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Jan 2, 2021 · In this paper, we develop the CVD scheme to detect clickbait video. The scheme leverages on three components for learning three sets of latent features based on User Profiling, Video-Content, and Human Consensus that further be used to retrieve three sets of cognitive evidence, as an innovative idea for the detection of clickbait videos on YouTube.
- Deepika Varshney, Dinesh Kumar Vishwakarma
- 10.1007/s10489-020-02057-9
- 2021
- Appl Intell (Dordr). 2021; 51(7): 4214-4235.
Jul 26, 2021 · Clickbait Detection in YouTube Videos. YouTube videos often include captivating descriptions and intriguing thumbnails designed to increase the number of views, and thereby increase the revenue for the person who posted the video. This creates an incentive for people to post clickbait videos, in which the content might deviate significantly ...
- Ruchira Gothankar, Fabio Di Troia, Mark Stamp
- arXiv:2107.12791 [cs.LG]
- 2021
- LITERATURE REVIEW
- ALGORITHM 1: Clickbait Detection and Prevention Framework (CPDM)
- 4.3 Feature Set Mining
- EXPERIMENTAL RESULTS
Much of the previous work in clickbait YouTube detection has focused on detecting text-centric clickbaits in articles. However, with the emergence of visual-centric social media and video-streaming platforms like YouTube and Vimeo, newer forms of clickbaits in terms of images and videos surfaced. As a result, researchers redefined clickbaits and pr...
for each video in dataset, do extract video, audio, thumbnail, title and keyframes extract title text features using BERT-Base and thumbnail image features using Resnet-50 models perform feature set mining to extract video features, do generate a graph representation for the video to determine title-content disparity generate image caption for thum...
Six prominent feature sets were mined based on thumbnail, title, and video content using various deep learning techniques as follows: Content-Thumbnail Disparity: It uses a graph-based deep neural network to produce a vectorized representation of the disparity between the video's thumbnail and the actual content, i.e., the frames of the video. ...
In order to evaluate the performance of the proposed algorithm, we conducted experiments on a Bollywood-focused multilingual YouTube dataset (BollyBAIT) and a general YouTube clickbait-focused dataset, which is the MVD (Misleading Video Dataset). We analysed the performance of the CPDM framework on three aspects, namely classification, feature impo...
Aug 6, 2018 · The use of deceptive techniques in user-generated video portals is ubiquitous. Unscrupulous uploaders deliberately mislabel video descriptors aiming at increasing their views and subsequently their ad revenue. This problem, usually referred to as "clickbait," may severely undermine user experience. In this work, we study the clickbait problem on YouTube by collecting metadata for 206k videos ...
- Savvas Zannettou, Sotirios Chatzis, Kostantinos Papadamou, Michael Sirivianos
- 2018
Dec 16, 2021 · Unscrupulous content creators on YouTube employ deceptive techniques such as spam and clickbait to reach a broad audience and trick users into clicking on their videos to increase their advertisement revenue. Clickbait detection on YouTube requires an in depth examination and analysis of the intricate relationship between the video content and video descriptors title and thumbnail. However ...
- arXiv:2112.08611 [cs.SI]
- Social and Information Networks (cs.SI)
- 26 pages, 16 figures
Mar 23, 2018 · These techniques include: (i) use of eye-catching. thumbnails, such as depictions of abnormal stuff or attractive. adults, which are often irrelevant to video content; (ii) use of. headlines that ...
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Jan 1, 2023 · Contributing to this, we aim to detect YouTube clickbait videos by building several binary classification machine learning models trained on an open-sourced dataset of 31.987 English YouTube video titles from GitHubGist to differentiate between clickbait or non-clickbait YouTube titles.
