相关论文: Stance Prediction for Russian: Data and Analysis

Toxicity has become a grave problem for many online communities and has been growing across many languages, including Russian. Hate speech creates an environment of intimidation, discrimination, and may even incite some real-world violence.…

This work explores the application of textual entailment in news claim verification and stance prediction using a new corpus in Arabic. The publicly available corpus comes in two perspectives: a version consisting of 4,547 true and false…

Stance detection has been widely studied as the task of determining if a social media post is positive, negative or neutral towards a specific issue, such as support towards vaccines. Research in stance detection has however often been…

Argumentation analysis is a field of computational linguistics that studies methods for extracting arguments from texts and the relationships between them, as well as building argumentation structure of texts. This paper is a report of the…

With the rise of social media, rumor detection has drawn increasing attention. Although numerous methods have been proposed with the development of rumor classification datasets, they focus on identifying whether a post is a rumor, lacking…

Social media enables data-driven analysis of public opinion on contested issues. Target-Stance Extraction (TSE) is the task of identifying the target discussed in a document and the document's stance towards that target. Many works classify…

To advance argumentative stance prediction as a multimodal problem, the First Shared Task in Multimodal Argument Mining hosted stance prediction in crucial social topics of gun control and abortion. Our exploratory study attempts to…

Fake news can significantly misinform people who often rely on online sources and social media for their information. Current research on fake news detection has mostly focused on analyzing fake news content and how it propagates on a…

In the paper, different approaches for the analysis of news trends on Twitter has been considered. For the analysis and case study, informational trends on Twitter caused by Russian invasion of Ukraine in 2022 year have been studied. A deep…

We present a new task, suspicious news detection using micro blog text. This task aims to support human experts to detect suspicious news articles to be verified, which is costly but a crucial step before verifying the truthfulness of the…

The article presents a study of some characteristics of post and comment publishing in the Russian segment of Facebook. A number of non-trivial results has been obtained. For example, a significant anomaly has been detected in the number of…

Factual claims and misinformation circulate widely on social media and affect how people form opinions and make decisions. This paper presents a truthfulness stance map (TrustMap), an application that identifies and maps public stances…

Mis- and disinformation, commonly collectively called fake news, continue to menace society. Perhaps, the impact of this age-old problem is presently most plain in politics and healthcare. However, fake news is affecting an increasing…

This work introduces UstanceBR, a multimodal corpus in the Brazilian Portuguese Twitter domain for target-based stance prediction. The corpus comprises 86.8 k labelled stances towards selected target topics, and extensive network…

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…

Social media communications are becoming increasingly prevalent; some useful, some false, whether unwittingly or maliciously. An increasing number of rumours daily flood the social networks. Determining their veracity in an autonomous way…

Current text classification approaches usually focus on the content to be classified. Contextual aspects (both linguistic and extra-linguistic) are usually neglected, even in tasks based on online discussions. Still in many cases the…

The paper describes the open Russian medical language understanding benchmark covering several task types (classification, question answering, natural language inference, named entity recognition) on a number of novel text sets. Given the…

Most research on natural language processing treats bias as an absolute concept: Based on a (probably complex) algorithmic analysis, a sentence, an article, or a text is classified as biased or not. Given the fact that for humans the…

Internet is one of the important inventions and a large number of persons are its users. These persons use this for different purposes. There are different social media platforms that are accessible to these users. Any user can make a post…