Stance detection is the task of inferring viewpoint towards a given topic or entity either being supportive or opposing. One may express a viewpoint towards a topic by using positive or negative language. This paper examines how the stance is being expressed in social media according to the sentiment polarity. There has been a noticeable misconception of the similarity between the stance and sentiment when it comes to viewpoint discovery, where negative sentiment is assumed to mean against stance, and positive sentiment means in-favour stance. To analyze the relation between stance and sentiment, we construct a new dataset with four topics and examine how people express their viewpoint with regards these topics. We validate our results by carrying a further analysis of the popular stance benchmark SemEval stance dataset. Our analyses reveal that sentiment and stance are not highly aligned, and hence the simple sentiment polarity cannot be used solely to denote a stance toward a given topic.
University of Houston（休斯顿大学）；Temple University（天普大学）
We investigate whether pre-trained bidirectional transformers with sentiment and emotion information improve stance detection in long discussions of contemporary issues. As a part of this work, we create a novel stance detection dataset covering 419 different controversial issues and their related pros and cons collected by procon.org in nonpartisan format. Experimental results show that a shallow recurrent neural network with sentiment or emotion information can reach competitive results compared to fine-tuned BERT with 20x fewer parameters. We also use a simple approach that explains which i…
University of Michigan（密歇根大学）；Northeastern University（东北大学）
Stance detection is typically framed as predicting the sentiment in a given text towards a target entity. However, this setup overlooks the importance of the source entity, i.e., who is expressing the opinion. In this paper, we emphasize the need for studying interactions among entities when inferring stances. We first introduce a new task, entity-to-entity (E2E) stance detection, which primes models to identify entities in their canonical names and discern stances jointly. To support this study, we curate a new dataset with 10,619 annotations labeled at the sentence-level from news articles o…
StanceFlip: A Comprehensive Multi-Dimensional Benchmark for Multimodal Conversational Stance Flipping Forecasting
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件学院）；Harbin Institute of Technology Shenzhen（哈尔滨工业大学（深圳））；City University of Macau（澳门城市大学）
MT2-CSD: A New Dataset and Multi-Semantic Knowledge Fusion Method for Conversational Stance Detection
College of Big Data and Internet, Shenzhen Technology University（大数据与互联网学院，深圳技术大学）；University of Washington（华盛顿大学）；University of Science and Technology of China（中国科学技术大学）
Read Between the Lines: A Benchmark for Uncovering Political Bias in Bangla News Articles
University of Dhaka（达卡大学）；University of Maryland, Baltimore County（马里兰大学巴尔的摩县分校）；Cisco Systems（思科系统）；BanglaLLM；Maharishi International University（玛哈里希国际大学）；Unityflow AI
Social media empowers citizens to raise the voice and expressed civil outrage leads to collective action to change the society. Since social media welcomes anyone regardless of the political ideology or perspectives, social media is where the supporters and opponents of specific issue discuss. This study attempts to empirically examine a recent anti-racism movement initiated by the death of George Floyd with the lens of stance prediction and aspect-based sentiment analysis (ABSA). First, this study found the stance of the tweet and users do change over the course of the protest. Furthermore, t…
IBM Research（IBM研究院）；NSIT（Netaji Subhas Institute of Technology）；IIT（印度理工学院）
The topical stance detection problem addresses detecting the stance of the text content with respect to a given topic: whether the sentiment of the given text content is in FAVOR of (positive), is AGAINST (negative), or is NONE (neutral) towards the given topic. Using the concept of attention, we develop a two-phase solution. In the first phase, we classify subjectivity - whether a given tweet is neutral or subjective with respect to the given topic. In the second phase, we classify sentiment of the subjective tweets (ignoring the neutral tweets) - whether a given subjective tweet has a FAVOR …
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）
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…
Radboud University（拉德堡德大学）；Dutch National Institute for Public Health and Environment（荷兰国家公共卫生与环境研究所）
We developed a system to automatically classify stance towards vaccination in Twitter messages, with a focus on messages with a negative stance. Such a system makes it possible to monitor the ongoing stream of messages on social media, offering actionable insights into public hesitance with respect to vaccination. For Dutch Twitter messages that mention vaccination-related key terms, we annotated their stance and feeling in relation to vaccination (provided that they referred to this topic). Subsequently, we used these coded data to train and test different machine learning set-ups. With the a…
Stance detection, which aims to determine whether an individual is for or against a target concept, promises to uncover public opinion from large streams of social media data. Yet even human annotation of social media content does not always capture "stance" as measured by public opinion polls. We demonstrate this by directly comparing an individual's self-reported stance to the stance inferred from their social media data. Leveraging a longitudinal public opinion survey with respondent Twitter handles, we conducted this comparison for 1,129 individuals across four salient targets. We find tha…
There is great interest in the dynamics of health behaviors in social networks and how they affect collective public health outcomes, but measuring population health behaviors over time and space requires substantial resources. Here, we use publicly available data from 101,853 users of online social media collected over a time period of almost six months to measure the spatio-temporal sentiment towards a new vaccine. We validated our approach by identifying a strong correlation between sentiments expressed online and CDC- estimated vaccination rates by region. Analysis of the network of opinio…
Technion – Israel Institute of Technology（以色列理工学院）；Stanford University（斯坦福大学）；Ghent University（根特大学）
The increasing size and complexity of modern ML systems has improved their predictive capabilities but made their behavior harder to explain. Many techniques for model explanation have been developed in response, but we lack clear criteria for assessing these techniques. In this paper, we cast model explanation as the causal inference problem of estimating causal effects of real-world concepts on the output behavior of ML models given actual input data. We introduce CEBaB, a new benchmark dataset for assessing concept-based explanation methods in Natural Language Processing (NLP). CEBaB consis…
Social media provides many opportunities to monitor and evaluate political phenomena such as referendums and elections. In this study, we propose a set of approaches to analyze long-running political events on social media with a real-world experiment: the debate about Brexit, i.e., the process through which the United Kingdom activated the option of leaving the European Union. We address the following research questions: Could Twitter-based stance classification be used to demonstrate public stance with respect to political events? What is the most efficient and comprehensive approach to meas…
Regulation of tobacco products is rapidly evolving. Understanding public sentiment in response to changes is very important as authorities assess how to effectively protect population health. Social media systems are widely recognized to be useful for collecting data about human preferences and perceptions. However, how social media data may be used, in rapid policy change settings, given challenges of narrow time periods and specific locations and non-representative the population using social media is an open question. In this paper we apply quasi-experimental designs, which have been used p…
Amity University Kolkata（阿米提大学加尔各答分校）；IBM India Private Limited（IBM印度私人有限公司）；Jalpaiguri Government Engineering College（贾尔派古里政府工程学院）；Netaji Subhas Open University（内塔吉·苏巴斯开放大学）
Social media platforms are thriving nowadays, so a huge volume of data is produced. As it includes brief and clear statements, millions of people post their thoughts on microblogging sites every day. This paper represents and analyze the capacity of diverse strategies to volumetric, delicate, and social networks to predict critical opinions from online social networking sites. In the exploration of certain searching for relevant, the thoughts of people play a crucial role. Social media becomes a good outlet since the last decades to share the opinions globally. Sentiment analysis as well as op…
University of New Mexico（新墨西哥大学）；International Computer Science Institute (ICSI)（国际计算机科学研究所（ICSI））；University of Hong Kong（香港大学）
Widespread Chinese social media applications such as Weibo are widely known for monitoring and deleting posts to conform to Chinese government requirements. In this paper, we focus on analyzing a dataset of censored and uncensored posts in Weibo. Despite previous work that only considers text content of posts, we take a multi-modal approach that takes into account both text and image content. We categorize this dataset into 14 categories that have the potential to be censored on Weibo, and seek to quantify censorship by topic. Specifically, we investigate how different factors interact to affe…
