A Survey on Computational Politics

Haq, Ehsan ul; Braud, Tristan; Kwon, Young Dae; Hui, Pan

doi:10.1109/access.2020.3034983

Cited by 25 publications

(12 citation statements)

References 185 publications

(130 reference statements)

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“…According to a recent survey (Haq et al. 2020 ), existing literature on computational politics can be categorized into five classes, as discussed in the following.…”

Section: Related Workmentioning

confidence: 99%

Analyzing voter behavior on social media during the 2020 US presidential election campaign

Belcastro

Branda

Cantini

et al. 2022

Soc. Netw. Anal. Min.

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Every day millions of people use social media platforms by generating a very large amount of opinion-rich data, which can be exploited to extract valuable information about human dynamics and behaviors. In this context, the present manuscript provides a precise view of the 2020 US presidential election by jointly applying topic discovery, opinion mining, and emotion analysis techniques on social media data. In particular, we exploited a clustering-based technique for extracting the main discussion topics and monitoring their weekly impact on social media conversation. Afterward, we leveraged a neural-based opinion mining technique for determining the political orientation of social media users by analyzing the posts they published. In this way, we were able to determine in the weeks preceding the Election Day which candidate or party public opinion is most in favor of. We also investigated the temporal dynamics of the online discussions, by studying how users’ publishing behavior is related to their political alignment. Finally, we combined sentiment analysis and text mining techniques to discover the relationship between the user polarity and sentiment expressed referring to the different candidates, thus modeling political support of social media users from an emotional viewpoint.

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“…According to a recent survey (Haq et al. 2020 ), existing literature on computational politics can be categorized into five classes, as discussed in the following.…”

Section: Related Workmentioning

confidence: 99%

Analyzing voter behavior on social media during the 2020 US presidential election campaign

Belcastro

Branda

Cantini

et al. 2022

Soc. Netw. Anal. Min.

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“…Social media platforms have become a key resource for research on political participation and discourse (see Haq et al (2020) and Theocharis and Jungherr (2021) for overviews). Studies have investigated a wide range of phenomena including political polarization (Adamic and Glance 2005;Conover et al 2011;Himelboim, McCreery, and Smith 2013;Weber, Garimella, and Batayneh 2013;Garcia et al 2015;Garimella et al 2018;Morales et al 2019), ideological radicalization (Grover and Mark 2019;Crawford, Keen, and Suarez-Tangil 2021;Mamié, Ribeiro, and West 2021), and political framing (Demszky et al 2019;Mendelsohn, Budak, and Jurgens 2021;Reiter-Haas, Kopeinik, and Lex 2021).…”

Section: Research On Online Political Discoursementioning

confidence: 99%

The Reddit Politosphere: A Large-Scale Text and Network Resource of Online Political Discourse

Hofmann

Schütze

Pierrehumbert

2022

ICWSM

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We introduce the Reddit Politosphere, a large-scale resource of online political discourse covering more than 600 political discussion groups over a period of 12 years. It is to the best of our knowledge the largest and ideologically most comprehensive dataset of its type now available. One key feature of the Reddit Politosphere is that it consists of both text and network data, allowing for methodologically-diverse analyses. We describe in detail how we create the Reddit Politosphere, present descriptive statistics, and sketch potential directions for future research based on the resource.

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“…Partisan media intentionally adopt ideological frames and cover stories in ways that favor the politicians in their own party and create negative impressions of those in the other party, contributing to polarization in American politics ( Levendusky, 2013 ; Forgette, 2018 ). At the interface of political communication and computational sciences, the emerging field of computational politics has produced many analyses of polarization over the recent years ( Haq et al, 2020 ; Pozen, Talley & Nyarko, 2019 ; Yarchi, Baden & Kligler-Vilenchik, 2021 ). Comparatively less computational research has been devoted to how politicians influence debate on social media, but interest in this question has grown during Donald Trump’s presidency as he extensively communicated directly to the public via Twitter ( Ouyang & Waterman, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

(Re)shaping online narratives: when bots promote the message of President Trump during his first impeachment

Galgoczy

Phatak

Vinson

et al. 2022

PeerJ Computer Science

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Influencing and framing debates on Twitter provides power to shape public opinion. Bots have become essential tools of ‘computational propaganda’ on social media such as Twitter, often contributing to a large fraction of the tweets regarding political events such as elections. Although analyses have been conducted regarding the first impeachment of former president Donald Trump, they have been focused on either a manual examination of relatively few tweets to emphasize rhetoric, or the use of Natural Language Processing (NLP) of a much larger corpus with respect to common metrics such as sentiment. In this paper, we complement existing analyses by examining the role of bots in the first impeachment with respect to three questions as follows. (Q1) Are bots actively involved in the debate? (Q2) Do bots target one political affiliation more than another? (Q3) Which sources are used by bots to support their arguments? Our methods start with collecting over 13M tweets on six key dates, from October 6th 2019 to January 21st 2020. We used machine learning to evaluate the sentiment of the tweets (via BERT) and whether it originates from a bot. We then examined these sentiments with respect to a balanced sample of Democrats and Republicans directly relevant to the impeachment, such as House Speaker Nancy Pelosi, senator Mitch McConnell, and (then former Vice President) Joe Biden. The content of posts from bots was further analyzed with respect to the sources used (with bias ratings from AllSides and Ad Fontes) and themes. Our first finding is that bots have played a significant role in contributing to the overall negative tone of the debate (Q1). Bots were targeting Democrats more than Republicans (Q2), as evidenced both by a difference in ratio (bots had more negative-to-positive tweets on Democrats than Republicans) and in composition (use of derogatory nicknames). Finally, the sources provided by bots were almost twice as likely to be from the right than the left, with a noticeable use of hyper-partisan right and most extreme right sources (Q3). Bots were thus purposely used to promote a misleading version of events. Overall, this suggests an intentional use of bots as part of a strategy, thus providing further confirmation that computational propaganda is involved in defining political events in the United States. As any empirical analysis, our work has several limitations. For example, Trump’s rhetoric on Twitter has previously been characterized by an overly negative tone, thus tweets detected as negative may be echoing his message rather than acting against him. Previous works show that this possibility is limited, and its existence would only strengthen our conclusions. As our analysis is based on NLP, we focus on processing a large volume of tweets rather than manually reading all of them, thus future studies may complement our approach by using qualitative methods to assess the specific arguments used by bots.

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A Survey on Computational Politics

Cited by 25 publications

References 185 publications

Analyzing voter behavior on social media during the 2020 US presidential election campaign

Analyzing voter behavior on social media during the 2020 US presidential election campaign

The Reddit Politosphere: A Large-Scale Text and Network Resource of Online Political Discourse

(Re)shaping online narratives: when bots promote the message of President Trump during his first impeachment

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