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A Machine Learning Approach to Twitter User Classification
Open accessAbstract
This paper addresses the task of user classification in social media, with an application to Twitter. We automatically infer the values of user attributes such as political orientation or ethnicity by leveraging observable information such as the user behavior, network structure and the linguistic content of the user’s Twitter feed. We employ a machine learning approach which relies on a comprehensive set of features derived from such user information. We report encouraging experimental results on 3 tasks with different characteristics: political affiliation detection, ethnicity identification and detecting affinity for a particular business. Finally, our analysis shows that rich linguistic features prove consistently valuable across the 3 tasks and show great promise for additional user classification needs.
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Cites
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“The trend is clear even for the remaining features where this complete compliance is not observed, and we expect that given more data and the ability to examine sufficiently larger contexts (i.e., values of L) we would still get a complete compliance.”
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Metadata
- Authors
- Pennacchiotti, M., & Popescu, A.-M.
- Venue
- Proceedings of the International AAAI Conference on Web and Social Media
- Published
- 2021-08-03
- Volume
- 5
- Issue
- 1
- Pages
- 281-288
- ISSN
- 2162-3449 (ISSN-L)
- 2334-0770
- OpenAlex
- W184758014
- Cited by
- 548
- References
- 1
- FWCI
- 11.11
- Type
- conference paper
- Language
- en
- Open access
- diamond
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