Hate Speech and Counter Speech Detection: Conversational Context Does Matter (2022)

Abstract: Hate speech is plaguing the cyberspace along with user-generated content. Adding counter speech has become an effective way to combat hate speech online. Existing datasets and models target either (a) hate speech or (b) hate and counter speech but disregard the context. This paper investigates the role of context in the annotation and detection of online hate and counter speech, where context is defined as the preceding comment in a conversation thread. We created a context-aware dataset for a 3-way classification task on Reddit comments: hate speech, counter speech, or neutral. Our analyses indicate that context is critical to identify hate and counter speech: human judgments change for most comments depending on whether we show annotators the context. A linguistic analysis draws insights into the language people use to express hate and counter speech. Experimental results show that neural networks obtain significantly better results if context is taken into account. We also present qualitative error analyses shedding light into (a) when and why context is beneficial and (b) the remaining errors made by our best model when context is taken into account.

## introduction the advent of social media has democratized public discourse on an unparalleled scale. meanwhile, it is considered a particularly conducive arena for hate speech @xcite . online hate speech is prevalent and can lead to serious consequences. at the individual level, the victims targeted by hate speech are frightened by online threats that may materialize in the real world @xcite . at the societal level, it has been reported that there is an upsurge in offline hate crimes targeting minorities @xcite . there are two common strategies to combat online hate: disruption and counter speech. disruption refers to blocking hateful content or users. to scale this strategy, researchers have proposed methods to identify hate @xcite @xcite . while these interventions could de-escalate the impact of parent as an average height male, idgaf how tall you are, if that's your issue then spend the money and get a better seat, or just f**king make the seat selection online to get more space. target found the short guy! -target is neutral if considering only target. parent i deal with women all day with my job and this is how they are -extremely stupid, hate-filled, bizarre and they appreciate nothing. target maybe you're an a**hole if they treat you like that? in this study, we answer the following questions: 1. does conversational context affect if a comment is perceived as hate, neutral, or counterhate by humans? (it does.) 2. do models to identify hate, neutral, and counter-hate benefit from incorporating context? (they do.) to answer the first question, we create a collection of (parent, target) reddit comments and annotate the targets with three labels (hate, neutral, counter-hate) in two independent phases: showing annotators (a) only the target or (b) the parent and the target. we limit context to the parent comment. while the full conversation could provide additional information, it is also known to affect annotators' stance @xcite and introduce biases. we find that human judgments are substantially different when the parent is shown. thus the task of annotating hate and counter-hate requires taking into account the context. to answer the second question, we experiment with context-unaware and context-aware classifiers to detect if a given target is hate, neutral, or counter-hate. results show that adding context does benefit the classifiers significantly. in summary, the main contributions of this paper are: foot_1 (a) a corpus with 6,846 pairs of (parent, target) reddit comments and annotations indicating whether the targets are hate, neutral, or counterhate; (b) annotation analysis showing that the problem requires taking into account context, as the ground truth changes; (c) corpus analysis detailing the kind of language people use to express hate and counter-hate; (d) experiments showing that context-aware neural models obtain significantly better results; and (e) qualitative analysis revealing when context is beneficial and the remaining errors made by the best context-aware model. ## related work hate speech in user-generated content has been an active research area recently @xcite . researchers have built several datasets for hate speech detection from diverse sources such as twitter @xcite , yahoo! @xcite , fox news @xcite , gab @xcite and reddit @xcite . compared to hate speech detection, few studies focus on detecting counter speech @xcite @xcite . @xcite al. (2019 ) collect and hand-code 6,898 counter hate comments from youtube videos targeting jews, blacks and lgbt communities. garland et al. (2020) work with german tweets and define hate and counter speech based on the communities to which the authors belong. he et al. (2021) use a collection of hate and counter hate keywords relevant to covid-19 and create a dataset containing 359 counter hate tweets targeting asians. another line of research focuses on curating datasets for counter speech generation using crowdsourcing @xcite or with the help of trained operators @xcite . however, synthetic language is rarely as rich as language in the wild. even if it were, conclusions and models from synthetic data may not transfer to the real world. in this paper, we work with usergenerated content expressing hate and counter-hate rather than synthetic content. ## dataset collection and annotation we first describe our procedure to collect (parent, target) pairs, where both parents and targets are reddit comments in english. then, we describe the annotation guidelines and the two annotation phases: showing annotators (a) only the target and (b) the parent and target. the two independent phases allow us to quantify how often context affects the annotation of hate and counter-hate. ## corpus analysis does conversational context affect if a comment is perceived as hate or counter-hate? yes, it does. with even more hatred (and the ground truth label is hate). in the second example, the target alone is insufficient to tell if it is counter-hate. when annotators see the parent, however, they understand the author of target counters the hateful content in the parent by showing empathy towards the mail carrier. in the last example, the target alone is considered hate because it attacks someone by using the phrase "sick person". when the parent is shown, however, the annotators understand the target as calling out the parent to be inappropriate. ## experiments and results we build neural network models to identify if a target comment is hate, counter-hate, or neutral. we randomly split gold instances (4,751) as follows: 70% for training, 15% for validation and 15% for testing. silver instances are only used for training. ## qualitative analysis when is adding the context beneficial? when does our best model make mistakes? to investigate these questions, we manually analyze the following: ## conclusions and future work conversational context does matter in hate and counter-hate detection. we have demonstrated so by (a) analyzing whether humans perceive usergenerated content as hate or counter-hate depending on whether we show them the parent comment and (b) investigating whether neural networks benefit from incorporating the parent. we find that 38.3% of human judgments change when we show the parent to annotators. experimental results demonstrate that networks incorporating the parent yield better results. additionally, we show that noisy instances (silver data) and pretraining with relevant datasets improves model performance. we have created and released a corpus of 6,846 (parent, target) pairs of reddit comments with the target annotated as hate, neutral or counter-hate. our work have several limitations. first, we only consider context as the parent comment. while considering additional context might be sometimes beneficial, doing so would require careful design to not bias annotations @xcite . our research agenda includes exploring reliably strate-gies to consider more context and identify which parts are most important. second, people may have different opinions about what constitutes hate and counter speech due to different tolerances in online aggression. we obtained the ground truth according to annotators' reliability (mace scores), which may lead to controversial samples falling in the silver set and thus being considered only for training (not for testing). finally, the keywords sampling used to create our corpus may introduce biases. despite we partially mitigate the issue by considering hateful comments in both the parent and target, community-based sampling @xcite could be applied in our future work.