 dominant keywords as well as their percentage are also shown. The maximum

entropy is 10.64, when the 1596 keywords are discussed uniformly.

Name Entropy N0 keywords Dominant keyword % François Asselineau 5.98 63.2 Frexit, Europe, poll, media, Brexit, nation, elected officials, democracy, treaties, the Euro 48.7

Benoı̂t Hamon 7.77 218.6 republic, youth employment, universal income, democracy, Europe, elected officials, ecology, nation, children 21.1

François Fillon 7.75 215.8 media, poll, republic, upturn, elected officials, unemployed, police, nation, work, security 19.6

Emmanuel Macron 7.71 209.1 Penelope Fillon, republic, elected officials, work, poll, justice, unemployed, Europe, police, indictment 19.8

Marine Le Pen 7.82 226.2 nation, islamists, patriotism, police, media, poll, unemployed, migrants, elected officials, terrorism 20.7

Jean-Luc Mélenchon 7.53 184.8 French labor law, nation, work, republic, nuitdebout, poll, elected officials, media, Penelope Fillon, unemployed 24.3

All 8.23 301.23 nation, republic, poll, elected officials, media, unemployed, work, Frexit, police, Europe 17.5

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There is an average number of 27.3 sharing per fake news story whereas the average number

of share for debunks is 7.12. Consequently, for this fake news/debunks dataset, fake news sto-

ries were shared almost four times more than debunk news.

This difference may be structural or situational in nature. It could also be due to the fact

that we might miss a significant proportion of debunks for these set of stories published by

newspapers other than Le Monde. It is nevertheless comparable with the results of Vosoughi et al. 2018 [73] on US political fake news on Twitter: “falsehood diffuse significantly farther, faster, deeper, and more broadly than the truth in all categories of information, and the effects

[is] more pronounced for false political news.”

Still, our reconstruction of political communities invite us to reconsider Vosoughi et al. results given of the new perspectives if offers on fake news diffusion. Computing the entropies

of the fake news and debunks diffusions at the community level, we found that the phenomena

is reversed (see Table 10): on average, fake news spread in much less communities (3.78) than

debunks (7.67), that in turn themselves spread in much less communities than the average of

all political tweets (13.45).

Fake news might reach on average more people than other political news but they spread

more locally. This immergence effect of the community structure on micro-dynamics, that entails a reversal of perspective during the transition from the micro-level (fake news reach

more accounts than other news) to the macro-level (fake news reach less communities than

other news) is very important when addressing this issue from a societal perspective.

On Twitter, fakes news are primarily from some political communities, not from the

sea. The number of shares is not the only relevant variable. The analysis of fake news and

debunks circulating inside the five major political communities (Emmanuel Macron, Marine

Le Pen, François Fillon, Jean-Luc Mélenchon and Benoı̂t Hamon) reveals some clear patterns

in terms of localization and temporal distribution.

18.9% (922) of the shares of fake news correspond to tweets with no community affiliation,

i.e. from what we have called “the sea” (we remind that communities are processed each day and the community of a tweet is the community attributed to the account issuing the tweet at

the time of the tweet issuance). Since globally 43.44% (25 695 888) of tweets have no commu-

nity, this means that the accounts that are the least politicized or not involved into some group

of activists share on average much less fake news than the most politically integrated accounts.

There is also a significant heterogeneity in the practice of fake news sharing within commu-

nities. The communities of Fillon (right-wing) and Le Pen (extreme-right wing) are the most

active with respectively 50.75% and 22.21% of the total number of fake links shares (89.9% of

fake news shared within communities); while the community of Hamon is the least active with

only 0.43% of the total shares (cf. Table 11).

The temporal dynamics is also noteworthy. Insides stories, we ranked tweets by their date

of issuance with a time resolution of one second. Removing the stories that have been shared

less than 30 times to mitigate small size effects, we computed the percentage of early spreaders per communities defined as the users that rank in the 1/30 percentile (cf. Table 11). The Fillon

community is the one with the highest percentage of early spreaders (6% of accounts from that

community are early spreaders), before the Le Pen community (5%); while none of the

Hamon and Macron communities accounts are among early spreaders.

To summarized, based on the Decodex fake news list, the Fillon community is both the one that was the quickest to spread fake news and the major source of fake news sharing.

We must not make too hasty conclusions about the origins of these fakes news. Several

hypothesis are compatible with these preliminary results. Within communities, the status

of the accounts spreading fake news are of utmost sociological importance. It could be the

work of leaders, second fiddle, activists, bots or accounts that try to influence a particular

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community through astroturfing (these accounts could even be held by foreign persons). The

estimation of this social status is out of the scope of the present paper but these accounts have

sufficiently close interactions with political communities to be associated with them through

our community detection method.

The analysis of the tweets containing debunk links offer a radically different pattern (cf.

Table 12). While 38.11% of debunks circulated within the sea, the most active community is

the one of Macron with 39.18% of the total of the debunks alone. Moreover, 12% of the users

spreading debunks inside the Macron community are among the first 1/10 percentile of user

sharing a debunk when they are sorted by the time of the tweet (early spreaders). The

Mélenchon community is also very reactive with 14% of debunks early spreaders even though

they are much less active.

To compare the fake news and debunks sharing propensities, Fig 13 shows the normalized

propensity of each community to share fake news and debunks (a propensity of 1 is the aver-

age). The Macron community shared almost four times more debunks than the average, while

the Fillon community shared more than four times more fake news than the average.

This preliminary study depicts a “split-brain” political landscape from the perspective of

fake news: one “hemisphere” produces or spreads fake news, the other produces or spreads

debunks. This dissociation between the audience that reads fake news and the one that reads

their debunks must be taken into account in mitigation strategies for the fake news

phenomenon.

Fighting fake news is a very tricky task with at least two counter-intuitive effects: the back- fire effect [74]—repeating a myth increases familiarity, reinforcing it—and the continued influ- ence effect [75]—despite a retraction, people continue to rely on misinformation. In addition, in absence of media exposure, fake news are spreading locally, from close to close. But when

exposing a large audience to the debunk of a fake news, there is no guaranty that a significant

Table 10. Entropy and average number of communities [in brackets] reached on average by fake news, debunks and tweets with or without URL mention over the

period. We also present the result when considering all communities as a single one except for the 6 main political online communities identified during the French presi-

dential election. Tweets without communities (43.44%) were considered as a single community for the processing of entropy. For comparison with the debunks and fakes

news dataset, identified by the URLs they spread, we also provide the global statistics for the subset of tweets that mention URLs.

entropy [# communities] All communities 6 majors communities

Fake news retweet (n = 4,888) 1.92 [3.78] 1.53 [2.88]

Debunks retweet (n = 1,275) 2.94 [7.67] 2.23 [4.69]

All retweets with url (n = 7,318,878) 3.75 [13.45] 2.28 [4.85]

All retweets (n = 43,101,891) 3.72 [13.17] 2.54 [5.82]

https://doi.org/10.1371/journal.pone.0201879.t010

Table 11. Number of users, number of tweets mentioning a fake news link and the proportion of early spreaders

for the tweets containing fake news links. Early spreaders are users that have been among the first 1/30 percentile of

users to share a fake news.

Candidates #Users #Tweets % early spreaders

François Fillon 1236 2481 0.06

Benoı̂t Hamon 19 21 0

Marine Le Pen 784 1086 0.05

Emmanuel Macron 61 65 0

Jean-Luc Mélenchon 11 143 0.03

Other communities 123 170 0.03

Sea 614 922 0.03

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proportion of this audience will not choose to trust the fake news source rather than the

debunk (e.g. believers in conspiracy theories). Thus exposing some communities that were pre- viously not aware of the existence of a fake news could start a new outbreak of fake news in

areas of social media that were spared until then.

Unlike vaccination for which people have to be treated preventively, in the domain of fake

news, people have to be debunked after exposure. In that sens, the fact that fakes news tend to

spread in less communities than other news is a good news: counter measures can target only

those particular communities or the media they rely on without interfering with a broader

audience. The second good news, when we look closely at the figures, is that at least on Twitter,

political fake news do not seems to be that much popular. Our study identified that in France,

only 0.08% of political tweets contained fake news. Although it is likely that de Decodex failed to identify a significant proportion of fake news, this is nevertheless a quite low proportion.

This estimate is also in line with Vosoughi et al. (2018) who identified “only” 4.5M shares of stories over 10 years of Twitter records, to be compared to an estimate of 500M tweets sent per

day in 2017.

Conclusions

We have demonstrated that methods involving the analysis of exchanges of political content

on social networking sites such as Twitter allow automatic reconstruction of political commu-

nities which, by their semantic profile and patterns of development, provide deep insights into

the dynamics at play inside the space of political activists.

By applying these methods to data from the 2017 French presidential election, we recon-

structed with a high accuracy the multi-polar French political space, retrieved the main events

which occurred during the campaign, and revealed the multiple reconfigurations of political

communities, some of which took place under relatively confidential circumstances. By using

interaction data only, our approach has the advantage of being independent from the language

used during these exchanges, provided the studied social activity involves a certain level of

commitment from the participants, as it is the case in politics.

After checking the accuracy of this reconstruction, we demonstrated how this approach can

help to better characterize online political communities, both from the perspective of their

structure and from point of view of the kind of information they propagate, fake news

included:

1. Political communities can be reconstructed from Twitter data:

• The usage of Twitter by political communities makes it possible to reconstruct the evolu-

tion of the political landscape of a country during presidential campaigns through

Table 12. Number of users, number of tweets and the proportion of early spreaders for the tweets containing

debunk links. Early spreaders are users that have been among the first 1/10 percentile of users to share a debunk.

Candidates #Users #Tweets % early spreaders

François Fillon 15 15 0.07

Benoı̂t Hamon 47 69 0.11

Marine Le Pen 7 8 0

Emmanuel Macron 361 499 0.12

Jean-Luc Mélenchon 83 98 0.14

Other communities 82 100 0.12

Sea 345 486 0.05

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correspondence between the different political currents and communities reconstructed

algorithmically from their digital traces,

• Official and non official key political events correspond to identifiable mathematical struc-

tures in the reconstructed political landscape like split, merge, fade-out, fade-in or abrupt

change of sizes of communities,

2. This reconstruction gives insights into the structures of political communities and their

dynamics (the case of France):

• Online communities are able to reconfigure themselves in a very short period of time as a

result of key events such as victories at primaries or alliances between leaders,

• There is a generic political community structure on Twitter, comprising a core of activists

who remain for several weeks within the community, a considerably more volatile

Fig 13. Radar chart of the normalized propensity to spread fake news (dotted red) and debunks (blue) per community. 1 means

a spread at average level.

https://doi.org/10.1371/journal.pone.0201879.g013

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PLOS ONE | https://doi.org/10.1371/journal.pone.0201879 September 19, 2018 31 / 38

peripheral membership, and an intermediate set of accounts covering a broad spectrum of

participation,

• Political communities are heterogeneous with respect to the online activity, commitment,

political integration and politicization of their members suggesting both different profiles

of political commitment from one community to another and distinct dynamics for

recruiting new members. The characterization from digital traces of the variety of pro-

cesses sustaining the cohesion of a community is an open challenge,

• People with extreme political views tend to be the most committed to their ideology,

3. This reconstruction gives insights into the impact of communities on information diffusion

and in particular on their role in the fake news phenomena (the case of France):

• The online community structure constrains the diffusion of information among Twitters

activists, most retweet cascades occur within one or two communities,

• The French political Twitter landscape looks like a “split-brain” from the perspective of

fake news: one “hemisphere” produces or spreads fake news, the other produces or spreads

debunks,

• Fake news seems to be more shared than their debunks in terms of number of accounts

reached, but the phenomena is reversed at the community level: the spread of fake news

tends to be confined to fewer communities than other political news. Consequently, in

fake news impact assessment, the variety of communities they reach should be taken into

account in addition to the number of people they reach.

• Overall, on Twitter, for French politics, the fake news phenomena seems rather limited

both in terms of volume (order of magnitude of 0.1%) and in terms of the diversity of the

communities concerned. 72,9% of all fake news analyzed was produced and disseminated

by only two political communities. Their diffusion is therefore above all the work of politi-

cal communities, not laymens.

As a consequence of its 11-months duration, its temporal resolution (real-time capture of

data, once every second), and its scale (�2.4 million Twitter accounts of which �180, 000 had

a sufficient degree of interaction with the Politoscope to be cataloged into a political commu- nity), this study establishes the basis for a series of analyzes providing unprecedented insight

into opinion dynamics and the reconfiguration of political communities. It provides access to

an intermediate level of resolution between sociological surveys in the field and global statisti-

cal studies (such as those conducted by national census agencies), from which we expect that,

in the future, it will represent a major source of political and sociological knowledge supple-

menting the more traditional approaches.

We conclude this paper with a comment concerning the political and ethical implications

of the development of these methods. In recent years, the boundary between private and

public life has become increasingly thin, as the digital trails left by citizens have been systemati-

cally analyzed, by combining an ever increasing number of databases (purchase history, web

navigation history, votes, store loyalty cards, public transport, GPS location, etc.), and by

considering an increasingly significant history based on population sample sizes that tend to

be of the order of magnitude of the population size itself. Marketing and the economic sector

were the first areas of activity to seize these new opportunities, in the interests of financial

gains. More recently, the political sphere has adopted these new opportunities and has tried

to take advantage of them to gain greater insight into the preferences and political persuasion

of voters.

Reconstruction of the socio-semantic dynamics of political activist Twitter networks

PLOS ONE | https://doi.org/10.1371/journal.pone.0201879 September 19, 2018 32 / 38

A perfect example of this tendency can be found in the leak of sensitive personal data con-

cerning nearly 200 million Americans, compiled for the Republican party during the Trump

2016 campaign (� 99% of the electorate), which occurred in July 2017 [76].

We took considerable care during the entire Politoscope project to anonymize the processed data and to produce aggregate data only, thus making it impossible to identify specific individ-

uals, with the exception of a small number of well-known political figures. However, the

research presented in this paper demonstrates that it is possible, using the conspicuous trails

left in the public digital domain, and with relatively accessible computational means, to catalog

a significant proportion of the electorate of an entire country, according to their political ori-

entations, even though, as we have pointed out, there is still a certain margin of uncertainty.

From this observation, it is important for citizens to realize that they can be accountable for

any trace they leave on the web. The open platform politoscope.org is, in particular, designed

to promote this awareness. We can only count on the honesty and good faith of current and

future political leaders to refrain from using, as has happened so often in the past and contin-

ues to be the case in certain countries [77–80], data of this type for the purposes of discrimina-

tion, manipulation, or even repression and persecution.

Supporting information

S1 File. Exhaustive data description and complementary analyses. Table of content:

• Text A: Main political events of the 2017 French presidential campaign,

• Text B: Data collection platform

• B.1 Collection focused on main political accounts (follow API),

• B.2 Collection focused on political terms (track API),

• B.3 List of the followed political figures (track and follow API),

• B.4 Platform architecture,

• B.5 Availability of the data,

• Text C: Bots detection,

• Text D: Lifetime of tweet and Twitter activity patterns,

• Text E: Community stability,

• Text F: Correlation between community structure and retweets patterns: confirmation of the

echo chamber effect,

• Text G: Analysis of the communities’ semantic background

• 7.1 Definition of the themes through queries,

• 7.2 Distribution of topics by political communities,

• 7.3 Evolution of community vocabulary,

• Text H: Fake news data cleaning,

• Text I: Vocabulary of the political discourse of the presidential election 2017,

• Text J: Analysis of late retweets.

(PDF)

Reconstruction of the socio-semantic dynamics of political activist Twitter networks

PLOS ONE | https://doi.org/10.1371/journal.pone.0201879 September 19, 2018 33 / 38

Acknowledgments

This research was supported by the Complex Systems Institute of Paris Ile-de-France (https://

iscpif.fr), the Algodiv project (ANR-15-CE38-0001), the project Science en Poche (Emergence(s) program, City of Paris) and the Problématiques transversales aux systèmes complexes DIM (Field of major interest) from the Île-de-France Region.

We warmly thanks the reviewers and all our colleagues for their fruitful comments and sug-

gestions about our work.

Author Contributions

Conceptualization: Noé Gaumont, Maziyar Panahi, David Chavalarias.

Data curation: Noé Gaumont, Maziyar Panahi, David Chavalarias.

Formal analysis: Noé Gaumont, David Chavalarias.

Funding acquisition: David Chavalarias.

Investigation: Noé Gaumont, Maziyar Panahi, David Chavalarias.

Methodology: Noé Gaumont, David Chavalarias.

Project administration: David Chavalarias.

Resources: Maziyar Panahi, David Chavalarias.

Software: Noé Gaumont, Maziyar Panahi.

Supervision: David Chavalarias.

Validation: Noé Gaumont, David Chavalarias.

Visualization: Noé Gaumont, Maziyar Panahi, David Chavalarias.

Writing – original draft: Noé Gaumont, David Chavalarias.

Writing – review & editing: Noé Gaumont, David Chavalarias.

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