Comparative Evaluation of Label-Agnostic Selection Bias in Multilingual Hate Speech Datasets (2020)

Abstract: Work on bias in hate speech typically aims to improve classification performance while relatively overlooking the quality of the data. We examine selection bias in hate speech in a language and label independent fashion. We first use topic models to discover latent semantics in eleven hate speech corpora, then, we present two bias evaluation metrics based on the semantic similarity between topics and search words frequently used to build corpora. We discuss the possibility of revising the data collection process by comparing datasets and analyzing contrastive case studies.

## introduction hate speech in social media dehumanizes minorities through direct attacks or incitement to defamation and aggression. despite its scarcity in comparison to normal web content, @xcite demonstrated that it tends to reach large audiences faster due to dense connections between users who share such content. hence, a search based on generic hate speech keywords or controversial hashtags may result in a set of social media posts generated by a limited number of users @xcite . this would lead to an inherent bias in hate speech datasets similar to other tasks involving social data @xcite as opposed to a selection bias @xcite particular to hate speech data. mitigation methods usually point out the classification performance and investigate how to debias the detection given false positives caused by gender group identity words such as "women" @xcite , racial terms reclaimed by communities in certain contexts @xcite , or names of groups that belong to the intersection of gender and racial terms such as "black men" @xcite . the various aspects of the dataset construction are less studied though it has recently been shown, by looking at historical documents, that we may somehow neglect the data collection process @xcite . thus, in the present work, we are interested in improving hate speech data collection with evaluation before focusing on classification performance. we conduct a comparative study on english, french, german, arabic, italian, portuguese, and indonesian datasets using topic models, specifically latent dirichlet allocation (lda) @xcite . we use multilingual word embeddings or word associations to compute the semantic similarity scores between topic words and predefined keywords and define two metrics that calculate bias in hate speech based on these measures. we use the same list of keywords reported by @xcite for german, @xcite for italian, @xcite for indonesian, @xcite for portuguese; allow more flexibility in both english @xcite @xcite and arabic @xcite @xcite in order to compare different datasets based on shared concepts that have been reported in their respective paper descriptions; and for french, we make use of a subset of keywords that covers most of the targets reported by @xcite . our first bias evaluation metric measures the average similarity between topics and the whole set of keywords, and the second one evaluates how often keywords appear in topics. we analyze our methods through different use cases which explain how we can benefit from the assessment. our main contributions consist of (1) designing bias metrics that evaluate hateful web content using topic models; (2) examining selection bias in eleven datasets; and (3) turning present hate speech corpora into an insightful resource that may help us balance training data and reduce bias in the future. ## related work hate speech labeling schemes depend on the general purpose of the dataset. the annotations may include hateful vs. non hateful @xcite , racist, sexist, and none @xcite ; as well as discriminating target attributes @xcite , the degree of intensity @xcite , and the annotator's sentiment towards the tweets @xcite . besides english @xcite @xcite @xcite , we notice a growing interest in the study of hate speech in other languages, such as portuguese @xcite , italian @xcite , german @xcite , indonesian (ibrohim and budi, 2019), french @xcite , @xcite , and arabic @xcite @xcite . challenging questions being tackled in this area involve the way abusive language spreads online @xcite , fast changing topics during data collection @xcite , user bias in publicly available datasets @xcite , bias in hate speech classification and different methods to reduce it @xcite @xcite . bias in social data is broad and addresses a wide range of issues @xcite . @xcite present a framework to predict the origin of different types of bias including label bias @xcite , selection bias @xcite , model overamplification @xcite , and semantic bias @xcite . existing work deals with bias through the construction of large datasets and the definition of social frames @xcite , the investigation of how current nlp models might be non-inclusive of marginalized groups such as people with disabilities @xcite , mitigation @xcite , or better data splits @xcite . however, blodgett et al. (2020) report a missing normative process to inspect the initial reasons behind bias in nlp without the main focus being on the performance which is why we choose to investigate the data collection process in the first place. in order to operationalize the evaluation of selection bias, we use topic models to capture latent semantics. regularly used topic modeling techniques such as latent dirichlet allocation (lda) @xcite have proven their efficiency to handle several nlp applications such as data exploration @xcite , twitter hashtag recommendation @xcite , authorship attribution @xcite , and text categorization @xcite . in order to evaluate the consistency of the generated topics, @xcite used crowdsourcing and semantic similarity metrics, essentially based on pointwise mutual information (pmi), to assess the coherence; @xcite estimated coherence scores using conditional log-probability instead of pmi; @xcite enhanced this formulation based on normalized pmi (npmi); and lau and baldwin (2016) investigated the effect of cardinality on topic generation. similarly, we use topics and semantic similarity metrics to determine the quality of hate speech datasets, and test on corpora that vary in language, size, and general collection purposes for the sake of examining bias up to different facets. ## bias estimation method the construction of toxic language and hate speech corpora is commonly conducted based on keywords and/or hashtags. however, the lack of an unequivocal definition of hate speech, the use of slurs in friendly conversations as opposed to sarcasm and metaphors in elusive hate speech @xcite , and the data collection timeline @xcite contribute to the complexity and imbalance of the available datasets. therefore, training hate speech classifiers easily produces false positives when tested on posts that contain controversial or search-related identity words @xcite @xcite . to claim whether a dataset is rather robust to keyword-based selection or not, we present two label-agnostic metrics to evaluate bias using topic models. first, we generate topics using latent dirichlet allocation (lda) @xcite . then, we compare topics to predefined sets of keywords using a semantic similarity measure. we test our methods on different numbers of topics and topic words. ## results in this section, we demonstrate the impact of our evaluation metrics applied to various datasets and using different similarity measures. ## discussion we consider our bias evaluation metrics to be labelagnostic and tested this claim in the different use cases we presented in section 4. although our bias metrics do not take annotations into account, we notice a global trend of overgeneralizing labels as presented in section 4.6. despite the fact that this is partly due to the absence of a formal definition of hate speech, we do believe that there could be a general framework which specifies several aspects that must be annotated. moreover, we notice recurring topics in many languages, such as those centered around immigrants and refugees which may later lead to false positives during the classification and hurt the detection performance. hence, we believe that our evaluation metrics can help us recognize complementary biases in various datasets, facilitate transfer learning, as well as enable the enhancement of the quality of the data during collection by performing an evaluation step at the end of each search round. ## conclusion we proposed two label-agnostic metrics to evaluate bias in eleven hate speech datasets that differ in language, size, and content. the results reveal potential similarities across available hate speech datasets which may hurt the classification performance. as unpreventable as selection bias in social data can be, we believe there is a way to mitigate it by incorporating evaluation as a step which directs the construction of a new dataset or when combining existing corpora. our metrics are extensible to other forms of bias such as user, label, and semantic biases, and could be adapted in cross-lingual contexts using different similarity measures.