Titlebook: Data Science for Fake News; Surveys and Perspect Deepak P,Tanmoy Chakraborty,Santhosh Kumar G Book 2021 Springer Nature Switzerland AG 2021

心胸狭窄

Dynamics of Fake News Diffusionffusion and addressing the challenges involved. We then model information cascade in various ways such as a diffusion tree. We then present a series of traditional and recent approaches which attempt to model the spread of fake news on social media.

烧烤

短程旅游

Intractable

Mammal

舰旗

兽群

Multi-modal Fake News Detectionuman cognition tends to consume news more when it is visually depicted through multimedia content than just plain text. Fake news spreaders leverage this cognitive state to prepare false information in such a way that it looks attractive in the first place. Therefore, multi-modal representation of f

needle

Deep Learning for Fake News Detectionitigate its use are essential because of their potential to influence the information ecosystem. A vast amount of work using deep learning techniques paved a way to understand the anatomy of fake news and its spread through social media. This chapter attempts to take stock of such efforts and look b

DAUNT

Dynamics of Fake News Diffusionall spread of news contents through network links such as followers, friends, etc. Those fake stories which gain quick visibility are deployed on social media in a strategic way in order to create maximum impact. In this context, the selection of initiators, the time of deployment, the estimation of

表皮

Neural Language Models for (Fake?) News Generation generation and many downstream applications of NLP. Deep learning models with multitudes of parameters have achieved remarkable progress in machine-generated news items indistinguishable from human experts’ articles. Though the developed techniques are for authentic text generation and entertainmen