Exploiting Emojis for Abusive Language Detection (2021)

Abstract: We propose to use abusive emojis, such as the “middle finger” or “face vomiting”, as a proxy for learning a lexicon of abusive words. Since it represents extralinguistic information, a single emoji can co-occur with different forms of explicitly abusive utterances. We show that our approach generates a lexicon that offers the same performance in cross-domain classification of abusive microposts as the most advanced lexicon induction method. Such an approach, in contrast, is dependent on manually annotated seed words and expensive lexical resources for bootstrapping (e.g. WordNet). We demonstrate that the same emojis can also be effectively used in languages other than English. Finally, we also show that emojis can be exploited for classifying mentions of ambiguous words, such as “fuck” and “bitch”, into generally abusive and just profane usages.

## introduction abusive or offensive language is defined as hurtful, derogatory or obscene utterances made by one person to another. 1 in the literature, closely related terms include hate speech @xcite or cyber bullying @xcite . while there may be nuanced differences in meaning, they are all compatible with the general definition above. due to the rise of user-generated web content, the amount of abusive language is also steadily growing. nlp methods are required to focus human review efforts towards the most relevant microposts. building classifiers for abusive language detection requires expensive manually labeled data. in this paper we explore distant supervision @xcite for abusive language detection in which abusive emojis serve as a heuristic to identify abusive language (1)-( 8 ). these texts are subsequently used as training data. the advantage of emojis is that some of them are unambiguously abusive. they are also often redundant @xcite , i.e. they convey something already expressed verbally in the micropost. since the concept conveyed by an emoji can be expressived verbally in many different ways, abusive emojis may co-occur with many different abusive words (e.g. idiot, cunt). moreover, the meaning of emojis is (mostly) shared across languages. in order to avoid an evaluation affected by such topic or author biases, we focus on learning a lexicon of abusive language. a lexicon-based approach to detect abusive language primarily focuses on the detection of explicitly abusive language, i.e. abusive language that is conveyed by abusive words. such an approach is currently the most effective clue known for cross-domain classification @xcite . in general, other types of abusive language that are more implicit, such as sarcasm, jokes or stereotypes, require more contextual interpretation of words. supervised classification is theoretically able to conduct such contextual interpretation. however, it has been reported to perform very poorly @xcite on this task because the biases these classifiers exploit are unlikely to be present across different datasets @xcite . therefore, we focus on explicitly abusive language in this work, since there are no ways of reliably detecting implicitly abusive language. despite the existence of lexicons for abusive words, induction methods are required, since new abusive words enter language constantly. further, there are only few lexicons available in languages other than english. the aim of our work is not to detect completely new types of abusive language but to find an inexpensive and language-independent method for lexicon induction. our contributions in this paper are: the supplementary material 2 to this paper includes all resources newly created for our research and notes on implementation details. recently, there has been a general interest in exploiting extralinguistic information for natural language processing. emoticons, such as :-), have been found useful for sentiment analysis, particularly emotion classification @xcite . emojis represent an even more fine-grained set of icons. @xcite exploit them for pretraining neural models to produce a text representation of emotional content. since this approach relies on a representative sample of tweets containing emojis, only the 64 most frequently occurring emojis are considered. this set, however, does not contain the very predictive emojis for abusive language detection (e.g. middle finger). @xcite follow an approach similar to @xcite in that they pretrain a language model with the help of emoji informarion. however, unlike @xcite , their emoji-based masked language model is evaluated for zero-shot abusive language detection. the task is also considered in a multilingual setting: the target languages are english, german, italian and spanish. the improvements that @xcite report over baseline language models that do not explicitly incorporate emoji information are only limited. our work extends @xcite and @xcite in that we focus on predictive emojis for abusive languag detection. unlike @xcite and @xcite , we do not pretrain a text classifier with these additional emojis. supervised text classifiers are known to severely suffer from domain mismatches in abusive language detection whereas lexicon-based classification is much more stable @xcite . for further emojis we only obtained an insufficient amount of english tweets that were necessary for our experiments (i.e. several thousand tweets after running a query containing these emojis using the twitter-streaming api for a few days). examples of such sparse emojis are (bomb) connoting violence or (high voltage) connoting anger. 4 although our procedure involved a manual selection of emojis, in our evaluation we will demonstrate that this choice does not overfit but generalizes across different datasets and languages. ## disambiguation of abusive words many potentially abusive words are not meant to be abusive, i.e. deliberately hurt someone, in all situations in which they are used. for instance, the word fuck is abusive in (9) but it is not in (10). while operators of social media sites are increasingly facing pressure to react to abusive content on their platforms, they are not necessarily targeting profane language as in (10). in fact, users may see advances of operators against their profane posts as unnecessary and as an infringement of their freedom of speech. therefore, automated methods to filter textual content of social media sites should ideally distinguish between abusive and profane usage of potentially abusive words. ## conclusion we presented a distant-supervision approach for abusive language detection. our main idea was to exploit emojis that strongly correlate with abusive content. the most predictive emoji is the middlefinger emoji. we employed mentions of such emojis as a proxy for abusive utterances and thus generated a lexicon of abusive words that offers the same performance on cross-domain classification of abusive microposts as the best previously reported lexicon. unlike that lexicon, our new approach neither requires labeled training data nor any expensive resources. we also demonstrated that emojis can similarly be used in other languages where they outperform a crosslingual classifier and a translated lexicon. finally, we showed that emojis can also be used to disambiguate mentions of potentially abusive words.