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Predicting Demographics and Affect in Social Networks
| Content Provider | Semantic Scholar |
|---|---|
| Author | Volkova, Svitlana |
| Copyright Year | 2015 |
| Abstract | The recent explosion of social media services like Twitter, Facebook and Google+ has led to an interest in predicting hidden information from the large amounts of freely available public content. As compared to the earlier explosion of documents arising from the web, social media content is significantly more personalized – written in the first person, informal, and often revealing of latent attributes of users. The task of inferring latent user properties from social media data has become known as user modeling, personal analytics or user profiling task. Previous approaches treated the task of user attribute prediction as static supervised classification, applied textual features extracted from user tweets and relied on an unrealistic amount of content per user (thousands of tweets). This dissertation relies mainly on Twitter data and focuses on several important but previously unexplored aspects of the task of user attribute prediction: (1) developing novel models and practical techniques that reflect the dynamic streaming nature of social media; (2) studying predictive power and latent relationships between user demographics, emotions and interests in social media; and (3) showing that extra-linguistic features such |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://jscholarship.library.jhu.edu/bitstream/handle/1774.2/39639/VOLKOVA-DISSERTATION-2015.pdf?isAllowed=y&sequence=1 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
