Using Information-Seeking Argument Mining to Improve Service
研究提出信息寻求论证挖掘（IS-AM）技术，通过分析新闻和评论中用户支持或反对服务的理由，帮助服务提供者理解并改变用户行为。实证分析电动滑板车共享系统，发现40个使用或不使用的理由及其重要性，且新闻文章比评论更有效。
If service providers can identify reasons users are in favor of or against a service, they have insightful information that can help them understand user behavior and what they need to do to change such behavior. This article argues that the novel text-mining technique referred to as information-seeking argument mining (IS-AM) can identify these reasons. The empirical study applies IS-AM to news articles and reviews about electric scooter-sharing systems (i.e., a service enabling the short-term rentals of electric motorized scooters). Its results point to IS-AM as a promising technique to improve service; the data enable the authors to identify 40 reasons to use or not use electric scooter-sharing systems, as well as their importance to users. Furthermore, the results show that news articles are better data sources than reviews because they are longer and contain more arguments and, thus, reasons.