The possibility to monitor hateful content online on the basis of what people write is becoming an important topic for several actors such as governments, ICT companies, and NGO's operators conducting active campaigns in response to the worrying rise of online abuse and hate speech. Abusive language is a broad umbrella term which is commonly used for denoting different kinds of hostile user-generated contents that intimidate or incite to violence and hatred, targeting many vulnerable groups in social platforms. Such hateful contents are pervasive nowadays and can also be detected even in other kinds of texts, such as online newspapers. Various approaches have been proposed in the last years to support the identification and monitoring of these phenomena, but unfortunately, they are far from solving the problem due to the inner complexity of abusive language, and to the difficulties to detect its implicit forms. In our doctoral investigation, we have studied the issues related to automatic identification of abusive language online, investigating various forms of hostility against women, immigrants and cultural minority communities in languages such as Italian, English, and Spanish. The multilingual frame allowed us to have a comparative setting to reflect on how hateful contents are expressed in distinct languages and how these different ways are transposed in the automated processing of the text. The analysis of the results of different methods of classification of hateful and non-hateful messages revealed important challenges that lie principally on the implicitness of some manifestations of abusive language expressed through the use of figurative devices (i.e., irony and sarcasm), recall of inner ideologies (i.e., sexist ideology) or cognitive schemas (i.e., stereotypes), and expression of unfavorable stance. To face these challenges, in this work, we have proposed distinct solutions applicable also to different textual genres. We observed that, in particular, cognitive and creative aspects of abusive language are harder to infer automatically from texts. At the same time they are often recurrent elements, such in the case of sarcasm, a figurative device that tends to affect the accuracy of the systems. Indeed, for its peculiarities, sarcasm is apt to disguise hurtful messages, especially in short and informal texts such as the ones posted on Twitter. Our hypothesis is that information about the presence of sarcasm could help to improve the detection of hateful messages, even when they are camouflaged as sarcastic. In this perspective, it is interesting to study how the injection of linguistic knowledge into detection models can be useful to capture implicit levels of meaning. In particular, we created novel resources that allowed us to examine deeply our hypothesis and develop specific approaches for the detection of two forms of abusive language in tweets and headlines: hate speech and stereotypes. Our idea was to fruitfully combine general knowledge from language models and linguistic information, obtained with specific linguistic features and the injection of ironic language recognition within a multi-task learning framework. The experimental results confirm that the awareness of sarcasm helps systems to retrieve correctly hate speech and stereotypes in social media texts, such as tweets. Moreover, linguistic features make the system sensible to stereotypes in both tweets and news headlines.
Sarcasm and Implicitness in Abusive Language Detection: A Multilingual Perspective
Simona Frenda
2022-01-01
Abstract
The possibility to monitor hateful content online on the basis of what people write is becoming an important topic for several actors such as governments, ICT companies, and NGO's operators conducting active campaigns in response to the worrying rise of online abuse and hate speech. Abusive language is a broad umbrella term which is commonly used for denoting different kinds of hostile user-generated contents that intimidate or incite to violence and hatred, targeting many vulnerable groups in social platforms. Such hateful contents are pervasive nowadays and can also be detected even in other kinds of texts, such as online newspapers. Various approaches have been proposed in the last years to support the identification and monitoring of these phenomena, but unfortunately, they are far from solving the problem due to the inner complexity of abusive language, and to the difficulties to detect its implicit forms. In our doctoral investigation, we have studied the issues related to automatic identification of abusive language online, investigating various forms of hostility against women, immigrants and cultural minority communities in languages such as Italian, English, and Spanish. The multilingual frame allowed us to have a comparative setting to reflect on how hateful contents are expressed in distinct languages and how these different ways are transposed in the automated processing of the text. The analysis of the results of different methods of classification of hateful and non-hateful messages revealed important challenges that lie principally on the implicitness of some manifestations of abusive language expressed through the use of figurative devices (i.e., irony and sarcasm), recall of inner ideologies (i.e., sexist ideology) or cognitive schemas (i.e., stereotypes), and expression of unfavorable stance. To face these challenges, in this work, we have proposed distinct solutions applicable also to different textual genres. We observed that, in particular, cognitive and creative aspects of abusive language are harder to infer automatically from texts. At the same time they are often recurrent elements, such in the case of sarcasm, a figurative device that tends to affect the accuracy of the systems. Indeed, for its peculiarities, sarcasm is apt to disguise hurtful messages, especially in short and informal texts such as the ones posted on Twitter. Our hypothesis is that information about the presence of sarcasm could help to improve the detection of hateful messages, even when they are camouflaged as sarcastic. In this perspective, it is interesting to study how the injection of linguistic knowledge into detection models can be useful to capture implicit levels of meaning. In particular, we created novel resources that allowed us to examine deeply our hypothesis and develop specific approaches for the detection of two forms of abusive language in tweets and headlines: hate speech and stereotypes. Our idea was to fruitfully combine general knowledge from language models and linguistic information, obtained with specific linguistic features and the injection of ironic language recognition within a multi-task learning framework. The experimental results confirm that the awareness of sarcasm helps systems to retrieve correctly hate speech and stereotypes in social media texts, such as tweets. Moreover, linguistic features make the system sensible to stereotypes in both tweets and news headlines.File | Dimensione | Formato | |
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