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Hate versus politics: detection of hate against policy makers in Italian tweets
- Source :
- SN Social Sciences
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- Accurate detection of hate speech against politicians, policy making and political ideas is crucial to maintain democracy and free speech. Unfortunately, the amount of labelled data necessary for training models to detect hate speech are limited and domain-dependent. In this paper, we address the issue of classification of hate speech against policy makers from Twitter in Italian, producing the first resource of this type in this language. We collected and annotated 1264 tweets, examined the cases of disagreements between annotators, and performed in-domain and cross-domain hate speech classifications with different features and algorithms. We achieved a performance of ROC AUC 0.83 and analyzed the most predictive attributes, also finding the different language features in the anti-policymakers and anti-immigration domains. Finally, we visualized networks of hashtags to capture the topics used in hateful and normal tweets.<br />to appear in SN social sciences - special issue on hate speech
- Subjects :
- FOS: Computer and information sciences
J.4
Policy making
Computer science
68T50
media_common.quotation_subject
02 engineering and technology
computer.software_genre
Industrial and Manufacturing Engineering
K.4.1
Social media
Politics
Free speech
Resource (project management)
0202 electrical engineering, electronic engineering, information engineering
Hate speech
media_common
Original Paper
Computer Science - Computation and Language
business.industry
Natural language processing
020206 networking & telecommunications
16. Peace & justice
Democracy
020201 artificial intelligence & image processing
Artificial intelligence
business
Computation and Language (cs.CL)
computer
Subjects
Details
- ISSN :
- 26629283
- Volume :
- 1
- Database :
- OpenAIRE
- Journal :
- SN Social Sciences
- Accession number :
- edsair.doi.dedup.....ada1b5e91af86b1ce957bd7fc783c1ef