Abstract
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 that we are able to predict owner and staff attributed tweets with good accuracy, even when not using any training data from that account.- Anthology ID:
- P19-1274
- Volume:
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Anna Korhonen, David Traum, Lluís Màrquez
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2848–2853
- Language:
- URL:
- https://aclanthology.org/P19-1274
- DOI:
- 10.18653/v1/P19-1274
- Cite (ACL):
- Daniel Preoţiuc-Pietro and Rita Devlin Marier. 2019. Analyzing Linguistic Differences between Owner and Staff Attributed Tweets. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2848–2853, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Analyzing Linguistic Differences between Owner and Staff Attributed Tweets (Preoţiuc-Pietro & Devlin Marier, ACL 2019)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/P19-1274.pdf
- Code
- danielpreotiuc/signed-tweets