{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120547"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120547","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"The language of vocational interests on social media","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","abstract_has_math":false,"creators":["Du, Yan Yi Lance"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":["Drasgow, Fritz","Roberts, Brent W"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:57Z","subjects":["Vocational Interests","Interest Assessment","Language Analysis","Social Media","Facebook"],"languages":["en","eng"],"rights":["Copyright 2023 Yan Yi Lance Du"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120547","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Drasgow, Fritz","Roberts, Brent W"]},{"key":"dc:creator","label":"Author","values":["Du, Yan Yi Lance"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-04-28"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Psychology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Vocational Interests","Interest Assessment","Language Analysis","Social Media","Facebook"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Yan Yi Lance Du"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120547"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","The student, Yan Yi Lance Du, accepted the attached license on 2023-04-27 at 17:24.","The student, Yan Yi Lance Du, submitted this Thesis for approval on 2023-04-27 at 17:26.","This Thesis was approved for publication on 2023-04-28 at 15:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19120 on 2023-09-01 at 17:21:29","There is a burgeoning interest in using natural language to study vocational interests. However, little to no research has used social media language to predict users’ interests. The present study investigated how accurately language used on Facebook predicts individuals’ self-ratings on eight basic interests: Agriculture, Engineering, Human Resource, Life Science, Management/Administration, Mechanics/Electronics, Media, and Social Science. This study employed closed-vocabulary (Linguistic Inquiry and Word Count 2015) and open-vocabulary approaches (Latent Dirichlet Allocation topic modeling) to analyze 3.2 million Facebook posts from 2,834 participants, who completed a 32-item basic interest measure (adapted from the Comprehensive Assessment of Basic Interests; CABIN; Su et al., 2019). Findings showed that the predictive accuracies of the linguistic models (mean r = .24; LDA topics) in assessing vocational interests are comparable to previous language research predicting personality traits (r = 0.27; Tay et al., 2020). Further, the present study revealed the unique language markers which characterize different basic interests. Together, the findings represent a novel advancement in vocational interest assessment. The results further suggest that automated language-based assessments can complement traditional self-report interest inventories to match people’s interests to their ideal occupations. Implications for research and applied settings were discussed."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["The language of vocational interests on social media"]}]}],"canonical_facts":{"dc:contributor":["Drasgow, Fritz","Roberts, Brent W"],"dc:creator":["Du, Yan Yi Lance"],"dc:date":["2023-05","2023-04-28"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01","The student, Yan Yi Lance Du, accepted the attached license on 2023-04-27 at 17:24.","The student, Yan Yi Lance Du, submitted this Thesis for approval on 2023-04-27 at 17:26.","This Thesis was approved for publication on 2023-04-28 at 15:15.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19120 on 2023-09-01 at 17:21:29","There is a burgeoning interest in using natural language to study vocational interests. However, little to no research has used social media language to predict users’ interests. The present study investigated how accurately language used on Facebook predicts individuals’ self-ratings on eight basic interests: Agriculture, Engineering, Human Resource, Life Science, Management/Administration, Mechanics/Electronics, Media, and Social Science. This study employed closed-vocabulary (Linguistic Inquiry and Word Count 2015) and open-vocabulary approaches (Latent Dirichlet Allocation topic modeling) to analyze 3.2 million Facebook posts from 2,834 participants, who completed a 32-item basic interest measure (adapted from the Comprehensive Assessment of Basic Interests; CABIN; Su et al., 2019). Findings showed that the predictive accuracies of the linguistic models (mean r = .24; LDA topics) in assessing vocational interests are comparable to previous language research predicting personality traits (r = 0.27; Tay et al., 2020). Further, the present study revealed the unique language markers which characterize different basic interests. Together, the findings represent a novel advancement in vocational interest assessment. The results further suggest that automated language-based assessments can complement traditional self-report interest inventories to match people’s interests to their ideal occupations. Implications for research and applied settings were discussed."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120547"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Yan Yi Lance Du"],"dc:subject":["Vocational Interests","Interest Assessment","Language Analysis","Social Media","Facebook"],"dc:title":["The language of vocational interests on social media"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Psychology"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:57Z"}