Automatic Classification of Students on Twitter Using Simple Profile Information (2020.aacl-srw)
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| Challenge: | Existing models for age classification of students and non-students are restrictive and require access to many tweets. |
| Approach: | They propose a model which uses 3 tweet-content features to classify users as students or non-students. |
| Outcome: | The proposed model achieves an accuracy of 88.1% and an F1 score of .704 compared to previous models, which require access to many user tweets. |
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| Challenge: | Existing approaches to predicting Twitter users' demographic attributes exploit, select, and combine various features generated from text and network to achieve the best performance. |
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