Coordinator: Dr. Savvas Zannettou
The spread of offensive language and hate speech online is an important and timely issue that exists on social networks. The continuous exposure to hateful content can have catastrophic consequences as it can lead to user radicalization and real-world violence (e.g., hate attacks against specific demographic groups). Despite the problem’s importance and societal implications, there are several research gaps in understanding, detecting, and mitigating the spread of hate speech. First, the research community has not reached a consensus on the definition of hate speech and this lack of definition results in concerns regarding suppression of free speech. Second, online hate speech can take several forms like targeted hate to specific user groups with each form having its own peculiarities. Due to this and the lack of a clear definition of what constitutes hate speech, we do not yet have the appropriate tools and techniques to automatically and effectively detect hate speech. Finally, given that we detect hate speech, there is a need to develop specific mitigation techniques to minimize the impact that hate speech has on online Web communities and the mental toll to end-users. In this line of work, we aim to study and understand specific forms of hate speech (e.g., Anti Semitism, anti-chinese sentiments, etc.) and develop techniques to automatically detect instances of hate speech.
A Quantitative Approach to Understanding Online Antisemitism (ICWSM 2020):
Authors: Savvas Zannettou, Joel Finkelstein (Princeton University), Barry Bradlyn (UIUC), Jeremy Blackburn (Binghamton University)
Paper Link: https://arxiv.org/abs/1809.01644
Short description: In this work, we study online antisemitism on two fringe Web communities, namely, 4chan Politically Incorrect board (/pol/) and Gab. We propose a quantitative approach to understanding online antisemitism, which can also be applied to study other forms of hate speech on the Web. We leverage word embeddings and graph analysis techniques to visualize topics of discussions and automatically discover new slurs related to online antisemitism. Overall, alarmingly, we find a rise of antisemitic rhetoric and antisemitic memes over time in both 4chan’s /pol/ and Gab.
Go Eat a Bat, Chang!": An Early Look on the Emergence of Sinophobic Behavior on Web Communities in the Face of Covid-19 (under submission):
Authors: Leonard Schild (CISPA), Chen Ling (Boston University), Jeremy Blackburn (Binghamton University), Gianluca Stringhini (Boston University), Yang Zhang (CISPA), Savvas Zannettou
Short description: The COVID-19 pandemic has changed our lives in an unprecedented way. During these challenging times, the Web is an indispensable medium, however, it can also be exploited for disseminating hateful content like the spread of Sinophobic content, since the virus is believed to originate from China. In this work, we investigate the spread of Sinophobic content on 4chan and Twitter and assess whether the COVID-19 pandemic leads to an insurgence of online Sinophobia. We find that, indeed, the COVID-19 pandemic caused an increase in the use of Sinophobic slurs on both 4chan and Twitter. Also, we find differences across Twitter and 4chan: on Twitter we observed a shift towards blaming China for the pandemic, while on 4chan we observed a shift towards using more and new Sinophobic slurs.
Measuring and Characterizing Hate Speech on News Websites (WebSci 2020):
Authors: Savvas Zannettou, Mai ElSherief (Georgia Institute of Technology), Elizabeth Belding (UCSB), Shirin Nilizadeh (University of Texas at Arlington), Gianluca Stringhini (Boston University)
Short description: In this work, we aim to quantify and measure the prevalence of hateful content on comments posted on news articles and whether the appearance of news articles on 4chan and Reddit, lead to changes in (hateful) commenting activity. By collecting a large corpus of comments from news articles and annotating them using Google’s Perspective API, we find that there is an increase in hateful commenting activity shortly after the news articles are posted on Reddit and 4chan.