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.
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.
| Original language | English |
|---|---|
| Pages (from-to) | 431-440 |
| Number of pages | 10 |
| Journal | International Journal of Public Health |
| Volume | 64 |
| Issue number | 3 |
| Early online date | 26 Dec 2018 |
| DOIs | |
| Publication status | Published - 1 Apr 2019 |
Keywords
- public health
- health conditions
- social media
- network analysis
- data science