In this work, we present NewsClaims, a new benchmark for attribute-aware claim detection in the news domain.
In this paper, we propose the task of logical fallacy detection, and provide a new dataset (Logic) of logical fallacies generally found in text, together with an additional challenge set for detecting logical fallacies in climate change claims (LogicClimate).
Although there were already several studies related to the detection of misinformation in social media data, most studies focused on the English dataset.
However, early misinformation often demonstrates both conditional and label shifts against existing misinformation data (e. g., class imbalance in COVID-19 datasets), rendering such methods less effective for detecting early misinformation.
With recent advancements in diffusion models, users can generate high-quality images by writing text prompts in natural language.
With information consumption via online video streaming becoming increasingly popular, misinformation video poses a new threat to the health of the online information ecosystem.
Multimedia content has become ubiquitous on social media platforms, leading to the rise of multimodal misinformation (MM) and the urgent need for effective strategies to detect and prevent its spread.
Multimodal misinformation on online social platforms is becoming a critical concern due to increasing credibility and easier dissemination brought by multimedia content, compared to traditional text-only information.
We find that between January 1, 2022, and May 1, 2023, the relative number of synthetic news articles increased by 57. 3% on mainstream websites while increasing by 474% on misinformation sites.
Despite this, most previous studies have been predominantly geared towards creating detectors that differentiate between purely ChatGPT-generated texts and human-authored texts.