过滤社会动荡的推文
Filtering Tweets for Social Unrest
摘要：
自阿拉伯之春事件以来，人们越来越有兴趣利用社交媒体预测社会动荡。虽然已经在努力实现自动化的动荡预测，但我们专注于过滤大量的推文，以识别与动荡相关的推文，这些推文可以提供给下游用户进行进一步分析。我们训练一个有监督的分类器，能够将阿拉伯语推文标记为与高可靠性的骚乱相关。我们研究了训练数据大小和性能之间的关系，并研究了在最小化成本的同时优化模型构建过程的方法。我们还探讨了如何设置置信阈值，以实现所需的性能水平。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
