Titlebook: Disinformation, Misinformation, and Fake News in Social Media; Emerging Research Ch Kai Shu,Suhang Wang,Huan Liu Book 2020 Springer Nature

Lipase

書(shū)目名稱Disinformation, Misinformation, and Fake News in Social Media影響因子(影響力)




書(shū)目名稱Disinformation, Misinformation, and Fake News in Social Media影響因子(影響力)學(xué)科排名




書(shū)目名稱Disinformation, Misinformation, and Fake News in Social Media網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Disinformation, Misinformation, and Fake News in Social Media網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Disinformation, Misinformation, and Fake News in Social Media被引頻次




書(shū)目名稱Disinformation, Misinformation, and Fake News in Social Media被引頻次學(xué)科排名




書(shū)目名稱Disinformation, Misinformation, and Fake News in Social Media年度引用




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書(shū)目名稱Disinformation, Misinformation, and Fake News in Social Media讀者反饋




書(shū)目名稱Disinformation, Misinformation, and Fake News in Social Media讀者反饋學(xué)科排名

paroxysm

Diatribe

https://doi.org/10.1007/978-3-030-42699-6Fake news; Social media mining; Social cyber security; misinformation and disinformation; community dete

Atmosphere

978-3-030-42701-6Springer Nature Switzerland AG 2020

行乞

Bots, Elections, and Social Media: A Brief Overview of bots around major political events, including elections in various countries. In this chapter, we summarize our findings of bot operations in the context of the 2016 and 2018 US Presidential and Midterm elections and the 2017 French Presidential election.

搬運(yùn)工

Kai Shu,Suhang Wang,Huan LiuCovers an interdisciplinary emerging research topic on disinformation and misinformation attracting increasing attention.Contains recent advanced social computing, data mining, and machine learning me

搬運(yùn)工

手段

https://doi.org/10.1007/978-0-230-00094-0e spread of disinformation and fake news can cause detrimental societal effects. Despite the recent progress in detecting disinformation and fake news, it is still non-trivial due to its complexity, diversity, multi-modality, and costs of fact-checking or annotation. The goal of this chapter is to p

idiopathic

https://doi.org/10.1007/978-0-230-00094-0itter users based on their role identities. We first collect a coarse-grained public figure dataset automatically, then manually label a more fine-grained identity dataset. We propose a hierarchical self-attention neural network for Twitter user role identity classification. Our experiments demonstr

bioavailability

Systems Collaboration and Integrationine social networks are used as a means to spread digital deception within local, national, and global communities which has led to a renewed focus on the means of detection and defense. The audience (i.e., social media users) form the first line of defense in this process and it is of utmost import