Abstract
Temporal distance (TD) is a type of psychological distance which shows how an individual construes past and future. It is not explored with empirical research as to how an individual's focus on temporal distance (near-past, far-past, near-future, and far-future) can be measured from human-written text and further used for studying human tendencies. Traditionally, focus on a Temporal Distance is studied by self-report measurements. In this article, we present a study on human focus on a temporal distance from their Twitter posts (English tweets). We first identify the tweet-level temporal focus by deep neural classifiers which make use of linguistic knowledge for classification. The model classifies each tweet into one of near-past, far-past, near-future or far-future. Classified tweets are then grouped by users to obtain the user-level temporal focus. Finally, we correlate the user's focus on temporal distance (near-past, far-past, near-future, and far-future) with his/her demographic (age, gender, education, and relationship status) and psychological attributes (intelligence, optimism, joy, sadness, disgust, anger, surprise, and fear). Our empirical analysis reveals that users' near-past focus is more positively correlated to their age. We also observe that users' near-future focus is correlated to joy while users' focus on far-past is associated with negative emotions like sadness, disgust, anger, and fear.
Original language | English |
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Pages (from-to) | 1086-1097 |
Number of pages | 12 |
Journal | IEEE Transactions on Affective Computing |
Volume | 13 |
Issue number | 2 |
DOIs | |
Publication status | Published - 04 May 2020 |
Externally published | Yes |
Keywords
- classification
- psycho-demographic attributes
- Temporal distance focus
- tweet-level semantics
- tweets
ASJC Scopus subject areas
- Software
- Human-Computer Interaction