Project

Social Media Support in Health Crisis Communication

Goal: Employ data analytics techniques for discovering patterns in communication on Twitter during health crisis. The goal of this project is to provide health service authorities with a set of tools for more effective communication to the public on social media.

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Nikola S. Nikolov
added a research item
Background: Social media platforms play a vital role in the dissemination of health information. However, evidence suggests that a high proportion of Twitter posts (ie, tweets) are not necessarily accurate, and many studies suggest that tweets do not need to be accurate, or at least evidence based, to receive traction. This is a dangerous combination in the sphere of health information. Objective: The first objective of this study is to examine health-related tweets originating from Saudi Arabia in terms of their accuracy. The second objective is to find factors that relate to the accuracy and dissemination of these tweets, thereby enabling the identification of ways to enhance the dissemination of accurate tweets. The initial findings from this study and methodological improvements will then be employed in a larger-scale study that will address these issues in more detail. Methods: A health lexicon was used to extract health-related tweets using the Twitter application programming interface and the results were further filtered manually. A total of 300 tweets were each labeled by two medical doctors; the doctors agreed that 109 tweets were either accurate or inaccurate. Other measures were taken from these tweets’ metadata to see if there was any relationship between the measures and either the accuracy or the dissemination of the tweets. The entire range of this metadata was analyzed using Python, version 3.6.5 (Python Software Foundation), to answer the research questions posed. Results: A total of 34 out of 109 tweets (31.2%) in the dataset used in this study were classified as untrustworthy health information. These came mainly from users with a non-health care background and social media accounts that had no corresponding physical (ie, organization) manifestation. Unsurprisingly, we found that traditionally trusted health sources were more likely to tweet accurate health information than other users. Likewise, these provisional results suggest that tweets posted in the morning are more trustworthy than tweets posted at night, possibly corresponding to official and casual posts, respectively. Our results also suggest that the crowd was quite good at identifying trustworthy information sources, as evidenced by the number of times a tweet’s author was tagged as favorited by the community. Conclusions: The results indicate some initially surprising factors that might correlate with the accuracy of tweets and their dissemination. For example, the time a tweet was posted correlated with its accuracy, which may reflect a difference between professional (ie, morning) and hobbyist (ie, evening) tweets. More surprisingly, tweets containing a kashida—a decorative element in Arabic writing used to justify the text within lines—were more likely to be disseminated through retweets. These findings will be further assessed using data analysis techniques on a much larger dataset in future work.
Nikola S. Nikolov
added a project goal
Employ data analytics techniques for discovering patterns in communication on Twitter during health crisis. The goal of this project is to provide health service authorities with a set of tools for more effective communication to the public on social media.
 
Nikola S. Nikolov
added a research item
In recent years, there has been significant growth in the uptake of personal communication technologies across the world. This has been largely afforded by the wide availability of social media (SM) and facilitated by the increase in smartphone ownership. However, this growth does not come without disadvantages. For example, there is growing evidence that misinformation generated across SM platforms can generate negative impacts, for example, misinformation relating to people's health. In this paper, we explore this phenomenon and examine the impact of SM on health communications. Specifically, we present a structured literature review that identifies the key gaps in current literature. Our results indicated that while Twitter is the dominant SM tool in health communications, there is a lack of research on non-English-language contexts. We also found that there is a lack of evidence on identifying the key stakeholders providing health information on Twitter. We explain that due to the spread of misinformation during health crises, there is a need to identify the factors which contribute to improved dissemination of health information.