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Hate Speech and Cyberbullying Detection

Hate Speech and Cyberbullying Detection is a research topic within Artificial Intelligence. Science Explorer counts 18k research works in it since 1950. 26.8% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the automated detection of hate speech, offensive language, and cyberbullying in social media platforms such as Twitter. It explores various techniques including machine learning, natural language processing, and deep learning to identify and categorize abusive content, with a specific emphasis on mitigating online harassment and promoting online safety.

  • Hate Speech
  • Detection
  • Social Media
  • Cyberbullying
  • Offensive Language
  • Machine Learning
  • Natural Language Processing
  • Online Harassment
  • Twitter
  • Deep Learning
Research works
18k
fractional, since 1950
In the world top 10%
4.7k
per year above
Top-10% rate
26.8%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+208%
the tick is no change

Which countries lead Hate Speech and Cyberbullying Detection research?

By volume, the United States and India publish the most (1.5k and 976 works in 2022–2025).

By volume, 2022–2025

  1. 1 United States 1.5k works
  2. 2 India 976 works
  3. 3 China 471 works
  4. 4 United Kingdom 419 works
  5. 5 Indonesia 400 works
  6. 6 Germany 322 works
  7. 7 Türkiye 284 works
  8. 8 Spain 247 works
  9. 9 Australia 205 works
  10. 10 Canada 178 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

United States: 29.9%India: 19.5%China: 9.4%United Kingdom: 8.4%6 others listed: 32.7%30%largest
United States1,496 · 29.9%India976 · 19.5%China471 · 9.4%United Kingdom419 · 8.4%6 others listed1,636 · 32.7%

Shares of the rows listed above, not of the whole node.

Which institutions lead Hate Speech and Cyberbullying Detection research?

By volume in 2022–2025, Amrita Vishwa Vidyapeetham publishes the most Hate Speech and Cyberbullying Detection research, followed by Binus University and University of Amsterdam.

Who are the leading researchers in Hate Speech and Cyberbullying Detection?

The most-cited researchers publishing on Hate Speech and Cyberbullying Detection include Philip S. Yu and Jure Leskovec.

  1. 1 Philip S. Yu United States 6.6k citations
  2. 2 Jure Leskovec United States 4.3k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Hate Speech and Cyberbullying Detection research done?

The largest centres of Hate Speech and Cyberbullying Detection research in 2022–2025 are Beijing (China), London (United Kingdom), Dhaka (Bangladesh) and Chennai (India). Among places with at least 20 works in it, it is an unusually large share of all research in Chittagong, Bloomington and Greater Noida.

Largest cities, 2022–2025

  1. 1 Beijing China 111 works
  2. 2 London United Kingdom 95 works
  3. 3 Dhaka Bangladesh 94 works
  4. 4 Chennai India 91 works
  5. 5 New Delhi India 78 works
  6. 6 Istanbul Türkiye 69 works
  7. 7 Jakarta Indonesia 65 works
  8. 8 Bengaluru India 57 works
  9. 9 New York United States 56 works
  10. 10 Coimbatore India 55 works

Where it is the local speciality

  1. ChittagongBD · 46.5 works17×
  2. BloomingtonUS · 21.6 works6.5×
  3. Greater NoidaIN · 27.5 works5.8×
← less than its size predictsmore →

Location quotient: how much more of its research is in Hate Speech and Cyberbullying Detection than the world average.

See Hate Speech and Cyberbullying Detection on the map

Where is the best place to study Hate Speech and Cyberbullying Detection?

Among universities, judged by research, University of Amsterdam, Carnegie Mellon University and University of Vienna score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%25%50%mean 53.11%fractional works in this node (log) →share in the world top 10% →University of Amsterdam: 30, 58.0%Carnegie Mellon University: 25, 46.1%University of Vienna: 16, 55.0%Ollscoil na Gaillimhe – University of Galway: 14, 40.7%Indiana University Bloomington: 14, 49.4%Northeastern University: 12, 59.5%Ludwig-Maximilians-Universität München: 14, 58.4%Cornell University: 20, 52.9%University of Washington: 25, 56.8%University of Southern California: 16, 54.3%University of Amster…University of ViennaCarnegie Mellon Univ…Ollscoil na Gaillimh…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 University of AmsterdamNetherlands 80.458.0%7.6×30 +122.7%
2 Carnegie Mellon UniversityUnited States 70.846.1%8.1×25 +127.4%
3 University of ViennaAustria 65.555.0%4.4×16 +179.1%
4 Ollscoil na Gaillimhe – University of GalwayIreland 64.440.7%10.2×14
5 Indiana University BloomingtonUnited States 63.849.4%6.2×14 +813.6%
6 Northeastern UniversityUnited States 62.859.5%5.2×12 +150.0%
7 Ludwig-Maximilians-Universität MünchenGermany 61.958.4%2.9×14 +278.1%
8 Cornell UniversityUnited States 61.852.9%2.6×20 +243.4%
9 University of WashingtonUnited States 59.356.8%3.3×25 +65.2%
10 University of Southern CaliforniaUnited States 59.354.3%2.9×16 +392.5%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Hate Speech and Cyberbullying Detection research growing?

Output in 2018–2022 was 208% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Hate Speech and Cyberbullying Detection.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.