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TheNorth at HASOC 2019: Hate Speech Detection in Social Media Data
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0002-6785-4356
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0001-8532-0895
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The detection of hate speech in social media is a crucial task.The uncontrolled spread of hate speech can be detrimental to maintaining the peace and harmony in society. Particularly when hate speech isspread with the intention to defame people, or spoil the image of a person, a community, or a nation. A major ground for spreading hate speechis that of social media. This significantly contributes to the difficultyof the task, as social media posts not only include paralinguistic tools(e.g. emoticons, and hashtags), their linguistic content contains plentyof poorly written text that does not adhere to grammar rules. With therecent development in Natural Language Processing (NLP), particularly with deep architecture, it is now possible to anlayze unstructured composite natural language text. For this reason, we propose a deep NLPmodel for the detection of automatic hate speech in social media data. We have applied our model on the HASOC2019 hate speech corpus, and attained a macro F1 score of 0.63 in the detection of hate speech.

Place, publisher, year, edition, pages
2019.
National Category
Engineering and Technology
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-77403OAI: oai:DiVA.org:ltu-77403DiVA, id: diva2:1385556
Conference
Forum for Information Retrieval Evaluation 2019
Available from: 2020-01-14 Created: 2020-01-14 Last updated: 2020-01-23

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Alonso, PedroSaini, RajkumarKovács, György

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