Dataset Information

A comparison of information sharing behaviours across 379 health conditions on Twitter.

ABSTRACT:

Objectives

To compare information sharing of over 379 health conditions on Twitter to uncover trends and patterns of online user activities.

Methods

We collected 1.5 million tweets generated by over 450,000 Twitter users for 379 health conditions, each of which was quantified using a multivariate model describing engagement, user and content aspects of the data and compared using correlation and network analysis to discover patterns of user activities in these online communities.

Results

We found a significant imbalance in terms of the size of communities interested in different health conditions, regardless of the seriousness of these conditions. Improving the informativeness of tweets by using, for example, URLs, multimedia and mentions can be important factors in promoting health conditions on Twitter. Using hashtags on the contrary is less effective. Social network analysis revealed similar structures of the discussion found across different health conditions.

Conclusions

Our study found variance in activity between different health communities on Twitter, and our results are likely to be of interest to public health authorities and officials interested in the potential of Twitter to raise awareness of public health.

SUBMITTER: Zhang Z

PROVIDER: S-EPMC6451705 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Publications

A comparison of information sharing behaviours across 379 health conditions on Twitter.

Zhang Ziqi Z Ahmed Wasim W

International journal of public health 20181226 3

<h4>Objectives</h4>To compare information sharing of over 379 health conditions on Twitter to uncover trends and patterns of online user activities.<h4>Methods</h4>We collected 1.5 million tweets generated by over 450,000 Twitter users for 379 health conditions, each of which was quantified using a multivariate model describing engagement, user and content aspects of the data and compared using correlation and network analysis to discover patterns of user activities in these online communities.< ...[more]

PMID: 30585297

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Project description:BackgroundTwitter is a common platform for people to share opinions, discuss health-related topics, and engage in conversations with a wide audience. Twitter users frequently share health information related to chronic diseases, mental health, and general wellness topics. However, sharing health information on Twitter raises privacy concerns as it involves sharing personal and sensitive data on a web-based platform.ObjectiveThis study aims to adopt an interactive approach and develop a model consisting of privacy concerns related to web-based vendors and web-based peers. The research model integrates the 4 dimensions of concern for information privacy that express concerns related to the practices of companies and the 4 dimensions of peer privacy concern that reflect concerns related to web-based interactions with peers. This study examined how this interaction may affect individuals' information-sharing behavior on Twitter.MethodsData were collected from 329 Twitter users in the United States using a web-based survey.ResultsResults suggest that privacy concerns related to company practices might not significantly influence the sharing of general health information, such as details about hospitals and medications. However, privacy concerns related to companies and third parties can negatively shape the disclosure of specific health information, such as personal medical issues (β=-.43; P<.001). Findings show that peer-related privacy concerns significantly predict sharing patterns associated with general (β=-.38; P<.001) and specific health information (β=-.72; P<.001). In addition, results suggest that people may disclose more general health information than specific health information owing to peer-related privacy concerns (t165=4.72; P<.001). The model explains 41% of the variance in general health information disclosure and 67% in specific health information sharing on Twitter.ConclusionsThe results can contribute to privacy research and propose some practical implications. The findings provide insights for developers, policy makers, and health communication professionals about mitigating privacy concerns in web-based health information sharing. It particularly underlines the importance of addressing peer-related privacy concerns. The study underscores the need to build a secure and trustworthy web-based environment, emphasizing the significance of peer interactions and highlighting the need for improved regulations, clear data handling policies, and users' control over their own data.

Dataset Information

A comparison of information sharing behaviours across 379 health conditions on Twitter.

Objectives

Methods

Results

Conclusions

Publications

A comparison of information sharing behaviours across 379 health conditions on Twitter.

Similar Datasets

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