The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
A Measurement Study of Hate Speech in Social Media
Abstract
Social media platforms provide an inexpensive communication medium that allows anyone to quickly reach millions of users. Consequently, in these platforms anyone can publish content and anyone interested in the content can obtain it, representing a transformative revolution in our society. However, this same potential of social media systems brings together an important challenge---these systems provide space for discourses that are harmful to certain groups of people. This challenge manifests itself with a number of variations, including bullying, offensive content, and hate speech. Specifically, authorities of many countries today are rapidly recognizing hate speech as a serious problem, specially because it is hard to create barriers on the Internet to prevent the dissemination of hate across countries or minorities. In this paper, we provide the first of a kind systematic large scale measurement and analysis study of hate speech in online social media. We aim to understand the abundance of hate speech in online social media, the most common hate expressions, the effect of anonymity on hate speech and the most hated groups across regions. In order to achieve our objectives, we gather traces from two social media systems: Whisper and Twitter. We then develop and validate a methodology to identify hate speech on both of these systems. Our results identify hate speech forms and unveil a set of important patterns, providing not only a broader understanding of online hate speech, but also offering directions for detection and prevention approaches.
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Cites
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Locate the Hate: Detecting Tweets against Blacks
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Cited by
Hate speech detection: Challenges and solutions
MacAvaney, S., Yao, H.-R., Yang, E., Russell, K., Goharian, N., & Frieder, O. (2019) · PLOS ONE · doi:10.1371/journal.pone.0221152 · Cited by 566
Spread of Hate Speech in Online Social Media
Mathew, B., Dutt, R., Goyal, P., & Mukherjee, A. (2019) · Proceedings of the 10th ACM Conference on Web Science · doi:10.1145/3292522.3326034 · Cited by 348
Jiang, S., & Wilson, C. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274351 · Cited by 125
Understanding the Effect of Deplatforming on Social Networks
Ali, S., Saeed, M. H., Aldreabi, E., Blackburn, J., De Cristofaro, E., Zannettou, S., & Stringhini, G. (2021) · 13th ACM Web Science Conference 2021 · doi:10.1145/3447535.3462637 · Cited by 109
Nguyen, T. T., Criss, S., Michaels, E. K., Cross, R. I., Michaels, J. S., Dwivedi, P., Huang, D., Hsu, E., Mukhija, K., Nguyen, L. H., et al. (2021) · SSM - Population Health · doi:10.1016/j.ssmph.2021.100922 · Cited by 103
Metadata
- Authors
- Mondal, M., Silva, L. A., & Benevenuto, F.
- Venue
- Proceedings of the 28th ACM Conference on Hypertext and Social Media
- Publisher
- ACM
- Published
- 2017-07-04
- Pages
- 85-94
- OpenAlex
- W2729412353
- Cited by
- 357
- References
- 24
- FWCI
- 12.58
- Type
- conference paper
- Language
- en
- Open access
- closed
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