An Unsupervised Domain-Independent Framework for Automated Detection of Persuasion Tactics in Text
arXiv:1912.06745
Abstract
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.
19 pages, 8 Figures
References in corpus (4)
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Cited by in corpus (8)
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- A Heterogeneous Graphical Model to Understand User-Level Sentiments in Social Media
- Modeling Product Search Relevance in e-Commerce
- A Correspondence Analysis Framework for Author-Conference Recommendations
- Simultaneous Identification of Tweet Purpose and Position
- An End-to-End ML System for Personalized Conversational Voice Models in Walmart E-Commerce
- Transition-Based Dependency Parsing using Perceptron Learner