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Cited by 104Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion MiningText and Document Classification Technologies

The DOI matches its Crossref record; 164 of the 173 references it cites were found in a registry or an index.

Offensive, aggressive, and hate speech analysis: From data-centric to human-centered approach

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Kocoń, Jan, Figas, Alicja, Gruza, Marcin, Puchalska, Daria, Kajdanowicz, Tomasz, Kazienko, Przemysław·2021·Information Processing & Management
DOI 10.1016/j.ipm.2021.102643
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Abstract

Analysis of subjective texts like offensive content or hate speech is a great challenge, especially regarding annotation process. Most of current annotation procedures are aimed at achieving a high level of agreement in order to generate a high quality reference source. However, the annotation guidelines for subjective content may restrict the annotators’ freedom of decision making . Motivated by a moderate annotation agreement in offensive content datasets, we hypothesize that personalized approaches to offensive content identification should be in place. Thus, we propose two novel perspectives of perception: group-based and individual. Using demographics of annotators as well as embeddings of their previous decisions (annotated texts), we are able to train multimodal models (including transformer-based) adjusted to personal or community profiles. Based on the agreement of individuals and groups, we experimentally showed that annotator group agreeability strongly correlates with offensive content recognition quality. The proposed personalized approaches enabled us to create models adaptable to personal user beliefs rather than to agreed offensiveness understanding. Overall, our individualized approaches to offensive content classification outperform classic data-centric methods that generalize offensiveness perception and it refers to all six tested models. Additionally, we developed requirements for annotation procedures, personalization and content processing to make the solutions human-centered.

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Research works printed but not linked: 5

  1. Breckheimer, 2001, A haven for hate: The foreign and domestic implications of protecting Internet hate speech under the first amendment, Southern California Law Review, 75, 1493
  2. Buczynski, A., & Wawer, A. (2008). Shallow parsing in sentiment analysis of product reviews. In Proceedings of the partial parsing workshop at LREC (vol. 2008) (pp. 14–18).
  3. Marcinczuk, M., Kocon, J., & Gawor, M. (2018). Recognition of named entities for Polish - comparison of deep learning and conditional random fields approaches. In Proceedings of the PolEval 2018 workshop (pp. 77–92).
  4. Ptaszyński, M., Masui, F., Janz, A., Kocoń, J., Piasecki, M., & Zaśko-Zielińska, M., et al. (2017). Three attempts in PolEval 2017 sentiment analysis task. In LTC’17 the 8th language & technology conference (pp. 117–121).
  5. Troszyński, 2017, Czy komputer rozpozna hejtera? Wykorzystanie uczenia maszynowego (ML) w jakościowej analizie danych. [Can a Computer Recognize Hate Speech? Machine Learning (ML) in Qualitative Data Analysis], Przegla̧d Socjologii Jakościowej, XIII, 62

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Metadata

Authors
Kocoń, J., Figas, A., Gruza, M., Puchalska, D., Kajdanowicz, T., & Kazienko, P.
Affiliations
Wrocław University of Science and Technology (PL)
Corresponding author
Kocoń, Jan
ORCID
Venue
Information Processing & Management
Publisher
Elsevier BV
Published
2021-09
Volume
58
Issue
5
Pages
102643
ISSN
  • 0020-0271 (ISSN-L)
  • 0306-4573
DOI
10.1016/j.ipm.2021.102643
OpenAlex
W3171716568
Cited by
103
References
173
Citations by year
2026: 9 · 2025: 17 · 2024: 21 · 2023: 32 · 2022: 17 · 2021: 7
FWCI
6.81
Type
journal article
Language
en
Open access
Licence
Locations
Information Processing & Management (publishedVersion, cc-by)
Funders
  • Narodowe Centrum Nauki
  • Wroclaw University of Science and Technology
  • Polish Ministry of Science and Higher Education
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
  • Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
Keywords
offensive content detection, hate speech, annotation agreement, annotator demographics, personalized classification, individualized models, multimodal models, transformer models, human-centered AI, annotation guidelines

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