Counterspeech on Twitter: A Field Study

As hateful and extremist content proliferates online, 'counterspeech' is gaining currency as a means of diminishing it. No wonder: counterspeech doesn't impinge on freedom of expression and can be practiced by almost anyone, requiring neither law nor institutions. The idea that 'more speech' is a remedy for harmful speech has been familiar in liberal democratic thought at least since U.S. Supreme Court Justice Louis Brandeis declared it in 1927. We are still without evidence, however, that counterspeech actually diminishes harmful speech or its effects. This would be very hard to measure offline but is a bit easier online, where speech and responses to it are recorded. In this paper we make a modest start. Specifically we ask: in what forms and circumstances does counterspeech - which we define as a direct response to hateful or dangerous speech - favorably influence discourse and perhaps even behavior?To our knowledge, this is the first study of Internet users (not a government or organization) counterspeaking spontaneously on a public platform like Twitter. Our findings are qualitative and anecdotal, since reliable quantitative detection of hateful speech or counterspeech is a problem yet to be fully solved due to the wide variations in language employed, although we made progress, as reported in an earlier paper that was part of this project (Saleem, Dillon, Benesch, & Ruths, 2016).We have identified four categories or "vectors" in each of which counterspeech functions quite differently, as hateful speech also does: one-to-one exchanges, many-to-one, one-to-many, and many-to-many. We also present a set of counterspeech strategies extrapolated from our data, with examples of tweets that illustrate those strategies at work, and suggestions for which ones may be successful

Thou shalt not hate: Countering Online Hate Speech

Hate content in social media is ever-increasing. While Facebook, Twitter, Google have attempted to take several steps to tackle the hateful content, they have mostly been unsuccessful. Counterspeech is seen as an effective way of tackling the online hate without any harm to the freedom of speech. Thus, an alternative strategy for these platforms could be to promote counterspeech as a defense against hate content. However, in order to have a successful promotion of such counterspeech, one has to have a deep understanding of its dynamics in the online world. Lack of carefully curated data largely inhibits such understanding. In this paper, we create and release the first ever dataset for counterspeech using comments from YouTube. The data contains 13,924 manually annotated comments where the labels indicate whether a comment is a counterspeech or not. This data allows us to perform a rigorous measurement study characterizing the linguistic structure of counterspeech for the first time. This analysis results in various interesting insights such as: the counterspeech comments receive much more likes as compared to the non-counterspeech comments, for certain communities majority of the non-counterspeech comments tend to be hate speech, the different types of counterspeech are not all equally effective and the language choice of users posting counterspeech is largely different from those posting non-counterspeech as revealed by a detailed psycholinguistic analysis. Finally, we build a set of machine learning models that are able to automatically detect counterspeech in YouTube videos with an F1-score of 0.71. We also build multilabel models that can detect different types of counterspeech in a comment with an F1-score of 0.60.Comment: Accepted at ICWSM 2019. 12 Pages, 5 Figures, and 7 Tables. The dataset and models are available here: https://github.com/binny-mathew/Countering_Hate_Speech_ICWSM201

Counterspeech: A Literature Review

Every day, internet users encounter hateful and dangerous speech online, and some of them choose to respond directly in order to refute or undermine it. We call this counterspeech. Only a few studies have attempted to measure the effectiveness of counterspeech directly, and as far as we know, this is the first review of relevant literature.We've collected and reviewed related articles from a range of fields including political science, sociology, countering violent extremism, and computational social science. These articles do not all use the term "counterspeech," but they shed light on various features of successful counterspeech, for example, qualities that make speakers/authors more influential in online interactions or the extent to which pro- and anti-social behavior is contagious on the internet

Counterspeeches up my sleeve! Intent Distribution Learning and Persistent Fusion for Intent-Conditioned Counterspeech Generation

Counterspeech has been demonstrated to be an efficacious approach for combating hate speech. While various conventional and controlled approaches have been studied in recent years to generate counterspeech, a counterspeech with a certain intent may not be sufficient in every scenario. Due to the complex and multifaceted nature of hate speech, utilizing multiple forms of counter-narratives with varying intents may be advantageous in different circumstances. In this paper, we explore intent-conditioned counterspeech generation. At first, we develop IntentCONAN, a diversified intent-specific counterspeech dataset with 6831 counterspeeches conditioned on five intents, i.e., informative, denouncing, question, positive, and humour. Subsequently, we propose QUARC, a two-stage framework for intent-conditioned counterspeech generation. QUARC leverages vector-quantized representations learned for each intent category along with PerFuMe, a novel fusion module to incorporate intent-specific information into the model. Our evaluation demonstrates that QUARC outperforms several baselines by an average of 10% across evaluation metrics. An extensive human evaluation supplements our hypothesis of better and more appropriate responses than comparative systems.Comment: ACL 202

On Simulating the Propagation and Countermeasures of Hate Speech in Social Networks

Hate speech expresses prejudice and discrimination based on actual or perceived innate characteristics such as gender, race, religion, ethnicity, colour, national origin, disability or sexual orientation. Research has proven that the amount of hateful messages increases inevitably on online social media. Although hate propagators constitute a tiny minority with less than 1% participants they create an unproportionally high amount of hate motivated content. Thus, if not countered properly, hate speech can propagate through the whole society. In this paper we apply agent-based modelling to reproduce how the hate speech phenomenon spreads within social networks. We reuse insights from the research literature to construct and validate a baseline model for the propagation of hate speech. From this, three countermeasures are modelled and simulated to investigate their effectiveness in containing the spread of hatred: Education, deferring hateful content, and cyber activism. Our simulations suggest that: (1) Education consititutes a very successful countermeasure, but it is long term and still cannot eliminate hatred completely; (2) Deferring hateful content has a similar although lower positive effect than education, and it has the advantage of being a short-term countermeasure; (3) In our simulations, extreme cyber activism against hatred shows the poorest performance as a countermeasure, since it seems to increase the likelihood of resulting in highly polarised societies

Dangerous Speech: A Practical Guide

No one has ever been born hating or fearing other people. That has to be taught – and those harmful lessons seem to be similar, though they're given in highly disparate cultures, languages, and places. Leaders have used particular kinds of rhetoric to turn groups of people violently against one another throughout human history, by demonizing and denigrating others. Vocabulary varies but the same themes recur: members of other groups are depicted as threats so serious that violence against them comes to seem acceptable or even necessary. Such language (or images or any other form of communication) is what we have termed "Dangerous Speech."Naming and studying Dangerous Speech can be useful for violence prevention, in several ways. First, a rise in the abundance or severity of Dangerous Speech can serve as an early warning indicator for violence between groups. Second, violence might be prevented or at least diminished by limiting Dangerous Speech or its harmful effects on people. We do not believe this can or should be achieved through censorship. Instead, it's possible to educate people so they become less susceptible to (less likely to believe) Dangerous Speech. The ideas described here have been used around the world, both to monitor and to counter Dangerous Speech.This guide, a revised version of an earlier text (Benesch, 2013) defines Dangerous Speech, explains how to determine which messages are indeed dangerous, and illustrates why the concept is useful for preventing violence. We also discuss how digital and social media allow Dangerous Speech to spread and threaten peace, and describe some promising methods for reducing Dangerous Speech – or its harmful effects on people

Accountability Issues, Online Covert Hate Speech, and the Efficacy of Counter‐Speech

Concerning individual or institutional accountability for online hate speech, research has revealed that most such speech is covert (veiled or camouflaged expressions of hate) and cannot be addressed with existing measures (e.g., deletion of messages, prosecution of the perpetrator). Therefore, in this article, we examine another way to respond to and possibly deflect hate speech: counter-speech. Counter-narratives aim to influence those who write hate speech, to encourage them to rethink their message, and to offer to all who read hate speech a critical deconstruction of it. We created a unique set of parameters to analyze the strategies used in counter-speech and their impact. Upon analysis of our database (manual annotations of 15,000 Twitter and YouTube comments), we identified the rhetoric most used in counter-speech, the general impact of the various counter-narrative strategies, and their specific impact concerning several topics. The impact was defined by noting the number of answers triggered by the comment and the tone of the answers (negative, positive, or neutral). Our data reveal an overwhelming use of argumentative strategies in counter-speech, most involving reasoning, history, statistics, and examples. However, most of these argumentative strategies are written in a hostile tone and most dialogues triggered are negative. We also found that affective strategies (based on displaying positive emotions, for instance) led to a positive outcome, although in most cases these narratives do not receive responses. We recommend that education or training - even machine learning such as empathetic bots - should focus on strategies that are positive in tone, acknowledging grievances especially