Improved Topic Modeling in Twitter Through Community Pooling

Author:

Albanese FedericoORCID,Feuerstein EstebanORCID

Publisher

Springer International Publishing

Reference21 articles.

1. Akhtar, N., Beg, M.: User graph topic model. J. Intell. Fuzzy Syst. 36(3), 2229–2240 (2019)

2. Al-Sultany, G.A., Aleqabie, H.J.: Events tagging in twitter using twitter latent Dirichlet allocation. Int. J. Eng. Technol. 8(1.5), 503–508 (2019)

3. Albanese, F., Lombardi, L., Feuerstein, E., Balenzuela, P.: Predicting shifting individuals using text mining and graph machine learning on twitter. arXiv preprint arXiv:2008.10749 (2020)

4. Alvarez-Melis, D., Saveski, M.: Topic modeling in twitter: aggregating tweets by conversations. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 10 (2016)

5. Aruguete, N., Calvo, E.: Time to # protest: selective exposure, cascading activation, and framing in social media. J. Commun. 68(3), 480–502 (2018)

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