Social Media, Political Polarization, and Political Disinformation: A Review of the Scientific Literature

Tucker, Joshua A.; Guess, Andrew M.; Barberá, Pablo; Vaccari, Cristian; Siegel, Alexandra A.; Sanovich, Sergey; Стукал, Денис; Nyhan, Brendan

doi:10.2139/ssrn.3144139

Cited by 545 publications

(335 citation statements)

References 235 publications

(218 reference statements)

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“…The use of trolls and bots in political manipulation campaigns around the globe is well documented through an array of reports by mainstream media outlets and academics (see Tucker et al [2018] for a comprehensive review on the role of misinformation, bots, and trolls on social media). This phenomenon is not entirely new: researchers warned about the potential for online political manipulation for over a decade [Howard 2006;Hwang et al 2012].…”

Section: Related Literaturementioning

confidence: 99%

Who Falls for Online Political Manipulation?

Badawy

Lerman

Ferrara

2019

Companion Proceedings of the 2019 World Wide Web Conference

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Social media, once hailed as a vehicle for democratization and the promotion of positive social change across the globe, are under attack for becoming a tool of political manipulation and spread of disinformation. A case in point is the alleged use of trolls by Russia to spread malicious content in Western elections. This paper examines the Russian interference campaign in the 2016 US presidential election on Twitter. Our aim is twofold: first, we test whether predicting users who spread trolls' content is feasible in order to gain insight on how to contain their influence in the future; second, we identify features that are most predictive of users who either intentionally or unintentionally play a vital role in spreading this malicious content. We collected a dataset with over 43 million elections-related posts shared on Twitter between September 16 and November 9, 2016, by about 5.7 million users. This dataset includes accounts associated with the Russian trolls identified by the US Congress. Proposed models are able to very accurately identify users who spread the trolls' content (average AUC score of 96%, using 10-fold validation). We show that political ideology, bot likelihood scores, and some activity-related account meta data are the most predictive features of whether a user spreads trolls' content or not.

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Section: Related Literaturementioning

confidence: 99%

Who Falls for Online Political Manipulation?

Badawy

Lerman

Ferrara

2019

Companion Proceedings of the 2019 World Wide Web Conference

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“…Beyond just reflecting the media environment at large however, other research has highlighted ways Twitter and other social media platforms may work to promote polarization and ultimately harm democracy (Barberá et al 2018;Sunstein 2018;Tucker et al 2018) . Research has identified three mechanisms: first, social media exposes people to uncivil conversations around contentious issues which leads to increases in affective polarization (Lelkes 2016;Suhay, Bello-Pardo, and Maurer 2018;Weeks 2015) ; second, by generating a fragmented news environment that lowers the overall quality and creates spaces for the spread of disinformation (Lazer et al 2018;Vosoughi, Roy, and Aral 2018) and incorporation of otherwise fringe views (Bail 2012;Farrell 2016a) ; third, it exposes users to larger number of opposing viewpoints which can activate the type of "boomerang" or "hostile media" response seen in general media (Bail et al 2018) .…”

Section: Social Media Polarizationmentioning

confidence: 99%

Twitter Discourses on Climate Change: Exploring Topics and the Presence of Bots

Marlow¹,

Miller²,

Roberts³

2020

Preprint

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This study explores the role of Twitter bots (automated users) in online discourse on climate change. We examined 6.5 million tweets posted during the days leading up to and the month following President Donald Trump’s June 1, 2017 announcement of the United States’ withdrawal from the 2015 Paris Climate Agreement. Of a 10% sample of users, we used the machine learning algorithm “Botometer” to identify likely mechanized “bots.” Botometer identified 17,509 suspected bot accounts, representing about 9% of users and 17% of all tweets. Query limits on Botometer and the capacity of STM modeling reduced our final sample size to 167,259 tweets. We then used the ‘stm’ package in the R statistical programming language to implement structural topic models to cluster tweet content into topics. We identified 34 topics. Topics broadly fell into categories related to news of the withdrawal and the responses from various media and government personnel, posts about climate change research, discussions of the denial of climate change, and finally activist topics with campaign goals. Within these topics, bots were often common, representing as much as 38% of tweets. Additionally, among the most common 15 topics, we found evidence that after adjusting for topic size, timing of tweets, and whether a user was suspended, tweets produced by bots were more likely relative to non-bot users to fall into four topics. These topics included tweets sharing news links on the announcement, links related to climate research, links sharing denialist research, and links about White House aids and their views of fossil fuels. These findings suggest a substantial impact of mechanized bots in amplifying denialist messages about climate change, including support for Trump’s withdrawal from the Paris Agreement

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“…In 280 characters or less, a candidate, journalist, or voter can publish their thoughts for others to see. While discussions questioning the validity of polls and their predictive power on election outcomes have increasingly circulated since Donald Trump's victory in 2016, Twitter's role has only broadened, transforming it into a catalyst for political communication as well as a gauge for public opinion [24,53].…”

Section: Polarization Party and Gendermentioning

confidence: 99%

“…While we expect affective polarization to be evident, we are cognizant that using social media data comes with some concerns. In particular, the n=all fallacy [34], which is the assumption that, through the use of "big data," scholars can position their arguments as universal -based on digital trace data -even though these traces might be influenced by social media companies or other organizations [53], and are likely skewed to a younger population than the general public. These limitations thus motivate the exploratory nature of this study.…”

Section: Measuring Twitter-based Affective Polarizationmentioning

confidence: 99%

“…It is vital that we understand the drivers of this type of polarization since affective polarization can lead to democratic erosion [19]. As the general public increasingly turns to social media for their source of information [53], and partisan discourse leads to an increase in affective polarization [38,50], it is important to understand the discourse on social media during election campaigns [49].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Measuring and Unpacking Affective Polarization on Twitter: The Role of Party and Gender in the 2018 Senate Races

Mentzer¹,

Fallon²,

Prichard³

et al. 2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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This study examines how the Twittersphere talked about candidates running for the U.S senate in the 2018 congressional elections. We classify Twitter users as Liberal or Conservative to better understand how the two groups use social media during a major national political election. Using tweet sentiment, we assess how the Twittersphere felt about in-group party versus outgroup party candidates. When we further break these findings down based on the candidates' gender, we find that male senatorial candidates were talked about more positively than female candidates. We also find that Conservatives talked more positively about female Republican candidates than they did about Republican male candidates. Female candidates of the out-group party were talked about the least favorably of all candidates. Conservative tweeters exhibit the most positive level of in-group party sentiment and the most negative level of out-group party sentiment. We therefore attribute the most intense affective polarization to this ideological group.

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Social Media, Political Polarization, and Political Disinformation: A Review of the Scientific Literature

Cited by 545 publications

References 235 publications

Who Falls for Online Political Manipulation?

Who Falls for Online Political Manipulation?

Twitter Discourses on Climate Change: Exploring Topics and the Presence of Bots

Measuring and Unpacking Affective Polarization on Twitter: The Role of Party and Gender in the 2018 Senate Races

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