# Stance Detection in the Context of Fake News--A New Approach +
## 2 Literature Review
### Stance Detection  
Stance detection, a subtask within the FNC, plays a crucial role in identifying fake news. Stance detection involves determining the attitude of a text towards a specific target. Stance detection in news articles is different from stance detection in online debates and tweets. The stance detection in article news is related to the titles in NLP. The validity of titles is predicted with the utilization of the sources' trustworthiness and the stance of the article [43].  
The Fake News Challenge (FNC-1), launched in 2017, was a pivotal competition in the field of fake news detection that focused on stance detection as a subtask. Participants were tasked with classifying the stance of body text relative to a headline into four categories: agrees, disagrees, discusses, or unrelated. The competition provided a dataset of 50,000 headline-body pairs and used a weighted scoring system for evaluation. FNC-1 had a significant impact on the field, providing a standardized dataset and evaluation metric, sparking numerous research papers, and highlighting the importance of stance detection in fake news identification. The motivation behind the first FNC stance identification job was retained from the effort recommended in [44], wherein the authors categorize the stance of distinct sentences only of the headline news to a certain claim.  
[PERSON] et al. [45] used deep learning to tackle the problem of stance detection in FNC. They integrate the neural attention mechanism with max-pooling and bidirectional RNN to create depictions from the news article body and titles with external features based on similarity. Similarly, [PERSON] et al. [46], proposed a framework to deliver and offer the reader a complete viewpoint and understanding of the question. They introduced a model consisting of two parts: part one consists of tree-based concentrating on features that are handcrafted, while part two involves RNN concentrating on a few of the main sentences merely. Moreover, [PERSON] et al. [47] proposed a model to detect fake news stances. This model depends on the body of the news and the titles. They combine Principal Component Analysis (PCA) as well as chi-square along with LSTM and CNN, wherein chi-square and PCA obtain the features that are delivered to the CNN-LSTM model. They mitigate the feature dimensionality before delivering them to the classifier through applying dimensionality mitigation methods. Their approach shows that the principal component analysis did better than chi-square and achieved 97.8% accuracy.  
Recently, [PERSON] et al. [48] proposed a cross-lingual multi-task learning framework for stance detection. Their approach, which leverages pre-trained language models and multi-task learning, achieved state-of-the-art results on several benchmark datasets. In the domain of multimedia stance detection, [PERSON] et al. [49] introduced MMSD (Multi-Modal Stance Detection), a framework that incorporates textual, visual, and social context information. Their model demonstrated superior performance in detecting stances in social media posts containing both text and images. Other researchers studied fake news and stance detection in multilingual text or other specific non-English text; see, for example, [50; 51].  
The project outlined herein is an analysis of the data made available by the FNC. As described on the website, the goal of the challenge is \"to address the problem of fake news by organizing a competition to foster the development of tools to help human fact-checkers identify hoaxes and deliberate misinformation in news stories using machine learning, NLP, and artificial intelligence\" [27].