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