Health information diffusion on Twitter: The content and design of WHO tweets matter
Holger Hönings,Daniel Knapp,Bích Châu Nguyễn,Daniel Richter,Kelly Williams,Isabelle Dorsch,Kaja J. Fietkiewicz +6 more
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In this paper, the authors used k-means clustering and manual coding to classify tweets by theme, sentiment, length and count of emojis, pictures, videos and links.Abstract:
BACKGROUND Micro-blogging services empower health institutions to quickly disseminate health information to many users. By analysing user data, infodemiology (i.e. improving public health using user contributed health related content) can be measured in terms of information diffusion. OBJECTIVES Tweets by the WHO were examined in order to identify tweet attributes that lead to a high information diffusion rate using Twitter data collected between November 2019 and January 2020. METHODS One thousand hundred and seventy-seven tweets were collected using Python's Tweepy library. Afterwards, k-means clustering and manual coding were used to classify tweets by theme, sentiment, length and count of emojis, pictures, videos and links. Resulting groups with different characteristics were analysed for significant differences using Mann-Whitney U- and Kruskal-Wallis H-tests. RESULTS The topic of the tweet, the included links, emojis and (one) picture as well as the tweet length significantly affected the tweets' diffusion, whereas sentiment and videos did not show any significant influence on the diffusion of tweets. DISCUSSION The findings of this study give insights on why specific health topics might generate less attention and do not showcase sufficient information diffusion. CONCLUSION The subject and appearance of a tweet influence its diffusion, making the design equally essential to the preparation of its content.read more
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