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Access to word–sentiment associations is use- ful for many applications, including senti- ment analysis, stance detection, and linguistic analysis. However, manually assigning fine- grained sentiment association scores to words has …...

We describe the system submitted to the SemEval-2016 for detecting stance in tweets (Task 6, Subtask A). One of the main goals of stance detection is to automatically determine the …...

In this paper, we develop a convolutional neu- ral network for stance detection in tweets. Ac- cording to the official results, our system ranks 1st on subtask B (among 9 …...

Stance detection is the task of classifying the attitude Previous work has assumed that ei- ther the target is mentioned in the text or that training data for every target …...

AbstractWe pose the general task of user-factor adaptation – adapting supervised learning models to real-valued user factors inferred from a background of their language, reflecting the idea that a piece …...

AbstractThis paper describes our approach for SemEval-2017 Task 8. We aim at detecting the stance of tweets and determining the veracity of the given rumor. We utilize a convolutional neural …...

AbstractStance detection aims to assign a stance label (for or against) to a post toward a specific target. Recently, there is a growing interest in using neural models to detect …...

AbstractAutomated fact-checking based on machine learning is a promising approach to identify false information distributed on the web. In order to achieve satisfactory performance, machine learning methods require a large …...

AbstractStance detection in social media is a well-studied task in a variety of domains. Nevertheless, previous work has mostly focused on multiclass versions of the problem, where the labels are …...

AbstractStance detection aims to detect whether the opinion holder is in support of or against a given target. Recent works show improvements in stance detection by using either the attention …...

AbstractStance detection in twitter aims at mining user stances expressed in a tweet towards a single or multiple target entities. To tackle this problem, most of the prior studies have …...

AbstractOur goal is to evaluate the usefulness of unsupervised representation learning techniques for detecting stances of Fake News. Therefore we examine several pre-trained language models with respect to their performance …...

AbstractStance detection aims to determine the attitude of a given text with respect to a specific topic or claim. While stance detection has been fairly well researched in the last …...

AbstractSocial media sites like Twitter possess the potential to complement surveys that measure political opinions and, more specifically, political actors’ approval. However, new challenges related to the reliability and validity …...

AbstractWe present a new challenging news dataset that targets both stance detection (SD) and fine-grained evidence retrieval (ER). With its 3,291 expert-annotated articles, the dataset constitutes a high-quality benchmark for …...

AbstractTransformer models, trained and publicly released over the last couple of years, have proved effective in many NLP tasks. We wished to test their usefulness in particular on the stance …...

AbstractStance detection is an important component of understanding hidden influences in everyday life. Since there are thousands of potential topics to take a stance on, most with little to no …...

AbstractStance 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 …...

AbstractStance detection concerns the classification of a writer’s viewpoint towards a target. There are different task variants, e.g., stance of a tweet vs. a full article, or stance with respect …...

We introduce deInStance, a corpus of 1000 politicians’ answers in German (de) contain- ing sentences labeled with explicitly expressed and inferred stances - pro and con relations - by 3 …...

AbstractStance detection determines whether the author of a text is in favor of, against or neutral to a specific target and provides valuable insights into important events such as legalization …...

Stance detection determines whether the au- thor of a text is in favor of, against or neu- tral to a specific target and provides valuable insights into important events such …...

AbstractThe prevalence of the COVID-19 pandemic in day-to-day life has yielded large amounts of stance detection data on social media sites, as users turn to social media to share their …...

AbstractThe stance detection task aims at detecting the stance of a tweet or a text for a target. These targets can be named entities or free-form sentences (claims). Though the …...

AbstractThe stance detection task aims to classify the stance toward given documents and topics. Since the topics can be implicit in documents and unseen in training data for zero-shot settings, …...

AbstractStance detection aims to identify people’s standpoints expressed in the text towards a target, which can provide powerful information for various downstream tasks. Recent studies have proposed multi-task learning models …...

AbstractResearch in stance detection has so far focused on models which leverage purely textual input. In this paper, we investigate the integration of textual and financial signals for stance detection …...

AbstractStance detection aims to identify the attitude from an opinion towards a certain target. Despite the significant progress on this task, it is extremely time-consuming and budget-unfriendly to collect sufficient …...

AbstractStance detection is the task of automatically eliciting stance information towards a specific claim made by a primary author. While most studies have been done for high-resource languages, this work …...

AbstractStance detection determines whether the author of a piece of text is in favor of, against, or neutral towards a specified target, and can be used to gain valuable insights …...

AbstractStance detection deals with identifying an author’s stance towards a target. Most existing stance detection models are limited because they do not consider relevant contextual information which allows for inferring …...

AbstractThe task of Stance Detection is concerned with identifying the attitudes expressed by an author towards a target of interest. This task spans a variety of domains ranging from social …...