Abstract
Nowadays, events usually burst and are propagated online through multiple modern media like social networks and search engines. There exists various research discussing the event dissemination trends on individual medium, while few studies focus on event popularity analysis from a cross-platform perspective. In this paper, we design DancingLines, an innovative scheme that captures and quantitatively analyzes event popularity between pairwise text media. It contains two models: TF-SW, a semantic-aware popularity quantification model, based on an integrated weight coefficient leveraging Word2Vec and TextRank; and \(\omega \)DTW-CD, a pairwise event popularity time series alignment model matching different event phases adapted from Dynamic Time Warping. Experimental results on eighteen real-world datasets from an influential social network and a popular search engine validate the effectiveness and applicability of our scheme. DancingLines is demonstrated to possess broad application potentials for discovering knowledge related to events and different media.
This work has been supported in part by the Program of International S&T Cooperation (2016YFE0100300), the China 973 project (2014CB340303), the National Natural Science Foundation of China (Grant number 61472252, 61672353), the Shanghai Science and Technology Fund (Grant number 17510740200), CCF-Tencent Open Research Fund (RAGR20170114), and Key Technologies R&D Program of China (2017YFC0405805-04).
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References
Ahn, B., Van Durme, B., Callison-Burch, C.: Wikitopics: what is popular on Wikipedia and why. In: Proceedings of the Workshop on Automatic Summarization for Different Genres, Media, and Languages, pp. 33–40 (2011)
Bao, B., Xu, C., Min, W., Hossain, M.S.: Cross-platform emerging topic detection and elaboration from multimedia streams. TOMCCAP 11(4), 54 (2015)
Charikar, M.S.: Similarity estimation techniques from rounding algorithms. In: STOC, pp. 380–388 (2002)
Dau, H.A., Begum, N., Keogh, E.: Semi-supervision dramatically improves time series clustering under dynamic time warping. In: CIKM, pp. 999–1008 (2016)
Giummolè, F., Orlando, S., Tolomei, G.: A study on microblog and search engine user behaviors: how Twitter trending topics help predict Google hot queries. Human 2(3), 195 (2013)
Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: STOC, pp. 604–613 (1998)
Jeong, Y.S., Jeong, M.K., Omitaomu, O.A.: Weighted dynamic time warping for time series classification. Pattern Recogn. 44(9), 2231–2240 (2011)
Keogh, E.J., Pazzani, M.J.: Derivative dynamic time warping. In: SDM, pp. 1–11 (2001)
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: WWW, pp. 591–600 (2010)
Lee, P., Lakshmanan, L.V.S., Milios, E.E.: Keysee: supporting keyword search on evolving events in social streams. In: KDD, pp. 1478–1481 (2013)
Li, R., Lei, K.H., Khadiwala, R., Chang, K.: Tedas: a Twitter-based event detection and analysis system. In: ICDE, pp. 1273–1276 (2012)
Lin, S., Wang, F., Hu, Q., Yu, P.: Extracting social events for learning better information diffusion models. In: KDD, pp. 365–373 (2013)
Liu, N., An, H., Gao, X., Li, H., Hao, X.: Breaking news dissemination in the media via propagation behavior based on complex network theory. Physica A 453, 44–54 (2016)
Maus, V., Câmara, G., Cartaxo, R., Sanchez, A., Ramos, F., Queiroz, G.: A time-weighted dynamic time warping method for land-use and land-cover mapping. J-STARS 9(8), 3729–3739 (2016)
Mihalcea, R., Tarau, P.: TextRank: bringing order into texts. In: EMNLP, pp. 404–411 (2004)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS, pp. 3111–3119 (2013)
Newman, M.: Power laws, pareto distributions and Zipf’s law. Contemp. Phys. 46(5), 323–351 (2005)
Osborne, M., Petrovic, S., McCreadie, R., Macdonald, C., Ounis, I.: Bieber no more: first story detection using Twitter and Wikipedia. In: SIGIR 2012 Workshop on Time-Aware Information Access (2012)
Rong, Y., Zhu, Q., Cheng, H.: A model-free approach to infer the diffusion network from event cascade. In: CIKM, pp. 1653–1662 (2016)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26(1), 43–49 (1978)
Silva, D.F., Batista, G.E., Keogh, E.: On the effect of endpoints on dynamic time warping. In: SIGKDD Workshop on Mining Data and Learning from Time Series (2016)
Tang, Y., Ma, P., Kong, B., Ji, W., Gao, X., Peng, X.: ESAP: a novel approach for cross-platform event dissemination trend analysis between social network and search engine. In: Cellary, W., Mokbel, M.F., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds.) WISE 2016. LNCS, vol. 10041, pp. 489–504. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48740-3_36
Wang, J., et al.: Mining multi-aspect reflection of news events in Twitter: discovery, linking and presentation. In: ICDM, pp. 429–438 (2015)
Zhang, J., Chen, J., Zhi, S., Chang, Y., Yu, P.S., Han, J.: Link prediction across aligned networks with sparse and low rank matrix estimation. In: ICDE, pp. 971–982 (2017)
Zhong, Y., Liu, S., Wang, X., Xiao, J., Song, Y.: Tracking idea flows between social groups. In: AAAI, pp. 1436–1443 (2016)
Zhou, X., Liang, X., Zhang, H., Ma, Y.: Cross-platform identification of anonymous identical users in multiple social media networks. TKDE 28(2), 411–424 (2016)
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Gao, T. et al. (2018). DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2018. Lecture Notes in Computer Science(), vol 11029. Springer, Cham. https://doi.org/10.1007/978-3-319-98809-2_18
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