Quantifying the impacts of online fake news on the equity value of social media platforms – Evidence from Twitter

Abstract

Online Fake News characterized by falsehood and ambiguity is significantly shaking up various aspects of social, economic, and political life across the globe. In addition, it can also be detrimental to the existence of social media platforms. In this research, by synthesizing the prior literature on negativity bias in online settings, reasons for platform failure and the characteristics of social media platforms, we explain the mechanisms through which online fake news impacts the equity value of these platforms. We also develop and implement a two-stage Bayesian Vector Autoregression technique to test these mechanisms using a dataset from Twitter. Our results show that falsehood results in an equity value loss of approximately 2.11 Million USD over a ten-day period while a mere 67.17 Million falsehood tweets can interact with ambiguity to produce a loss of 10 Million USD. We also find that ambiguity helps mitigate the negative impact of fake news which we attribute to the fact that users converse and disambiguate news that arrives on social media platforms. Our research has theoretical and practical implications for the impact of fake news on these platforms and their valuations.