Job recommendation using facebook personality scores
Facebook is one of the most popular social media sites that has become part of our lives. User-generated Facebook data are useful and can be used to gauge personality. However, previous studies did not use Facebook data for personality assessment and mapping for professional purposes. The current st...
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| Format: | Article |
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Faculty of Computer Science and Information Technology, University of Malaya
2018
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| _version_ | 1825721502962548736 |
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| author | Ting, Thiam Li Varathan, Kasturi Dewi |
| author_facet | Ting, Thiam Li Varathan, Kasturi Dewi |
| author_sort | Ting, Thiam Li |
| collection | UM |
| description | Facebook is one of the most popular social media sites that has become part of our lives. User-generated Facebook data are useful and can be used to gauge personality. However, previous studies did not use Facebook data for personality assessment and mapping for professional purposes. The current study mainly aims to identify personality features using user-generated content in Facebook. A computational score is created and a model is developed by utilizing these scores in job recommendations that match the personality of the user. The personality score of Facebook is benchmarked against the Big Five Inventory (BFI) test score to determine accuracy. The scores of Facebook personality scores and BFI test reached 93.1%. The findings of this study benefits job candidates, especially fresh graduates by assisting them in identifying a career that suits their personality. This study also helps create awareness among individuals by identifying the personality strengths and weaknesses through the use of Facebook information. This study can help employers find candidates who fit the needs of the company by gauging their personality through Facebook data. |
| first_indexed | 2024-03-06T05:51:17Z |
| format | Article |
| id | um.eprints-20465 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:51:17Z |
| publishDate | 2018 |
| publisher | Faculty of Computer Science and Information Technology, University of Malaya |
| record_format | dspace |
| spelling | um.eprints-204652019-02-25T02:49:13Z http://eprints.um.edu.my/20465/ Job recommendation using facebook personality scores Ting, Thiam Li Varathan, Kasturi Dewi QA75 Electronic computers. Computer science Facebook is one of the most popular social media sites that has become part of our lives. User-generated Facebook data are useful and can be used to gauge personality. However, previous studies did not use Facebook data for personality assessment and mapping for professional purposes. The current study mainly aims to identify personality features using user-generated content in Facebook. A computational score is created and a model is developed by utilizing these scores in job recommendations that match the personality of the user. The personality score of Facebook is benchmarked against the Big Five Inventory (BFI) test score to determine accuracy. The scores of Facebook personality scores and BFI test reached 93.1%. The findings of this study benefits job candidates, especially fresh graduates by assisting them in identifying a career that suits their personality. This study also helps create awareness among individuals by identifying the personality strengths and weaknesses through the use of Facebook information. This study can help employers find candidates who fit the needs of the company by gauging their personality through Facebook data. Faculty of Computer Science and Information Technology, University of Malaya 2018 Article PeerReviewed Ting, Thiam Li and Varathan, Kasturi Dewi (2018) Job recommendation using facebook personality scores. Malaysian Journal of Computer Science, 31 (4). pp. 311-331. ISSN 0127-9084, DOI https://doi.org/10.22452/mjcs.vol31no4.5 <https://doi.org/10.22452/mjcs.vol31no4.5>. https://doi.org/10.22452/mjcs.vol31no4.5 doi:10.22452/mjcs.vol31no4.5 |
| spellingShingle | QA75 Electronic computers. Computer science Ting, Thiam Li Varathan, Kasturi Dewi Job recommendation using facebook personality scores |
| title | Job recommendation using facebook personality scores |
| title_full | Job recommendation using facebook personality scores |
| title_fullStr | Job recommendation using facebook personality scores |
| title_full_unstemmed | Job recommendation using facebook personality scores |
| title_short | Job recommendation using facebook personality scores |
| title_sort | job recommendation using facebook personality scores |
| topic | QA75 Electronic computers. Computer science |
| work_keys_str_mv | AT tingthiamli jobrecommendationusingfacebookpersonalityscores AT varathankasturidewi jobrecommendationusingfacebookpersonalityscores |