Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies

Field, Anjalie; Kliger, Doron; Wintner, Shuly; Pan, Jennifer; Jurafsky, Dan; Tsvetkov, Yulia

doi:10.18653/v1/d18-1393

Cited by 91 publications

(104 citation statements)

References 23 publications

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“…Critically, there is much room for future research/investigation into the matter of interference in the 2016 presidential election. Recent work on frame identification, for example, could help to more deeply understand how the Facebook ads and troll accounts may have developed their targeting strategies as a function of both U.S. and Russian media [31]. Deeper analyses of other types of metadata (e.g., number of click-throughs on Facebook ads, social network / retweeting patterns in the Twitter data) will help us to understand the extent to which the influence operations were effective in polarizing and/or suppressing the American electorate.…”

Section: Discussionmentioning

confidence: 99%

Characterizing the Internet Research Agency’s Social Media Operations During the 2016 U.S. Presidential Election using Linguistic Analyses

Boyd¹,

Spangher²,

Fourney³

et al. 2018

Preprint

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Converging investigations on the part of multiple agencies/agents have provided overwhelming evidence for Russian interference in the 2016 U.S. presidential election. As a part (and consequence) of recent reports, multiple datasets that capture actions taken by actors of the Internet Research Agency (IRA), have been released to the public. In the cur-rent paper, we present and abridged report of several preliminary forensic analyses of Facebook ad data and Twitter troll accounts that were run by the IRA during the election cycle. Through the use of language analysis, we characterize the evolution of IRA content over the course of the election cycle, providing a basis for understanding how left- and right-leaning ideologies were differentially targeted to spread enmity among the American electorate. Additionally, through an analysis of syntactic constructions, we find that the content produced by the IRA on Twitter was linguistically unique from a control sample of English-speaking Twitter accounts. Altogether, our findings suggest that the IRA’s operations were largely unsophisticated and “low-budget” in nature, with no serious attempts at point-of-origin obfuscation being taken.

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Section: Discussionmentioning

confidence: 99%

Characterizing the Internet Research Agency’s Social Media Operations During the 2016 U.S. Presidential Election using Linguistic Analyses

Boyd¹,

Spangher²,

Fourney³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Topic choice can be a tool for agenda-setting by establishing what an author or institution deems worthy of discussion (McCombs, 2002), and works in NLP have used topic modeling as an approach to measure this effect (Tsur et al, 2015;Field et al, 2018). The strategy of highlighting particular aspects within topics as a means of framing (Entman, 2007) has also been quantified in the NLP literature (Boydstun et al, 2013;Card et al, 2015;Naderi and Hirst, 2017).…”

Section: Topics and Framingmentioning

confidence: 99%

Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings

Demszky

Garg

Voigt³

et al. 2019

Proceedings of the 2019 Conference of the North

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110

103

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We provide an NLP framework to uncover four linguistic dimensions of political polarization in social media: topic choice, framing, affect and illocutionary force. We quantify these aspects with existing lexical methods, and propose clustering of tweet embeddings as a means to identify salient topics for analysis across events; human evaluations show that our approach generates more cohesive topics than traditional LDA-based models. We apply our methods to study 4.4M tweets on 21 mass shootings. We provide evidence that the discussion of these events is highly polarized politically and that this polarization is primarily driven by partisan differences in framing rather than topic choice. We identify framing devices, such as grounding and the contrasting use of the terms "terrorist" and "crazy", that contribute to polarization. Results pertaining to topic choice, affect and illocutionary force suggest that Republicans focus more on the shooter and event-specific facts (news) while Democrats focus more on the victims and call for policy changes. Our work contributes to a deeper understanding of the way group divisions manifest in language and to computational methods for studying them. 1

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“…Topic choice has been commonly used in NLP (Tsur et al, 2015;Field et al, 2018;Demszky et al, 2019) as a proxy for agenda-setting, the strategic highlighting of what aspects of a subject are worth discussing (McCombs, 2002). Here, we first describe our preliminary topic analysis for discovering the range of topics discussed.…”

Section: Topical Analysismentioning

confidence: 99%

The Discourse of Online Content Moderation: Investigating Polarized User Responses to Changes in Reddit’s Quarantine Policy

Shen¹,

Rosé²

2019

Proceedings of the Third Workshop on Abusive Language Online

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Recent concerns over abusive behavior on their platforms have pressured social media companies to strengthen their content moderation policies. However, user opinions on these policies have been relatively understudied. In this paper, we present an analysis of user responses to a September 27, 2018 announcement about the quarantine policy on Reddit as a case study of to what extent the discourse on content moderation is polarized by users' ideological viewpoint. We introduce a novel partitioning approach for characterizing user polarization based on their distribution of participation across interest subreddits. We then use automated techniques for capturing framing to examine how users with different viewpoints discuss moderation issues, finding that right-leaning users invoked censorship while left-leaning users highlighted inconsistencies on how content policies are applied. Overall, we argue for a more nuanced approach to moderation by highlighting the intersection of behavior and ideology in considering how abusive language is defined and regulated.

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Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies

Cited by 91 publications

References 23 publications

Characterizing the Internet Research Agency’s Social Media Operations During the 2016 U.S. Presidential Election using Linguistic Analyses

Characterizing the Internet Research Agency’s Social Media Operations During the 2016 U.S. Presidential Election using Linguistic Analyses

Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings

The Discourse of Online Content Moderation: Investigating Polarized User Responses to Changes in Reddit’s Quarantine Policy

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