Fake News Detection using Stance Classification: A Survey
It addresses the problem of fake news detection for social media users and researchers, but is incremental as it is a survey with a proposed system based on existing methods.
This paper surveys recent work on stance classification for fake news detection, highlighting challenges like echo chambers and data quality, and notes that machine learning approaches achieve promising results in classifying stance.
This paper surveys and presents recent academic work carried out within the field of stance classification and fake news detection. Echo chambers and the model organism problem are examples that pose challenges to acquire data with high quality, due to opinions being polarised in microblogs. Nevertheless it is shown that several machine learning approaches achieve promising results in classifying stance. Some use crowd stance for fake news detection, such as the approach in [Dungs et al., 2018] using Hidden Markov Models. Furthermore feature engineering have significant importance in several approaches, which is shown in [Aker et al., 2017]. This paper additionally includes a proposal of a system implementation based on the presented survey.