The online hate speech is proliferating with several organization and countries implementing laws to ban such harmful speech. While these restrictions might reduce the amount of such hateful content, it does so by restricting freedom of speech. Thus, an promising alternative supported by several organizations is to counter such hate speech with more speech. In this paper, We analyze hate speech and the corresponding counters (aka counterspeech) on Twitter. We perform several lexical, linguistic and psycholinguistic analysis on these user accounts and obverse that counter speakers employ several strategies depending on the target community. The hateful accounts express more negative sentiments and are more profane. We also find that the hate tweets by verified accounts have much more virality as compared to a tweet by a non-verified account. While the hate users seem to use words more about envy, hate, negative emotion, swearing terms, ugliness, the counter users use more words related to government, law, leader. We also build a supervised model for classifying the hateful and counterspeech accounts on Twitter and obtain an F-score of 0.77. We also make our dataset public to help advance the research on hate speech.
Silence in response to digital hate carries significant expressive costs as well. When powerful intermediaries rebut demeaning stereotypes (like the Michelle Obama image) and invidious falsehoods (such as holocaust denial), they send a powerful message to readers. Because intermediaries often enjoy respect and a sense of legitimacy, using counterspeech, they can demonstrate what it means to treat others with respect and dignity @cite_9 .
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No longer confined to isolated corners of the web, cyber hate now enjoys a major presence on popular social media sites. The Facebook group “Kill a Jew Day,” for instance, acquired thousands of friends within days of its formation, while YouTube has hosted videos with names like “How to Kill Beaners,” “Execute the Gays,” and “Murder Muslim Scum.” The mainstreaming of cyber hate has the troubling potential to shape public expectations of online discourse. Internet intermediaries have the freedom and influence to seize this defining moment in cyber hate’s history. We believe that a thoughtful and nuanced intermediary-based approach to hate speech can foster respectful online discourse without suppressing valuable expression. To this end, we urge intermediaries to help address cyber hate by adopting accessible and transparent policies that educate users about their rights and responsibilities as digital citizens. Intermediaries’ options include challenging hateful speech by responding with counter-speech and empowering community members to enforce norms of digital citizenship. In online social networks, people tend to connect with like-minded people and read agreeable information. Direct recommendation of challenging content has not worked well because users do not value diversity and avoid challenging content. In this poster, we investigate the possibility of an indirect approach by introducing intermediary topics, which are topics that are common to people having opposing views on sensitive issues, i.e., those issues that tend to divide people. Through a case study about a sensitive issue discussed in Twitter, we show that such intermediary topics exist, opening a path for future work in recommendation promoting diversity of content to be shared. Hateful and antagonistic content published and propagated via the World Wide Web has the potential to cause harm and suffering on an individual basis, and lead to social tension and disorder beyond cyber space. Despite new legislation aimed at prosecuting those who misuse new forms of communication to post threatening, harassing, or grossly offensive language - or cyber hate - and the fact large social media companies have committed to protecting their users from harm, it goes largely unpunished due to difficulties in policing online public spaces. To support the automatic detection of cyber hate online, specifically on Twitter, we build multiple individual models to classify cyber hate for a range of protected characteristics including race, disability and sexual orientation. We use text parsing to extract typed dependencies, which represent syntactic and grammatical relationships between words, and are shown to capture ‘othering’ language - consistently improving machine classification for different types of cyber hate beyond the use of a Bag of Words and known hateful terms. Furthermore, we build a data-driven blended model of cyber hate to improve classification where more than one protected characteristic may be attacked (e.g. race and sexual orientation), contributing to the nascent study of intersectionality in hate crime. ABSTRACTWith the advances in interpersonal communication of the “Web 2.0” era, questions about the importance of civility are perhaps more important than ever. Mass digital interaction between strangers has become an everyday occurrence, bound by few behavioral norms. I argue that the widespread presence of incivility in online political communication limits the deliberative potential of online interactions. To test this hypothesis, I manipulate exposure to uncivil political discourse in an online discussion forum. I find that exposure to disagreeable uncivil political talk induces feelings of anger and aversion, which in turn reduces satisfaction with the message board discourse. On the other hand, exposure to like-minded incivility increases the use of uncivil behavior in political comments by message board posters. Notably, these effects mainly occur when histrionic, emotional incivility is present. I discuss why like-minded and disagreeable incivility have different effects, and reflect on what the pr...