Assessing interests using social media

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Psychology

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2023

Author dc:creator

• Hyland, William Elliott

Contributors dc:contributor

• Rounds, James
• Briley, D.A.
• Bosch, Nigel
• Alexander, Leo
• Hoff, Kevin
• Tigunova, Anna

• Vocational Interests
• Social Media Text Mining
• Natural Language Processing

Statement dc:rights

• Copyright 2023 William Hyland

Language dc:language

en, eng

Handle dc:identifier

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

OAI record oai:identifier

https://www.ideals.illinois.edu/oai-pmh?verb=GetRecord&metadataPrefix=etdms&identifier=oai%3Awww.ideals.illinois.edu%3A2142%2F121316