FNDNet – A deep convolutional neural network for fake news detection

TL;DR: A theoretical model of determinants influencing multimodal fake review generation using the theories of signaling, actor-network, motivation, and human–environment interaction hypothesis indicates that determinants influencing multimodal fake review generation are complex and interconnected.

TL;DR: This paper proposes a hybrid transfer learning model combining PhoBERT, TF-IDF, and CNN for Vietnamese fake news detection on social networks, achieving an outstanding AUC score of 0.9538 on the ReINTEL dataset with raw data.

TL;DR: This perspective describes a selection of COVID-19 fake news that originated in Peru and the government’s response to this information and believes that similar actions by other countries in collaboration with social media companies may offer a solution to the infodemic problem.

TL;DR: A hybrid methodology that combines natural language processing with qualitative content coding approaches is described to characterize conspiracy discourse related to 5G wireless technology and COVID-19 on Twitter (currently known as ‘X’).

TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.

TL;DR: In this paper, the Skip-gram model is used to learn high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships and improve both the quality of the vectors and the training speed.