Is it Fake? News Disinformation Detection on South African News Websites
원문 초록
Disinformation through fake news is an ongoing problem in our society and has become easily spread through social media. The most cost and time effective way to filter these large amounts of data is to use a combination of human and technical interventions to identify it. From a technical perspective, Natural Language Processing (NLP) is widely used in detecting fake news. Social media companies use NLP techniques to identify the fake news and warn their users, but fake news may still slip through undetected. It is especially a problem in more localised contexts (outside the United States of America). How do we adjust fake news detection systems to work better for local contexts such as in South Africa. In this work we investigate fake news detection on South African websites. We curate a dataset of South African fake news and then train detection models. We contrast this with using widely available fake news datasets (from mostly USA website). We also explore making the datasets more diverse by combining them and observe the differences in behaviour in writing between nations' fake news using interpretable machine learning.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
DCR: Quantifying Data Contamination in LLMs Evaluation
Leveraging Predictive Equivalence in Decision Trees
midr: Learning from Black-Box Models by Maximum Interpretation Decomposition
Synergizing LLMs with Global Label Propagation for Multimodal Fake News Detection
KGAlign: Joint Semantic-Structural Knowledge Encoding for Multimodal Fake News Detection
Manifold Learning with Normalizing Flows: Towards Regularity, Expressivity and Iso-Riemannian Geometry
토론
이 논문과 연결된 포럼 글이 아직 없습니다.