A Simple Three-Step Approach for the Automatic Detection of Exaggerated Statements in Health Science News

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, pages 369–380 April 19 - 23, 2021. ©2021 Association for Computational Linguistics 369 Exploiting Emojis for Abusive Language Detection Michael Wiegand Digital Age Research Center (D!ARC) Alpen-Adria-Universit¨at Klagenfurt AT-9020 Klagenfurt, Austria michael.wiegand@aau.at Josef Ruppenhofer Leibniz Institute for German Language D-68161 Mannheim, Germany ruppenhofer@ids-mannheim.de 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 sin- gle 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 classi- fication of abusive microposts as the most ad- vanced lexicon induction method. Such an ap- proach, in contrast, is dependent on manually annotated seed words and expensive lexical re- sources for bootstrapping (e.g. WordNet). We demonstrate that the same emojis can also be effectively used in languages other than En- glish. Finally, we also show that emojis can be exploited for classifying mentions of ambigu- ous words, such as fuck and bitch, into gener- ally abusive and just profane usages. 1 Introduction Abusive or offensive language is defined as hurt- ful, derogatory or obscene utterances made by one person to another.1 In the literature, closely re- lated terms include hate speech (Waseem and Hovy, 2016) or cyber bullying (Zhong et al., 2016). 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 hu- man review efforts towards the most relevant mi- croposts. Building classifiers for abusive language detection requires expensive manually labeled data. In this paper we explore distant supervision (Mintz et al., 2009) for abusive language detec- tion in which abusive emojis serve as a heuristic to identify abusive language (1)-(8). These texts are subsequently used as training data. The advantage 1http://thelawdictionary.org of emojis is that some of them are unambiguously abusive. They are also often redundant (Donato and Paggio, 2017), i.e. they convey something already expressed verbally in the micropost. Since the con- cept conveyed by an emoji can be expressived ver- bally 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. (1) You are such a hypocrite ... Have your dinner dick (2) @USER @USER you need a good old fashion man sized ass kicking you little Twitt (3) @USER I challenge you to go on a diet you fat cunt (4) @USER You are so so stupid you monkey face (5) Send your location, I’ll send some killers (6) @USER @USER A vote for toddstone or any liberal. Id rather flush a toilet. (7) Fuck the 12 fuck the cops we aint forgot about you, kill em all kill em all (8) …

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