Mapping Twitter hate speech towards social and sexual minorities: a lexicon-based approach to semantic content analysis
Open accessAbstract
Though there are currently no statistics offering a global overview of online hate speech, both social networking platforms and organisations that combat hate speech have recognised that prevention strategies are needed to address this negative online phenomenon. While most cases of online hate speech target individuals on the basis of ethnicity and nationality, incitements to hatred on the basis of religion, class, gender and sexual orientation are increasing. This paper reports the findings of the ‘Italian Hate Map’ project, which used a lexicon-based method of semantic content analysis to extract 2,659,879 Tweets (from 879,428 Twitter profiles) over a period of 7 months; 412,716 of these Tweets contained negative terms directed at one of the six target groups. In the geolocalized Tweets, women were the most insulted group, having received 71,006 hateful Tweets (60.4% of the negative geolocalized tweets), followed by immigrants (12,281 tweets, 10.4%), gay and lesbian persons (12,140 tweets, 10.3%), Muslims (7,465 tweets, 6.4%), Jews (7,465 tweets, 6.4%) and disabled persons (7,230 tweets, 6.1%). The findings provide a real-time snapshot of community behaviours and attitudes against social, ethnic, sexual and gender minority groups that can be used to inform intolerance prevention campaigns on both local and national levels.
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Las comunidades musulmanas son uno de los grupos más vulnerables al discurso de odio en línea, junto con las mujeres, las personas migrantes y los miembros de la comunidad LGBTQ+.
“Diversos estudios han identificado a las comunidades musulmanas como uno de los grupos más vulnerables al discurso de odio en línea, junto con las mujeres, las personas migrantes y los miembros de la comunidad LGBTQ+ (Baumgarten et al., 2019;Lingiardi et al., 2020).”
From Redes de odio y contranarrativas en Twitter sobre el ataque a iglesias católicas en Algeciras: Dinámicas de conectividad, influencia y polarización (2026) · cites this paper as Lingiardi et al., 2020
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- Authors
- Lingiardi, V., Carone, N., Semeraro, G., Musto, C., D’Amico, M., & Brena, S.
- Affiliations
- Sapienza University of Rome (IT)
- University of Pavia (IT)
- University of Bari Aldo Moro (IT)
- University of Milan (IT)
- VOX Diritti – Osservatorio italiano sui diritti, Milan, Italy
- Corresponding author
- Lingiardi, Vittorio
- Venue
- Behaviour & Information Technology
- Publisher
- Informa UK Limited
- Published
- 2019-04-22
- Volume
- 39
- Issue
- 7
- Pages
- 711-721
- ISSN
- 0144-929X (ISSN-L)
- 1362-3001
- OpenAlex
- W2941193813
- Cited by
- 113
- References
- 30
- Citations by year
- 2026: 6 · 2025: 16 · 2024: 16 · 2023: 27 · 2022: 16 · 2021: 8 · 2020: 22 · 2019: 2
- FWCI
- 5.54
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc-nd
- Locations
- Behaviour and Information Technology (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Cineca Institutional Research Information System (Tor Vergata University) (submittedVersion)
- CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- online hate speech, Twitter, sexual minorities, minority groups, semantic content analysis, women, immigrants, gay and lesbian people, Muslims, Jews, people with disabilities, intolerance prevention
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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