hatem1974/T-HSAB-A-Tunisian-Hate-Speech-and-Abusive-Dataset

The first Tunisian Hate Speech and Abusive Dataset # T-HSAB-A-Tunisian-Hate-Speech-and-Abusive-Dataset The first Tunisian Hate Speech and Abusive Dataset. # T-HSAB Dataset: Context and Topics The Tunisian Hate Speech and ABusive (T-HSAB) is the first Arabic Tunisian Hate Speech and Abusive Language Dataset proposed in the The 7th International Conference on Arabic Language Processing October 16-17, 2019 (Nancy, France). Since the "Jasmine Revolution" at 2011, Tunisia has entered a new era of ultimate freedom of expression with a full access into social media. This has been associated with an unrestricted spread of toxic contents such as Abusive and Hate speech. T-HSAB combines 6,024 Tunisian comments labeled as normal, abusive or hate. The collected tweets were posted between October 2018 and March 2019. # Data Annotation Guidelines Our annotation process was conducted by 3 Tunisian-speaking annotators. The annotation instructions defined the 3 label categories as: • Normal tweets are those instances with no offensive, aggressive, insulting and profanity content. • Abusive tweets are those instances that combine offensive, aggressive, insulting or profanity content. • Hate tweets are those instances that: (a) contain an abusive language, (b) dedicate the abusive language towards a specific person or a group of people and (c) demean or dehumanize that person or that group of people based on their descriptive identity (race, gender, religion, disability, skin color, belief). • The annotators were provided by the nicknames usually used, within hate/abusive contexts, to refer to certain political parties, minorities and ethnic/religion groups. For example, “كحلوش” (of a dark skin), which represents the African ethnic group, is usually used within hate speech contexts. # Annotation Evaluation: Methods & Results The annotation credibility was evaluated using several evaluation measures: 1- Pairwise Percent Agreement Measure (PRAM): best value between annotator 1 & annotator 2 = 97.9% 2- Cohen's Kappa (K): best v …