Identifying Influential Factors in the Discussion Dynamics of Emerging Health Issues on Social Media: Computational Study

Abstract

Background Social media has become a major resource for observing and understanding public opinions using infodemiology and infoveillance methods, especially during emergencies such as disease outbreaks. For public health agencies, understanding the driving forces of web-based discussions will help deliver more effective and efficient information to general users on social media and the web. Objective The study aimed to identify the major contributors that drove overall Zika-related tweeting dynamics during the 2016 epidemic. In total, 3 hypothetical drivers were proposed: (1) the underlying Zika epidemic quantified as a 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 (RTs) containing the keyword Zika posted in 2016 were collected via the Gnip application programming interface (API). We developed an analytical pipeline, EventPeriscope, to identify co-occurring trending events with Zika and quantify the strength of these events. We also retrieved Zika case data and identified the top influencers of the Zika discussion on Twitter. The influence of 3 potential drivers was examined via a multivariate time series analysis, signal processing, a content analysis, and text mining techniques. Results Zika-related tweeting dynamics were not significantly correlated with the underlying Zika epidemic in the United States in any of the four quarters in 2016 nor in the entire year. Instead, peaks of Zika-related 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. The Rio Olympics was mentioned in >15% of all Zika-related tweets and PHEIC occurred in 27% of Zika-related tweets around their respective peaks. In addition, the overall tweeting dynamics of the top 100 most actively tweeting users on the Zika topic, the top 100 users receiving most RTs, and the top 100 users mentioned were the most highly correlated to and preceded the overall tweeting dynamics, making these groups of users the potential drivers of tweeting dynamics. The 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 were decoupled from the actual disease epidemic in the United States but were 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 web-based public discourse during health emergencies. Therefore, health agencies could deliver more effective and efficient web-based communications in emerging crises.

A Novel Machine Learning Framework for Comparison of Viral COVID-19–Related Sina Weibo and Twitter Posts: Workflow Development and Content Analysis

“…Our initial assessment showed that >75% of tweets and >80% of Weibo posts regarding COVID-19 did not receive any attention on social media. This number is similar to our previous finding that 76% of all Zika-related tweets were never retweeted [ 25 ].…”

“…However, for a large and ongoing pandemic, time is another major influential factor that is associated with the actual progress of the pandemic. Our previous studies showed that the Zika case series was strongly associated with the Zika discussion trend on Twitter in 2016 [ 24 , 25 ]. Similarly, future studies can be expended to explicitly characterize how various content features evolve with time in different regions.…”

“…Practically, we ordered the posts by original posting date first and then ranked them based on the number of shares they received on each day. Due to the fast pace of social media, most viral posts received a majority of their reposts within a short period of time, and the overall lifespan of a viral post usually lasted less than 48 hours [ 25 ]. Eventually, a total of 1000 viral COVID-19 social media posts were selected, 500 for Weibo and 500 for Twitter.…”

“…Pandemics, including the current COVID-19 pandemic, are almost always intermingled with complicated interactions of underlying societal and cultural factors that vary within and among countries. Consequently, discussions of these pandemics on social media include content not restricted to health, as observed during the 2014 Ebola and 2016 Zika epidemics [ 10 , 18 - 25 ]. During the current COVID-19 pandemic, it has also been demonstrated that various social and political issues are associated with the pandemic, including different views on nonpharmaceutical interventions (NPIs) such as mask-wearing, social distancing, and stay-at-home-orders [ 26 - 29 ].…”

“…However, completely data-driven techniques can result in a lack of interpretability. For instance, the exact meanings of vectors resulting from the doc2vec algorithm are unclear, and it is usually used for classification purposes [ 25 ]. Similar to LIWC, it is still challenging to use the Chinese language as an input into these data-driven algorithms without extensive data preprocessing, which may result in a loss of subtlety of the content of the original Chinese post.…”