منابع مشابه
Hate Me, Hate Me Not: Hate Speech Detection on Facebook
While favouring communications and easing information sharing, Social Network Sites are also used to launch harmful campaigns against specific groups and individuals. Cyberbullism, incitement to self-harm practices, sexual predation are just some of the severe effects of massive online offensives. Moreover, attacks can be carried out against groups of victims and can degenerate in physical viol...
متن کاملHate speech, volition, and neurology
In ‘A Hypothetical Neurological Association between Dehumanization and Human RightsAbuse’,1 GailMurrowandRichardMurrowposit a biological explanationof how hate speech can spur violence, not only among individuals but, even, on a societal scale. They elaborate historical examples, cite to neuronal studies on patterns of responses in observation of pain and suffering to explain the dehumanization...
متن کاملa comparative pragmatic analysis of the speech act of “disagreement” across english and persian
the speech act of disagreement has been one of the speech acts that has received the least attention in the field of pragmatics. this study investigates the ways power relations, social distance, formality of the context, gender, and language proficiency (for efl learners) influence disagreement and politeness strategies. the participants of the study were 200 male and female native persian s...
15 صفحه اولChallenges in discriminating profanity from hate speech
In this study we approach the problem of distinguishing general profanity from hate speech in social media, something which has not been widely considered. Using a new dataset annotated specifically for this task, we employ supervised classification along with a set of features that includes n-grams, skip-grams and clustering-based word representations. We apply approaches based on single class...
متن کاملAutomatic Detection of Online Jihadist Hate Speech
We have developed a system that automatically detects online jihadist hate speech with over 80% accuracy, by using techniques from Natural Language Processing and Machine Learning. The system is trained on a corpus of 45,000 subversive Twitter messages collected from October 2014 to December 2016. We present a qualitative and quantitative analysis of the jihadist rhetoric in the corpus, examine...
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ژورنال
عنوان ژورنال: SSRN Electronic Journal
سال: 2013
ISSN: 1556-5068
DOI: 10.2139/ssrn.2413302