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Analyzing and learning the language for different types of harassment
- Source :
- PLoS ONE, PLoS ONE, Vol 15, Iss 3, p e0227330 (2020)
- Publication Year :
- 2018
-
Abstract
- Disclaimer: This paper is concerned with violent online harassment. To describe the subject at an adequate level of realism, examples of our collected tweets involve violent, threatening, vulgar and hateful speech language in the context of racial, sexual, political, appearance and intellectual harassment. The presence of a significant amount of harassment in user-generated content and its negative impact calls for robust automatic detection approaches. This requires that we can identify different forms or 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 context that represents the interrelated conditions in which they occur. In this paper, we introduce the notion of contextual type to harassment involving five categories: (i) sexual, (ii) racial, (iii) appearance-related, (iv) intellectual and (v) political. We utilize an annotated corpus from Twitter distinguishing these types of harassment. To study the context for each type that sheds light on the linguistic meaning, interpretation, and distribution, we conduct two lines of investigation: an extensive linguistic analysis, and a statistical distribution of unigrams. We then build type-ware 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 major observations about the effectiveness of type-aware classifiers using a detailed comparison setup providing insight into the role of type-dependent features.<br />Submitted for PLOS ONE Journal 17 pages
- Subjects :
- FOS: Computer and information sciences
Male
Facebook
Computer science
Emotions
Social Sciences
02 engineering and technology
computer.software_genre
Machine Learning
Sociology
0202 electrical engineering, electronic engineering, information engineering
Psychology
Language
Multidisciplinary
Computer Science - Computation and Language
Data Collection
Social Communication
Semantics
Identification (information)
Social Networks
Medicine
020201 artificial intelligence & image processing
Female
Harassment, Non-Sexual
Computation and Language (cs.CL)
Natural language processing
Network Analysis
Research Article
Computer and Information Sciences
Science
Twitter
Context (language use)
Artificial Intelligence
020204 information systems
Support Vector Machines
Humans
Social media
Lexicons
business.industry
Interpretation (philosophy)
Offensive
Cognitive Psychology
Biology and Life Sciences
Correction
Linguistics
Communications
Support vector machine
Sexual Harassment
Harassment
Cognitive Science
Artificial intelligence
business
computer
Social Media
Neuroscience
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- PLoS ONE, PLoS ONE, Vol 15, Iss 3, p e0227330 (2020)
- Accession number :
- edsair.doi.dedup.....4b755fbf9142bf48972d837c0b0d8e27