Assessing interests using social media

Hyland, William Elliott

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https://hdl.handle.net/2142/121316 Copy

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.

Graduation Semester

2023-08

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/121316

Copyright and License Information

Copyright 2023 William Hyland

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