On Analyzing Antisocial Behaviors Amid COVID-19 Pandemic

Awal, Rabiul; Cao, Rui; Mitrović, Sandra; Lee, Roy Ka-Wei

doi:10.48550/arxiv.2007.10712

Cited by 6 publications

(6 citation statements)

References 21 publications

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“…A recent work showed how the anti-social behavior on social media during COVID-19 led to the spread of hate speech. Awal et al [15] coined the term, 'disability hate speech' and showed its social, cultural and political contexts. Ziems et al [16] explained how COVID-19 tweets increased racism, hate, and xenophobia in social media.…”

Section: Related Workmentioning

confidence: 99%

Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter

Masud

Dutta

Makkar

et al. 2021

2021 IEEE 37th International Conference on Data Engineering (ICDE)

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Online hate speech, particularly over microblogging platforms like Twitter, has emerged as arguably the most severe issue of the past decade. Several countries have reported a steep rise in hate crimes infuriated by malicious hate campaigns. While the detection of hate speech is one of the emerging research areas, the generation and spread of topic-dependent hate in the information network remain under-explored. In this work, we focus on exploring user behavior, which triggers the genesis of hate speech on Twitter and how it diffuses via retweets. We crawl a large-scale dataset of tweets, retweets, user activity history, and follower networks, comprising over 161 million tweets from more than 41 million unique users. We also collect over 600k contemporary news articles published online. We characterize different signals of information that govern these dynamics. Our analyses differentiate the diffusion dynamics in the presence of hate from usual information diffusion. This motivates us to formulate the modeling problem in a topic-aware setting with real-world knowledge. For predicting the initiation of hate speech for any given hashtag, we propose multiple feature-rich models, with the best performing one achieving a macro F1 score of 0.65. Meanwhile, to predict the retweet dynamics on Twitter, we propose RETINA, a novel neural architecture that incorporates exogenous influence using scaled dot-product attention. RETINA achieves a macro F1-score of 0.85, outperforming multiple stateof-the-art models. Our analysis reveals the superlative power of RETINA to predict the retweet dynamics of hateful content compared to the existing diffusion models.

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Section: Related Workmentioning

confidence: 99%

Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter

Masud

Dutta

Makkar

et al. 2021

2021 IEEE 37th International Conference on Data Engineering (ICDE)

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show abstract

“…2). A study on online antisocial activity indicated increased sinophobic sentiment (Schild et al, 2020) and increased hate speech directed at vulnerable groups as well as xenophobia (Awal et al, 2020). While these studies do not necessarily use the same definition of anti-social behaviour referred to here, they give a useful indication of related research.…”

Section: Asb In the Pandemicmentioning

confidence: 89%

Anti-social behaviour in the coronavirus pandemic

Halford¹,

Dixon²,

Farrell³

2022

Preprint

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Anti-social behaviour recorded by police more than doubled early in the coronavirus pandemic in England and Wales. This was a stark contrast to the steep falls in most types of recorded crime. Why was ASB so different? Was it changes in ‘traditional’ ASB such as noisy neighbours, or was it ASB records of breaches of COVID-19 regulations? Further, why did police-recorded ASB find much larger increases early in the pandemic than the Telephone Crime Survey for England and Wales? This study uses two approaches to address the issues. The first is a survey of police forces, via Freedom of Information requests, to determine whether covid-regulation breaches were recorded as ASB. The second is natural language processing (NLP) used to interrogate the text details of police ASB records. We find police recording practice varied greatly between areas. We conclude that the early-pandemic increases in recorded ASB were primarily due to breaches of covid regulations with around half of these also involving traditional forms of ASB. We also suggest that the study offers proof of concept that NLP may have significant potential to inform policing and crime policy globally.

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“…3, this suggests that Gettr as a whole is generally representative of the US far-right. These results suggest that the ability to mention one's political opponents on Twitter is part of the reason that the matched cohort are more toxic on Twitter than they are on Gettr where direct interactions with political opponents are not possible [61,62].…”

Section: Content Toxicity and Twitter Mentionsmentioning

confidence: 96%

The Systemic Impact of Deplatforming on Social Media

Mekacher¹,

Falkenberg²,

Baronchelli³

2023

Preprint

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Deplatforming, or banning malicious accounts from social media, is a key tool for moderating online harms. However, the consequences of deplatforming for the wider social media ecosystem have been largely overlooked so far, due to the difficulty of tracking banned users. Here, we address this gap by studying the ban-induced platform migration from Twitter to Gettr. With a matched dataset of 15M Gettr posts and 12M Twitter tweets, we show that users active on both platforms post similar content as users active on Gettr but banned from Twitter, but the latter have higher retention and are 5 times more active. Then, we reveal that matched users are more toxic on Twitter, where they can engage in abusive cross-ideological interactions, than Gettr. Our analysis shows that the matched cohort are ideologically aligned with the farright, and that the ability to interact with political opponents may be part of the appeal of Twitter to these users. Finally, we identify structural changes in the Gettr network preceding the 2023 Brasília insurrections, highlighting how deplatforming from mainstream social media can fuel poorly-regulated alternatives that may pose a risk to democratic life.

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On Analyzing Antisocial Behaviors Amid COVID-19 Pandemic

Cited by 6 publications

References 21 publications

Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter

Hate is the New Infodemic: A Topic-aware Modeling of Hate Speech Diffusion on Twitter

Anti-social behaviour in the coronavirus pandemic

The Systemic Impact of Deplatforming on Social Media

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