CoChat
Sign Up
Cited by 113Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion MiningMisinformation and Its Impacts

The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.

Mapping Twitter hate speech towards social and sexual minorities: a lexicon-based approach to semantic content analysis

Open access
Lingiardi, Vittorio, Carone, Nicola, Semeraro, Giovanni, Musto, Cataldo, D’Amico, Marilisa, Brena, Silvia·2020·Behaviour & Information Technology
DOI 10.1080/0144929x.2019.1607903
Open this paper in CoChat — free account

Abstract

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.

Overview

Integrity signals

Registry

Crossref · 10.1080/0144929x.2019.1607903

Cites

30 of 30 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 30 references

Research works printed but not linked: 1

  1. Silva L., 2016, International AAAI Conference on Web and Social Media 2016

Cited by

Sorted by citations

Related

See all

Metadata

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
ORCID
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
DOI
10.1080/0144929x.2019.1607903
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
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 ›

Are you an author, or do you see a problem with this page? Request a review