Analyzing Linguistic Differences between Owner and Staff Attributed Tweets

Daniel Preoţiuc-Pietro, Rita Devlin Marier
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
Research on social media has to date assumed that all posts from an account are authored by the same person. In this study, we challenge this assumption and study the linguistic differences between posts signed by the account owner or attributed to their staff. We introduce a novel data set of tweets posted by U.S. politicians who self-reported their tweets using a signature. We analyze the linguistic topics and style features that distinguish the two types of tweets. Predictive results show
more » ... t we are able to distinguish between owner and staff attributed tweets with good accuracy, even when not using any training data from that account.
doi:10.18653/v1/p19-1274 dblp:conf/acl/Preotiuc-Pietro19 fatcat:7k7pvmfr2behtbv7wpgbgs5roq