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https://hdl.handle.net/2142/121316
Description
Title
Assessing interests using social media
Author(s)
Hyland, William Elliott
Issue Date
2023-06-22
Director of Research (if dissertation) or Advisor (if thesis)
Rounds, James
Doctoral Committee Chair(s)
Rounds, James
Committee Member(s)
Briley, D.A.
Bosch, Nigel
Alexander, Leo
Hoff, Kevin
Tigunova, Anna
Department of Study
Psychology
Discipline
Psychology
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
vocational interests
social media text mining
natural language processing
Abstract
Interests are explicit in much of the information that is circulated on social media, including Facebook likes, Twitter follows, and discussions of interests on sites such as Reddit, Tumblr, and Pinterest. This wealth of data presents unique opportunities to expand applications of interest research and produce new insights into the structure of interests in novel contexts where people spend considerable time. Digital assessment of interests could also be valuable for career guidance by providing individuals with instant feedback about their interests and how they connect to different careers. In this article, we apply an unsupervised method of digital assessment to develop and validate a measure of interests using Reddit data. Specifically, we analyze thematically organized discussion forums called “subreddits”, using a combination of Natural Language Processing and clustering techniques to group subreddits based on similarity of language usage. Traits were identified at 2 levels of the interest hierarchy, leading to a 4-interest and a 13-interest measure. These interests predicted occupational choice with accuracy similar to self-report interest inventories and were stable over time. Overall, findings demonstrate that interests can be assessed digitally with good psychometric properties, providing a useful complement to self-report methodology. We discuss similarities and differences between digitally assessed interests and the RIASEC self-report model, as well as applications for research and practice.
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