# Stance Detection in the Context of Fake News--A New Approach +
###### Abstract  
Online social networks (OSNs) are annotated with an enormous daily influx of news shared by users worldwide. Information can originate from any OSN user and quickly spread, making the task of fact-checking news both time-consuming and resource-intensive. To address this challenge, researchers are exploring machine learning techniques to automate fake news detection. This paper specifically focuses on detecting the stance of content producers--whether they support or oppose the subject of the content. Our study aims to develop and evaluate advanced text-mining models that leverage pre-trained language models enhanced with meta features derived from headlines and article bodies. We sought to determine whether incorporating the cosine distance feature could improve model prediction accuracy. After analyzing and assessing several previous competition entries, we identified three key tasks for achieving high accuracy: (1) a multi-stage approach that integrates classical and neural network classifiers, (2) the extraction of additional text-based meta features from headline and article body columns, and (3) the utilization of recent pre-trained embeddings and transformer models. s +
Footnote †: These authors contributed equally to this work. Footnote †: These authors contributedstances may seem similar, both reflect the connection between user emotions or opinions and the content's subject, stances are often broader, influenced by multiple factors beyond mere sentiment. Stance detection is a crucial area within Natural Language Processing (NLP) and has been extensively studied. It involves determining the polarity of an audience or listener based on textual analysis, identifying whether their preference toward a subject is positive, negative, or neutral. Related concepts, such as stance classification, stance prediction, stance identification, debate stance classification, and debate-side classification, are integral to this research domain [8; 9; 10; 11; 12]. Stance detection has found applications across various fields, including the identification of fake news. Numerous researchers have focused on detecting fake news by examining the stance of texts, particularly when the stance is directed toward a specific topic or entity [13]. Fake News Challenge (FNC) involved the development of machine learning models that can predict stance labels given a headline and body of a news article. The stance label to be assigned could be one of the sets: 'agree', 'disagree', 'discuss,' or 'unrelated'. The provided dataset consists of pairs of a headline of a news article as a claim and a snippet of text related to the headline. Most of the teams participating in the FNC, including the winner team, used deep learning (DL) approaches to develop their learning and prediction models. Several papers discussed FNC and main submissions, such as [14; 15; 16; 17; 18; 19]. Previous research indicates that traditional text processing methods have been effective. The crucial insight appears to be in comparing features from the headline with those from the body of the text. This approach is intuitive, as the stance is often determined by how the body of the text relates to the headline. Recent developments in the identification of false news have demonstrated that hybrid models, which incorporate a variety of machine learning approaches, significantly improve detection accuracy. Studies show that by efficiently digesting both short and long news texts, pre-trained Transformer models such as BERT and XLNet achieve great accuracy in identifying false news [20]. Furthermore, zero-shot detection techniques have been made possible by integrating fact verification and false news identification, which helps to get beyond the drawbacks of limited datasets [21]. Likewise, detection accuracy has been further enhanced by multi-modal hybrid techniques that include text and picture analysis, particularly for news that contains satirical or distorted material [22].