COVID-19 is unique in that it is the first global pandemic occurring amidst a crowded information environment that has facilitated the proliferation of misinformation on social media. Dangerous misleading narratives have the potential to disrupt ‘official’ information sharing at major government announcements. Using an interrupted time-series design, we test the impact of the announcement of the first UK lockdown on short-term trends of misinformation on Twitter. We utilise a novel dataset of all COVID-19-related social media posts on Twitter from the (...) UK 48 hours before and 48 hours after the announcement. We find that while the number of tweets increased immediately post announcement, there was no evidence of an increase in misinformation-related tweets. We found an increase in COVID-19-related bot activity post-announcement. Topic modelling of misinformation tweets revealed four distinct clusters: ‘government and policy’, ‘symptoms’, ‘pushing back against misinformation’ and ‘cures and treatments’. (shrink)

As the coronavirus pandemic continues apace in the United States, the dizzying amount of data being generated, analyzed and consumed about the virus has led to calls to proclaim this the first ‘data-driven pandemic’. But at the same time, it seems that this plethora of data has not meant a better grasp on the reality of the pandemic and its effects. Even as we have the potential to digitally track and trace nearly every single individual who has contracted the virus, (...) we have no idea exactly how many people have had the virus, been hospitalized, or died because of it, largely due to a confluence of factors, particularly active obfuscation and mismanagement by public authorities and misinformation spread through social media and right-wing media channels. But beyond these dynamics, there also lies the less nefarious ways that the everyday, subjective practices of data collection, analysis and visualization have the potential to themselves produce these very same dynamics where data is at once valorized and ignored, preeminent and completely useless. That is, the pandemic has revealed only the general inadequacy of our data infrastructures and assemblages to solving pressing social issues, but also the more general shift towards a ‘post-truth’ disposition in contemporary social life. But, as this paper argues, it would be a mistake to see the centrality of data as being somehow the opposite from the larger post-truth apparatus, as the two are instead fundamentally intertwined and co-produced. (shrink)

Public discourse about the COVID-19 that appears on Twitter and other social media platforms provides useful insights into public concerns and responses to the pandemic. However, acknowledging that public discourse around COVID-19 is multi-faceted and evolves over time poses both analytical and ontological challenges. Studies that use text-mining approaches to analyse responses to major events commonly treat public discourse on social media as an undifferentiated whole, without systematically examining the extent to which that discourse consists of distinct sub-discourses or which (...) phases characterize its development. They also confound structured behavioural data with unstructured user-generated data in their sampling methods. The present study aims to demonstrate how one might go about addressing both of these sets of challenges by combining corpus linguistic methods with a data-driven text-mining approach to gain a better understanding of how the public discourse around COVID-19 developed over time and what topics combine to form this discourse in the Australian Twittersphere over a period of nearly four months. By combining text mining and corpus linguistics, this study exemplifies how both approaches can complement each other productively. (shrink)

This special theme issue of Big Data & Society presents leading-edge, interdisciplinary research that focuses on examining how health-related information is circulating on social media. In particular, we are focusing on how computational and Big Data approaches can help to provide a better understanding of the ongoing COVID-19 infodemic and to develop effective strategies to combat it.

The spreading of COVID-19 misinformation on social media could have severe consequences on people's behavior. In this paper, we investigated the emotional expression of misinformation related to the COVID-19 crisis on Twitter and whether emotional valence differed depending on the type of misinformation. We collected 17,463,220 English tweets with 76 COVID-19-related hashtags for March 2020. Using Google Fact Check Explorer API we identified 226 unique COVID-19 false stories for March 2020. These were clustered into six types of misinformation. Applying the (...) 226 classifiers to the Twitter sample we identified 690,004 tweets. Instead of running the sentiment on all tweets we manually coded a random subset of 100 tweets for each classifier to increase the validity, reducing the dataset to 2,097 tweets. We found that only a minor part of the entire dataset was related to misinformation. Also, misinformation in general does not lean towards a certain emotional valence. However, looking at comparisons of emotional valence for different types of misinformation uncovered that misinformation related to “virus” and “conspiracy” had a more negative valence than “cures,” “vaccine,” “politics,” and “other.” Knowing from existing studies that negative misinformation spreads faster, this demonstrates that filtering for misinformation type is fruitful and indicates that a focus on “virus” and “conspiracy” could be one strategy in combating misinformation. As emotional contexts affect misinformation spreading, the knowledge about emotional valence for different types of misinformation will help to better understand the spreading and consequences of misinformation. (shrink)