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UBERT: A Novel Language Model for Synonymy Prediction at Scale in the UMLS Metathesaurus ...
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"Is depression related to cannabis?": A Knowledge-infused Model for Entity and Relation Extraction with Limited Supervision
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In: Publications (2021)
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Analyzing and Learning the Language for Different Types of Harassment
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In: Publications (2020)
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Abstract:
THIS ARTICLE USES WORDS OR LANGUAGE THAT IS CONSIDERED PROFANE, VULGAR, OR OFFENSIVE BY SOME READERS. The presence of a significant amount of harassment in user-generated content and its negative impact calls for robust automatic detection approaches. This requires the identification of different types of harassment. Earlier work has classified harassing language in terms of hurtfulness, abusiveness, sentiment, and profanity. However, to identify and understand harassment more accurately, it is essential to determine the contextual type that captures the interrelated conditions in which harassing language occurs. In this paper we introduce the notion of contextual type in harassment by distinguishing between five contextual types: (i) sexual, (ii) racial, (iii) appearance-related, (iv) intellectual and (v) political. We utilize an annotated corpus from Twitter distinguishing these types of harassment. We study the context of each kind to shed light on the linguistic meaning, interpretation, and distribution, with results from two lines of investigation: an extensive linguistic analysis, and the statistical distribution of uni-grams. We then build type- aware classifiers to automate the identification of type-specific harassment. Our experiments demonstrate that these classifiers provide competitive accuracy for identifying and analyzing harassment on social media. We present extensive discussion and significant observations about the effectiveness of type-aware classifiers using a detailed comparison setup, providing insight into the role of type-dependent features.
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Keyword:
Computer Engineering; Electrical and Computer Engineering; harassment; Twitter
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URL: https://scholarcommons.sc.edu/cgi/viewcontent.cgi?article=1044&context=aii_fac_pub https://scholarcommons.sc.edu/aii_fac_pub/45
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ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter
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In: Publications (2020)
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Analyzing and Learning the Language for Different Types of Harassment
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In: Amit P. Sheth (2020)
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ALONE: A Dataset for Toxic Behavior among Adolescents on Twitter
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In: Amit P. Sheth (2020)
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Personalized Prediction of Suicide Risk for Web-based Intervention
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In: Krishnaprasad Thirunarayan (2019)
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Personalized Prediction of Suicide Risk for Web-based Intervention
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In: Amit P. Sheth (2019)
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Personalized Prediction of Suicide Risk for Web-based Intervention
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In: Kno.e.sis Publications (2018)
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Database and Expert Systems Applications - 28th International Conference, DEXA 2017, Lyon, France, Proceedings part II
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In: ISSN: 0302-9743 ; Lecture Notes in Computer Science ; 28th International Conference on Database and Expert Systems Applications and Workshops (DEXA 2017) ; https://hal.archives-ouvertes.fr/hal-03120290 ; Benslimane, Djamal; Damiani, Ernesto; Grosky, William I.; Hameurlain, Abdelkader; Sheth, Amit P.; Wagner, Roland R. 28th International Conference on Database and Expert Systems Applications and Workshops (DEXA 2017), Aug 2017, Lyon, France. Lecture Notes in Computer Science, 10439 (Part II), Springer, 2017, Database and Expert Systems Applications 28th International Conference, DEXA 2017, Lyon, France, 978-3-319-64470-7. ⟨10.1007/978-3-319-64471-4⟩ ; https://link.springer.com/book/10.1007%2F978-3-319-64471-4 (2017)
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RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem
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In: Publications (2017)
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RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem
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In: Kno.e.sis Publications (2017)
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What Kind of #Communication is Twitter? A Psycholinguistic Perspective on Communication in Twitter for the Purpose of Emergency Coordination
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In: Valerie Shalin (2017)
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What Kind of #Conversation is Twitter? Mining #Psycholinguistic Cues for Emergency Coordination
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In: Valerie Shalin (2017)
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RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem
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In: Amit P. Sheth (2017)
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Intent Classification of Short-Text on Social Media
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In: Valerie Shalin (2017)
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Context-Aware Semantic Association Ranking
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In: Amit P. Sheth (2016)
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ezDI's Semantics-Enhanced Linguistic, NLP, and ML Approach for Health Informatics
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In: Amit P. Sheth (2016)
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Intent Classification of Short-Text on Social Media
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In: Amit P. Sheth (2016)
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Intent Classification of Short-Text on Social Media
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In: Krishnaprasad Thirunarayan (2016)
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