Measuring political polarization: Twitter shows the two sides of Venezuela

Morales, Alfredo Jose; Borondo, J.; Losada, Juan Carlos; Benito, R. M.

doi:10.1063/1.4913758

Cited by 159 publications

(166 citation statements)

References 55 publications

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“…11 The retweet network visualizations are created first in Gephi, 12 using a force directed layout and then exported to Sigma.js via a plugin. 13 …”

Section: Architecturementioning

confidence: 99%

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Mary, Mary, Quite Contrary

Garimella

iMorales²,

Gionis

et al. 2017

Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion

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Polarized topics often spark discussion and debate on social media. Recent studies have shown that polarized debates have a specific clustered structure in the endorsement network, which indicates that users direct their endorsements mostly to ideas they already agree with. Understanding these polarized discussions and exposing social media users to content that broadens their views is of paramount importance.The contribution of this demonstration is two-fold. (i) A tool to visualize retweet networks about controversial issues on Twitter. By using our visualization, users can understand how polarized discussions are shaped on Twitter, and explore the positions of the various actors. (ii) A solution to reduce polarization of such discussions. We do so by exposing users to information which presents a contrarian point of view. Users can visually inspect our recommendations and understand why and how these would play out in terms of the retweet network.Our demo 1 provides one of the first steps in developing automated tools that help users explore, and possibly escape, their echo chambers. The ideas in the demo can also help content providers design tools to broaden their reach to people with different political and ideological backgrounds.

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“…11 The retweet network visualizations are created first in Gephi, 12 using a force directed layout and then exported to Sigma.js via a plugin. 13 …”

Section: Architecturementioning

confidence: 99%

“…This selective exposure has led to increased fragmentation and polarization online. A wide body of recent studies have studied [1,3,12] and quantified [2,4,5,10,13] this issue.…”

Section: Related Workmentioning

confidence: 99%

Mary, Mary, Quite Contrary

Garimella

iMorales²,

Gionis

et al. 2017

Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion

View full text Add to dashboard Cite

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“…Previous studies aim at identifying controversial issues, mostly around political debates [1,3,8,9] but also other topics [5]. While most recent papers focus on Twitter [3,5,8,9], controversy in other social-media platforms, such as blogs [1] and opinion fora [2], has also been analyzed.…”

Section: Related Workmentioning

confidence: 99%

“…While most recent papers focus on Twitter [3,5,8,9], controversy in other social-media platforms, such as blogs [1] and opinion fora [2], has also been analyzed. The main limitation of previous work is that the majority of studies have focused on known, long-lasting debates, such as elections [1,3].…”

Section: Related Workmentioning

confidence: 99%

Exploring Controversy in Twitter

Garimella

Mathioudakis²,

Morales

et al. 2016

Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion

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Among the topics discussed on social media, some spark more heated debate than others. For example, experience suggests that major political events, such as a vote for healthcare law in the US, would spark more debate between opposing sides than other events, such as a concert of a popular music band. Exploring the topics of discussion on Twitter and understanding which ones are controversial is extremely useful for a variety of purposes, such as for journalists to understand what issues divide the public, or for social scientists to understand how controversy is manifested in social interactions.The system we present processes the daily trending topics discussed on the platform, and assigns to each topic a controversy score, which is computed based on the interactions among Twitter users, and a visualization of these interactions, which provides an intuitive visual cue regarding the controversy of the topic. The system also allows users to explore the messages (tweets) associated with each topic, and sort and explore the topics by different criteria (e

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“…To date, much of the previous research on controversiality has used data from political debates (Adamic & Glance, 2005;Conover et al, 2011;Mejova, Zhang, Diakopoulos, & Castillo, 2014;Morales, Borondo, Losada, & Benito, 2015), news (Awadallah, Ramanath, & Weikum, 2012;Choi, Jung, & Myaeng, 2010;Mejova et al, 2014), and social media, such as blogs (Adamic & Glance, 2005), and Wikipedia (Dori-Hacohen & Allan, 2013;Kittur, Suh, Pendleton, & Chi, 2007;Rad & Barbosa, 2012).…”

Section: Introductionmentioning

confidence: 99%

Telling Apart Tweets Associated with Controversial versus Non-Controversial Topics

Addawood¹,

Rezapour²,

Abdar³

et al. 2017

Proceedings of the Second Workshop on NLP and Computational Social Science

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In this paper, we evaluate the predictability of tweets associated with controversial versus non-controversial topics. As a first step, we crowd-sourced the scoring of a predefined set of topics on a Likert scale from non-controversial to controversial. Our feature set entails and goes beyond sentiment features, e.g., by leveraging empathic language and other features that have been previously used, but are new for this particular study. We find focusing on the structural characteristics of tweets to be beneficial for this task. Using a combination of emphatic, language-specific, and Twitter-specific features for supervised learning resulted in 87% accuracy (F1) for cross-validation of the training set and 63.4% accuracy when using the test set. Our analysis shows that features specific to Twitter or social media in general are more prevalent in tweets on controversial topics than in non-controversial ones. To test the premise of the paper, we conducted two additional sets of experiments, which led to mixed results. This finding will inform our future investigations into the relationship between language use on social media and the perceived controversiality of topics.

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Measuring political polarization: Twitter shows the two sides of Venezuela

Cited by 159 publications

References 55 publications

Mary, Mary, Quite Contrary

Mary, Mary, Quite Contrary

Exploring Controversy in Twitter

Telling Apart Tweets Associated with Controversial versus Non-Controversial Topics

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