Social media fueled a bank run on Silicon Valley Bank (SVB), and the effects werefelt broadly in the U.S. banking industry. We employ comprehensive Twitter data toshow that preexisting exposure to social media predicts bank stock market losses inthe run period even after controlling for bank characteristics related to run risk (i.e.,mark-to-market losses and uninsured deposits). Moreover, we show that social mediaamplifies these bank run risk factors. During the run period, we find the intensity ofTwitter conversation about a bank predicts stock market losses at the hourly frequency.This effect is stronger for banks with bank run risk factors. At even higher frequency,tweets in the run period with negative sentiment translate into immediate stock marketlosses. These high frequency effects are stronger when tweets are authored by membersof the Twitter startup community (who are likely depositors) and contain keywordsrelated to contagion. These results are consistent with depositors using Twitter tocommunicate in real time during the bank run.