Antisemitism on Twitter: Collective Efficacy and the Role of Community Organisations in Challenging Online Hate Speech

Abstract

In this article, we conduct a comprehensive study of online antagonistic content related to Jewish identity posted on Twitter between October 2015 and October 2016 by UK-based users. We trained a scalable supervised machine learning classifier to identify antisemitic content to reveal patterns of online antisemitism perpetration at the source. We built statistical models to analyze the inhibiting and enabling factors of the size (number of retweets) and survival (duration of retweets) of information flows in addition to the production of online antagonistic content. Despite observing high temporal variability, we found that only a small proportion (0.7%) of the content was antagonistic. We also found that antagonistic content was less likely to disseminate in size or survive for a longer period. Information flows from antisemitic agents on Twitter gained less traction, while information flows emanating from capable and willing counter-speech actors—that is, Jewish organizations—had a significantly higher size and survival rates. This study is the first to demonstrate that Sampson’s classic sociological concept of collective efficacy can be observed on social media (SM). Our findings suggest that when organizations aiming to counter harmful narratives become active on SM platforms, their messages propagate further and achieve greater longevity than antagonistic messages. On SM, counter-speech posted by credible, capable and willing actors can be an effective measure to prevent harmful narratives. Based on our findings, we underline the value of the work by community organizations in reducing the propagation of cyberhate and increasing trust in SM platforms.

Hate speech’s double damage: A semi-automated approach toward direct and indirect targets

“…While the devaluation of ideas or behavior constitutes the most prominent type of hate speech in all three rather distinct datasets, the amount of other forms of visible hate speech (i.e., insult, slander, devaluation of communication, vulgarity) is comparably small and accumulates around certain events (Twitter) or single videos (YouTube). These latter findings are also roughly in line with prior research from other platforms, other cultural contexts, and other time periods (e.g., Coe et al, 2014;Ozalp et al, 2020;Williams & Burnap, 2016).…”

Contextual-Aware and Expert Resources for Brazilian Portuguese Hate Speech Detection

“…For example, "sol nascer quadrado" (sun rise square) is an expression in which we may identify the pejorative term "ladrão" (thief), and in "mulher fácil" (easy woman) the pejorative term "vadia" (slut). It should be noted that a couple of identified terms and expressions were classified by the linguist as deeply culture-rooted (e.g., "macumbeira" 12 , "bolsonazi" 13 , "moças que ficam na rodovia" (girls who are on the highway ) 14 ). Approximately 10% of identified terms and expressions from the proposed contextual lexicon were classified as being deeply culture-rooted.…”

“…The state-of-the-art for offensive language and hate speech detection has focused on different tasks, such as (i) automatically detecting hate speech groups as racism [8], antisemitism [5,13], religious intolerance [14], misogyny and sexism [9,15], and cyberbullying [16]; (ii) filtering pages with hate and violence [17]; (iii) offensive language detection [2,7,18]; and (iv) toxic comment detection [19]. Comprehensive surveys on automatic detection of hate speech in text were proposed by [3,10,12,20].…”

Understanding and Detecting Hateful Content using Contrastive Learning

“…Antisemitism have grown and proliferated rapidly online and have done so mostly unchecked; Zannettou et al [71] call for new techniques to understand it better and combat it. Ozalp et al [44] train a scalable supervised machine learning classifier to identify antisemitic content on Twitter. Chandra et al [9] propose a multimodal system that uses text, images, and OCR to detect the presence of Antisemitic textual and visual content.…”