Abstract
Social media platforms have become a common venue for sharing experiences and knowledge about health-related topics. This research focuses on examining social media-based communication patterns related to diabetes on the Twitter platform. Specifically, we apply an updated methodology to examine changes in the current use of hash-tags, trending hash-tags, and the frequency of diabetes-related tweets using a previous study as a baseline. Our results show significant growth in the diabetes community on Twitter over time and also evidence that this community is increasing in its capacity to spread awareness regarding diabetes- related health topics. Our methodological contributions include an improved framework for collecting, cleaning and analyzing Twitter data related to diabetes as well as the application of regular expressions to categorize subsets of tweets. We have also developed a model based on word-embedding and long short term memory to identify tweets of diabetic patients.
This is a preview of subscription content, log in via an institution to check access.
References
Budhiraja SS, Mago V (2018) A supervised learning approach for heading detection. arXiv preprint arXiv:180901477
Eriksson-Backa K, Holmberg K, Ek S (2016) Communicating diabetes and diets on twitter-a semantic content analysis. Int J Networking Virtual Organ 16(1):8–24
Federer LM, Joubert DJ (2018) Providing library support for interactive scientific and biomedical visualizations with tableau. J eSci Librariansh 7(1):2
Gabarron E, Årsand E, Wynn R (2018) Social media use in interventions for diabetes: rapid evidence-based review. J Med Internet Res 20(8):e10303
German A, Salmeron S, Ha W, Henderson B (2016) Mean web development: a tutorial for educators. In: Proceedings of the 17th annual conference on information technology education. ACM, pp 128–129
Ghosh D, Guha R (2013) What are we tweeting about obesity? Mapping tweets with topic modeling and geographic information system. Cartogr Geogr Inf Sci 40(2):90–102
Harris JK, Mueller NL, Snider D, Haire-Joshu D (2013) Peer reviewed: local health department use of twitter to disseminate diabetes information, United States. Prev Chronic Dis 10:120215
Jones MT (2013) Process real-time big data with Twitter storm. IBM Technical Library, New York
Karami A, Dahl AA, Turner-McGrievy G, Kharrazi H, Shaw G Jr (2018) Characterizing diabetes, diet, exercise, and obesity comments on twitter. Int J Inf Manag 38(1):1–6
Kemp S (2019) The incredible growth of the internet over the past five years–explained in detail. Retrieved from https://thenextweb.com/insider/2017/03/06/the-incredible-growth-of-the-internet-over-the-past-five-years-explained-in-detail/
Kemp S (2018) Digital in 2018: World’s internet users pass the 4 billion mark. We are social 30
Kumar S, Morstatter F, Liu H (2014) Twitter data analytics. Springer, Berlin
Liu Y, Mei Q, Hanauer DA, Zheng K, Lee JM (2016) Use of social media in the diabetes community: an exploratory analysis of diabetes-related tweets. JMIR Diabetes 1(2):e4
Malik QM (2014) India’s defence spending trends from 2004–2014: a status without human development. Strateg Stud 34(2/3):94–113
Moorhead SA, Hazlett DE, Harrison L, Carroll JK, Irwin A, Hoving C (2013) A new dimension of health care: systematic review of the uses, benefits, and limitations of social media for health communication. J Med Internet Res 15(4):e85
Moro S, Rita P, Vala B (2016) Predicting social media performance metrics and evaluation of the impact on brand building: a data mining approach. J Bus Res 69(9):3341–3351
Pandey SK, Sharma V (2018) World diabetes day 2018: battling the emerging epidemic of diabetic retinopathy. Indian J Ophthalmol 66(11):1652
Patel KD, Heppner A, Srivastava G, Mago V (2019) Analyzing use of twitter by diabetes online community. In: ASONAM ’19: international conference on advances in social networks analysis and mining, Vancouver, British Columbia, Canada, 27–30 August, 2019, pp 937–944. https://doi.org/10.1145/3341161.3343673
Pawar A, Mago V (2019) Challenging the boundaries of unsupervised learning for semantic similarity. IEEE Access 7:16291–16308
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830
Reblin M, Uchino BN (2008) Social and emotional support and its implication for health. Curr Opin Psychiatry 21(2):201
Reddy KSP (2017) Working with mongodb. In: Beginning Spring Boot 2. Springer, pp 99–106
Reddy T, Giabbanelli PJ, Mago VK (2019) The artificial facilitator: guiding participants in developing causal maps using voice-activated technologies. In: International conference on human–computer interaction. Springer, pp 111–129
Roesslein J (2009) Tweepy documentation. Online http://www.tweepyreadthedocsio/en/v35. Accessed 19 June 2018
Saif H, He Y, Alani H (2012) Semantic sentiment analysis of Twitter. In: International semantic web conference. Springer, pp 508–524
Salas-Zárate MdP, Medina-Moreira J, Lagos-Ortiz K, Luna-Aveiga H, Rodriguez-Garcia MA, Valencia-Garcia R (2017) Sentiment analysis on tweets about diabetes: an aspect-level approach. Comput Math Methods Med 2017:1–10
Sandhu M, Giabbanelli PJ, Mago VK (2019) From social media to expert reports: The impact of source selection on automatically validating complex conceptual models of obesity. In: International conference on human–computer interaction. Springer, pp 434–452
Serrano WC, Chren MM, Resneck JS, Aji NN, Pagoto S, Linos E (2016) Online advertising for cancer prevention: Google ads and tanning beds. JAMA Dermatol 152(1):101–102
Shah N, Willick D, Mago V (2018) A framework for social media data analytics using Easticsearch and Kibana. Wirel Netw 13:1–9
Shah N, Srivastava G, Savage DW, Mago V (2019) Assessing canadians health activity and nutritional habits through social media. Front Public Health 7:400
Shaw RJ, Johnson CM (2011) Health information seeking and social media use on the internet among people with diabetes. Online J Public Health Inform 3(1):1–9
Tabish SA (2007) Is diabetes becoming the biggest epidemic of the twenty-first century? Int J Health Sci 1(2):V
Taylor J, Pagliari C (2018) Comprehensive scoping review of health research using social media data. BMJ Open 8(12):e022931
Acknowledgements
The authors would like to thank the Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant Program for providing support for Kazi Zainab at Lakehead University held by Dr. Vijay Mago.
Author information
Authors and Affiliations
Contributions
KDP extracted, analyzed, and studied the data and drafted the article. KZ developed the content for the Vector Space Model. AH contributed to the language and structure of the writing. GS and VM supported the study design and technologies and assisted in drafting the article.
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Patel, K.D., Zainab, K., Heppner, A. et al. Using Twitter for diabetes community analysis. Netw Model Anal Health Inform Bioinforma 9, 36 (2020). https://doi.org/10.1007/s13721-020-00241-y
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s13721-020-00241-y