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2022 | OriginalPaper | Buchkapitel

Combinations of Content Representation Models for Event Detection on Social Media

verfasst von : Elliot Maître, Max Chevalier, Bernard Dousset, Jean-Philippe Gitto, Olivier Teste

Erschienen in: Research Challenges in Information Science

Verlag: Springer International Publishing

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Abstract

Social media are becoming the preferred channel to report and discuss events happening around the world. The data from these channels can be used to detect ongoing events in real-time. A typical approach is to use event detection methods, usually consisting of a clustering phase, in which similar documents are grouped together, and then an analysis of the clusters to decide whether they deal with real-world events. To cluster together similar documents, content representation models are critical. In this paper, we individually compare the performances of different social media documents content representation models used during the clustering phase, exploiting lexical, semantic and social media specific features, like tags and URLs. To the best of our knowledge, these models are usually individually exploited in this context. We investigate their complementarity and propose to combine them.

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Vorheriges Kapitel A Conceptual Characterization of Fake News: A Positioning Paper

Nächstes Kapitel Increasing Awareness and Usefulness of Open Government Data: An Empirical Analysis of Communication Methods

https://github.com/ina-foss/twembeddings.

Aggarwal, C.C., Zhai, C.X.: A survey of text clustering algorithms. In: Aggarwal, C.C., Zhai, C.X. (eds.) Mining Text Data, pp. 77–128. Springer, Boston (2012). https://doi.org/10.1007/978-1-4614-3223-4_4CrossRef

Allan, J., Lavrenko, V., Malin, D., Swan, R.: Detections, bounds, and timelines: UMass and TDT-3. In: Proceedings of Topic Detection and Tracking Workshop, November 2000 (2000)

Amigó, E., Gonzalo, J., Artiles, J., Verdejo, F.: A comparison of extrinsic clustering evaluation metrics based on formal constraints. Inf. Retr. 12(4), 461–486 (2009)CrossRef

Atefeh, F., Khreich, W.: A survey of techniques for event detection in Twitter. Comput. Intell. 31(1), 132–164 (2015)MathSciNetCrossRef

Baeza-Yates, R., Ribeiro-Neto, B., et al.: Modern Information Retrieval, vol. 463. ACM Press, New York (1999)

Becker, H., Naaman, M., Gravano, L.: Learning similarity metrics for event identification in social media. In: Proceedings of the 3rd ACM International Conference on Web Search and Data Mining, pp. 291–300 (2010)

Becker, H., Naaman, M., Gravano, L.: Beyond trending topics: real-world event identification on Twitter. In: ICWSM, January 2011, vol. 11 (2011)

Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theor. Exp. P10008, 1–12 (2008)MATH

Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)CrossRef

10.

Boom, C.D., Canneyt, S.V., Demeester, T., Dhoedt, B.: Representation learning for very short texts using weighted word embedding aggregation. CoRR abs/1607.00570 (2016)

11.

Cer, D., et al.: Universal sentence encoder. CoRR abs/1803.11175 (2018)

12.

Gionis, A., Mannila, H., Tsaparas, P.: Clustering aggregation. ACM Trans. Knowl. Discov. Data (TKDD) 1(1), 4-es (2007)

13.

Hasan, M., Orgun, M.A., Schwitter, R.: A survey on real-time event detection from the Twitter data stream. J. Inf. Sci. 44(4), 443–463 (2018)CrossRef

14.

Hasan, M., Orgun, M.A., Schwitter, R.: Real-time event detection from the Twitter data stream using the Twitternews+ framework. Inf. Process. Manage. 56(3), 1146–1165 (2019)CrossRef

15.

Mazoyer, B., Cagé, J., Hervé, N., Hudelot, C.: A French corpus for event detection on Twitter. In: Proceedings of the 12th Language Resources and Evaluation Conference, pp. 6220–6227 (2020)

16.

McMinn, A.J., Jose, J.M.: Real-time entity-based event detection for Twitter. In: Mothe, J., et al. (eds.) Experimental IR Meets Multilinguality, Multimodality, and Interaction, pp. 65–77. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24027-5_6CrossRef

17.

McMinn, A.J., Moshfeghi, Y., Jose, J.M.: Building a large-scale corpus for evaluating event detection on Twitter. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 409–418 (2013)

18.

Petrović, S., Osborne, M., Lavrenko, V.: Streaming first story detection with application to Twitter. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, HLT 2010, pp. 181–189. Association for Computational Linguistics, USA (2010)

19.

Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)

Titel: Combinations of Content Representation Models for Event Detection on Social Media
verfasst von: Elliot Maître
Max Chevalier
Bernard Dousset
Jean-Philippe Gitto
Olivier Teste
Verlag: Springer International Publishing
Buch: Research Challenges in Information Science
Print ISBN: 978-3-031-05759-5

Electronic ISBN: 978-3-031-05760-1

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-031-05760-1_42

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