Language about the future on social media as a novel marker of anxiety and depression: A big-data and experimental analysis

• Authors: Cole Robertson, Emory University; James Carney, The London Interdisciplinary School; Shane Trudell, Nightingale Counselling and Research
• Journal Title: Current Research in Behavioral Sciences
• Volume: Volume 4
• Publisher: Elsevier | 2023-06-30
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1016/j.crbeha.2023.100104
• Subjects: Education, Guidance and Counseling
• Keywords: Future time reference; Temporal discounting; Mood disorders; Anxiety; Depression; Big-data; Time perspective

Abstract

Anxiety and depression negatively impact many. Studies suggest depression is associated with future time horizons, or how “far” into the future people tend to think, and anxiety is associated with temporal discounting, or how much people devalue future rewards. Separate studies from linguistics and economics have shown that how people refer to future time predicts temporal discounting. Yet no one—that we know of—has investigated whether future time reference habits are a marker of anxiety and/or depression. We introduce the FTR classifier, a novel classification system researchers can use to analyse linguistic temporal reference. In Study 1, we used the FTR classifier to analyse data from the social-media website Reddit. Users who had previously posted popular contributions to forums about anxiety and depression referenced the future and past more often than controls, had more proximal future and past time horizons, and significantly differed in their linguistic future time reference patterns: They used fewer future tense constructions (e.g. will), fewer high-certainty constructions (certainly), more low-certainty constructions (could), more bouletic modal constructions (hope), and more deontic modal constructions (must). This motivated Study 2, a survey-based mediation analysis. Self-reported anxious participants represented future events as more temporally distal and therefore temporally discounted to a greater degree. The same was not true of depression. We conclude that methods which combine big-data with experimental paradigms can help identify novel markers of mental illness, which can aid in the development of new therapies and diagnostic criteria.