Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText.
Kick-your-searchterm to multiple Engines kick-your-query now !>
A dictionary by aggregated review articles of nephrology, medicine and the life sciences
Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

suck abstract from ncbi


10.1007/s10489-020-02057-9

http://scihub22266oqcxt.onion/10.1007/s10489-020-02057-9
suck pdf from google scholar
C7778503AunifiedapproachfordetectionofClickbaitvideosonYouTubeusingcognitiveevidences.!7778503!34764575
unlimited free pdf from europmc34764575    free
PDF from PMC    free
html from PMC    free

suck abstract from ncbi


Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534

Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534

Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534

Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534
pmid34764575      Appl+Intell+(Dordr) 2021 ; 51 (7): 4214-35
Nephropedia Template TP

gab.com Text

Twit Text FOAVip

Twit Text #

English Wikipedia


  • A unified approach for detection of Clickbait videos on YouTube using cognitive evidences #MMPMID34764575
  • Varshney D; Vishwakarma DK
  • Appl Intell (Dordr) 2021[]; 51 (7): 4214-35 PMID34764575show ga
  • Clickbait is one of the form of false content, purposely designed to attract the user?s attention and make them curious to follow the link and read, view, or listen to the attached content. The teaser aim behind this is to exploit the curiosity gap by giving information within the short statement. Still, the given statement is not sufficient enough to satisfy the curiosity without clicking through the linked content and lure the user to get into the respective page via playing with human psychology and degrades the user experience. To counter this problem, we develop a Clickbait Video Detector (CVD) scheme. The scheme leverages to learn three sets of latent features based on User Profiling, Video-Content, and Human Consensus, these are further used to retrieve cognitive evidence for the detection of clickbait videos on YouTube. The first step is to extract audio from the videos, which is further transformed to textual data, and later on, it is utilized for the extraction of video content-based features. Secondly, the comments are analyzed, and features are extracted based on human responses/reactions over the posted content. Lastly, user profile based features are extracted. Finally, all these features are fed into the classifier. The proposed method is tested on the publicly available fake video corpus [FVC], [FVC-2018] dataset, and a self-generated misleading video dataset [MVD]. The achieved result is compared with other state-of-the-art methods and demonstrates superior performance.
  • ä


  • DeepDyve
  • Pubget Overpricing
  • suck abstract from ncbi

    Linkout box