Related papers: MINT -- Mainstream and Independent News Text Corpu…
The MIND dataset is at the moment of writing the most extensive dataset available for the research and development of news recommender systems. This work analyzes the suitability of the dataset for research on diverse news recommendations.…
The spread of misinformation through online social media platforms has had substantial societal consequences. As a result, platforms have introduced measures to alert users of news content that may be misleading or contain inaccuracies as a…
Misinformation is considered a threat to our democratic values and principles. The spread of such content on social media polarizes society and undermines public discourse by distorting public perceptions and generating social unrest while…
The widespread dissemination of multimodal content on social media has made misinformation detection increasingly challenging, as misleading narratives often arise not only from textual or visual content alone, but also from semantic…
The proliferation of misinformation necessitates scalable, automated fact-checking solutions. Yet, current benchmarks often overlook multilingual and topical diversity. This paper introduces a novel, dynamically extensible data set that…
Claims are the central component of an argument. Detecting claims across different domains or data sets can often be challenging due to their varying conceptualization. We propose to alleviate this problem by fine tuning a language model…
Fine-grained financial sentiment analysis on news headlines is a challenging task requiring human-annotated datasets to achieve high performance. Limited studies have tried to address the sentiment extraction task in a setting where…
We propose MINT, a new Multilingual INTimacy analysis dataset covering 13,372 tweets in 10 languages including English, French, Spanish, Italian, Portuguese, Korean, Dutch, Chinese, Hindi, and Arabic. We benchmarked a list of popular…
We present MERIT, an inference-time modular framework for multimodal misinformation detection that decomposes verification into four specialized modules: visual forensics, cross-modal alignment, retrieval-augmented claim verification, and…
Informational bias is widely present in news articles. It refers to providing one-sided, selective or suggestive information of specific aspects of certain entity to guide a specific interpretation, thereby biasing the reader's opinion.…
This work consists of a study that addresses the context of false news in the reality of today's world. False news is a widely used expression currently. During the study, it was possible to identify problems generalized about this theme,…
As social media platforms are evolving from text-based forums into multi-modal environments, the nature of misinformation in social media is also transforming accordingly. Taking advantage of the fact that visual modalities such as images…
News headlines can be a good data source for detecting the news spreading barriers in news media, which may be useful in many real-world applications. In this paper, we utilize semantic knowledge through the inference-based model COMET and…
Identifying misinformation is increasingly being recognized as an important computational task with high potential social impact. Misinformation and fake contents are injected into almost every domain of news including politics, health,…
News sources undergo the process of selecting newsworthy information when covering a certain topic. The process inevitably exhibits selection biases, i.e. news sources' typical patterns of choosing what information to include in news…
Fact-checking remains a demanding and time-consuming task, still largely dependent on manual verification and unable to match the rapid spread of misinformation online. This is particularly important because debunking false information…
The spread of fake news, polarizing, politically biased, and harmful content on online platforms has been a serious concern. With large language models becoming a promising approach, however, no study has properly benchmarked their…
Texts like news, encyclopedias, and some social media strive for objectivity. Yet bias in the form of inappropriate subjectivity - introducing attitudes via framing, presupposing truth, and casting doubt - remains ubiquitous. This kind of…
Fake news detection is an important and challenging task for defending online information integrity. Existing state-of-the-art approaches typically extract news semantic clues, such as writing patterns that include emotional words,…
The topic of fake news has drawn attention both from the public and the academic communities. Such misinformation has the potential of affecting public opinion, providing an opportunity for malicious parties to manipulate the outcomes of…