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Profiling Social Sentiment in Times of Health Emergencies with Information from Social Networks and Official Statistics

Author

Listed:
  • Jorge-Eusebio Velasco-López

    (Instituto Nacional de Estadística, 28050 Madrid, Spain)

  • Ramón-Alberto Carrasco

    (Department of Marketing, Faculty of Statistics, Universidad Complutense de Madrid, 28040 Madrid, Spain)

  • Jesús Serrano-Guerrero

    (Department of Information Technologies and Systems, Universidad de Castilla-La Mancha, 13071 Ciudad Real, Spain)

  • Francisco Chiclana

    (Institute of Artificial Intelligence, Faculty of Computing, Engineering and Media, De Montfort University, Leicester LE1 9BH, UK)

Abstract

Social networks and official statistics have become vital sources of information in times of health emergencies. The ability to monitor and profile social sentiment is essential for understanding public perception and response in the context of public health crises, such as the one resulting from the COVID-19 pandemic. This study will explore how social sentiment monitoring and profiling can be conducted using information from social networks and official statistics, and how this combination of data can offer a more complete picture of social dynamics in times of emergency, providing a valuable tool for understanding public perception and guiding a public health response. To this end, a three-layer architecture based on Big Data and Artificial Intelligence is presented: the first layer focuses mainly on collecting, storing, and governing the necessary data such as social media and official statistics; in the second layer, the representation models and machine learning necessary for knowledge generation are built, and in the third layer the previously generated knowledge is adapted for better understanding by crisis managers through visualization techniques among others. Based on this architecture, a KDD (Knowledge Discovery in Databases) framework is implemented using methodological tools such as sentiment analysis, fuzzy 2-tuple linguistic models and time series prediction with the Prophet model. As a practical demonstration of the proposed model, we use tweets as data source (from the social network X, formerly known as Twitter) generated during the COVID-19 pandemic lockdown period in Spain, which are processed to identify the overall sentiment using sentiment analysis techniques and fuzzy linguistic variables, and combined with official statistical indicators for prediction, visualizing the results through dashboards.

Suggested Citation

  • Jorge-Eusebio Velasco-López & Ramón-Alberto Carrasco & Jesús Serrano-Guerrero & Francisco Chiclana, 2024. "Profiling Social Sentiment in Times of Health Emergencies with Information from Social Networks and Official Statistics," Mathematics, MDPI, vol. 12(6), pages 1-23, March.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:6:p:911-:d:1360260
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    References listed on IDEAS

    as
    1. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    2. Huang, Yu-ting & Bai, Yu-long & Yu, Qing-he & Ding, Lin & Ma, Yong-jie, 2022. "Application of a hybrid model based on the Prophet model, ICEEMDAN and multi-model optimization error correction in metal price prediction," Resources Policy, Elsevier, vol. 79(C).
    Full references (including those not matched with items on IDEAS)

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