Mining facebook data of people with rare diseases: A content-based and temporal analysis

International Journal of Environmental Research and Public Health

Volumn 15, Issue 9, 2018, Pages

  Subirats, Laia ^a,b   Reguera, Natalia ^b   Bañón, Antonio Miguel ^c   Gómez Zúñiga, Beni ^b   Minguillón, Julià ^b   Armayones, Manuel ^b  

^a CENTRE TECNOLÒGIC DE CATALUNYA   (Spain)

^b UNIVERSITAT OBERTA DE CATALUNYA   (Spain)

^c UNIVERSITY OF ALMERÍA   (Spain)

Author keywords

Data mining; Facebook; Rare diseases; Social media

Indexed keywords

DATA MINING; DECISION MAKING; DISEASE INCIDENCE; RESEARCH WORK; SOCIAL MEDIA; SOCIAL NETWORK; TEMPORAL VARIATION;

ANALYSIS OF VARIANCE; ARTICLE; CONTENT BASED ANALYSIS; CONTROLLED STUDY; CORRELATION ANALYSIS; CORRELATION COEFFICIENT; DATA MINING; INFORMATION PROCESSING; RARE DISEASE; SOCIAL MEDIA; SOCIAL NETWORK; STATISTICAL ANALYSIS; TEMPORAL ANALYSIS; ALGORITHM; HUMAN; PROCEDURES; RESEARCH; SPAIN;

ALGORITHMS; DATA MINING; HUMANS; RARE DISEASES; RESEARCH; SOCIAL MEDIA; SOCIAL NETWORKING; SPAIN;

EID: 85052697244     PISSN: 16617827     EISSN: 16604601     Source Type: Journal    
DOI: 10.3390/ijerph15091877     Document Type: Article

References (37)

1. Henrard, S.; Arickx, F. Negotiating Prices of Drugs for Rare Diseases. Available online: http://www.who. int/bulletin/volumes/94/10/15-163519/en (accessed on 1 January 2016).
2. FEDER. Decalogue of Priorities. Available online: http://www.enfermedades-raras.orgimages/dia_ mundial/pdf/Decalogoprioridades.pdf (accessed on 1 January 2015).
3. Davies, W. Insights into rare diseases from social media surveys. Orphanet J. Rare Dis. 2016, 11, 151. [CrossRef] [PubMed]
4. Hand, R.; Kenne, D.; Wolfram, T.; Abram, J.; Fleming, M. Assessing the Viability of Social Media for Disseminating Evidence-Based Nutrition Practice Guideline Through Content Analysis of Twitter Messages and Health Professional Interviews: An Observational Study. J. Med. Internet Res. 2016, 18, e295. [CrossRef] [PubMed]
5. Reguera, N.; Subirats, L.; Armayones, M. Mining Facebook Data of People with Rare Diseases. In Proceedings of the 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), Thessaloniki, Greece, 22–24 June 2017; pp. 588–593. [CrossRef]
6. Reguera, N. Las Enfermedades Raras en Grupos de Facebook: Evaluación de la Línea de Actuación de FEDER Mediante Minería de Textos. Master’s Thesis, Universitat Oberta de Catalunya, Barcelona, Spain, 2017.
7. Rooney, E. How to Promote Your Rare Disease Story through Social Media. Available online: https://globalgenes.org/wp-content/uploads/2016/02/GG_toolkit_social-mediastorytelling_021016_WEB.pdf (accessed on 1 January 2016).
8. Chempetitive Group. Rare Diseases: The Role of Social Media in Patient Recruitment. Available online: https://chempetitive.com/chemunity/rare-diseases-role-social-media-patient-recruitment (accessed on 1 January 2016).
9. Smale, F. Social Media and the Rare Disease Community. Available online: http://www.orphan-drugs.org/2015/01/29/social-media-rare-disease-community (accessed on 1 January 2015).
10. Armayones, M.; Requena, S.; Gómez-Zúñiga, B.; Pousada, M.; Bañón, A.M. El uso de facebook en asociaciones españolas de enfermedades raras: cómo y para qué lo utilizan? Gac. Sanit. 2014, 29, 335–340. [CrossRef] [PubMed]
11. Subirats, L.; Ceccaroni, L.; Lopez-Blazquez, R.; Miralles, F.; García-Rudolph, A.; Tormos, J.M. Circles of Health: Towards an advanced social network about disabilities of neurological origin. J. Biomed. Inform. 2013, 46, 1006–1029. [CrossRef] [PubMed]
12. Subirats, L.; Lopez-Blazquez, R.; Ceccaroni, L.; Gifre, M.; Miralles, F.; García-Rudolph, A.; Tormos, J.M. Monitoring and Prognosis System Based on the ICF for People with Traumatic Brain Injury. Int. J. Environ. Res. Public Health 2015, 12, 9832–9847. [CrossRef] [PubMed]
13. Palomino, M.; Taylor, T.; Göker, A.; Isaacs, J.; Warber, S. The Online Dissemination of Nature–Health Concepts: Lessons from Sentiment Analysis of Social Media Relating to “Nature-Deficit Disorder”. Int. J. Environ. Res. Public Health 2016, 13, 142. [CrossRef] [PubMed]
14. Larsen, M.; Boonstra, T.; Batterham, P.; ODea, B.; Paris, C.; Christensen, H. We feel: Mapping emotion on twitter. IEEE J. Biomed. Health Inform. 2015, 19, 1246–1252. [CrossRef] [PubMed]
15. Yang, F.; Lee, A.; Kuo, S. Mining health social media with sentiment analysis. J. Med. Syst. 2016, 40, 236. [CrossRef] [PubMed]
16. Lasker, J.; Sogolow, E.; Sharim, R. The role of an online community for people with a rare disease: Content analysis of messages posted on a primary biliary cirrhosis mailinglist. J. Med. Internet Res. 2005, 7, e10. [CrossRef] [PubMed]
17. Jacobs, R.; Boyd, L.; Brennan, K.; Sinha, C.; Giuliani, S. The importance of social media for patients and families affected by congenital anomalies: A facebook cross-sectional analysis and user survey information platform. J. Pediatr. Surg. 2016, 51, 1766–1771. [CrossRef] [PubMed]
18. Pang, B.; Lee, L. Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2008, 2, 1–135. [CrossRef]
19. Greaves, F.; Ramirez-Cano, D.; Millett, C.; Darzi, A.; Donaldson, L. Use of sentiment analysis for capturing patient experience from free-text comments posted online. J. Med. Internet Res. 2013, 15, e239. [CrossRef] [PubMed]
20. Tai, C.H.; Tan, Z.H.; Chang, Y.S. Systematical Approach for Detecting the Intention and Intensity of Feelings on Social Network. IEEE J. Biomed. Health Inform. 2016, 20, 987–995. [CrossRef] [PubMed]
21. Akay, A.; Dragomir, A.; Erlandsson, B. Network-Based Modeling and Intelligent Data Mining of Social Media for Improving Care. IEEE J. Biomed. Health Inform. 2015, 19, 210–218. [CrossRef] [PubMed]
22. Rayson, P.; Garside, R. Comparing corpora using frequency profiling. In Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics (ACL 2000), Hong Kong, China, 7 October 2000; pp. 1–6.
23. Gabrielatos, C.; Marchi, A. Keyness: Appropriate metrics and practical issues. In Proceedings of the CADS International Conference, Bologna, Italy, 13–14 September 2012.
24. Wilson, A. Embracing Bayes Factors for Key Item Analysis in Corpus Linguistics; Language Competence and Language Awareness in Europe; Peter Lang: Bern, Switzland, 2013; pp. 3–11.
25. Johnston, J.; Berry, K.; Mielke, P. Measures of effect size for chi-squared and likelihood-ratio goodness-of-fit tests. Percept. Mot. Skills 2006, 103, 412–414. [CrossRef] [PubMed]
26. Diakopoulos, N.; Elgesem, D.; Salway, A.; Zhang, A.; Hofland, K. Compare Clouds: Visualizing Text Corpora to Compare Media Frames. In Proceedings of the IUI Workshop on Visual Text Analytics, Atlanta, GA, USA, 29 March 2015.
27. Scanfeld, D.; Scanfeld, V.; Larson, E.L. Dissemination of health information through social networks: Twitter and antibiotics. Am. J. Infect. Control 2010, 38, 182–188. [CrossRef] [PubMed]
28. Townsend, L.; Wallace, C. Social Media Research: A Guide to Ethics; Technical Report, ESRC Grant Report; University of Aberdeen: Aberdeen, UK, 2016.
29. Beel, J.; Gipp, B.; Langer, S.; Breitinger, C. Research-paper recommender systems: A literature survey. Int. J. Digit. Libr. 2015, 17, 305–338. [CrossRef]
30. Pojanapunya, P.; Todd, R.W. Log-likelihood and odds ratio: Keyness statistics for different purposes of keyword analysis. Corpus Linguist. Linguist. Theory 2016, 14, 133–167. [CrossRef]
31. Ramírez-Tinoco, F.J.; Alor-Hernández, G.; Sánchez-Cervantes, J.L.; Olivares-Zepahua, B.A.; Rodríguez-Mazahua, L. A Brief Review on the Use of Sentiment Analysis Approaches in Social Networks. In Trends and Applications in Software Engineering; Mejia, J., Muñoz, M., Rocha, Á., Quiñonez, Y., Calvo-Manzano, J., Eds.; Springer International Publishing: Cham, Switzerland, 2018; pp. 263–273.
32. Smedt, T.D.; Daelemans, W. Pattern for Python. J. Mach. Learn. Res. 2012, 13, 2031–2035.
33. Sabate, F.; Berbegal-Mirabent, J.; Cañabate, A.; Lebherz, P.R. Factors influencing popularity of branded content in Facebook fan pages. Eur. Manag. J. 2014, 32, 1001–1011. [CrossRef]
34. Pletikosa-Cvijikj, I.; Michahelles, F. Online engagement factors on Facebook brand pages. Soc. Netw. Anal. Min. 2013, 3, 843–861. [CrossRef]
35. Burke, M.; Kraut, R.E. Growing Closer on Facebook: Changes in Tie Strength Through Social Network Site Use. In Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems (CHI’14), Toronto, ON, Canada, 26 April–1 May 2014; ACM: New York, NY, USA, 2014; pp. 4187–4196.10.1145/2556288.2557094. [CrossRef]
36. Leiva, V.; Freire, A. Towards Suicide Prevention: Early Detection of Depression on Social Media. In Lecture Notes in Computer Science, Proceedings of the Internet Science (INSCI 2017), Thessaloniki, Greece, 22–24 November 2017; Springer: New York, NY, USA, 2017; Volume 10673.
37. Montoyo, A.; Martínez-Barco, P.; Balahur, A. Subjectivity and sentiment analysis: An overview of the current state of the area and envisaged developments. Decis. Support Syst. 2012, 53, 675–679. [CrossRef]