The case of the COVID-19 epidemic is showing the critical role of information diffusion in a disintermediated news cycle. As stated by the WHO, the outbreak and response have been accompanied by a massive infodemic: an exponential increase in the volume of information – some accurate and some not – that makes it hard for people to find trustworthy sources and reliable guidance when they need it. Without thoughtful strategies to prevent the spread of bad information, a lot could go wrong. Misinformation isn’t just a problem of content; it’s also one of transmission. In the information age, this phenomenon is amplified through social networks, spreading farther and faster like a virus. Inaccurate and false information has been circulating about all aspects of the disease: how the virus originated, its cause, its treatment, and its mechanism of spread. Thus, an important research challenge is to determine how people seek or avoid information and how those decisions affect their behavior. Further, the information spreading can strongly influence people’sbehavior and alter the effectiveness of the countermeasures deployed by governments. The issues related to the current infodemics are indeed being tackled by the scientificliterature from multiple perspectives including the dynamics of hate speech and conspiracy theories, the effect of bots and automated accounts and the threats of misinformation interms of diffusion and opinions formation.
The spread of the COVID‐19 global pandemic has generated an exponentially mounting and extraordinary volume of data that can beharnessed to improve our understanding of health communication as well as exemplifying the necessity among scholars for a better and deeper understanding of a range ofanalytical tools that could be utilized to better anticipate and respond to such unforeseen events The analysis of large, complex, and often unstructured data sets allows us to identify valuable information and accurately determine trends. This project uses three interconnected studies, all based on big data and innovative methods designs to address the infodemic challenge. Analyse of trends based on Web Searches (study one) Data collection will be guided by a set of selected keywords based on Google Trends. Identification, analysis and evaluation of how these trends have changed over the period between February 2020 and February 2022 in Portugal were considered. Key search terms will be selected from questions about the prevention and treatments of COVID-19 commonly raised in the clinical setting. GT data will be correlated with daily data on COVID-19 cases. Selecting information and digital footprints (Study two) Since the rise of contemporary social media sites as an additional news channel, people face a highly diverse information landscape with abundant choices. However, the nearly unlimited content on SNS also amplifies the amount of inaccurate and questionable information and presents individuals with the challenge of selecting verified news. On top of that, we have a proliferation of strategies to win our attention, like clickbait. Understanding these features of the social environmentcan help identify risk factors and successful messages and interventions. Therefore, this study addresses the efforts to identify effective public health messages and strategies forengaging in effective communication around public health.
The quasi-experimental design of our approach interconnects a webpage dedicated to covid with several campaignsimplemented through Google Ads. Clicking in those ads will redirect the users to the webpage allowing us to access the trail of digital footprints and cross that information with otherweb metrics. Using epidemiological models to study the spreading of information (Study three) The spread of information will be analysed with epidemic models. While most of thestudies on misinformation diffusion focus on a single platform, the dynamics behind information consumption might be particular to the environment in which they spread.
Consequently, in this project, we perform a comparative analysis of three social media platforms (Twitter, Instagram, YouTube). Various data collection processes will be performed depending on the platform. In all cases, we will guide the data collection by a set of selected keywords based on Google Trends’ COVID-19 related queries such as coronavirus,pandemic, vaccines, coronavirus update, coronavirus transmission, coronavirus news, coronavirus outbreak. The spread of information will be addressed with epidemic models,characterising for each platform its basic reproduction number (R0), i.e. the average number of secondary cases (users that start posting about COVID-19) an “infectious” individual (an individual already posting on COVID-19) will create.
