Assessing the risks of “infodemics” in response to COVID-19 epidemics

Abstract

Our society is built on a complex web of interdependencies whose effects become manifest during extraordinary events such as the COVID-19 pandemic, with shocks in one system propagating to the others to an exceptional extent. We analyzed more than 100 millions Twitter messages posted worldwide in 64 languages during the epidemic emergency due to SARS-CoV-2 and classified the reliability of news diffused. We found that waves of unreliable and low-quality information anticipate the epidemic ones, exposing entire countries to irrational social behavior and serious threats for public health. When the epidemics hit the same area, reliable information is quickly inoculated, like antibodies, and the system shifts focus towards certified informational sources. Contrary to mainstream beliefs, we show that human response to falsehood exhibits early-warning signals that might be mitigated with adequate communication strategies.

Causal Modeling of Twitter Activity during COVID-19

“…We collected 954,902 tweets that have location information from Twitter by searching for #covid19 and #coronavirus hashtags. Similar to other studies [18,20,46], geolocation of the tweets is inferred either by using user geo-tagging or geo-coding the information available in users' profiles. Timeline of daily log-distribution of collected tweet counts among 177 countries can be examined from Figure 1.…”

“…Themes of previous studies that focus on exploration of, description of, correlation of, or predictive modeling with Twitter data during COVID-19 pandemic include sentiment analysis [17,[25][26][27][28], public attitude/interest measurement [21,[29][30][31], content analysis [15,[32][33][34][35][36], topic modeling [16,26,27,[37][38][39][40], analysis of misinformation, disinformation, or conspiracies [20,[41][42][43][44][45][46], outbreak detection or disease nowcasting/forecasting [18,19], and more [47][48][49][50][51][52]. Similarly, data from other social media channels (e.g., Weibo, Reddit, Facebook) or search engine statistics are utilized for parallel analyses related to COVID-19 pandemic as well [53][54][55][56][57][58][59][60][61]…”

Living with COVID-19-triggered pseudoscience and conspiracies

“…Moreover, some of the pseudoscience and conspiracy theories could create unanticipated opportunities if they are thoroughly ratified and classified into reliable, questionable, misleading, conspiratorial, or inaccurate information. Interestingly, efforts have been made to quantify the spread and infectivity of unfounded speculations (Gallotti et al 2020). We recommend creating a worldwide database to archive all sorts of speculations and to develop a pipeline to curate this information, and to utilize those validated thoughts and practices in downstream mitigation strategies.…”

Artificial intelligence cooperation to support the global response to COVID-19

“…AI methods can support audience analysis (for example, social media, TV, radio) and accelerate fact checking 16 . Social media analysis, for example, is providing insights into global trends and sentiment around the pandemic and its socioeconomic impacts 17 . Research is being conducted to identify the emergence and escalation of hate speech and verbal attacks against minorities and communities suffering from discrimination that may result in violent action or their exclusion from access to healthcare 18 .…”