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Title: Mining facebook data of people with rare diseases: A content-based and temporal analysis
Author: Subirats Maté, Laia
Reguera Terrazo, Natalia
Bañón Hernández, Antonio Miguel
Minguillón Alfonso, Julià
Armayones Ruiz, Manuel
Others: Universidad de Almería
Eurecat, Centre Tecnològic de Catalunya
Universitat Oberta de Catalunya. Estudis de Ciències de la Salut
Keywords: rare diseases
social media
data mining
Issue Date: 30-Aug-2018
Publisher: Analysis. International Journal of Environmental Research and Public Health
Citation: Subirats Maté, L., Reguera Terrazo, N., Bañón Hernández, A.M., Gómez Zúñiga, B., Minguillón, J. & Armayones Ruiz, M. (2018). Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis. International Journal of Environmental Research and Public Health, 15(9), 1-13. doi: 10.3390/ijerph15091877
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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.
Language: English
ISSN: 1660-4601MIAR
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Articles cientÍfics

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