Targets and Aspects in Social Media Hate Speech (2021)

Abstract: Mainstream research on hate speech focused so far predominantly on the task of classifying mainly social media posts with respect to predefined typologies of rather coarse-grained hate speech categories. This may be sufficient if the goal is to detect and delete abusive language posts. However, removal is not always possible due to the legislation of a country. Also, there is evidence that hate speech cannot be successfully combated by merely removing hate speech posts; they should be countered by education and counter-narratives. For this purpose, we need to identify (i) who is the target in a given hate speech post, and (ii) what aspects (or characteristics) of the target are attributed to the target in the post. As the first approximation, we propose to adapt a generic state-of-the-art concept extraction model to the hate speech domain. The outcome of the experiments is promising and can serve as inspiration for further work on the task

## introduction online hate speech and, in particular, hate speech in social media, is the cause for growing concern. already six years ago, 73% of adult internet users have seen someone harassed online, and 40% have personally experienced it @xcite . therefore, research on hate speech identification is of increasing importance. a significant body of work has been conducted over the last decade; cf., e.g., @xcite @xcite @xcite . most of this work focused on the task of classifying, for instance, social media posts, with respect to predefined typologies of rather coarse-grained hate speech categories, such as 'hate speech', 'racism', 'sexism', 'offense', etc. this may be sufficient if the task is to detect and remove abusive language posts. however, for instance, in the us, hate speech has been repeatedly judged as being covered by the first amendment. 1 furthermore, a number of studies suggest that hate speech cannot be successfully combated by merely removing identified hate speech posts foot_1 and should be countered by education and counternarratives @xcite . but to provide a basis for education and counter-narratives, we need a more detailed analysis of hate speech. in particular, we need to identify (i) who is the target in the identified hate speech post, and (ii) what aspects of the target are referred to or what aspects are attributed to the target in the post. for instance, we need to be able to determine that post (1) below targets muslims of palestine and that it attributes to them to be terrorists. similarly, for post (2), we need to determine that it targets female sports reporters and that they "should come to an end" (i.e., that they should be removed from their jobs). 3 the analogy to aspect-oriented sentiment analysis @xcite is evident. some recent works on hate speech go beyond the mere classification task and, actually, some of them also use the term aspect, but, again, with a different interpretation. in this paper, we present an approach that is different from these works and that aims to identify (i) the entity (most often, a group of individuals or an individual) who is targeted in the post, without drawing upon a predefined range of categories (which will necessarily be always limited and coarse-grained and will not cover new or intersecting categories, like 'black women'), (ii) the aspect (or characteristics) assigned to the targeted entity. we use an open-domain neural network-based concept extraction model for the identification of target and aspect candidates in each post. the obtained candidates are then further processed taking into account the idiosyncrasies of the codification of both targets and aspects in the domain. the remainder of the paper is structured as follows. in the next section, we introduce the notions of target and aspect we are working with. section 3 summarizes the work that is related to ours, including aspect-oriented sentiment analysis, to which our proposal shows some clear analogies. section 4 outlines the generic concept extraction model from which we start and presents its adaptation to the problem of target and aspect extraction from hate speech data, while section 5 describes the experiments we carried out and discusses their outcome. section 6, finally, draws some conclusions and outlines several lines of research that we aim to address in the future. ## targets and aspects let us define more precisely what we mean by'target' and 'aspect' in the context of our work. definition 1 (target). a target is the entity that is in the focus of a hate post, i.e., the entity that incurs the hate of the author. very often, the target is an individual or a group of individuals, e.g., women, people of color, refugees, muslims, jews, etc.: however, the target can also be a specific political conviction, a religion, an object related to an individual or a group of individuals, etc.; see, e.g., feminist novels in (4): foot_3 (4) i'm not sexist, but nothing bores me more than feminist novels. definition 2 (aspect). aspect is a characteristic, attitude, or behavior or the lack of it (as a rule, with a pejorative connotation) that the author attributes to the target. the aspect is often expressed as a modifier(e.g., boring, stupid, lazy, not funny, etc.) of the target in the focus of the author, as in: in some posts, no targets and/or aspects can be identified; see, e.g., ( 8 ). we discard such posts in our current experiments. ## related work as mentioned in section 1, most of the works on online hate speech focused on the task of classifying social media posts with respect to predefined typologies of rather coarse-grained hate speech categories, such as 'hate speech', 'racism', 'sexism', 'offense', etc. @xcite @xcite @xcite @xcite . 5 vidgen and derczynski (2020) distinguish between binary classification (as in @xcite ), multi-class classification into several hate speech categories (e.g., 'racism', 'sexism', and 'none' in @xcite ), different strengths of abuse classification (e.g., 'hateful', 'offensive' and 'neutral' contents as in @xcite ), classification into different types of statements (e.g., 'denouncing', 'facts', 'humor', 'hypocrisy' and others) and themes (e.g., 'crimes', 'culture', 'islamization', 'rapism' and others) as in @xcite ), and classification of different focuses of abuse (e.g., 'stereotype & objectification', 'dominance', 'derailing', 'sexual harassment', 'threats of violence', and 'discredit' as in @xcite ). all these works do not aim to identify the specific targeted group of individuals or the individual and neither do they aim to identify characteristics of the targets that provoked hate. rather, they identify posts related to hate speech in general or to one of its more specific categories -which is a step prior to detection of targets and aspects, where we start. some previous works use a similar terminology as we do, but with a different meaning. for instance, @xcite @xcite aim to identify the sentiment (positive or negative) of the author of a given post towards a range of specific hate speech categories (e.g., 'race' and 'gender'), which they call "aspect". in @xcite , tweets related to the metoo movement are annotated manually with respect to five different linguistic "aspects": relevance, stance, hate speech, sarcasm, and dialogue acts. in this case, too, the interpretation of the notion of aspect is different from ours. @xcite define five different "aspects" that include specific targets, among others: (i) whether the text is direct or indirect; our work also aligns with mathew et al. (2020) and @xcite in the sense that @xcite annotate a hate speech dataset at the word and phrase level, capturing human rationales for the labelling (which is similar to the target-aspect labelling), while @xcite propose to understand and fight hate speech prejudices with accurate underlying explanations. however, @xcite take into account only three labels ('hate', 'offensive', and 'normal') and ten target communities performing supervised classification, while we aim at retrieving and distinguishing open-class targets and aspects in a semi-supervised manner. @xcite perform supervised training of a conditional language generation model that often results in generic stereotypes about the targeted groups rather than in implications meant in the post, while we use a language generation model only to produce candidates and further expand, rank, and select them such that a connection of a target and an aspect to the text is guaranteed. to summarize, although the identification of the targets and characteristics of hate speech in the above works are significant advancements compared to the more traditional hate speech classification, all of these works still assume predefined target categories and do not identify which characteristics of the targets are concerned. in contrast, open-class target and aspect extraction may allow for modeling of the particular forms of discrimination and hate experienced by individuals or groups of individuals covered or not covered by previously identified target categories. as already mentioned in section 1, our work is also related to aspect-oriented sentiment analysis, in which "targets" are specific entities (e.g., products, sights, celebrities) and "aspects" are characteristics or components of a given entity @xcite . for each identified aspect, the "sentiment value" aligned with it is extracted; see, e.g., @xcite for a recent comprehensive survey of aspect-oriented sentiment analysis. in some (more traditional) works, aspects and their values are identified in separate stages @xcite . in more recent works, both tasks are addressed by one model, with aspects being partially identified by attention mechanisms realized, e.g., in an lstm @xcite , cnn @xcite or an alternative common deep nn model. the targets are, as a rule, predefined, such that the challenge consists in analysing the sentiment of tweets towards these predefined targets; cf., e.g., @xcite . the problem of open-class target identification has not been broadly investigated and sometimes solved simply as a named entity recognition problem due to the nature of the data in wihch the targets are often represented by proper names @xcite . however, targets in hate speech texts go far beyond named entities, and the overall task is inverse to targetoriented sentiment classification: given a known category (hate speech of negative sentiment as a rule), we have to identify the hate target and its corresponding "opinioned" aspect. still, our proposal is similar to the modern approaches to aspectoriented sentiment analysis in the sense that we also use an nn model (in our case, lstm-based encoder) with attention mechanisms for initial hate speech target and aspect candidates identification, before a domain-adaptation post-processing stage. ## outline of the model the study of social media hate speech posts reveals that targets are entities that are, as a rule, verbalized in terms of classifying nominal groups @xcite . aspects may also be expressed by classifying nominal groups, but adjectival (attributive) and participle groups (actions) are also common. in other words, overall, targets can be considered concepts @xcite . therefore, we envision the detection of surface forms of targets in the posts primarily as a concept extraction (ce) task. for aspects, it is often not sufficient to apply concept extraction if we want to also capture the adjectival and verbal group aspects. given that hate speech datasets are, in general, too small to serve for training neural networks for reliable concept extraction, we opt for applying an open-domain-oriented concept extraction model with a follow-up algorithmic domain adaptation. ## conclusions and future work classification of hate speech in terms of broad categories is not sufficient; in order to effectively combat hate speech, a detailed target-aspect analysis is necessary. we presented a model that adapts a generic concept extraction model and showed that it is able to reach a reasonable quality for target and aspect identification in the 'sexism' and 'racism' categories of the (waseem and hovy, 2016a) hate speech dataset. the model is semi-supervised and works already with a small annotated dataset. this is an advantage in view of the absence of large hate speech datasets annotated with the target-aspect information. despite the promising figures, our model still has some limitations. thus, aspect identification quality should be further improved. furthermore, we plan to use distance learning in order to make the model language-independent, which will be an advantage compared to the presented implementation, which is to a certain extent language-specific. in addition, experiments on other hate speech datasets should be carried out in order to demonstrate that the proposed variable tuning and implemented syntactic target and aspect patterns generalize well across datasets. finally, although the vast majority of posts indeed contains just one target, to capture multiple targets would be desirable. the annotated development and test sets and the code are available in the following github repository: https://github.com/ talnupf/hatespeechtargetsaspects/ .