4 datasets tagged as the tag “Arabic”.
4 datasets.
A Wikipedia (Arabic edition talk pages) dataset of 1,095,799 nodes and 1,564,598 edges, collected by KONECT via Wikipedia XML dumps. Each record contains directed: True, weighted: True, signed: False, temporal: True. Directed temporal network of Wikipedia Arabic talk page interactions between editors.. Suitable for wiki collaboration analysis, multilingual network analysis.
A TextDetox 2024 shared task (Arabic) dataset of 5,000 Arabic posts (2,500 toxic, 2,500 non-toxic), collected by Dementieva et al. for PAN/CLEF TextDetox 2024. Each record contains post text, binary toxicity label (0=non-toxic, 1=toxic). TextDetox Arabic provides 5,000 balanced binary toxicity annotations — part of a 15-language multilingual benchmark for cross-lingual toxicity detection research. Suitable for toxicity detection, multilingual content moderation, cross-lingual NLP, hate speech classification, text detoxification.
A CLEF CheckThat! 2023 subjectivity task (arabic) dataset of 1,185 sentences with subjectivity labels (23.6% subjective), collected by CLEF CheckThat! Lab organizers (2023). Each record contains sentence text, binary subjectivity label (SUBJ/OBJ), sentence ID, conflict resolution flag. CLEF 2023 CheckThat! subjectivity (arabic) provides 1,185 news sentences annotated for subjectivity — whether the sentence expresses opinions (SUBJ) or facts (OBJ). Suitable for subjectivity detection, opinion mining, NLP classification, news objectivity analysis, multilingual text classification.
A CLEF CheckThat! 2025 Task 2 (arabic) dataset of 470 social media post and normalized claim pairs, collected by CLEF CheckThat! Lab organizers (2025). Each record contains social media post text containing a factual claim, normalized canonical claim statement. CLEF 2025 CheckThat! Task 2 (arabic) provides 470 pairs of social media posts and their normalized factual claims, enabling claim normalization and canonicalization research. Suitable for claim normalization, fact-checking automation, NLP text generation, multilingual claim extraction, social media fact verification.
Wikipedia