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Not All Comments Are Equal: Insights into Comment Moderation from a Topic-aware Model ...
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Not All Comments Are Equal: Insights into Comment Moderation from a Topic-aware Model ...
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Not All Comments are Equal: Insights into Comment Moderation from a Topic-Aware Model ...
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Not All Comments are Equal: Insights into Comment Moderation from a Topic-Aware Model ...
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Not all comments are equal: Insights into comment moderation from a topic-aware model ...
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Abstract:
Moderation of reader comments is a signifi- cant problem for online news platforms. Here, we experiment with models for automatic mod- eration, using a dataset of comments from a popular Croatian newspaper. Our analy- sis shows that while comments that violate the moderation rules mostly share common linguistic and thematic features, their con- tent varies across the different sections of the newspaper. We therefore make our models topic-aware, incorporating semantic features from a topic model into the classification de- cision. Our results show that topic informa- tion improves the performance of the model, increases its confidence in correct outputs, and helps us understand the model’s outputs. ...
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URL: https://zenodo.org/record/5648098 https://dx.doi.org/10.5281/zenodo.5648098
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Not All Comments are Equal: Insights into Comment Moderation from a Topic-Aware Model ...
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Not all comments are equal: Insights into comment moderation from a topic-aware model ...
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XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages ...
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Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction ...
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Classification-Based Self-Learning for Weakly Supervised Bilingual Lexicon Induction
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XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages
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Glavas, Goran; Karan, Mladen; Vulic, Ivan. - : International Committee on Computational Linguistics, 2020. : https://www.aclweb.org/anthology/2020.coling-main.559, 2020. : Proceedings of the 28th International Conference on Computational Linguistics (COLING 2020), 2020
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XHate-999: analyzing and detecting abusive language across domains and languages
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