Latent Hatred: A Benchmark for Understanding Implicit Hate Speech (2021)

Abstract: 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 overt hate speech, failing to address a more pervasive form based on coded or indirect language. To fill this gap, this work introduces a theoretically-justified taxonomy of implicit hate speech and a benchmark corpus with fine-grained labels for each message and its implication. We present systematic analyses of our dataset using contemporary baselines to detect and explain implicit hate speech, and we discuss key features that challenge existing models. This dataset will continue to serve as a useful benchmark for understanding this multifaceted issue.

## introduction hate speech is pervasive in social media. platforms have responded by banning hate groups and flagging abusive text @xcite , and the research community has developed increasingly competitive hate speech detection systems @xcite . while prior efforts have focused extensively on overt abuse or explicit hate speech @xcite , recent works have started to highlight the diverse range of implicitly hateful messages that have previously gone unnoticed by moderators and researchers alike @xcite @xcite . figure 1 provides an example from each hate speech type (explicit vs. implicit). implicit hate speech is defined by coded or indirect language that disparages a person or group on the basis of protected characteristics like race, equal contribution. figure 1 : sample posts from our dataset outlining the differences between explicit and implicit hate speech. explicit hate is direct and leverages specific keywords while implicit hate is more abstract. explicit text has been modified to include a star (*). the primary challenge for statistical and neural classifiers is the linguistic nuance and diversity of the implicit hate class, which includes indirect sarcasm and humor @xcite , euphemisms @xcite , circumlocution @xcite , and other symbolic or metaphorical language @xcite . the type of implicit hate speech also varies, from dehumanizing comparisons @xcite and stereotypes @xcite , to threats, intimidation, and incitement to violence @xcite . importantly, the field lacks a theoreticallygrounded framework and a large-scale dataset to help inform a more empirical understanding of implicit hate in all of its diverse manifestations. to fill this gap, we establish new resources to sustain research and facilitate both fine-grained classification and generative intervention strategies. specifically, we develop a 6-class taxonomy of implicit hate speech that is grounded in the social science literature. we use this taxonomy to annotate a new twitter dataset with broad coverage of the most prevalent hate groups in the united states. this dataset makes three original contributions: (1) it is a large and representative sample of implicit hate speech with (2) fine-grained implicit hate labels and (3) natural language descriptions of the implied aspects for each hateful message. finally, we train competitive baseline classifiers to detect implicit hate speech and generate its implied statements. while state-of-the-art neural models are effective at a high level hate speech classification, they are not effective at spelling out more finegrained categories with detailed explanations the implied message. the results suggest our dataset can serve as a useful benchmark for understanding implicit hate speech. ## related work numerous hate speech datasets exist, and we summarize them in a few neutrally-seeded datasets also exist @xcite @xcite . although some may contain implicit hate speech, there are no implicit hate labels and thus the distribution is un-known. furthermore, these datasets tend to focus more on controversial events (e.g. the lee rigby murder; burnap and williams) or specific hate targets (e.g. immigrants; basile et al.), which may introduce topic bias and artificially inflate model performance on implicit examples @xcite . consider @xcite for example: 31% of posts take the form of the question leading up to a mean joke. there is still need for a representative and syntactically diverse implicit hate benchmark. our contribution is similar to the gab hate corpus of @xcite , which provides both explicit and implicit hate and target labels for a random sample of 27k gab messages. we extend this work with a theoretically-grounded taxonomy and fine-grained labels for implicit hate speech beyond the umbrella categories, assault on human dignity (hd) and call for violence (cv). following the work of @xcite , we provide free-text annotations to capture messages' pragmatic implications. however, we are the first to take this framework, which was originally applied stereotype bias, and extend it to implicit hate speech more broadly. implicitly stereotypical language is just a subset of the implicit hate we cover, since we also include other forms of sarcasm, intimidation or incitement to violence, hidden threats, white grievance, and subtle forms of misinformation. our work also complements recent efforts to capture and understand microaggressions @xcite , a similarly elusive class that draws on subtle and unconscious linguistic reflections of social bias, prejudice and inequality @xcite . similar to @xcite , we provide a representative and domain-general typology and dataset, but ours are more representative of active hate groups in the united states, and our definitions extend to intentionally veiled acts of intimidation, threats, and abuse. ## taxonomy of implicit hate speech implicit hate speech is a subclass of hate speech defined by the use of coded or indirect language such as sarcasm, metaphor and circumlocution to disparage a protected group or individual, or to convey prejudicial and harmful views about them @xcite . the nlp community has not yet confronted, in a consistent and unified manner, the multiplicity of subtle challenges that implicit hate presents for online communities. to this end, we introduce a new typology for characterizing and detecting different forms of implicit hate, based on social science and relevant nlp literature. our categories are not necessarily mutually exclusive, but they represent principle axes of implicit hate, and while they may not be collectively exhaustive, we find they cover 98.6% of implicit hate in a representative sample of the most prevalent hate ideologies in the u.s. white grievance includes frustration over a minority group's perceived privilege and casting majority groups as the real victims of racism @xcite . this language is linked to extremist behavior and support for violence @xcite ). an example is black lives matter and white lives don't? sounds racist. incitement to violence includes flaunting ingroup unity and power or elevating known hate groups and ideologies @xcite . phrases like 'white brotherhood operate in the former manner, while statements like hitler was germany -germans shall rise again! operate in the latter, elevating nationalism and nazism. article 20 of the un international covenant on civil and political rights @xcite states that speech which incites violence shall be prohibited by law. inferiority language implies one group or individual is inferior to another @xcite , and it can include dehumanization (denial of a person's humanity), and toxification (language that com-pares the target with disease, insects, animals), both of which are early warning signs of genocide @xcite . inferiority language is also related to assaults on human dignity @xcite , dominance @xcite , and declarations of superiority of the in-group @xcite . for example, it's not a coincidence the best places to live are majority white. irony refers to the use of sarcasm @xcite , humor @xcite , and satire @xcite to attack or demean a protected class or individual. for example, in the context of one hate group, the tweet horrors... disney will be forced into hiring americans works to discredit disney for allegedly hiring only non-citizens or, really, nonwhites. irony is not exempt from our hate speech typology, since it is commonly used by modern online hate groups to mask their hatred and extremism @xcite . stereotypes and misinformation associate a protected class with negative attributes such as crime, or terrorism @xcite as in the rhetorical question, can someone tell the black people in chicago to stop killing one another before it becomes detroit? this class also includes misinformation that feeds stereotypes and vice versa, like holocaust denial and other forms of historical negationism @xcite . threatening and intimidation convey a speaker commitment to a target's pain, injury, damage, loss, or violation of rights. while explicitly violent threats are well-recognized in the hate speech literature @xcite , here we highlight threats related to implicit violation of rights and freedoms, removal of opportunities, and more subtle forms of intimidation, such as all immigration of non-whites should be ended. ## data collection and annotation we collect and annotate a benchmark dataset for implicit hate language using our taxonomy. our main source of data uses content published by online hate groups and their followers on twitter for two reasons. first, as modern hate groups have become more active online, they provide an increasingly vivid picture of the more subtle and coded forms of hate that we are interested in. second, the problem of hateful misinformation is compounded on social media platforms like twitter where around 3 out of 4 users get their news @xcite . this motivates a representative sample of online communication exchanged on twitter between members of the most prominent u.s hate groups. we focus on the eight largest ideological clusters of u.s. hate groups as given by the splc (2019) report. these ideological classes are black separatist (27.1%), white nationalist (16.4%), neo-nazi (6.2%), anti-muslim (8.9%), racist skinhead (5.1%), ku klux klan (5.0%), anti-lgbt (7.4%), and anti-immigrant (2.12%). detailed background and discussion on each hate ideology can be found at the the splc extremist files page (splc, 2020). ## implicit hate speech classification we experiment with two classification tasks: (1) distinguishing implicit hate speech from non-hate, and (2) categorizing implicit hate speech using one of the 6 classes in our fine-grained taxonomy. ## explaining implicit hate speech this section presents our generation results for natural language explanations of both (1) who is being targeted and (2) what the implied message is for each implicitly hateful tweet. generating such explanations can help content moderators better understand the severity and nature of automatically-flagged messages. additionally, we echo efforts from social media companies (e.g., instagram @xcite ) where the application alerts the user when the post is flagged "offensive," and asks them if they really want to post it. this strategy has proven successful in deterring hurtful comments. our work could inspire a similar strategy for implicit hate speech. by showing the user the implied meaning of their post before it is posted, we would enable them to recognize the severity of their words and possibly reconsider their decision to post. ## conclusion in this work, we introduce a theoretical taxonomy of implicit hate speech and a large-scale benchmark corpus with fine-grained labels for each message and its implication. as an initial effort, our work enables the nlp communities to better understand and model implicit hate speech at scale. we also provide several state-of-the-art baselines for detecting and explaining implicit hate speech. experimental results show these neural models can effectively categorize hate speech and spell out more fine-grained implicit hate speech and explaining these hateful messages. additionally, we identified eight challenges in implicit hate speech detection: coded hate symbols, discourse relations, entity framing, commonsense, metaphorical language, colloquial speech, irony, and identity term bias. to mitigate these challenges, future work could explore deciphering models for coded language @xcite , lifelong learning of hateful language @xcite , contextualized sarcasm detection, and bias mitigation for named entities in hate speech detection systems @xcite and their connection with our dataset. we demonstrate that our corpus can serve as a useful research benchmark for understanding implicit hate speech online. our work also has implications towards the emerging directions of countering online hate speech @xcite , detecting online radicalization @xcite and modeling societal systematic racism, prejudicial expressions, and biases @xcite @xcite .