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HateBERT: Retraining BERT for Abusive Language Detection in English ...
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Multilingual Irony Detection with Dependency Syntax and Neural Models
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In: Proceedings of the 28th International Conference on Computational Linguistics ; 28th International Conference on Computational Linguistics (COLING 2020) ; https://hal.archives-ouvertes.fr/hal-03102480 ; 28th International Conference on Computational Linguistics (COLING 2020), Dec 2020, Barcelona (Online), Spain. pp.1346-1358 ; https://www.aclweb.org/anthology/2020.coling-main.116/ (2020)
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Modeling Annotator Perspective and Polarized Opinions to Improve Hate Speech Detection
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In: Proceedings of the AAAI Conference on Human Computation and Crowdsourcing; Vol 8 No 1 (2020): Proceedings of the Eighth AAAI Conference on Human Computation and Crowdsourcing; 151-154 (2020)
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Modeling Annotator Perspective and Polarized Opinions to Improve Hate Speech Detection
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In: Proceedings of the AAAI Conference on Human Computation and Crowdsourcing; Vol. 8 No. 1 (2020): Proceedings of the Eighth AAAI Conference on Human Computation and Crowdsourcing; 151-154 (2020)
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Multilingual Irony Detection with Dependency Syntax and Neural Models ...
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Multilingual Irony Detection with Dependency Syntax and Neural Models ...
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HurtBERT: Incorporating Lexical Features with BERT for the Detection of Abusive Language
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EVALITA4ELG: Italian Benchmark Linguistic Resources, NLP Services and Tools for the ELG Platform
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“Contro L’Odio”: A Platform for Detecting, Monitoring and Visualizing Hate Speech against Immigrants in Italian Social Media
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Exploring the association between problem drinking and language use on Facebook in young adults
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Abstract:
Recent literature suggests that variations in both formal and content aspects of texts shared on social media tend to reflect user-level differences in demographic, psychosocial, and behavioral characteristics. In the present study, we examined associations between language use on Facebook and problematic alcohol use. We collected texts shared on Facebook by a sample of 296 adult social media users (66.9% females; mean age = 28.44 years (SD = 7.38)). Texts were mined using the closed-vocabulary approach based on the Linguistic Inquiry Word Count (LIWC) semantic dictionary, and an open-vocabulary approach performed via Latent Dirichlet Allocation (LDA). Then, we examined associations between emerging textual features and alcohol-drinking scores as assessed using the AUDIT-C questionnaire. As a final aim, we employed the Random Forest machine-learning algorithm to determine and compare the predictive accuracy of closed- and open-vocabulary features over users' AUDIT-C scores. We found use of words about family, school, and positive feelings and emotions to be negatively associated with alcohol use and problematic drinking, while words suggesting interest in sport events, politics and economics, nightlife, and use of coarse language were more frequent among problematic drinkers. Results coming from LIWC and LDA analyses were quite similar, but LDA added information that could not be retrieved only with LIWC analysis. Furthermore, open-vocabulary features outperformed closed-vocabulary features in terms of predictive power over participants’ AUDIT-C scores (r = .46 vs. r = .28, respectively). Emerging relationships between text features and offline behaviors may have important implications for alcohol screening purposes in the online environment.
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URL: https://doi.org/10.1016/j.heliyon.2019.e02523 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6812202/
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The DipInfoUniTo Realizer at SRST’19: Learning to Rank and Deep Morphology Prediction for Multilingual Surface Realization
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14-ExLab@UniTo for AMI at IberEval2018: Exploiting lexical knowledge for detecting misogyny in English and Spanish tweets
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Sentiment Polarity Classification at EVALITA: Lessons Learned and Open Challenges
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