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

Laia Subirats, Natalia Reguera, Antonio Miguel Bañón, Beni Gómez-Zúñiga, Julià Minguillón and Manuel Armayones
Additional contact information
Laia Subirats: Eurecat, Centre Tecnològic de Catalunya, Unitat de eHealth, C/Bilbao, 72, 08005 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

IJERPH, 2018, vol. 15, issue 9, 1-13

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.

Keywords: social media; Facebook; rare diseases; data mining (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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