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Social Media Multidimensional Analysis for Intelligent Health Surveillance

Author

Listed:
  • María José Aramburu

    (Departamento de Ciencia e Ingeniería de los Computadores, Universitat Jaume I, E-12071 Castellón de la Plana, Spain)

  • Rafael Berlanga

    (Departamento de Lenguajes y Sistemas Informáticos, E-12071 Castellón de la Plana, Spain)

  • Indira Lanza

    (Departamento de Lenguajes y Sistemas Informáticos, E-12071 Castellón de la Plana, Spain)

Abstract

Background : Recent work in social network analysis has shown the usefulness of analysing and predicting outcomes from user-generated data in the context of Public Health Surveillance (PHS). Most of the proposals have focused on dealing with static datasets gathered from social networks, which are processed and mined off-line. However, little work has been done on providing a general framework to analyse the highly dynamic data of social networks from a multidimensional perspective. In this paper, we claim that such a framework is crucial for including social data in PHS systems. Methods: We propose a dynamic multidimensional approach to deal with social data streams. In this approach, dynamic dimensions are continuously updated by applying unsupervised text mining methods. More specifically, we analyse the semantics and temporal patterns in posts for identifying relevant events, topics and users. We also define quality metrics to detect relevant user profiles. In this way, the incoming data can be further filtered to cope with the goals of PHS systems. Results: We have evaluated our approach over a long-term stream of Twitter. We show how the proposed quality metrics allow us to filter out the users that are out-of-domain as well as those with low quality in their messages. We also explain how specific user profiles can be identified through their descriptions. Finally, we illustrate how the proposed multidimensional model can be used to identify main events and topics, as well as to analyse their audience and impact. Conclusions: The results show that the proposed dynamic multidimensional model is able to identify relevant events and topics and analyse them from different perspectives, which is especially useful for PHS systems.

Suggested Citation

  • María José Aramburu & Rafael Berlanga & Indira Lanza, 2020. "Social Media Multidimensional Analysis for Intelligent Health Surveillance," IJERPH, MDPI, vol. 17(7), pages 1-17, March.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:7:p:2289-:d:338339
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    References listed on IDEAS

    as
    1. Amir Hassan Zadeh & Hamed M. Zolbanin & Ramesh Sharda & Dursun Delen, 2019. "Social Media for Nowcasting Flu Activity: Spatio-Temporal Big Data Analysis," Information Systems Frontiers, Springer, vol. 21(4), pages 743-760, August.
    2. Arkaitz Zubiaga & Damiano Spina & Raquel Martínez & Víctor Fresno, 2015. "Real-time classification of Twitter trends," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(3), pages 462-473, March.
    3. Javier Rodríguez‐Vidal & Julio Gonzalo & Laura Plaza & Henry Anaya Sánchez, 2019. "Automatic detection of influencers in social networks: Authority versus domain signals," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(7), pages 675-684, July.
    4. Sophie E. Jordan & Sierra E. Hovet & Isaac Chun-Hai Fung & Hai Liang & King-Wa Fu & Zion Tsz Ho Tse, 2018. "Using Twitter for Public Health Surveillance from Monitoring and Prediction to Public Response," Data, MDPI, vol. 4(1), pages 1-20, December.
    5. Weiling Chen & Chai Kiat Yeo & Chiew Tong Lau & Bu Sung Lee, 2017. "A study on real-time low-quality content detection on Twitter from the users’ perspective," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-22, August.
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