相关论文: Reading In-Between the Lines: An Analysis of Disse…

An increasing amount of attention has been devoted to the problem of "toxic" or antisocial behavior on social media. In this paper we analyze such behavior at very large scales: we analyze toxicity over a 14-year time span on nearly 500…

Hate speech has grown significantly on social media, causing serious consequences for victims of all demographics. Despite much attention being paid to characterize and detect discriminatory speech, most work has focused on explicit or…

Tweets are the most concise form of communication in online social media, wherein a single tweet has the potential to make or break the discourse of the conversation. Online hate speech is more accessible than ever, and stifling its…

Misbehavior in online social networks (OSN) is an ever-growing phenomenon. The research to date tends to focus on the deployment of machine learning to identify and classify types of misbehavior such as bullying, aggression, and racism to…

Modeling user engagement dynamics on social media has compelling applications in user-persona detection and political discourse mining. Most existing approaches depend heavily on knowledge of the underlying user network. However, a large…

With the rise of social media, a rise of hateful content can be observed. Even though the understanding and definitions of hate speech varies, platforms, communities, and legislature all acknowledge the problem. Therefore, adolescents are a…

The rapid growth of social media in recent years has fed into some highly undesirable phenomena such as proliferation of abusive and offensive language on the Internet. Previous research suggests that such hateful content tends to come from…

Over the past decade, the media landscape has seen a radical shift. As more of the public stay informed of current events via online sources, competition has grown as outlets vie for attention. This competition has prompted some online…

In order to study online hate speech, the availability of datasets containing the linguistic phenomena of interest are of crucial importance. However, when it comes to specific target groups, for example teenagers, collecting such data may…

Building on current work on multilingual hate speech (e.g., Ousidhoum et al. (2019)) and hate speech reduction (e.g., Sap et al. (2020)), we present XTREMESPEECH, a new hate speech dataset containing 20,297 social media passages from…

Social media systems allow Internet users a congenial platform to freely express their thoughts and opinions. Although this property represents incredible and unique communication opportunities, it also brings along important challenges.…

Social media has become an everyday means of interaction and information sharing on the Internet. However, posts on social networks are often aggressive and toxic, especially when the topic is controversial or politically charged.…

The censorship of toxic comments is often left to the judgment of imperfect models. Perspective API, a creation of Google technology incubator Jigsaw, is perhaps the most widely used toxicity classifier in industry; the model is employed by…

In today's digital landscape, the proliferation of conspiracy theories within the disinformation ecosystem of online platforms represents a growing concern. This paper delves into the complexities of this phenomenon. We conducted a…

In this work, we demonstrate how existing classifiers for identifying toxic comments online fail to generalize to the diverse concerns of Internet users. We survey 17,280 participants to understand how user expectations for what constitutes…

The Internet and online forums such as Reddit have become an increasingly popular medium for citizens to engage in political conversations. However, the online disinhibition effect resulting from the ability to use pseudonymous identities…

The tremendous growth of social media users interacting in online conversations has led to significant growth in hate speech, affecting people from various demographics. Most of the prior works focus on detecting explicit hate speech, which…

Most Americans agree that misinformation, hate speech and harassment are harmful and inadequately curbed on social media through current moderation practices. In this paper, we aim to understand the discursive strategies employed by people…

Online discussions are often characterized by strong behavioral asymmetries: a relatively small fraction of users actively produces content, while the majority primarily consumes and redistributes it. Here we propose a community-detection…

Toxic sentiment analysis on Twitter (X) often focuses on specific topics and events such as politics and elections. Datasets of toxic users in such research are typically gathered through lexicon-based techniques, providing only a…