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Experimental evidence of massive-scale emotional contagion through social networks Author(s): Adam D. I. Kramer, Jamie E. Guillory and Jeffrey T. Hancock Source: Proceedings of the National Academy of Sciences of the United States of America, Vol. 111, No. 24 (June 17, 2014), pp. 8788-8790 Published by: National Academy of Sciences Stable URL: https://www.jstor.org/stable/23802004 Accessed: 03-08-2019 19:27 UTC

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Experimental evidence of massive-scale emotional contagion through social networks Adam D. I. Kramer3,1, Jamie E. Guilloryb, and Jeffrey T. Hancockc,d

E

aCore Data Science Team, Facebook, Inc., Menlo Park, CA 94025; bCenter for Tobacco Control Research and Education, University of California, San Francisco, CA 94143; and Departments of Communication and information Science, Cornell University, Ithaca, NY 14853

Edited by Susan T. Fiske, Princeton University, Princeton, NJ, and approved March 25, 2014 (received for review October 23, 2013)

Emotional states can be transferred to others via emotional demonstrated that (i) emotional contagion occurs via text-based contagion, leading people to experience the same emotions computer-mediated communication (7); (ii) contagion of psy without their awareness. Emotional contagion is well established etiological and physiological qualities has been suggested based in laboratory experiments, with people transferring positive and on correlational data for social networks generally (7, 8); and negative emotions to others. Data from a large real-world social (iii) people's emotional expressions on Facebook predict friends' network, collected over a 20-y period suggests that longer-lasting emotional expressions, even days later (7) (although some shared moods (e.g., depression, happiness) can be transferred through experiences may in fact last several days). To date, however, there networks [Fowler JH, Christakis NA (2008) BMJ 337:a2338], al- is no experimental evidence that emotions or moods are contagious though the results are controversial. In an experiment with people in the absence of direct interaction between experiencer and target, who use Facebook, we test whether emotional contagion occurs On Facebook, people frequently express emotions, which are outside of in-person interaction between individuals by reducing later seen by their friends via Facebook's "News Feed" product the amount of emotional content in the News Feed. When positive (8). Because people's friends frequently produce much more expressions were reduced, people produced fewer positive posts content than one person can view, the News Feed filters posts, and more negative posts; when negative expressions were re- stories, and activities undertaken by friends. News Feed is the duced, the opposite pattern occurred. These results indicate that primary manner by which people see content that friends share, emotions expressed by others on Facebook influence our own Which content is shown or omitted in the News Feed is de emotions, constituting experimental evidence for massive-scale termined via a ranking algorithm that Facebook continually contagion via social networks. This work also suggests that, in develops and tests in the interest of showing viewers the content contrast to prevailing assumptions, in-person interaction and non- they will find most relevant and engaging. One such test is verbal cues are not strictly necessary for emotional contagion, and reported in this study; A test of whether posts with emotional that the observation of others' positive experiences constitutes content are more engaging. a positive experience for people. The experiment manipulated the extent to which people (N =

689,003) were exposed to emotional expressions in their News computer-mediated communication | social media | big data Feed. This tested whether exposure to emotions led people to

change their own posting behaviors, in particular whether ex motional states can be transferred to others via emotional posure to emotional content led people to post content that was

■ contagion, leading them to experience the same emotions as consistent with the exposure—thereby testing whether exposure those around them. Emotional contagion is well established in to verbal affective expressions leads to similar verbal expressions, laboratory experiments (1), in which people transfer positive and a form of emotional contagion. People who viewed Facebook in negative moods and emotions to others. Similarly, data from English were qualified for selection into the experiment. Two a large, real-world social network collected over a 20-y period parallel experiments were conducted for positive and negative suggests that longer-lasting moods (e.g., depression, happiness) emotion: One in which exposure to friends' positive emotional can be transferred through networks as well (2, 3). content in their News Feed was reduced, and one in which ex

The interpretation of this network effect as contagion of mood posure to negative emotional content in their News Feed was has come under scrutiny due to the study's correlational nature, reduced. In these conditions, when a person loaded their News including concerns over misspecification of contextual variables Feed, posts that contained emotional content of the relevant or failure to account for shared experiences (4, 5), raising im- emotional valence, each emotional post had between a 10% and portant questions regarding contagion processes in networks. An 90% chance (based on their User ID) of being omitted from experimental approach can address this scrutiny directly; how- their News Feed for that specific viewing. It is important to note ever, methods used in controlled experiments have been criti cized for examining emotions after social interactions. Interacting with a happy person is pleasant (and an unhappy person, un pleasant). As such, contagion may result from experiencing an interaction rather than exposure to a partner's emotion. Prior studies have also failed to address whether nonverbal cues are

necessary for contagion to occur, or if verbal cues alone suffice. Evidence that positive and negative moods are correlated in networks (2, 3) suggests that this is possible, but the causal question of whether contagion processes occur for emotions in massive social networks remains elusive in the absence of ex

perimental evidence. Further, Others have suggested that in Author contributions: A.D.I.K., J.E.G., and J.T.H. designed research; A.D.I.K. performed online social networks, exposure to the happiness of Others research; A.D.I.K. analyzed data; and A.D.I.K., J.E.G., and J.T.H. wrote the paper, may actually be depressing to us, producing an "alone together" The authors declare no conflict of interest, social comparison effect (6). This article Is a PNAS Direct Submission.

Three studies have laid the groundwork for testing these pro- Freely available online through the PNAS open access option. Cesses via Facebook, the largest online social network. This research 1To whom correspondence should be addressed. E-mail: [email protected].

8788-8790 | PNAS | June 17,2014 | vol.111 | no. 24 www.pnas.org/cgi/doi/10.1073/pnas.1320040111

Significance

We show, via a massive (N = 689,003) experiment on Facebook, that emotional states can be transferred to others via emotional

contagion, leading people to experience the same emotions without their awareness. We provide experimental evidence that emotional contagion occurs without direct interaction be tween people (exposure to a friend expressing an emotion is sufficient), and in the complete absence of nonverbal cues.

Emotional states can be transferred to others via emotional

contagion, leading people to experience the same emotions without their awareness. Emotional contagion is well established in laboratory experiments, with people transferring positive and negative emotions to others. Data from a large real-world social network, collected over a 20-y period suggests that longer-lasting moods (e.g., depression, happiness) can be transferred through networks [Fowler JH, Christakis NA (2008) BMJ 337:a2338], al though the results are controversial. In an experiment with people who use Facebook, we test whether emotional contagion occurs outside of in-person interaction between individuals by reducing the amount of emotional content in the News Feed. When positive expressions were reduced, people produced fewer positive posts and more negative posts; when negative expressions were re duced, the opposite pattern occurred. These results indicate that emotions expressed by others on Facebook influence our own emotions, constituting experimental evidence for massive-scale contagion via social networks. This work also suggests that, in contrast to prevailing assumptions, in-person interaction and non verbal cues are not strictly necessary for emotional contagion, and that the observation of others' positive experiences constitutes a positive experience for people.

Significance

We show, via a massive (N = 689,003) experiment on Facebook, that emotional states can be transferred to others via emotional

contagion, leading people to experience the same emotions without their awareness. We provide experimental evidence that emotional contagion occurs without direct interaction be tween people (exposure to a friend expressing an emotion is sufficient), and in the complete absence of nonverbal cues.

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that this content was always available by viewing a friend's con- As such, direct examination of the frequency of positive and tent directly by going to that friend's "wall" or "timeline," rather negative words would be inappropriate: It would be confounded than via the News Feed. Further, the omitted content may have with the change in overall words produced. To test our hypothesis appeared on prior or subsequent views of the News Feed. Fi- regarding emotional contagion, we conducted weighted linear nally, the experiment did not affect any direct messages sent regressions, predicting the percentage of words that were positive from one user to another. or negative from a dummy code for condition (experimental ver

Posts were determined to be positive or negative if they con- sus control), weighted by the likelihood of that person having an tained at least one positive or negative word, as defined by emotional post omitted from their News Feed on a given viewing, Linguistic Inquiry and Word Count software (LIWC2007) (9) such that people who had more content omitted were given higher word counting system, which correlates with self-reported and weight in the regression. When positive posts were reduced in physiological measures of well-being, and has been used in prior the News Feed, the percentage of positive words in people's research on emotional expression (7, 8, 10). LIWC was adapted status updates decreased by B = -0.1% compared with control to run on the Hadoop Map/Reduce system (11) and in the News ['(310,044) = —5.63, P < 0.001, Cohen's d = 0.02], whereas the Feed filtering system, such that no text was seen by the percentage of words that were negative increased by B = 0.04% researchers. As such, it was consistent with Facebook's Data Use (f = 2.71, P = 0.007, d = 0.001). Conversely, when negative posts Policy, to which all users agree prior to creating an account on were reduced, the percent of words that were negative decreased Facebook, constituting informed consent for this research. Both by B = —0.07% [7(310,541 ) = -5.51, P < 0.001, d = 0.02] and the experiments had a control condition, in which a similar pro- percentage of words that were positive, conversely, increased by portion of posts in their News Feed were omitted entirely at & ~ 0.06% (t = 2.19, P < 0.003, d = 0.008). random (i.e., without respect to emotional content). Separate The results show emotional contagion. As Fig. 1 illustrates, for control conditions were necessary as 22.4% of posts contained people who had positive content reduced in their News Feed, negative words, whereas 46.8% of posts contained positive a 'arger percentage of words in people s status updates were words. So for a person for whom 10% of posts containing posi- negative and a smaller percentage were positive. When negativity tive content were omitted, an appropriate control would with- was reduced, the opposite pattern occurred. These results sug hold 10% of 46.8% (i.e., 4.68%) of posts at random, compared êest that the emotions expressed by friends, via online social with omitting only 2.24% of the News Feed in the negativity- networks, influence our own moods, constituting, to our knowl reduced control edge, the first experimental evidence for massive-scale emotional

The experiments took place for 1 wk (January 11-18, 2012). contagion via social networks (3, 7, 8), and providing support for Participants were randomly selected based on their User ID, previously contested claims that emotions spread via contagion resulting in a total of ~155,000 participants per condition who 1 a netw°rk posted at least one status update during the experimental period. . The8e re8ults hl^ht 8eve/al features of !™tl0na conta:

For each experiment, two dependent variables were examined Slon' Flrst' because Ne™ Feed contenut 18 not, dlfrected toward pertaining to emotionality expressed in people's own status anyone, contagion could not be just the result of some specific updates: the percentage of all words produced by a given person mteraCtl"n w'tba haPPy or 8ad Partne/' Althouëh Pnor re8earch that was either positive or negative during the experimental examinted wbethJf an c"lotloa fn be contracted via a direc • • f V, i . . i ° . 1 , , interaction (1, 7), we show that simply failing to overhear period (as in ref. 7). In total, over 3 million posts were analyzed, c ■ ,, . ' , . ■ r ¿ . • , , , .... „ . • ■ ... a friend s emotional expression via Facebook is enough to buffer containing over 122 million words, 4 million of which were c .. cc * c j , , ,, , • „

,5 . ,10 .... '. . one from its effects. Second, although nonverbal behavior is well positive (3.6%) and 1.8 million negative (1.6%). . , , ,. c .'6 . ., , . »...

tí- cc A.- \ ^ • ■ , \ ■ established as one medium tor contagion, these data suggest that If affective states are contagious via verbal expressions on 6 &&

Facebook (our operationalization of emotional contagion), peo ple in the positivity-reduced condition should be less positive compared with their control, and people in the negativity reduced condition should be less negative. As a secondary mea sure, we tested for cross-emotional contagion in which the \ opposite emotion should be inversely affected: People in the ? positivity-reduced condition should express increased negativity, j whereas people in the negativity-reduced condition should ex- | press increased positivity. Emotional expression was modeled, on | a per-person basis, as the percentage of words produced by that s person during the experimental period that were either positive | or negative. Positivity and negativity were evaluated separately Q given evidence that they are not simply opposite ends of the same spectrum (8, 10). Indeed, negative and positive word use Scarcely correlated [/* = —0.04, t(620,587) = —38.01, P < 0.001]. o Negativity Reduced Positivity Reduced We examined these data by comparing each emotion condition

to its control. After establishing that our experimental groups did | not differ in emotional expression during the week before the ( experiment (all t < 1.5; all P > 0.13), we examined overall posting - rate via a Poisson regression, using the percent of posts omitted as | a regression weight. Omitting emotional content reduced the 5 amount of words the person subsequently produced, both when I positivity was reduced (z = -4.78, P < 0.001) and when negativity S was reduced (z = -7.219, P < 0.001). This effect occurred both 2 when negative words were omitted (99.7% as many words were produced) and when positive words were omitted (96.7%). An interaction was also observed, showing that the effect was stronger Fig. 1. Mean number of positive (Upper) and negative (Lower) emotion words when positive words were omitted (z = —77.9, P < 0.001). (percent) generated people, by condition. Bars represent standard errors.

Kramer et al. PNAS | June 17, 2014 | vol.111 | no. 24 | 8789

Control

Experimental

"D OJ _

■ Control

□ Experimental

Negativity Reduced Positivity Reduced

Fig. 1. Mean number of positive (Upper) and negative (Lower) emotion words (percent) generated people, by condition. Bars represent standard errors.

Q tn Z UJ

1 = gS — en

O uj 2 > o t

5§ p

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contagion does not require nonverbal behavior (7, 8): Textual (6, 13). In fact, this is the result when people are exposed to less content alone appears to be a sufficient channel. This is not positive content, rather than more. This effect also showed no a simple case of mimicry, either; the cross-emotional encourage- negativity bias in post hoc tests (z = -0.09, P = 0.93). ment effect (e.g., reducing negative posts led to an increase in Although these data provide, to our knowledge, some of the positive posts) cannot be explained by mimicry alone, although first experimental evidence to support the controversial claims mimicry may well have been part of the emotion-consistent effect. that emotions can spread throughout a network, the effect sizes Further, we note the similarity of effect sizes when positivity and from the manipulations are small (as small as d = 0.001). These negativity were reduced. This absence of negativity bias suggests effects nonetheless matter given that the manipulation of the that our results cannot be attributed solely to the content of the independent variable (presence of emotion in the News Feed) post: If a person is sharing good news or bad news (thus explaining was minimal whereas the dependent variable (people's emo his/her emotional state) friends response to the news (in- ti()nal expressions) is difficult to influence given the range of dependent of the sharer s emotional state) should be stronger daj] iences that influence mood (10) More importantly, when bad news is shown rather than good (or as commonly noted, • • i c • i i u t- , ; "if it bleeds, it leads;" ref. 12) if the results were being driven by §lven then™e sca1^ of social networks such as Facebook, reactions to news. In contrast, a response to a friend's emotion *ma11 effeC S Ca" aggregated consequences (14, expression (rather than news) should be proportional to exposure. 15): For examp|e> th£; we -documented connection between A post hoc test comparing effect sizes (comparing correlation emotions and physical well-being suggests the importance of coefficients using Fisher's method) showed no difference de- these findings for public health. Online messages influence our spite our large sample size (z = -0.36, P = 0.72). experience of emotions, which may affect a variety of offline

We also observed a withdrawal effect: People who were ex- behaviors. And after all, an effect size of d = 0.001 at Facebook's posed to fewer emotional posts (of either valence) in their News scale is not negligible: In early 2013, this would have corre Feed were less expressive overall on the following days, ad- sponded to hundreds of thousands of emotion expressions in dressing the question about how emotional expression affects status updates per day. social engagement online. This observation, and the fact that people were more emotionally positive in response to positive ACKNOWLEDGMENTS. We thank the Facebook News Feed team, especially emotion updates from their friends, stands in contrast to theories °aniel Stchafer' for encouragement and support; the Facebook Core Data , v ' . Science team, especially Cameron Marlow, Moira Burke, and Eytan Bakshy; that suggest viewing positive posts by friends on Facebook may p|us Michael Macy and Mathew Aldridge for their feedback. Data processing somehow affect US negatively, for example, via social comparison systems, per-user aggregates, and anonymized results available upon request.

1. Hatfield E, Cacioppo JT, Rapson RL (1993) Emotional contagion. Curr Dir Psychol Sci 2(3):96-100.

2. Fowler JH, Christakis NA (2008) Dynamic spread of happiness in a large social network: Longitudinal analysis over 20 years in the Framingham Heart Study. BMJ 337:a2338.

3. Rosenquist JN, Fowler JH, Christakis NA (2011) Social network determinants of de pression. Mol Psychiatry 16(3):273—281.

4. Cohen-Cole E, Fletcher JM (2008) Is obesity contagious? Social networks vs. environ mental factors in the obesity epidemic. J Health Econ 27(5):1382-1387.

5. Aral S, Muchnik L, Sundararajan A (2009) Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proc Natl Acad Sci USA 106(51):21544-21549.

6. Turkle S (2011) Alone Together: Why We Expect More from Technology and Less from Each Other (Basic Books, New York).

7. Guillory J, et al. (2011) Upset now? Emotion contagion in distributed groups. Proc ACM CHI Conf on Human Factors in Computing Systems (Association for Computing Machinery, New York), pp 745-748.

8. Kramer ADI (2012) The spread of emotion via Facebook. Proc CHI (Association for Computing Machinery, New York), pp 767-770.

9. Pennebaker JW, Chung CK, Ireland M, Gonzales A, Booth RJ (2007) The development and psychological properties of LIWC2007. Available at http://liwc.net/howliwcworks. php. Accessed May 10, 2014.

10. Golder SA, Macy MW (2011) Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333(6051 ):1878—1881.

11. Thusoo A; Facebook Data Infrastructure Team (2009) Hive-A warehousing solution over a map-reduce framework. Proc VLDB 2(2): 1626-1629.

12. Baumeister RF, Bratslavsky E, Finkenauer C, Vohs KD (2001) Bad is stronger than good. Rev Gen Psychol 5(4):323-370.

13. Festinger L (1954) A theory of social comparison processes. Hum Relat 7(2):117-140. 14. Prentice DA, Miller DT (1992) When small effects are impressive. Psychol Bull 112(1):

160-164.

15. Bond RM, et al. (2012) A 61-million-person experiment in social influence and political mobilization. Nature 489(7415):295-298.

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8790 I www.pnas.org/cgi/doi/10.1073/pnas.1320040111

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