Not All Counterhate Tweets Elicit the Same Replies: A Fine-Grained Analysis (2023)

Abstract: Counterhate arguments can effectively fight and limit the spread of hate speech. However, they can also exacerbate the hate, as some people may respond with aggression if they feel threatened or targeted by the counterhate. In this paper, we investigate replies to counterhate arguments beyond whether the reply agrees or disagrees with the counterhate argument. We present a corpus with 2,621 replies to counterhate arguments countering hateful tweets, and annotate them with fine-grained characteristics. We show that (a) half of the replies (51%) to the counterhate arguments disagree with the argument, and (b) this kind of reply often supports the hateful tweet (40%). We also analyze the language of counterhate arguments that elicit certain types of replies. Experimental results show that it is feasible to anticipate the kind of replies a counterhate argument will elicit.

## introduction hate messages and offensive language are commonplace in social media platforms. twitter reported that more than 1.1 million accounts spread hateful content in the second half of 2020, a 77% increase with respect to the first half of the same year. 1 in a recent survey of 10,093 adults in the u.s., 41% of participants reported online harassment on a personal level, and almost two-thirds of adults under the age of 30 reported experiencing internet harassment @xcite . these figures, alongside other surveys, foot_1 , foot_2 demonstrate the prevalence of hate speech on the internet. to address this problem, the european commission partnered with popular social media platforms to announce a "code of conduct on countering illegal hate speech online" (european commission, 2019), which contains several commitments to prevent the spread of online hate speech across europe. the enormous amount of daily data makes these platforms rely on users who manually flag hateful content @xcite . this approach requires spending millions of dollars yearly on manual hate speech verification and moderation @xcite ). an alternative is to automatically fight hate speech by using hate speech classifiers (section 2). however, removing users' content-as effective as it may be-restricts free speech. according to the pew research center @xcite , "despite this broad concern over online harassment, 45% of americans say it is more important to let people speak their minds freely online, and 53% feel that it is more important for people to feel welcome and safe online." a complementary strategy to address hateful content that does not interfere with free speech is to counter the hate with counterhate arguments in order to divert the discourse away from hate. counterhate arguments can effectively fight and limit the spread of hate speech without removing or blocking any content @xcite . counterhate arguments usually are positive arguments that oppose hate speech with logic and facts. however well-intentioned, counterhate arguments may worsen the situation, as some people may respond with aggression if they feel threatened or targeted by the argument @xcite . upon these motivations, we study the kind of replies counterhate arguments elicit. specifically, we investigate replies to counterhate arguments beyond whether the reply agrees or disagrees with the counterhate argument. we consider twitter threads consisting of (a) a hateful tweet, (b) a counterhate tweet countering (a), and (c) all replies to the counterhate tweet. we define a hateful tweet as any tweet that contains abusive language directed to individuals or groups of people. on the other hand, a counterhate tweet is a response tweet that explicitly or implicitly disagrees with the hateful figure 1 : twitter thread originating with a hateful tweet. this paper investigates the replies to counterhate tweets. in the first example, the reply not only agrees with the counterhate tweet, but also adds additional counterhate. on the other hand, the second reply not only disagrees with the counterhate tweet, but also shows support for the hateful tweet. in summary, the main contributions of this paper are: foot_3 (a) a corpus with 2,621 (hateful tweet, coun-terhate tweet, reply) triples annotated with finegrained characteristics (whether the reply agrees with the counterhate tweet, supports the hateful tweet, attacks the author of the counterhate tweet, or adds additional counterhate); (b) linguistic analysis of the counterhate tweets depending on our finegrained characterization of the replies they elicit; (c) experimental results showing it is feasible to anticipate the kind of replies a counterhate tweet will elicit, and modest improvements using data augmentation and blending related datasets; and (d) qualitative analysis revealing when it is harder to perform any of the four classification tasks. ## previous work recently, considerable literature has grown around identifying hateful content in user-generated content @xcite . existing research has created a variety of datasets to detect hate speech from several sources, including twitter @xcite , reddit @xcite , fox news @xcite , yahoo! @xcite , and gab @xcite . other studies have worked on identifying the target of hate, including whether the hateful content was directed toward a group, a person, or an object @xcite @xcite . another area of research aims to explore the role of context in hate and counterhate speech detection @xcite . previous efforts also detect and generate counterhate content. for counterhate detection, garland et al. (2020) work with hateful and counterhate german tweets from two well-known groups. @xcite collect and analyze pairs of hateful tweets and replies using the hate speech template i @xmath0, and detect whether a reply to a hateful tweet is a counterhate reply or not. in addition to analyzing or detecting counterhate replies, albanyan and blanco (2022) identify four fine-grained aspects of the relationship between a hateful tweet and a reply (e.g., whether the reply counters the hateful tweet with a justification). for counterhate generation, some studies have worked on collecting datasets with the help of crowd workers @xcite or trained operators @xcite . there are several attempts to predict whether content will lead to additional hateful content. @xcite identify whether a reply will result in a personal attack. @xcite predict the number of hateful comments that an instgram post would receive. on the other hand, there are few efforts on investigating the impact of counterhate content, as stated in a recent survey by @xcite . @xcite analyze youtube comments and found that counterhate comments received more likes and interactions than non-counterhate comments. other studies found that there is a positive association between counterhate efficiency and both its author's ethnicity @xcite and how immediate the response to the hateful content is posted @xcite . finally, @xcite analyze hateful and counterhate german tweets and find that organized counterhate tweets elicit more counterhate replies and decrease the severity of the hate speech. unlike these previous studies, we consider twitter threads consisting of hateful tweets, a counterhate argument, and all replies to the counterhate argument. to our knowledge, we are the first to analyze the replies with fine-grained characteristics and tackle the problem of forecasting what kind of replies a counterhate arguments will elicit. ## dataset collection and annotation we start our study by collecting triples consisting of hateful tweets, counterhate tweets, and replies to counterhate tweets. then, we annotate the triples with our fine-grained characterization of the replies to the counterhate tweets. unlike previous works (section 2), our corpus enables us to (a) investigate whether counterhate tweets are successful at stopping the hate (section 4), (b) analyze the language people use in counterhate tweets depending on the replies they elicit (section 4), and (c) predict the kind of replies a counterhate tweet will elicit (section 5). collecting hateful tweets, counterhate tweets, and replies we use three strategies to collect a sufficient number of hateful tweets, counterhate tweets, and replies. the first strategy is to start with corpora consisting of (hateful tweet, counterhate tweet) pairs that include the tweet identifiers @xcite . then, we use the twitter api to collect all replies to the counterhate tweets. this strategy resulted in only 260 triples because some tweets are no longer available and not all counterhate tweets have replies. note that other corpora not including identifiers cannot be used. in the second strategy, we start collecting hateful tweets from corpora that only provide hateful tweets @xcite @xcite including tweet identifiers. then, we follow these steps: the third strategy is the same than the second but with an alternative approach to collect the hateful tweets. instead of using existing corpora, we use (a) the hate pattern @xmath1. these strategy resulted in 3,820 triples. the total number of triples after combining the three strategies is 4,310. we finalized the collection process by validating the triples. the final size of our corpus after the validation process is 2,621 (hateful tweet, counterhate tweet, reply) triples. the total number of hateful tweets is 1,147, while the number of counterhate tweets is 1,685. the number of counterhate tweets per hateful tweet ranges between 1 and 20, while the number of replies per counterhate tweet ranges between 1 and 88. annotation guidelines along with determining whether a reply agrees with the counterhate tweet, we identify finer-grained characteristics of the replies. accordingly, we define three steps to answer four questions in the annotation process. the first step is determining whether the reply agrees with the counterhate tweet. we consider that a reply agrees if it does not oppose the counterhate tweet either explicitly or implicitly. on the other hand, we consider that a reply disagrees if it opposes the counterhate tweet, including sarcasm (e.g., you are missing something!) or casting doubt (e.g., are you kidding?). the second step provides fine-grained characteristics when the reply disagrees with the counterhate tweet. first, we ask whether the reply supports the hateful tweet. we consider the reply to support the hateful tweet if it includes a justification for the hateful content (e.g., the news says the opposite!) or introduces additional hateful content (e.g., first example in observed (%) cohen's κ agree? 91.1 0.82 support? the third step provides fine-grained characteristics when the reply agrees with the counterhate tweet. finally, when the reply agrees with the counterhate tweet, we distinguish whether the reply includes additional counterhate. namely, we identify whether the reply contains additional counterhate by providing a new opinion or factual argument to support the counterhate tweet (e.g., he is also known for his charitable work and donations). only agreeing with the counterhate tweet (e.g., you are correct!) does not contain additional arguments. examples in the second example, the reply attacks the author of the counterhate tweet without including any justification or support for the hateful tweet. this also indicates that the reply disagrees with the counterhate tweet. note that replies can disagree with the counterhate tweet without attacking the author (e.g., don't be their lawyer). finally, the reply in the third example not only agrees with the counterhate tweet, but also includes additional counterhate (honest vs. successful government). note that replies can agree with the counterhate tweet without adding additional counterhate (e.g., convincing response!). ## corpus analysis label distribution ## experiments and results we create a binary classifier for each task, namely, whether the reply: (a) agrees with the counterhate tweet, (b) supports the hateful tweet, (c) attacks the author of the counterhate tweet, or (d) includes additional counterhate arguments. we split the dataset into 70:10:20 ratios for training, validation, and testing. each instance consists of a hateful tweet, a counterhate tweet, and a reply. baselines the baseline models we use in our experiments are the majority and random models. in the majority model, the majority label is predicted (no label for all tasks, ## conclusions countering hateful content is an effective way to fight hate speech @xcite . additionally, countering hate speech-unlike blockingdoes not interfere with free speech. however wellintentioned, however, counterhate arguments may worsen the situation by eliciting additional hate. in this work, we analyze the discourse following a counterhate tweet. specifically, we analyze all replies to counterhate tweets and reveal finegrained characteristics beyond whether the reply agrees with the counterhate argument. namely, we determine whether the reply (a) not only disagrees with the counterhate tweet but also supports the hateful tweet or attacks the author of the counterhate arguments, or (b) not only agrees with the counterhate tweet but also adds additional counterhate arguments. to our knowledge, this work is the first to analyze the language of counterhate tweets based on the replies they elicit. the work presented here is empirical and explores genuine counterhate arguments and the replies they elicit. we believe that it is critical to analyze genuine social media discourse and how hate spreads (and does not spread). we avoid making any causal claims; instead, we draw insights from genuine social media discourse around hateful content. our future work includes generating counterhate arguments (a) customized to specific hateful content and (b) following the characteristics we found to be more effective at stopping hatred. we hypothesize that doing so will be more effective than generic or even expert-driven counterhate.