Towards Discovery of Influence and Personality Traits Through Social Link Prediction
Estimation of a person's influence and personality traits from social media data has many applications. We use social linkage criteria, such as number of followers and friends, as proxies to form corpora, from popular blogging site Livejournal, for examining two two-class classification problem...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
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IAAA
2011
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| Online Access: | http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/view/2772 http://hdl.handle.net/20.500.11937/21742 |
| _version_ | 1848750675138183168 |
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| author | Nguyen, Thin Phung, Dinh Adams, Brett Venkatesh, Svetha |
| author2 | Nicolas Nicolov |
| author_facet | Nicolas Nicolov Nguyen, Thin Phung, Dinh Adams, Brett Venkatesh, Svetha |
| author_sort | Nguyen, Thin |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Estimation of a person's influence and personality traits from social media data has many applications. We use social linkage criteria, such as number of followers and friends, as proxies to form corpora, from popular blogging site Livejournal, for examining two two-class classification problems: influential vs. non-influential, and extraversion vs. introversion. Classification is performed using automatically-derived psycholinguistic and mood-based features of a user's textual messages. We experiment with three sub-corpora of 10000 users each, and present the most effective predictors for each category. The best classification result, at 80%, is achieved using psycholinguistic features; e.g., influentials are found to use more complex language, than non-influentials, and use more leisure-related terms. |
| first_indexed | 2025-11-14T07:40:36Z |
| format | Conference Paper |
| id | curtin-20.500.11937-21742 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:40:36Z |
| publishDate | 2011 |
| publisher | IAAA |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-217422023-01-27T05:26:32Z Towards Discovery of Influence and Personality Traits Through Social Link Prediction Nguyen, Thin Phung, Dinh Adams, Brett Venkatesh, Svetha Nicolas Nicolov James G. Shanahan Estimation of a person's influence and personality traits from social media data has many applications. We use social linkage criteria, such as number of followers and friends, as proxies to form corpora, from popular blogging site Livejournal, for examining two two-class classification problems: influential vs. non-influential, and extraversion vs. introversion. Classification is performed using automatically-derived psycholinguistic and mood-based features of a user's textual messages. We experiment with three sub-corpora of 10000 users each, and present the most effective predictors for each category. The best classification result, at 80%, is achieved using psycholinguistic features; e.g., influentials are found to use more complex language, than non-influentials, and use more leisure-related terms. 2011 Conference Paper http://hdl.handle.net/20.500.11937/21742 http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/view/2772 IAAA restricted |
| spellingShingle | Nguyen, Thin Phung, Dinh Adams, Brett Venkatesh, Svetha Towards Discovery of Influence and Personality Traits Through Social Link Prediction |
| title | Towards Discovery of Influence and Personality Traits Through Social Link Prediction |
| title_full | Towards Discovery of Influence and Personality Traits Through Social Link Prediction |
| title_fullStr | Towards Discovery of Influence and Personality Traits Through Social Link Prediction |
| title_full_unstemmed | Towards Discovery of Influence and Personality Traits Through Social Link Prediction |
| title_short | Towards Discovery of Influence and Personality Traits Through Social Link Prediction |
| title_sort | towards discovery of influence and personality traits through social link prediction |
| url | http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/view/2772 http://hdl.handle.net/20.500.11937/21742 |