APPLIED PROJECT

1

The Impact of Political Disinformation Campaigns on Voting Patterns in America

Topic Proposal

ISS 501

November 7, 2020

1.) Topic: The topic for my proposed research study is the impact of the consumption of political disinformation on social media networking platforms on voting patterns in general elections (Presidential elections).

2.) Hypothesis: The consumption of political disinformation stories and fake news stories increases voter participation (casting votes) during general elections.

3.) Research Questions:

RQ1: How does the consumption of political disinformation campaigns impact voter’s perception of political candidates?

RQ2: In what ways do Fake News stories and misinformation shape voter turnout?

RQ3: What patterns emerge in the dissemination of political disinformation during presidential elections? (For example, is one political party targeted with unverified stories more than others)

RQ4: How does the rate of political disinformation, inaccurate claims, and rumors about candidates change as election day nears?

4.) Variables:

Independent Variable : The independent variable in this study is the consumption of political disinformation stories and rumors on social networking sites (Political disinformation campaigns). In social science research the independent variable is the causal factor, condition, or variable, which is applied to the study and does not change. This variable is said to influence or not impact the behavior or phenomenon being studied and analyzed.

Dependent Variable: The dependent variable in this specific hypothetical study is the voter participation in general presidential elections (casting votes). Again, the dependent variable is the condition or variable being tested or measured. In research studies, the dependent variable is thought to hinge on the application of the independent variable.

Operationalization of Variables:

The independent variable needs to be operationalized by accurately defining what is meant by the terms political disinformation, and fake news stories. I define political disinformation and fake news stories unverified, exaggerated, rumors and stories about opposing political candidates, which has not be independently verified by credible sources, including, journalists, and scholars. Stories that can be verified by credible sources obviously would not fit into the category of political disinformation.

5.) Style of Study:

I expect this proposed study to be a large-n regression type of study based on the consumption, and voting activities of the entire eligible voting public in the United States. I considered use a small case study style to determine how specific cities and communities consumed political disinformation on social media outlets during election cycles. However, I question the generalizability of such a study, and successfully being able to scale up the study to the rest of country. The demographic characteristics and social conditions in diverse cities is different nationwide, so carrying out a small case study may not be very reliable, or carry the desired levels of generalizability. Using a regression model for the study allows our researchers to examine a much larger, more representative population in American society.

I anticipate collecting qualitative and quantitative information from this particular study. The research will focus on looking at specific political disinformation messages and stories, and determining the ways that voters perceive this information. We are interested in analyzing if voters find this information credible and legitimate, and the motivational effects on casting votes. Quantitative measures will be used to summarize the connection between viewing and reading political disinformation stories and headlines, and counting the number of participants in the study, who actually vote.

The proposed sample size for this study is 1200 eligible voters in a combination of rural and urban settings. Questionnaires and smaller focus groups are the preferred tools for collecting data, information, and perspective from the participants. The design of questionnaires will provide participants with a series of political disinformation headlines and inaccurate stories about political candidates, followed by questions about the perception of candidates, and intention to vote. In the focus groups, participants will also be shown a series of political disinformation stories and claims, and asked a series of open-ended questions about candidates, stories, and voting after the showing. The focus groups will provide our researchers with more in depth, qualitative information about voters thinking processes associated with political disinformation campaigns. Randomized selection of voters will be selected by using voter registration rolls to send invitations to participate in the study.

Our researchers will also rely on existing data about elections between 2012-2020 by relying on the Cooperative Congressional Election Study (CCES). Although we are focusing on recent presidential elections, this resource provides voting data and perspective from midterm elections and general elections (Presidents) over the last two decades. Our findings will be compared to attitudes, voting patterns, and opinions shared from this resource, which collects opinions nationwide.

6. Scope Conditions

The specific scope conditions or boundaries for this proposed study are presidential general elections. In terms of the consumption of the political disinformation campaign, the boundaries are eligible voters between the ages of 18-65. This is the best range of voters for the election because they are most likely to access tools like social media networking platforms.

7.) Falsifiable

Falsifiability is a concept, which means that my thesis, prediction, and supportive evidence may prove to be inaccurate or off. For example, the data and information may prove that fake news and political misinformation may not shift or skew voting preferences or results. Joe Biden supporters might view fake news stories about the candidate on social media, and still vote for him, which shows no impact. Another possibility is that future researchers studying the same topic may create a design and framework for highlighting any errors that our current study may have produced. Finally, it is possible for other researchers to alter or tweak the same topic or thesis in a manner, which provides different findings or evidence, which could also falsify my conclusions or data.

8. Type of Reasoning (Induction)

After closely analyzing the variables, and conditions of this particular study, it is more likely to represent inductive reasoning, rather than deductive logic. Inductive reasoning or induction relies on drawing theories or making assumptions based on a specific set of observations. Essentially, researchers focus on an empirical tendency or phenomenon, and then develop a theory as to the influence or driving factor causing the particular behavior or outcome. In this specific scenario, the empirical phenomenon is that fake news stories and political disinformation campaigns on social media networking sites is shaping voter engagement and activity during election cycles. We will be focusing on the presence and access to fake news stories and political misinformation on popular social media networking platforms including, Twitter and Facebook.

There is the potential for a unique interplay between inductive and deductive reasoning once our researchers look at the relationship between fake news stories and voting patterns during elections. Deductive reasoning details the process of taking one or more premises or statements in order to reach a logical conclusion. In this scenario, if researchers find that voters view fake news stories and political misinformation as true, the logical conclusion is that such information does shape and direct voting decisions and behavior. We currently see this playing out in the post 2020 presidential election environment as waves of conservative Trump supporters continue to peddle and believe fake news stories about widespread election fraud and manipulation in several key battleground states that President Trump lost. Although voting among eligible voters already occurred, fake news stories are driving perception among some segments of society about the integrity of democratic elections in the U.S. today.

9.) Level of Analysis

The level of analysis is going to be national level because we are looking at large-scale behavior in elections.

10.) Unit of analysis

The unit of analysis is individual level because the consumption and voting decisions of individual level voting behavior is being studied.

11) Your hopes for counter-intuitive findings.

There is a possibility of counter-intuitive findings for this study in terms of issues like political party affiliation, ideology, and personal bias. There is a strong likelihood that some voters are already impacted by bias, political party, and ideological positions surrounding certain issues, which shape their consumption and thoughts of political disinformation and inaccurate claims. We may find that political disinformation only strengthens positions and voting preferences, which the participants already carry.

References

Goodwin-Ortiz de Leon, C. (2018). Fake news on social media: Illusory truth and the 2016 Presidential Election. Amazon Digital Services LLC - KDP Print US.

Ciampaglia, L., G. Fighting fake news: A role for computational social science in the fight against digital misinformation. Journal of Computational Social Science, 1, 147-153. 

Ott, L., B. (2017). The age of Twitter, Donald J. Trump, and the politics of debasement. Critical Studies in Media Communication, 34(1), 59-68. DOI: 10.1080/15295036.2016.1266686

Shin, J., Jian, L., Driscoll, K., & Bar, F. (2018). The diffusion of misinformation on social media: Temporal pattern, message, and source. Computers in Human Behavior, 83, 278-287. DOI: 10.1016/j.chb.2018.02.008.

Valenzuela, S., Halpern, D., Katz, E., J., & Miranda, P., J. (2019). The paradox of participation versus misinformation: Social media, political engagement, and the spread of misinformation, Digital Journalism, 7(6), 802-823. DOI: https://doi.org/10.1080/21670811.2019.1623701