Does Flagging POTUS’s Tweets Lead to Fewer or More Retweets? Preliminary Evidence from Machine Learning Models

Wallace Chipidza and Jie Yan

No 69hkb, SocArXiv from Center for Open Science

Abstract: There is vigorous debate as to whether influential social media platforms like Twitter and Facebook should censor objectionable posts by government officials in the United States and elsewhere. Although these platforms have resisted pressure to censor such posts in the past, Twitter recently flagged five posts by the United States President Donald J. Trump on the rationale that the tweets contained inaccurate or inflammatory content. In this paper, we examine preliminary evidence as to whether these posts were retweeted less or more than expected. We employ 10 machine learning (ML) algorithms to estimate the expected number of retweets based on 8 features of each tweet from historical data since President Trump was elected: number of likes, word count, readability, polarity, subjectivity, presence of link or multimedia content, time of day of posting, and number of days since Trump’s election. Our results indicate agreement from all 10 ML algorithms that the three flagged tweets for which we had retweet data were retweeted at higher rates than expected. These results suggest that flagging tweets by government officials might be counterproductive towards the spread of content deemed objectionable by social media platforms.

Date: 2020-07-04
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:69hkb

DOI: 10.31219/osf.io/69hkb

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