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

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.

Pain drawings as a diagnostic tool for the differentiation between two pain-associated rare diseases (Ehlers-Danlos-Syndrome, Guillain-Barré-Syndrome)

“…As chronic pain occurs in one of five patients with rare diseases, and is frequently the first symptom that leads to medical consultation [ 28 ], pain assessment has a considerable potential for the diagnosis of rare diseases. Shaballout et al recently showed that electronic PDs can indeed enhance physicians’ insight into acute pain sensation and improve pain communication [ 29 ], and the excellent results of the binary classifier of Pain2D for EDS.GBS classification highlights the diagnostic potential of the regional information contained in PDs and demonstrates the accessibility of this data type for computer based diagnostic aid tools.…”

The overlapping geography of cognitive ability and chronotype

“…User profiles were freely accessible to all web users with a free profile at the time of data collection, and the collected data was anonymized by deleting user names and any other information which would permit identification. The analysis of such web-based, pre-existing data constitutes archival rather than human research (Bruckman, 2002;Herring, 1996;Kosinski et al, 2015) and generally does not require permission from an ethical board (Catanese et al, 2011;Gjoka et al, 2010;Kim & Escobedo-Land, 2015;Kirkegaard & Lasker, 2020;Rahman, 2012;Subirats et al, 2018). Our research did not violate specific guidelines (Kosinski et al, 2015) which would render closer ethical scrutiny necessary:…”

Artificial Intelligence (AI) in Rare Diseases: Is the Future Brighter?

“…Efforts to increase patient involvement in various fields of healthcare show a growing need to proactively engage with patients [100], and patient organizations have a central role to play [101]. AI technologies can help assess patients' experience through the analysis of patient reported outcomes and increase patient recruitment and engagement through social media [100,102]. Furthermore, it can be used to monitor patient adherence in CTs [17].…”