Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis
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 prio...
| Main Authors: | , , , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
| Published: |
MDPI
2020
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10835/7366 |
| _version_ | 1789406707599802368 |
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| author | Subirats, Laia Reguera, Natalia Bañón Hernández, Antonio Miguel Gómez Zúñiga, Beni Minguillón, Julià Armayones, Manuel |
| author_facet | Subirats, Laia Reguera, Natalia Bañón Hernández, Antonio Miguel Gómez Zúñiga, Beni Minguillón, Julià Armayones, Manuel |
| author_sort | Subirats, Laia |
| collection | DSpace |
| description | 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. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-7366 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2020 |
| publisher | MDPI |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-73662023-04-12T19:22:42Z Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis Subirats, Laia Reguera, Natalia Bañón Hernández, Antonio Miguel Gómez Zúñiga, Beni Minguillón, Julià Armayones, Manuel social media Facebook rare diseases data mining 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. 2020-01-16T10:55:41Z 2020-01-16T10:55:41Z 2018-08-30 info:eu-repo/semantics/article 1660-4601 http://hdl.handle.net/10835/7366 en https://www.mdpi.com/1660-4601/15/9/1877 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess MDPI |
| spellingShingle | social media rare diseases data mining Subirats, Laia Reguera, Natalia Bañón Hernández, Antonio Miguel Gómez Zúñiga, Beni Minguillón, Julià Armayones, Manuel Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis |
| title | Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis |
| title_full | Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis |
| title_fullStr | Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis |
| title_full_unstemmed | Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis |
| title_short | Mining Facebook Data of People with Rare Diseases: A Content-Based and Temporal Analysis |
| title_sort | mining facebook data of people with rare diseases: a content-based and temporal analysis |
| topic | social media rare diseases data mining |
| url | http://hdl.handle.net/10835/7366 |
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