JMIR Publications

ABSTRACT

Background:

Social media have become a major resource to observe and understand public opinions, especially during emergencies such as disease outbreaks. For public health agencies, understanding driving forces of online discussions will help deliver more effective and efficient information to the general users on social media and online.

Objective:

This study aimed to identify major contributors that drive overall Zika tweeting dynamics during its 2016 epidemic. Three hypothetical drivers were proposed: 1) the underlying Zika epidemic quantified as time series of case counts; 2) sporadic but critical real-world events such as the 2016 Rio Olympics and World Health Organization’s Public Health Emergency of International Concern (PHEIC) announcement, and 3) a few influential users’ tweeting activities.

Methods:

All tweets and retweets containing the keyword “Zika” posted in 2016 were collected via Gnip API. An analytical pipeline, EventPeriscope, was developed to identify co-occurring trending events and quantify the strength of these trends. The influence of the three potential drivers was examined using multivariate time series analysis, signal processing, content analysis, and text mining techniques.

Results:

Zika-related tweeting dynamics was not significantly correlated with the underlying Zika epidemic in the U.S. in any of the four quarters in 2016, nor the entire year. Instead, peaks of Zika tweeting activity were strongly associated with a few critical real-world events, both planned such as the Rio Olympics and unplanned such as the PHEIC announcement. Rio Olympics was mentioned in >15% of all Zika-related tweets and PHEIC occurred in 27% Zika-related tweets around their respective peaks. In addition, overall tweeting dynamics of the top 100 most-tweeting users on the Zika topic, top 100 users receiving most retweets, and top 100 users being mentioned the most were highly correlated to and preceded the overall tweeting dynamics, making these groups of users the potential drivers of tweeting dynamics. Top 100 users who retweeted the most were not critical in driving the overall tweeting dynamics. There were very few overlaps among these different groups of potentially influential users.

Conclusions:

Using our proposed analytical workflow, EventPeriscope, we identified that Zika discussion dynamics on Twitter was decoupled from the actual disease epidemic in the U.S., but was closely related to and highly influenced by certain sporadic real-world events as well as by a few influential users. This study provided a methodology framework and insights to better understand the driving forces of online public discourse during health emergencies. Therefore, health agencies could deliver more effective and efficient online communications in emerging crisis.