Structural Analysis of Medical-Terminology Hashtag versus Lay-Language Hashtag Tweet Collections: An Information Theoretical Method with Entropy Matrix

Abstract

Abstract Healthcare communication on Twitter is challenging because the space for a tweet is limited, but the topic is too sophisticated to be concise. Comparing medical-terminology hashtags versus lay-language hashtags, this paper explores the characteristics of healthcare hashtags using an entropy matrix which derived from information theory. In this paper, the entropy matrix comprises of six different components used for constructing a tweet and serves as a framework for the structural analysis with the granularity of tweet composition. These granular components include image(s), text with semantic meanings, hashtag(s), @ username(s), hyperlink, and unused space. The entropy matrix proposed in this paper contributes to a new approach to visualizing the complexity level of hashtag collections. In addition, the calculated entropy could be an indicator of the diversity of a user’s choice across those tweet components. Furthermore, the visualizations (radar graph and scatterplot) illustrate statistical structures and the dynamics of the hashtag collections measured by entropy. The results from this study demonstrate a manifest relationship between tweet composition and the number of being retweeted.