Online reactions to the 2017 ‘Unite the right’ rally in Charlottesville: measuring polarization in Twitter networks using media followership

Tien, Joseph H.; Eisenberg, Marisa C.; Cherng, Sarah T.; Porter, Mason A.

doi:10.1007/s41109-019-0223-3

Cited by 26 publications

(27 citation statements)

References 89 publications

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“…From a practical perspective, the strong effect of common connections on transitive closures can lead to the formation of subgroups and clusters where information is shared between them, fostering homogeneity and segregation, where one’s own taste or worldview may be strengthened by being exposed to similar information sources. This is consistent with findings from prior studies showing polarization effects across OSNs, including Twitter, Facebook, and Blogs (see Gilbert et al, 2009; Tien et al, 2019; Yardi & boyd, 2010). If this is the natural evolution of ties in OSNs, it is arguably incumbent on the OSNs to promote more diversity in tweets shown to users and in the suggested list of users to follow.…”

Section: Discussionsupporting

confidence: 92%

Follow Me Too: Determinants of Transitive Tie Formation on Twitter

Malik

Lee

2020

Social Media + Society

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Transitivity, defined as the tendency for node A to be connected to node B given that A is connected to node X and X is connected to B, has been found to be a strong predictor of tie formation in various types of social networks. As transitive ties can influence information sharing, diffusion, and attitudes toward messages, understanding the motivations and mechanisms behind transitive tie formation in online social networks (OSNs) is important. Using a large longitudinal dataset from a popular OSN, Twitter, we examine the factors affecting transitivity. Results show that the strength of ties, activity like the number of tweets, and most importantly, the number of common connections are key factors affecting transitive tie formation. Theoretical implications regarding the evolution of network structure and polarization of views as well as practical suggestions for organizations aiming to accumulate followers for information sharing are discussed.

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Section: Discussionsupporting

confidence: 92%

Follow Me Too: Determinants of Transitive Tie Formation on Twitter

Malik

Lee

2020

Social Media + Society

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“…Other studies report similar results, using both local and global social network features, noticeably through incorporating the node2vec algorithm (Papegnies, Labatut, Dufour, & Linarès, 2017;Raisi & Huang, 2017). The use of network representations is supported by social science research which shows evidence of homophily online; it is likely that abusive users are connected to other abusive users (Caiani & Wagemann, 2009;Tien, Eisenberg, Cherng, & Porter, 2019). We propose that anonymity should also be explicitly modelled in future work as it has disinhibiting effects (Amichai-hamburger & McKenna, 2006) and is empirically associated with users posting abuse (Hine et al, 2017).…”

Section: Accounting For Contextmentioning

confidence: 52%

Challenges and frontiers in abusive content detection

Vidgen¹,

Harris²,

Nguyen³

et al. 2019

Proceedings of the Third Workshop on Abusive Language Online

169

210

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Online abusive content detection is an inherently difficult task. It has received considerable attention from academia, particularly within the computational linguistics community, and performance appears to have improved as the field has matured. However, considerable challenges and unaddressed frontiers remain, spanning technical, social and ethical dimensions. These issues constrain the performance, efficiency and generalizability of abusive content detection systems. In this article we delineate and clarify the main challenges and frontiers in the field, critically evaluate their implications and discuss solutions. We also highlight ways in which social scientific insights can advance research.

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“…Evidence that offline hate crime drives online radicalisation (Figure 1 step B) Online hate is also found to increase following offline violence, suggesting that offline violence can facilitate online radicalisation within extremist groups. For example, the Charlottesville 'Unite the Right' rally in 2017 led to a 400% increase in search terms indicating a desire to get involved with violent far-right extremist groups in the weeks following the event (Moonshot CVE, 2018), which reflected wider online interest in joining these groups (Tien, Eisenberg, Cherng, & Porter, 2020). Similar effects have been shown for Islamic extremism, and offline Jihadi-inspired terror attacks have led to increases in online posts advocating for further violence amongst Islamic extremist communities (Olteanu, Castillo, Boy, & Varshney, 2018),…”

Section: The Relationship Between Online Hate and Offline Violencementioning

confidence: 99%

Mutual radicalisation of opposing extremist groups via the Internet

Jd¹,

Heerdink²

2021

Preprint

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Social media has become a common arena for both far-right and Islamic extremist groups to stoke division through the spreading of propaganda and hate speech. This online hate is suggested to drive extremism online, and in some cases lead to offline hate crimes and violence. Whether this online radicalisation happens in isolation within a group, or whether there is an interdependent relationship of mutual radicalisation, is unclear. A possible process by which mutual radicalisation could occur would be if social media incite users to commit offline violence, and if this offline violence in return triggers online reactions from both the target and perpetrator groups. This however has not been tested. This study addresses these questions by investigating the nature of the online-offline relationship of extremist hate. We combine data from the social media platform Gab, variations in Internet search trends, and offline hate crimes in three countries, and test for temporal relationships between opposing extremist groups. Our findings show that online hate from far-right groups both precedes offline violence from these same groups, and spikes following offline violence from opposing Islamic extremist groups. Additionally, far-right Islamophobic violence offline is also followed by increased online interest in Islamic extremist topics. Together, these findings show that the Internet, and specifically hate speech, plays a potential key role in a cyclical process that increases mutual radicalisation.

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Online reactions to the 2017 ‘Unite the right’ rally in Charlottesville: measuring polarization in Twitter networks using media followership

Cited by 26 publications

References 89 publications

Follow Me Too: Determinants of Transitive Tie Formation on Twitter

Follow Me Too: Determinants of Transitive Tie Formation on Twitter

Challenges and frontiers in abusive content detection

Mutual radicalisation of opposing extremist groups via the Internet

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