Online forums and social media platforms are increasingly being used to discuss topics of varying polarities where different people take different stances. Several methodologies for automatic stance detection from text have been proposed in literature. To our knowledge, there has not been any systematic investigation towards their reproducibility, and their comparative performances. In this work, we explore the reproducibility of several existing stance detection models, including both neural models and classical classifier-based models. Through experiments on two datasets -- (i)~the popular SemEval microblog dataset, and (ii)~a set of health-related online news articles -- we also perform a detailed comparative analysis of various methods and explore their shortcomings. Implementations of all algorithms discussed in this paper are available at https://github.com/prajwal1210/Stance-Detection-in-Web-and-Social-Media.
L3S Research Center, Leibniz University of Hannover（莱布尼茨汉诺威大学L3S研究中心）；Institute of Computer Science, FORTH-ICS（希腊研究与技术基金会计算机科学研究所）；Dept. of CSE, IIT Patna（印度理工学院巴特那分校计算机科学与工程系）；Chongqing University of Technology（重庆理工大学）；GESIS - Leibniz Institute for the Social Sciences, Cologne & Heinrich-Heine-University Düsseldorf（科隆莱布尼茨社会科学研究所暨杜塞尔多夫海因里希·海涅大学）
Fact checking is an essential challenge when combating fake news. Identifying documents that agree or disagree with a particular statement (claim) is a core task in this process. In this context, stance detection aims at identifying the position (stance) of a document towards a claim. Most approaches address this task through a 4-class classification model where the class distribution is highly imbalanced. Therefore, they are particularly ineffective in detecting the minority classes (for instance, 'disagree'), even though such instances are crucial for tasks such as fact-checking by providing…
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）；Microsoft Research（微软研究院）
Controversial claims are abundant in online media and discussion forums. A better understanding of such claims requires analyzing them from different perspectives. Stance classification is a necessary step for inferring these perspectives in terms of supporting or opposing the claim. In this work, we present a neural network model for stance classification leveraging BERT representations and augmenting them with a novel consistency constraint. Experiments on the Perspectrum dataset, consisting of claims and users' perspectives from various debate websites, demonstrate the effectiveness of our …
Qatar Computing Research Institute, HBKU（卡塔尔计算研究所（哈马德·本·哈利法大学））；Department of Computer Science, Columbia University（哥伦比亚大学计算机科学系）；Georgia Institute of Technology（佐治亚理工学院）；The State University of New York at Buffalo（纽约州立大学布法罗分校）；The University of British Columbia（英属哥伦比亚大学）
With the continuing spread of misinformation and disinformation online, it is of increasing importance to develop combating mechanisms at scale in the form of automated systems that support multiple languages. One task of interest is claim veracity prediction, which can be addressed using stance detection with respect to relevant documents retrieved online. To this end, we present our new Arabic Stance Detection dataset (AraStance) of 4,063 claim--article pairs from a diverse set of sources comprising three fact-checking websites and one news website. AraStance covers false and true claims fro…
MIT Computer Science and Artificial Intelligence Laboratory（麻省理工学院计算机科学与人工智能实验室）；Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）；Amazon（亚马逊）
A reasonable approach for fact checking a claim involves retrieving potentially relevant documents from different sources (e.g., news websites, social media, etc.), determining the stance of each document with respect to the claim, and finally making a prediction about the claim's factuality by aggregating the strength of the stances, while taking the reliability of the source into account. Moreover, a fact checking system should be able to explain its decision by providing relevant extracts (rationales) from the documents. Yet, this setup is not directly supported by existing datasets, which …
To what extent user's stance towards a given topic could be inferred? Most of the studies on stance detection have focused on analysing user's posts on a given topic to predict the stance. However, the stance in social media can be inferred from a mixture of signals that might reflect user's beliefs including posts and online interactions. This paper examines various online features of users to detect their stance towards different topics. We compare multiple set of features, including on-topic content, network interactions, user's preferences, and online network connections. Our objective is …
Stance detection on social media is an emerging opinion mining paradigm for various social and political applications in which sentiment analysis may be sub-optimal. There has been a growing research interest for developing effective methods for stance detection methods varying among multiple communities including natural language processing, web science, and social computing. This paper surveys the work on stance detection within those communities and situates its usage within current opinion mining techniques in social media. It presents an exhaustive review of stance detection techniques on…
Social media polarization during conflict: Insights from an ideological stance dataset on Israel-Palestine Reddit comments
Department of Business and Technology Management, Islamic University of Technology, Gazipur, Dhaka, Bangladesh（伊斯兰科技大学商学院与技术管理系，加兹ipur，达卡，孟加拉国）；Department of Computer Science and Engineering, Islamic University of Technology, Gazipur, Dhaka, Bangladesh（伊斯兰科技大学计算机科学与工程系，加兹ipur，达卡，孟加拉国）；Department of Electrical and Electronic Engineering, University of Dhaka, Nilkhet Road, Dhaka, Bangladesh（达卡大学电子与电气工程系，Nilkhet Road，达卡，孟加拉国）；Department of Computer Science, American International University–Bangladesh, Kuratoli, Dhaka, Bangladesh（孟加拉国美国国际大学计算机科学系，Kuratoli，达卡，孟加拉国）
Increased usage of social media caused the popularity of news and events which are not even verified, resulting in spread of rumors allover the web. Due to widely available social media platforms and increased usage caused the data to be available in huge amounts.The manual methods to process such large data is costly and time-taking, so there has been an increased attention to process and verify such content automatically for the presence of rumors. A lot of research studies reveal that to identify the stances of posts in the discussion thread of such events and news is an important preceding…
A Survey of Stance Detection on Social Media: New Directions and Perspectives
College of Big Data and Internet, Shenzhen Technology University（深圳技术大学大数据与互联网学院）；College of Computer Science and Software, Shenzhen University（深圳大学计算机与软件学院）；Hong Kong University of Science and Technology（香港科技大学）；School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络空间安全学院）；Peking University Shenzhen Graduate School（北京大学深圳研究生院）
Opaque Epistemic Mediation: How LLM Deployment Configurations Shape the Validation of Pseudo-Science
NOVA School of Science and Technology - Universidade NOVA de Lisboa（新里斯本大学新文理学院）；CIUHCT - Interuniversity Centre for the History of Science and Technology（校际科学技术史研究中心（CIUHCT））；MARE - Marine and Environmental Sciences Centre（海洋与环境科学中心（MARE））；ARNET - Aquatic Research Network Associate Laboratory（水生研究网络联合实验室（ARNET））
In recent years, misinformation on the Web has become increasingly rampant. The research community has responded by proposing systems and challenges, which are beginning to be useful for (various subtasks of) detecting misinformation. However, most proposed systems are based on deep learning techniques which are fine-tuned to specific domains, are difficult to interpret and produce results which are not machine readable. This limits their applicability and adoption as they can only be used by a select expert audience in very specific settings. In this paper we propose an architecture based on …
