From Clickbait to Fake News Detection: An Approach based on Detecting the Stance of Headlines to Articles

Abstract

We present a system for the detection of the stance of headlines with regard to their corresponding article bodies. The approach can be applied in fake news, especially clickbait detection scenarios. The component is part of a larger platform for the curation of digital content; we consider veracity and relevancy an increasingly important part of curating online information. We want to contribute to the debate on how to deal with fake news and related online phenomena with technological means, by providing means to separate related from unrelated headlines and further classifying the related headlines. On a publicly available data set annotated for the stance of headlines with regard to their corresponding article bodies, we achieve a (weighted) accuracy score of 89.59.

From Stances' Imbalance to Their HierarchicalRepresentation and Detection

“…Two other methods consider all the classes, but use two different models: Bourgonje et al [10] use the lemmatized n-gram matching and a rule-based procedure to decide the evidence relatedness, and a three-way logistic regression classifier to distinguish among the relevant classes; Wang et al [43] firstly develop a gradient boosted decision tree (GBDT) model [28] to determine the evidence relatedness, then another GBDT model is used to distinguish stances of the text towards the claim. These methods involve feature engineering in separate models and cannot be jointly optimized to achieve the best performance.…”

“…This is the FNC-1 official baseline that uses one gradient boosting decision trees model for fourway classification; Logistic Regression (LR). Bourgonje et al [10] use n-gram matching and a rule-based procedure to decide relatedness, and three-way logistic regression to distinguish among the related classes; Gradient Boosted Decision Trees (GBDT). Wang et al [43] develop two GBDT models, one to determine the relatedness of an evidence to a claim, and another to distinguish among the related classes; Multi-Layer Perception (MLP).…”

Vulnerable to misinformation?

“…A number of fact-checking websites and organizations heavily rely on humans to detect misinformation [1,3]. Researchers have developed a model to detect click-baits by comparing the stance of a headline in comparison to the body [14]. By focusing on sarcasm and satirical cues researchers were able to create a model that identifies satirical misinformation [49].…”

“…. $15.00 https://doi.org/10.1145/3301275.3302320 practitioners from multiple disciplines including political science, psychology and computer science are grappling with the effects of misinformation and devising means to combat it [14,44,52,53,63].…”

An Infrastructure for Empowering Internet Users to Handle Fake News and Other Online Media Phenomena

“…3); the concept has been devised in a research and technology transfer project, in which smart technologies for curating large amounts of digital content are being developed and applied by companies that cover different sectors including journalism (Rehm and Sasaki 2015;Bourgonje et al 2016a,b;Rehm et al 2017). Among others, we currently develop services aimed at the detection and classification of abusive language (Bourgonje et al 2017a) and clickbait content (Bourgonje et al 2017b). The proposed hybrid infrastructure combines automatic language technology components and user-generated annotations and is meant to empower internet users better to handle the modern online media phenomena mentioned above.…”