Coronavirus on Social Media: Analyzing Misinformation in Twitter Conversations
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The ongoing Coronavirus Disease (COVID-19) pandemic highlights the interconnected-ness of our present-day globalized world. With social distancing policies in place, virtual communication has become an important source of (mis)information. As increasing number of people rely on social media platforms for news, identifying misinformation has emerged as a critical task in these unprecedented times. In addition to being malicious, the spread of such information poses a serious public health risk. To this end, we design a dashboard to track misinformation on popular social media news sharing platform - Twitter. Our dashboard allows visibility into the social media discussions around Coronavirus and the quality of information shared on the platform as the situation evolves. We collect streaming data using the Twitter API from March 1, 2020 to date and provide analysis of topic clusters and social sentiments related to important emerging policies such as "#socialdistancing" and "#workfromhome". We track emerging hashtags over time, and provide location and time sensitive analysis of sentiments. In addition, we study the challenging problem of misinformation on social media, and provide a detection method to identify false, misleading and clickbait contents from Twitter information cascades. The dashboard maintains an evolving list of detected misinformation cascades with the corresponding detection scores, accessible online athttps://ksharmar.github.io/index.html.
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