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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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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 significant problem for online news platforms. Here, we experiment with models for automatic moderation, using a dataset of comments from a popular Croatian newspaper. Our analysis shows that while comments that violate the moderation rules mostly share common linguistic and thematic features, their content 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 decision. Our results show that topic information 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/5562464 https://dx.doi.org/10.5281/zenodo.5562464
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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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