A Shape of Geo-tagged Media Bias in COVID-19 Related Twitter

Warning: this paper contains content that may be offensive or upsetting. Social media has become an essential data source for understanding many aspects of our lives, from personal opinions to local patterns. However, it also contains more subjective and biased information than traditional media due to community bubbles and echo chambers. This study aims to examine the correlation between media bias on Twitter and COVID-19-related critical events. We used an open-access dataset of COVID-19 tweets from March 2020 to July 2021. We first developed a classification model to identify media bias using an attention-based bidirectional long short-term memory (BiLSTM) model. Using this classification model, we classified 350k geo-tagged tweets into two classes: "biased" and "unbiased", focusing on four countries: the US, UK, Canada, and India. In our study, we found that critical events, such as the sharp increase of the coronavirus death toll, would exert the rise of biased information on Twitter. Additionally, we found that in the US, the states’ bachelor degree per capita correlated with the ratio of biased tweets, which is consistent with the Dunning–Kruger effect. The unemployment rate was only found positively correlated with the ratio of biased tweets in the UK. Presumably, other factors (e.g., income inequality, social trust, etc.) should be introduced to understand the dissemination of biased tweets.