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We build on a widely used model of skill in two-player games and augment it with linguistic features of a debater\u2019s content. We show that online debaters\u2019 skill levels do tend to improve over time. Incorporating linguistic profiles leads to more robust skill estimation than winning records alone. Notably, we find that an interaction feature combining uncertainty cues (hedging) with terms strongly associated with either side of a particular debate (fightin\u2019 words) is more predictive than either feature on its own, indicating the importance of fine- grained linguistic features. <\/jats:p>","DOI":"10.1162\/tacl_a_00281","type":"journal-article","created":{"date-parts":[[2019,9,11]],"date-time":"2019-09-11T19:59:46Z","timestamp":1568231986000},"page":"537-550","source":"Crossref","is-referenced-by-count":3,"title":["Measuring Online Debaters\u2019 Persuasive Skill from Text over Time"],"prefix":"10.1162","volume":"7","author":[{"given":"Kelvin","family":"Luu","sequence":"first","affiliation":[{"name":"Paul G. 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