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Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis

Author

Listed:
  • Laia Subirats

    (Eurecat, Centre Tecnològic de Catalunya, Unitat de eHealth, C/Bilbao, 72, 08005 Barcelona, Spain
    eHealth Center, Universitat Oberta de Catalunya, Rambla del Poblenou, 156, 08018 Barcelona, Spain)

  • Natalia Reguera

    (eHealth Center, Universitat Oberta de Catalunya, Rambla del Poblenou, 156, 08018 Barcelona, Spain)

  • Antonio Miguel Bañón

    (Department of Philology, Almería University, Ctra. Sacramento, s/n, La Cañada, 04120 Almería, Spain)

  • Beni Gómez-Zúñiga

    (eHealth Center, Universitat Oberta de Catalunya, Rambla del Poblenou, 156, 08018 Barcelona, Spain)

  • Julià Minguillón

    (eHealth Center, Universitat Oberta de Catalunya, Rambla del Poblenou, 156, 08018 Barcelona, Spain)

  • Manuel Armayones

    (eHealth Center, Universitat Oberta de Catalunya, Rambla del Poblenou, 156, 08018 Barcelona, Spain)

Abstract

This research characterized how Facebook deals with rare diseases. This characterization included a content-based and temporal analysis, and its purpose was to help users interested in rare diseases to maximize the engagement of their posts and to help rare diseases organizations to align their priorities with the interests expressed in social networks. This research used Netvizz to download Facebook data, word clouds in R for text mining, a log-likelihood measure in R to compare texts and TextBlob Python library for sentiment analysis. The Facebook analysis shows that posts with photos and positive comments have the highest engagement. We also observed that words related to diseases, attention, disability and services have a lot of presence in the decalogue of priorities (which serves for all associations to work on the same objectives and provides the lines of action to be followed by political decision makers) and little on Facebook, and words of gratitude are more present on Facebook than in the decalogue. Finally, the temporal analysis shows that there is a high variation between the polarity average and the hour of the day.

Suggested Citation

  • Laia Subirats & Natalia Reguera & Antonio Miguel Bañón & Beni Gómez-Zúñiga & Julià Minguillón & Manuel Armayones, 2018. "Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis," IJERPH, MDPI, vol. 15(9), pages 1-13, August.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:9:p:1877-:d:166607
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    References listed on IDEAS

    as
    1. Laia Subirats & Raquel Lopez-Blazquez & Luigi Ceccaroni & Mariona Gifre & Felip Miralles & Alejandro García-Rudolph & Jose María Tormos, 2015. "Monitoring and Prognosis System Based on the ICF for People with Traumatic Brain Injury," IJERPH, MDPI, vol. 12(8), pages 1-16, August.
    2. Marco Palomino & Tim Taylor & Ayse Göker & John Isaacs & Sara Warber, 2016. "The Online Dissemination of Nature–Health Concepts: Lessons from Sentiment Analysis of Social Media Relating to “Nature-Deficit Disorder”," IJERPH, MDPI, vol. 13(1), pages 1-23, January.
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    Cited by:

    1. Laia Subirats & Jordi Conesa & Manuel Armayones, 2020. "Biomedical Holistic Ontology for People with Rare Diseases," IJERPH, MDPI, vol. 17(17), pages 1-11, August.
    2. Mingzhe Wei & Georgios Sermpinis & Charalampos Stasinakis, 2023. "Forecasting and trading Bitcoin with machine learning techniques and a hybrid volatility/sentiment leverage," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 852-871, July.

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