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Communicating diabetes and diets on Twitter - a semantic content analysis

Author

Listed:
  • Kristina Eriksson-Backa
  • Kim Holmberg
  • Stefan Ek

Abstract

This paper analyses: 1) the semantic content of tweets discussing diabetes and diets: 2) the conversational connections of those tweeting and those being mentioned in the tweets. The content analysis of the tweets aims at mapping what kinds of diets are mentioned in conversations about diabetes and in what context. Our data consists of 9,042 tweets containing the words 'diabetes' and 'diet'. The findings indicate that analysing Twitter conversations can be a fruitful and an efficient way to map public opinions about diabetes and diets, as well as other medical issues that concern many people. The results also showed that many private persons act as diabetes advocates spreading information and news about diabetes and diets. Surveying these topics can be useful for healthcare practitioners; as these are in contact with patients with diabetes, it is important that they are aware of both the most discussed topics and the most common information sources, who are often laymen.

Suggested Citation

  • Kristina Eriksson-Backa & Kim Holmberg & Stefan Ek, 2016. "Communicating diabetes and diets on Twitter - a semantic content analysis," International Journal of Networking and Virtual Organisations, Inderscience Enterprises Ltd, vol. 16(1), pages 8-24.
    • Handle: RePEc:ids:ijnvor:v:16:y:2016:i:1:p:8-24
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    Citations

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    Cited by:

    1. Shenmeng Xu & Houqiang Yu & Bradley M. Hemminger & Xie Dong, 2018. "Who, what, why? An exploration of JoVE scientific video publications in tweets," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 845-856, November.
    2. Theo Lynn & Pierangelo Rosati & Guto Leoni Santos & Patricia Takako Endo, 2020. "Sorting the Healthy Diet Signal from the Social Media Expert Noise: Preliminary Evidence from the Healthy Diet Discourse on Twitter," IJERPH, MDPI, vol. 17(22), pages 1-28, November.
    3. Ruijun Zhang & Caiyan Lin & Zeping Tong, 2021. "A visual risk identification and early warning research for college net loan based on microblog texts," Risk Management, Palgrave Macmillan, vol. 23(4), pages 261-281, December.
    4. Grover, Purva & Kar, Arpan Kumar & Davies, Gareth, 2018. "“Technology enabled Health” – Insights from twitter analytics with a socio-technical perspective," International Journal of Information Management, Elsevier, vol. 43(C), pages 85-97.
    5. Salvatore Pirri & Valentina Lorenzoni & Gianni Andreozzi & Marta Mosca & Giuseppe Turchetti, 2020. "Topic Modeling and User Network Analysis on Twitter during World Lupus Awareness Day," IJERPH, MDPI, vol. 17(15), pages 1-18, July.

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