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TwitterMonitor: trend detection over the twitter stream
Abstract
We present TwitterMonitor, a system that performs trend detection over the Twitter stream. The system identifies emerging topics (i.e. 'trends') on Twitter in real time and provides meaningful analytics that synthesize an accurate description of each topic. Users interact with the system by ordering the identified trends using different criteria and submitting their own description for each trend.
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Cited by
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Morstatter, F., Pfeffer, J., Liu, H., & Carley, K. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v7i1.14401 · Cited by 863
Processing Social Media Messages in Mass Emergency: A Survey
Imran, M., Castillo, C., Diaz, F., & Vieweg, S. (2015) · ACM Computing Surveys · doi:10.1145/2771588 · Cited by 804
Automatic detection of rumor on Sina Weibo
Yang, F., Liu, Y., Yu, X., & Yang, M. (2012) · Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics · doi:10.1145/2350190.2350203 · Cited by 680
Predicting the Political Alignment of Twitter Users
Conover, M. D., Goncalves, B., Ratkiewicz, J., Flammini, A., & Menczer, F. (2011) · 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing · doi:10.1109/passat/socialcom.2011.34 · Cited by 588
Metadata
- Authors
- Mathioudakis, M., & Koudas, N.
- Venue
- Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
- Publisher
- ACM
- Published
- 2010-06-06
- Pages
- 1155-1158
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W2018165284
- Cited by
- 813
- References
- 0
- FWCI
- 50.58
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Mathioudakis, M., & Koudas, N. (2010). TwitterMonitor: trend detection over the twitter stream. Proceedings of the 2010 ACM SIGMOD International Conference on Management of data. https://doi.org/10.1145/1807167.1807306
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
