Filtering Tweets for Social Unrest
| dc.contributor.author | Mishler, Alan | |
| dc.contributor.author | Wonus, Kevin | |
| dc.contributor.author | Chambers, Wendy | |
| dc.contributor.author | Bloodgood, Michael | |
| dc.date.accessioned | 2017-04-02T01:32:24Z | |
| dc.date.available | 2017-04-02T01:32:24Z | |
| dc.date.issued | 2017-01 | |
| dc.description.abstract | Since the events of the Arab Spring, there has been increased interest in using social media to anticipate social unrest. While efforts have been made toward automated unrest prediction, we focus on filtering the vast volume of tweets to identify tweets relevant to unrest, which can be provided to downstream users for further analysis. We train a supervised classifier that is able to label Arabic language tweets as relevant to unrest with high reliability. We examine the relationship between training data size and performance and investigate ways to optimize the model building process while minimizing cost. We also explore how confidence thresholds can be set to achieve desired levels of performance. | en_US |
| dc.identifier | https://doi.org/10.13016/M2JZ6J | |
| dc.identifier.citation | Alan Mishler, Kevin Wonus, Wendy Chambers, and Michael Bloodgood. Filtering Tweets for Social Unrest. In Proceedings of the 2017 IEEE 11th International Conference on Semantic Computing (ICSC), pages 17-23, San Diego, CA, USA, January 2017. IEEE. | en_US |
| dc.identifier.other | DOI: 10.1109/ICSC.2017.75 | |
| dc.identifier.uri | http://ieeexplore.ieee.org/document/7889498/ | |
| dc.identifier.uri | http://hdl.handle.net/1903/19182 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.isAvailableAt | Center for Advanced Study of Language | |
| dc.relation.isAvailableAt | Digitial Repository at the University of Maryland | |
| dc.relation.isAvailableAt | University of Maryland (College Park, Md) | |
| dc.subject | computer science | en_US |
| dc.subject | statistical methods | en_US |
| dc.subject | artificial intelligence | en_US |
| dc.subject | machine learning | en_US |
| dc.subject | computational linguistics | en_US |
| dc.subject | natural language processing | en_US |
| dc.subject | human language technology | en_US |
| dc.subject | text processing | en_US |
| dc.subject | active learning | en_US |
| dc.subject | selective sampling | en_US |
| dc.subject | stopping criteria | en_US |
| dc.subject | stopping methods | en_US |
| dc.subject | text classification | en_US |
| dc.subject | text filtering | en_US |
| dc.subject | social media | en_US |
| dc.subject | social unrest | en_US |
| dc.title | Filtering Tweets for Social Unrest | en_US |
| dc.type | Article | en_US |