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When U.S. presidential candidates misrepresent the facts, their claims get discussed across media streams, creating a lasting public impression. We show this through a public performance: the 2020 presidential debates. For every five newspaper articles related to the presidential candidates, President Donald J. Trump and Joseph R. Biden Jr., there was one mention of a misinformation-related topic advanced during the debates. Personal attacks on Biden and election integrity were the most prevalent topics across social media, newspapers, and TV. These two topics also surfaced regularly in voters’ recollections of the candidates, suggesting their impression lasted through the presidential election. 

There is interest in using social media content to supplement or even substitute for survey data. In one of the first studies to test the feasibility of this idea, O’Connor, Balasubramanyan, Routledge, and Smith report reasonably high correlations between the sentiment of tweets containing the word “jobs” and survey-based measures of consumer confidence in 2008–2009. Other researchers report a similar relationship through 2011, but after that time it is no longer observed, suggesting such tweets may not be as promising an alternative to survey responses as originally hoped. But, it’s possible that with the right analytic techniques, the sentiment of “jobs” tweets might still be an acceptable alternative. To explore this, we first classify “jobs” tweets into categories whose content is either related to employment or not, to see whether sentiment of the former correlates more highly with a survey-based measure of consumer sentiment. We then compare the relationship when sentiment is determined with traditional dictionary-based methods versus newer machine learning-based tools developed for Twitter-like texts. We calculated daily sentiment in three different ways and used a measure of association less sensitive to outliers than correlation. None of these approaches improved the size of the relationship in the original or more recent data. We found that the many micro-decisions these analyses require, such as the size of the smoothing interval and the length of the lag between the two series, can significantly affect the outcomes. In the end, despite the earlier promise of tweets as an alternative to survey responses, we find no evidence that the original relationship in these data was more than a chance occurrence.