Measuring temporal distance focus from tweets and investigating its association with psycho-demographic attributes

Sabyasachi Kamila*, Mohammad Hasanuzzaman, Asif Ekbal, Pushpak Bhattacharyya

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Pages (from-to)1086-1097
Number of pages12
JournalIEEE Transactions on Affective Computing
Volume13
Issue number2
DOIs
Publication statusPublished - 04 May 2020
Externally publishedYes

Keywords

  • classification
  • psycho-demographic attributes
  • Temporal distance focus
  • tweet-level semantics
  • tweets

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction

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