Toward an Extended Infodemiology Framework: Leveraging Social Media Data and Web Search Queries as Digital Pulse on Cancer Communication

Search Engine Use for Health-Related Purposes: Behavioral Data on Online Health Information-Seeking in Germany

Internet searches for health-related purposes are common, with search engines like Google being the most popular starting point. However, results on the popularity of health information-seeking behaviors are based on self-report data, often criticized for suffering from incomplete recall, overreporting, and low reliability. Therefore, the current study builds on user-centric tracking of Internet use to reveal how individuals actually behave online. We conducted a secondary analysis of passively recorded Internet use logs to examine the prevalence of health-related search engine use, the types of health information searched for, and the sources visited after the searches. The analysis revealed two key findings. 1) We largely support earlier survey-based findings on the prevalence of online health information seeking with search engines and the relatively minor differences in information-seeking behaviors between socio-demographic groups. 2) We provide a more granular picture of the process of HISB using search engines by identifying different selection patterns depending on the scope of the searches.

Examining COVID-19 Tweet Diffusion Using an Integrated Social Amplification of Risk and Issue-Attention Cycle Framework

Drawing upon the social amplification of risk (SARF) and the issue-attention cycle framework, we examined the amplification of COVID-19 risk-related tweets through (a) topics: key interests of discussion; (b) temperament: emotions of tweets; (c) topography (i.e., location); and (d) temporality (i.e., over time). We computationally analyzed 1,641,273 tweets, and conducted manual content analysis on a subset of 6,000 tweets to identify how topics, temperament, and topography of COVID-19 tweets were associated with risk amplification - retweet and favorite count - using negative binomial regression. We found 11 dominant COVID-19 topics-health impact, economic impact, reports of lockdowns, report of new cases, the need to stay home, coping with COVID-19, news about President Trump, government support, fight with COVID-19 by non-government entities, origins, and preventive measure in our corpus of tweets across the issue-attention cycle. The negative binomial regression results showed that at the pre-problem stage, topics on President Trump, speculation of origins, and initiatives to fight COVID-19 by non-government entities were most likely to be amplified, underscoring the inherent politicization of COVID-19 and erosion of trust in governments from the start of the pandemic. We also found that while tweets with negative emotions were consistently amplified throughout the issue-attention cycle, surprisingly tweets with positive emotions were amplified during the height of the pandemic - this counter-intuitive finding indicated signs of premature and misplaced optimism. Finally, our results showed that the locations of COVID-19 tweet amplification corresponded to the shifting COVID-19 hotspots across different continents across the issue-attention cycle. Theoretical and practical implications of risk amplification on social media are discussed.

Understanding the effects of message cues on COVID-19 information sharing on Twitter

Analyzing and documenting human information behaviors in the context of global public health crises such as the COVID‐19 pandemic are critical to informing crisis management. Drawing on the Elaboration Likelihood Model, this study investigates how three types of peripheral cues—content richness, emotional valence, and communication topic—are associated with COVID‐19 information sharing on Twitter. We used computational methods, combining Latent Dirichlet Allocation topic modeling with psycholinguistic indicators obtained from the Linguistic Inquiry and Word Count dictionary to measure these concepts and built a research model to assess their effects on information sharing. Results showed that content richness was negatively associated with information sharing. Tweets with negative emotions received more user engagement, whereas tweets with positive emotions were less likely to be disseminated. Further, tweets mentioning advisories tended to receive more retweets than those mentioning support and news updates. More importantly, emotional valence moderated the relationship between communication topics and information sharing—tweets discussing news updates and support conveying positive sentiments led to more information sharing; tweets mentioning the impact of COVID‐19 with negative emotions triggered more sharing. Finally, theoretical and practical implications of this study are discussed in the context of global public health communication.