Paper: Inferring User Political Preferences from Streaming Communications

ACL ID P14-1018
Title Inferring User Political Preferences from Streaming Communications
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Existing models for social media per- sonal analytics assume access to thou- sands of messages per user, even though most users author content only sporadi- cally over time. Given this sparsity, we: (i) leverage content from the local neigh- borhood of a user; (ii) evaluate batch mod- els as a function of size and the amount of messages in various types of neighbor- hoods; and (iii) estimate the amount of time and tweets required for a dynamic model to predict user preferences. We show that even when limited or no self- authored data is available, language from friend, retweet and user mention commu- nications provide sufficient evidence for prediction. When updating models over time based on Twitter, we find that polit- ical preference can be often be predicted using roughly 100 tweets, d...