Temporal Orientation of Tweets for Predicting Income of Users

Automatically estimating a user’s socio-economic profile from their language use in social media can significantly help social science research and various downstream applications ranging from business to politics. The current paper presents the first study where user cognitive structure is used to build a predictive model of income. In particular, we first develop a classifier using a weakly supervised learning framework to automatically time-tag tweets as past, present, or future. We quantify a user’s overall temporal orientation based on their distribution of tweets, and use it to build a predictive model of income. Our analysis uncovers a correlation between future temporal orientation and income. Finally, we measure the predictive power of future temporal orientation on income by performing regression.

• Publication year

  2017

• Venue

  Annual Meeting of the Association for Computational Linguistics

• Publication date

  2017-07-01

• Fields of study

  Computer Science, Economics, Political Science

• Identifiers
  DOI 10.18653/v1/P17-2104
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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