Challenge numbe Automatically detect hate speech

[Shishirgano9]

But the targets of hate go beyond these populations. Hateful speech becomes the core of online interaction spaces where the disqualification of the interlocutor (his ethos ) constitutes the main objective.

This emotional universe (of "pathemization" ) draws its justification tunisia mobile database from a more general background of "social disorder" which also targets the "elites". journalists, media, politicians, etc.


The lack of a “gold standard” in defining online hate speech makes its detection difficult, both for human moderators and for machines (algorithms). Especially since the online content to be analyzed is vast and the context – situational (current events) and cultural (history, society, etc.) – affects the emergence and nature of this type of speech.

In the field of computational linguistics – particularly in English, Danish, German, Italian and Finnish – the fight against hate speech often involves the use of pre-established lists of “hateful” words. However, these lists have difficulty capturing the most subtle – and therefore implicit – forms of hatred (sarcasm, euphemisms, stereotypes, contextual references, for example historical), as well as the “masking strategies” put in place by Internet users (spelling games, crypto-languages ​​reserved for insiders, etc.).