to people’s individual differences and thus maximize their educational impact.

Related Work {#relwork-sec}
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Previous work on belief change has primarily focused on single, experimentally crafted, persuasive messages, rather than exploring whether user-generated dialogic arguments can be repurposed to persuade. Recently however several papers have begun to investigate two challenges in argument mining: (1) understanding the structure of an argument and extracting argument components [@lippi2015context; @nguyen2015extracting; @stab2014annotating; @lippi2015context; @BiranRambow11]; and (2) understanding what predicts the persuasiveness of web-sourced argumentative content [@habernal2016argument; @ostendorf2016learning; @wachsmuth2016using; @habernalmakes16; @Tanetal16].

Tan et al.  study belief change in the Reddit [ /r/ChangeMyView]{} subreddit (CMV), in which an original poster (OP) challenges others to change his/her opinion. They build logistic regression models to predict argument success, identifying two conversational dynamic factors: a) early potential persuaders are more successful and b) after 4 exchanges, the chance of persuasion drops virtually to zero. Linguistic factors of persuasive posts include: a) dissimilar content words to the OP, b) similar stop words, c) being lengthy (in words, sentences, and paragraphs), d) italics and bullets. Finally, susceptibility to persuasion is correlated with singular vs. plural first person pronouns, which the authors relate to the personality trait of Openness to Experience. The CMV 