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.

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Image indexing and knowledge representation on Instagram are organized by folksonomy-oriented hashtags. What kinds of hashtags do Instagram users apply for different picture categories? We distinguish between food, pets, selfies, friends, activity, art, fashion, quotes (captioned photos), landscape and architecture as image categories, as well as content-related (ofness, aboutness, iconology), emotiveness, isness, performativeness, fakeness, "Insta"-tags and sentences as hashtag categories. Are there any differences in relative frequencies of hashtags in the image categories? What hashtag categories dominate users' indexing activities? Given an image category, what is the distribution of hashtag categories? Given a hashtag category, what is the distribution of image categories? We analyzed 1,000 pictures on Instagram with all-in-all 14,649 hashtags deploying content analysis.

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