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With the increasing growth of social media, people have started relyingheavily on the information shared therein to form opinions and make decisions.While such a reliance is motivation for a variety of parties to promoteinformation, it also makes people vulnerable to exploitation by slander,misinformation, terroristic and predatorial advances. In this work, we aim tounderstand and detect such attempts at persuasion. Existing works on detectingpersuasion in text make use of lexical features for detecting persuasivetactics, without taking advantage of the possible structures inherent in thetactics used. We formulate the task as a multi-class classification problem andpropose an unsupervised, domain-independent machine learning framework fordetecting the type of persuasion used in text, which exploits the inherentsentence structure present in the different persuasion tactics. Our work showspromising results as compared to existing work.

An Unsupervised Domain-Independent Framework for Automated Detection of Persuasion Tactics in Text