The impact of toxic trolling comments on anti-vaccine YouTube videos.

Anti-vaccine trolling on video-hosting websites hinders efforts to increase vaccination rates by using toxic language and threatening claims to intimidate people and promote vaccine hesitancy. However, there is a shortage of research investigating the effects of toxic messages on these platforms. This study focused on YouTube anti-vaccine videos and examined the relationship between toxicity and fear in the comment section of these videos. We discovered that highly liked toxic comments were associated with a significant level of fear in subsequent comments. Moreover, we found complex patterns of contagion between toxicity and fear in the comments. These findings suggest that initial troll comments can evoke negative emotions in viewers, potentially fueling vaccine hesitancy. Our research bears essential implications for managing public health messaging and online communities, particularly in moderating fear-mongering messages about vaccines on social media.

Storm the Capitol: Linking Offline Political Speech and Online Twitter Extra-Representational Participation on QAnon and the January 6 Insurrection.

The transfer of power stemming from the 2020 presidential election occurred during an unprecedented period in United States history. Uncertainty from the COVID-19 pandemic, ongoing societal tensions, and a fragile economy increased societal polarization, exacerbated by the outgoing president's offline rhetoric. As a result, online groups such as QAnon engaged in extra political participation beyond the traditional platforms. This research explores the link between offline political speech and online extra-representational participation by examining Twitter within the context of the January 6 insurrection. Using a mixed-methods approach of quantitative and qualitative thematic analyses, the study combines offline speech information with Twitter data during key speech addresses leading up to the date of the insurrection; exploring the link between Trump's offline speeches and QAnon's hashtags across a 3-day timeframe. We find that links between online extra-representational participation and offline political speech exist. This research illuminates this phenomenon and offers policy implications for the role of online messaging as a tool of political mobilization.

Correction: Precision Public Health Campaign: Delivering Persuasive Messages to Relevant Segments Through Targeted Advertisements on Social Media.

[This corrects the article DOI: 10.2196/22313.].

Precision Public Health Campaign: Delivering Persuasive Messages to Relevant Segments Through Targeted Advertisements on Social Media.

Although established marketing techniques have been applied to design more effective health campaigns, more often than not, the same message is broadcasted to large populations, irrespective of unique characteristics. As individual digital device use has increased, so have individual digital footprints, creating potential opportunities for targeted digital health interventions. We propose a novel precision public health campaign framework to structure and standardize the process of designing and delivering tailored health messages to target particular population segments using social media-targeted advertising tools. Our framework consists of five stages: defining a campaign goal, priority audience, and evaluation metrics; splitting the target audience into smaller segments; tailoring the message for each segment and conducting a pilot test; running the health campaign formally; and evaluating the performance of the campaigns. We have demonstrated how the framework works through 2 case studies. The precision public health campaign framework has the potential to support higher population uptake and engagement rates by encouraging a more standardized, concise, efficient, and targeted approach to public health campaign development.

FrameAxis: characterizing microframe bias and intensity with word embedding.

Framing is a process of emphasizing a certain aspect of an issue over the others, nudging readers or listeners towards different positions on the issue even without making a biased argument. Here, we propose FrameAxis, a method for characterizing documents by identifying the most relevant semantic axes ("microframes") that are overrepresented in the text using word embedding. Our unsupervised approach can be readily applied to large datasets because it does not require manual annotations. It can also provide nuanced insights by considering a rich set of semantic axes. FrameAxis is designed to quantitatively tease out two important dimensions of how microframes are used in the text. Microframe bias captures how biased the text is on a certain microframe, and microframe intensity shows how prominently a certain microframe is used. Together, they offer a detailed characterization of the text. We demonstrate that microframes with the highest bias and intensity align well with sentiment, topic, and partisan spectrum by applying FrameAxis to multiple datasets from restaurant reviews to political news. The existing domain knowledge can be incorporated into FrameAxis by using custom microframes and by using FrameAxis as an iterative exploratory analysis instrument. Additionally, we propose methods for explaining the results of FrameAxis at the level of individual words and documents. Our method may accelerate scalable and sophisticated computational analyses of framing across disciplines.

Knowledge and Anxiety about COVID-19 in the State of Qatar, and the Middle East and North Africa Region-A Cross Sectional Study.

While the coronavirus disease 2019 (COVID-19) pandemic wreaked havoc across the globe, we have witnessed substantial mis- and disinformation regarding various aspects of the disease. We conducted a cross-sectional study using a self-administered questionnaire for the general public (recruited via social media) and healthcare workers (recruited via email) from the State of Qatar, and the Middle East and North Africa region to understand the knowledge of and anxiety levels around COVID-19 (April-June 2020) during the early stage of the pandemic. The final dataset used for the analysis comprised of 1658 questionnaires (53.0% of 3129 received questionnaires; 1337 [80.6%] from the general public survey and 321 [19.4%] from the healthcare survey). Knowledge about COVID-19 was significantly different across the two survey populations, with a much higher proportion of healthcare workers possessing better COVID-19 knowledge than the general public (62.9% vs. 30.0%, p < 0.0001). A reverse effect was observed for anxiety, with a higher proportion of very anxious (or really frightened) respondents among the general public compared to healthcare workers (27.5% vs. 11.5%, p < 0.0001). A higher proportion of the general public tended to overestimate their chance of dying if they become ill with COVID-19, with 251 (18.7%) reporting the chance of dying (once COVID-19 positive) to be ≥25% versus 19 (5.9%) of healthcare workers (p < 0.0001). Good knowledge about COVID-19 was associated with low levels of anxiety. Panic and unfounded anxiety, as well as casual and carefree attitudes, can propel risk taking and mistake-making, thereby increasing vulnerability. It is important that governments, public health agencies, healthcare workers, and civil society organizations keep themselves updated regarding scientific developments and that they relay messages to the community in an honest, transparent, unbiased, and timely manner.

High correlation of Middle East respiratory syndrome spread with Google search and Twitter trends in Korea.

The Middle East respiratory syndrome coronavirus (MERS-CoV) was exported to Korea in 2015, resulting in a threat to neighboring nations. We evaluated the possibility of using a digital surveillance system based on web searches and social media data to monitor this MERS outbreak. We collected the number of daily laboratory-confirmed MERS cases and quarantined cases from May 11, 2015 to June 26, 2015 using the Korean government MERS portal. The daily trends observed via Google search and Twitter during the same time period were also ascertained using Google Trends and Topsy. Correlations among the data were then examined using Spearman correlation analysis. We found high correlations (>0.7) between Google search and Twitter results and the number of confirmed MERS cases for the previous three days using only four simple keywords: "MERS", "" ("MERS (in Korean)"), "" ("MERS symptoms (in Korean)"), and "" ("MERS hospital (in Korean)"). Additionally, we found high correlations between the Google search and Twitter results and the number of quarantined cases using the above keywords. This study demonstrates the possibility of using a digital surveillance system to monitor the outbreak of MERS.