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ELEVATE-Live: Assessment and Visualization of Online News Virality via Entity-Level Analytics

  • GovindEmail author
  • Céline Alec
  • Marc Spaniol
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10845)

Abstract

Recent research has shown significant progress in forecasting the impact and spread of societal relevant events into online communities of different languages. Here, raising contents to the entity-level has been the driving force in “understanding” Web contents. In this demonstration paper, we present a novel Web-based tool that exploits entity information from online news in order to assess and visualize their virality.

Keywords

Entity-level web analytics Web semantics Analytics interface 

References

  1. 1.
    Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: a nucleus for a web of open data. In: Aberer, K., et al. (eds.) ASWC/ISWC -2007. LNCS, vol. 4825, pp. 722–735. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-76298-0_52CrossRefGoogle Scholar
  2. 2.
    Govind, Spaniol, M.: ELEVATE: a framework for entity-level event diffusion prediction into foreign language communities. In: Proceedings of the 9th International ACM Web Science Conference (WebSci 2017), pp. 111–120 (2017)Google Scholar
  3. 3.
    Hansen, L.K., Arvidsson, A., Nielsen, F.A., Colleoni, E., Etter, M.: Good friends, bad news - affect and virality in twitter. In: Park, J.J., Yang, L.T., Lee, C. (eds.) FutureTech 2011. CCIS, vol. 185, pp. 34–43. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-22309-9_5CrossRefGoogle Scholar
  4. 4.
    Hoffart, J., Milchevski, D., Weikum, G.: STICS: searching with strings, things, and cats. In: Proceedings of the 37th International ACM SIGIR Conference on Research & #38; Development in Information Retrieval, SIGIR 2014, pp. 1247–1248. ACM, New York (2014)Google Scholar
  5. 5.
    Hoffart, J., Suchanek, F.M., Berberich, K., Weikum, G.: YAGO2: a spatially and temporally enhanced knowledge base from wikipedia. Artif. Intell. 194, 28–61 (2013)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Hoffart, J., Yosef, M.A., Bordino, I., Fürstenau, H., Pinkal, M., Spaniol, M., Taneva, B., Thater, S., Weikum, G.: Robust disambiguation of named entities in text. In: Conference on Empirical Methods in Natural Language Processing, Edinburgh, Scotland, UK, pp. 782–792 (2011)Google Scholar
  7. 7.
    Jenders, M., Kasneci, G., Naumann, F.: Analyzing and predicting viral tweets. In: Proceedings of the 22nd International Conference on World Wide Web, WWW 2013 Companion, pp. 657–664. ACM, New York (2013)Google Scholar
  8. 8.
    Suchanek, F.M., Kasneci, G., Weikum, G.: YAGO: A core of semantic knowledge - unifying wordnet and wikipedia. In: 16th International World Wide Web Conference (WWW 2007), pp. 697–706. ACM (2007)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversité de Caen NormandieCaen CedexFrance

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