<div class="csl-bib-body">
<div class="csl-entry">Gemes, K. A., Kovacs, A., Reichel, M., & Recski, G. (2021). Offensive text detection on English Twitter with deep learning models and rule-based systems. In P. Mehta, T. Mandl, P. Majumder, & M. Mitra (Eds.), <i>FIRE-WN 2021 [FIRE 2021 Working Notes]</i> (pp. 283–296). CEUR-WS.org. https://doi.org/10.34726/4342</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/177654
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dc.identifier.uri
https://doi.org/10.34726/4342
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dc.description.abstract
This paper describes the systems the TUW-Inf team submitted for the HASOC 2021 shared task on identifying offensive comments in social media. Besides a simple BERT-based classifier that achieved one of the highest F-scores on the binary classification task, we also build a high-precision rule-based classifier using a custom framework for human-in-the-loop learning. Both of our approaches are also evaluated qualitatively by manual analysis of 150 tweets, which also highlights possible controversies in the ground truth labels of the HASOC dataset
en
dc.language.iso
en
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dc.relation.ispartofseries
CEUR Workshop Proceedings
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
social media data
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dc.subject
hate speech detection
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dc.subject
rule-based methods
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dc.subject
deep learning
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dc.subject
text classification
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dc.title
Offensive text detection on English Twitter with deep learning models and rule-based systems
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dc.type
Inproceedings
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dc.type
Konferenzbeitrag
de
dc.rights.license
Creative Commons Namensnennung 4.0 International
de
dc.rights.license
Creative Commons Attribution 4.0 International
en
dc.identifier.doi
10.34726/4342
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dc.contributor.affiliation
TU Wien, Austria
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dc.contributor.editoraffiliation
Pandit Deendayal Petroleum University, India
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dc.contributor.editoraffiliation
University of Hildesheim, Germany
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dc.contributor.editoraffiliation
Dhirubhai Ambani Institute of Information and Communication Technology, India
