Tackling the Headline Incongruity Problem using Stance Detection

Tackling the Headline Incongruity Problem using Stance Detection

Mariën, Simon (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Intelligent Systems)

Murukannaiah, P.K. (mentor)
Marroquim, Ricardo (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2021-07-01

The increasing amount of untrusted content on the internet is a worrisome trend. The headline of an article can be adjusted to influence a potential readers attention and click-through rate. This clickbait or sensationalism can mislead the reader as the headline does not accurately represent the information in the corresponding article body. This headline incongruity problem has received some recent attention from the research community with proposed datasets and approaches. However, there still lacks an overarching paper that tries to answer the current state of tackling the problem. This paper aims to fill this gap, look at recent proposed datasets and approaches, and compare them. These results will be discussed, reflected on, and formulated to answer the research question in conclusion. The main conclusions from this research are that the most suited dataset for comparison purposes is the Real-News based dataset and the Graph Neural Network by Yoon et al. performed the best.

Stance Detection
NLP
Headline Incongruity Problem

http://resolver.tudelft.nl/uuid:b34a7357-5f07-43e4-93d2-eecd0cedc126

Student theses

bachelor thesis

© 2021 Simon Mariën