Abstract
Local topic detection is an important task for many applications such as local event discovery, activity recommendation and emergency warning. Recent years have witnessed growing interest in leveraging spatio-temporal social media (eg. Twitter) for local topic detection. However, existing methods overlook the continuity of time and location, which is quite important and useful for local topic detection. For example, tweets posted at adjacent time and location should be considered correlated instead of isolated. To address this challenge, we propose a multi-layer heterogeneous network based embedding learner to preserve vicinity correlation as well as co-occurrence correlation, and map all the location, time, and keywords into a same latent space. Based on the heterogeneous network embedding, we develop a Bayesian mixture model to find local topics without specifying the number of topics in advance. Moreover, tweets are frequently updated, thus, we adopt an incremental update strategy to process continuous tweet stream in real time. The extensive experiments on real-world data sets demonstrate that our method outperforms the state-of-the-art existing methods.
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References
Batmanghelich, K., Saeedi, A., Narasimhan, K., Gershman, S.: Nonparametric spherical topic modeling with word embeddings. arXiv preprint arXiv:1604.00126 (2016)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)
Chen, W., Wang, J., Jiang, Z., Zhang, Y., Li, X.: Hierarchical mixed neural network for joint representation learning of social-attribute network. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10234, pp. 238–250. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57454-7_19
Das, R., Zaheer, M., Dyer, C.: Gaussian LDA for topic models with word embeddings. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), vol. 1, pp. 795–804 (2015)
Duan, J., Ai, Y., et al.: LDA topic model for microblog recommendation. In: 2015 International Conference on Asian Language Processing (IALP), pp. 185–188. IEEE (2015)
Gopal, S., Yang, Y.: Von Mises-Fisher clustering models. In: International Conference on Machine Learning, pp. 154–162 (2014)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864. ACM (2016)
Guo, J., Gong, Z.: A nonparametric model for event discovery in the geospatial-temporal space. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 499–508. ACM (2016)
Jabeen, I., Faiz, R., Mehmood, A., Yousaf, N.: Cohesion and semantic understanding. Acad. Res. Int. 4(6), 139 (2013)
Kawamae, N.: Trend analysis model: trend consists of temporal words, topics, and timestamps. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 317–326. ACM (2011)
Krumm, J., Horvitz, E.: Eyewitness: identifying local events via space-time signals in Twitter feeds. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, p. 20. ACM (2015)
Liu, H., Ge, Y., Zheng, Q., Lin, R., Li, H.: Detecting global and local topics via mining Twitter data. Neurocomputing 273, 120–132 (2018)
Liu, Y., Ester, M., Hu, B., Cheung, D.W.: Spatio-temporal topic models for check-in data. In: 2015 IEEE International Conference on Data Mining (ICDM), pp. 889–894. IEEE (2015)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710. ACM (2014)
Sizov, S.: Geofolk: latent spatial semantics in web 2.0 social media. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 281–290. ACM (2010)
Stevens, K., Kegelmeyer, P., Andrzejewski, D., Buttler, D.: Exploring topic coherence over many models and many topics. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 952–961. Association for Computational Linguistics (2012)
Stieglitz, S., Mirbabaie, M., Ross, B., Neuberger, C.: Social media analytics-challenges in topic discovery, data collection, and data preparation. Int. J. Inf. Manage. 39, 156–168 (2018)
Wang, J., Li, L., Tan, F., Zhu, Y., Feng, W.: Detecting hotspot information using multi-attribute based topic model. PloS one 10(10), e0140539 (2015)
Wang, W., Yin, H., Chen, L., Sun, Y., Sadiq, S., Zhou, X.: Geo-sage: a geographical sparse additive generative model for spatial item recommendation. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1255–1264. ACM (2015)
Wold, H.M., Vikre, L., Gulla, J.A., Özgöbek, Ö., Su, X.: Twitter topic modeling for breaking news detection. In: WEBIST, vol. 2. pp. 211–218 (2016)
Yan, X., Guo, J., Lan, Y., Cheng, X.: A biterm topic model for short texts. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 1445–1456. ACM (2013)
Zhang, C., et al.: Triovecevent: embedding-based online local event detection in geo-tagged tweet streams. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 595–604. ACM (2017)
Zhang, K.: Uncovering urban dynamics via cross-modal representation learning. Ph.D. thesis (2017)
Zhao, W.X., et al.: Comparing Twitter and traditional media using topic models. In: Clough, P., et al. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 338–349. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20161-5_34
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This work is partially supported by National Natural Science Foundation of China (No. U163620068) and National Key Research and Development Program of China.
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Chen, J., Gao, N., Xue, C., Tu, C., Zha, D. (2019). Perceiving Topic Bubbles: Local Topic Detection in Spatio-Temporal Tweet Stream. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11447. Springer, Cham. https://doi.org/10.1007/978-3-030-18579-4_43
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