Measuring and Detecting Virality on Social Media: The Case of Twitter's Viral Tweets Topic

Social media posts may go viral and reach large numbers of people within a short period of time. Such posts may threaten the public dialogue if they contain misleading content such as fake news. As such, early detection of viral posts may be crucial for tasks such as fact-checking. Previous works proposed their own metrics to measure virality. However, such metrics may not accurately represent viral tweets or may introduce too many false positives. In this work, we use the ground truth data provided by Twitter's "Viral Tweets" topic. We propose a dataset of tweets that are labeled by Twitter as viral and a dataset of all tweets from users who authored a viral tweet. We review the proposed metrics to represent the viral tweets and propose our own metric. We also propose a transformers-based model to predict viral tweets. The code and the tweet ids are publicly available at: https://github.com/tugrulz/ViralTweets