Email this article Model user behaviour of posting messages for topic trend detection
Abstract
However, now and again, people might need to know when to re-hot a topic, i.e., make the point mainstream once more. In this paper, we address this issue by presenting a fleeting User Topic Participation (UTP) demonstrates which models users' practices of posting messages. The UTP display takes into account users' interests, friend-circles, and unforeseen events in online interpersonal organizations. Additionally, it thinks about the persistent transient displaying of points, since themes are changing constantly after some time. Moreover, a weighting plan is proposed to smooth the changes in topic re-hotting forecast.
References
Y. Chen, H. Amiri, Z. Li, and T.-S.Chua, “Emerging Topic Detectionfor Organizations from Microblogs,” in Proceedings of the 36thinternational ACM SIGIR conference on Research and development ininformation retrieval. ACM, 2013, pp. 43–52.
J. Chen, J. Yu, and Y. Shen, “Towards Topic Trend Prediction on aTopic Evolution Model with Social Connection,” in Proceedings ofthe The 2012 IEEE/WIC/ACM International Joint Conferences on WebIntelligence and Intelligent Agent Technology (WI-IAT’12). IEEEComputer Society, 2012, pp. 153–157.
Y. Wang, E. Agichtein, and M. Benzi, “TM-LDA: Efficient Online Modeling of Latent Topic Transitions in Social Media,” in Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2012, pp. 123–131.
H. Kwak, C. Lee, H. Park, and S. Moon, “What is Twitter, a Social Network or a News Media?” in Proceedings of the 19th International Conference on World Wide Web. New York, NY, USA: ACM, 2010,
pp. 591–600.
T. Hofmann, “Probabilistic Latent Semantic Indexing,” in Proceedings of the 22nd annual international ACM SIGIR conference on Researchand development in information retrieval. ACM, 1999, pp. 50–57.
S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, andR. Harshman, “Indexing by Latent Semantic Analysis,” JASIS, vol. 41,no. 6, pp. 391–407, 1990.
D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet Allocation, ”the Journal of Machine Learning Research, vol. 3, pp. 993–1022, 2003.
W. Li and A. McCallum, “Pachinko Allocation: DAG-structured Mixture Models of Topic Correlations,” in Proceedings of International Conference on Machine Learning. ACM, 2006, pp. 577–584.
K. Liu, J. Xu, L. Zhang, Z. Ding, and M. Li, “Discovering Hot Topics from Geo-tagged Video,” Neuro computing, vol. 105, pp. 90–99, 2013.
X. Wang, L. Qi, C. Chen, J. Tang, and M. Jiang, “Grey System Theory based Prediction for Topic Trend on Internet,” Engineering Applications of Artificial Intelligence, vol. 29, pp. 191–200, 2014.
P. Velardi, G. Stilo, A. E. Tozzi, and F. Gesualdo, “Twitter Miningfor Fine-grained Syndromic Surveillance,” Artificial Intelligence inMedicine, vol. 61, pp. 153–163, 2014.
X. Zhang, X. Chen, Y. Chen, S. Wang, Z. Li, and J. Xia, “Event Detection and Popularity Prediction in Microblogging,” Neurocomputing, vol. 149, pp. 1469–1480, 2015.
G. Stilo and P. Velardi, “Efficient Temporal Mining of Micro-blog Textsand its Application to Event Discovery,” Data Mining and KnowledgeDiscovery, vol. 30, no. 2, pp. 372–402, 2016.
G. Stilo and P. Velardi, “Temporal Semantics: Time-varying HashtagSense Clustering,” in International Conference on Knowledge Engineering and Knowledge Management. Springer, 2014, pp. 563–578.
G. Stilo and P. Velardi, “Time Makes Sense: Event Discovery in Twitter Using Temporal Similarity,” in Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)-Volume 02. IEEE Computer Society, 2014,
pp. 186–193.
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