Abstract
Online Social Media provides real-time information about events and news in the physical world. A challenging problem is then to identify in a timely manner the few relevant bits of information in these massive and fast-paced streams. Most of the current topic clustering and event detection methods focus on user generated content, hence they are sensible to language, writing style and are usually expensive to compute. Instead, our approach focuses on mining the structure of the graph generated by the interactions between users. Our hypothesis is that bursts in user interest for particular topics and events are reflected by corresponding changes in the structure of the discussion dynamics. We show that our method is capable of effectively identifying event topics in Twitter ground truth data, while offering better overall performance than a purely content-based method based on LDA topic models.
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Atefeh, F., Khreich, W.: A survey of techniques for event detection in twitter. In: Computational Intelligence. Wiley Online Library (2013)
Chierichetti, F., et al.: Event detection via communication pattern analysis. In: Proc. of ICWSM, pp. 51–60. AAAI (2014)
Fung, G.P.C., et al.: Parameter free bursty events detection in text streams. In: Proc. of VLDB, pp. 181–192. VLDB Endowment (2005)
Hromic, H., et al.: Event panning in a stream of big data. In: Knowledge Discovery and Machine Learning Workshop in LWA (2012)
Hu, Y., et al.: Whoo.ly: facilitating information seeking for hyperlocal communities using social media. In: Proc. of SIGCHI, pp. 3481–3490. ACM (2013)
Hurlock, J., et al.: Searching twitter: separating the tweet from the chaff. In: Proc. of ICWSM, pp. 161–168. AAAI (2011)
Kwak, H., et al.: What is twitter, a social network or a news media? In: Proc. of WWW, pp. 591–600. ACM (2010)
Lancichinetti, A., et al.: Finding statistically significant communities in networks. In: PloS one, vol. 6, p. e18961. Public Library of Science (2011)
Lau, J.H., et al.: On-line trend analysis with topic models: #twitter trends detection topic model online. In: Proc. of COLING, pp. 1519–1534. Citeseer (2012)
Marcus, A., et al.: TwitInfo: aggregating and visualizing microblogs for event exploration. In: Proc. of SIGCHI, pp. 227–236. ACM (2011)
McMinn, A.J., et al.: Building a large-scale corpus for evaluating event detection on twitter. In: Proc. of CIKM, pp. 409–418. ACM (2013)
Popescu, A.M., et al.: Detecting controversial events from twitter. In: Proc. of CIKM, pp. 1873–1876. ACM (2010)
Prangnawarat, N., et al.: Event analysis in social media using clustering of heterogeneous information networks. In: Proc. of FLAIRS. AAAI (2015)
Sun, Y., et al.: RankClus: integrating clustering with ranking for heterogeneous information network analysis. In: Proc. of EDBT/ICDT, pp. 565–576. ACM (2009)
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Hromic, H., Prangnawarat, N., Hulpuş, I., Karnstedt, M., Hayes, C. (2015). Graph-Based Methods for Clustering Topics of Interest in Twitter. In: Cimiano, P., Frasincar, F., Houben, GJ., Schwabe, D. (eds) Engineering the Web in the Big Data Era. ICWE 2015. Lecture Notes in Computer Science(), vol 9114. Springer, Cham. https://doi.org/10.1007/978-3-319-19890-3_61
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DOI: https://doi.org/10.1007/978-3-319-19890-3_61
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