Fake News Detection using Stance Classification: A Survey
원문 초록
This paper surveys and presents recent academic work carried out within the field of stance classification and fake news detection. Echo chambers and the model organism problem are examples that pose challenges to acquire data with high quality, due to opinions being polarised in microblogs. Nevertheless it is shown that several machine learning approaches achieve promising results in classifying stance. Some use crowd stance for fake news detection, such as the approach in [Dungs et al., 2018] using Hidden Markov Models. Furthermore feature engineering have significant importance in several approaches, which is shown in [Aker et al., 2017]. This paper additionally includes a proposal of a system implementation based on the presented survey.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Orthrus: A Bimodal Learning Architecture for Malware Classification
Generative Adversarial Minority Oversampling for Spectral-Spatial Hyperspectral Image Classification
Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification
Image and Text fusion for UPMC Food-101 \\using BERT and CNNs
Kidney Level Lupus Nephritis Classification using Uncertainty Guided Bayesian Convolutional Neural Networks
Beyond the Attention: Distinguish the Discriminative and Confusable Features For Fine-grained Image Classification
토론
이 논문과 연결된 포럼 글이 아직 없습니다.