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ZENODO
Software . 2019
License: CC BY
Data sources: Datacite
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ZENODO
Software . 2019
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Software . 2019
License: CC BY
Data sources: Datacite
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Software for: "Disturbed YouTube For Kids: Characterizing And Detecting Inappropriate Videos Targeting Young Children"

Authors: Papadamou, Kostantinos; Papasavva, Antonis; Zannettou, Savvas; Blackburn, Jeremy; Kourtellis, Nicolas; Leontiadis, Ilias; Stringhini, Gianluca; +1 Authors

Software for: "Disturbed YouTube For Kids: Characterizing And Detecting Inappropriate Videos Targeting Young Children"

Abstract

In this repository we include a package with the latest version of the deep learning model implemented in this work which can be used by anyone who wants to detect inappropriate videos for kids on YouTube. (see https://ojs.aaai.org/index.php/ICWSM/article/view/7320/7174 for the detailed description on the results). Codebase also available on Github. Please appropriately cite the "Disturbed YouTube For Kids: Characterizing And Detecting Inappropriate Videos Targeting Young Children" paper in any publication, of any form and kind, using this software: @inproceedings{papadamou2020disturbedyoutube, title= {{Disturbed YouTube for Kids: Characterizing and Detecting Inappropriate Videos Targeting Young Children}}, author={Papadamou, Kostantinos and Papasavva, Antonis and Zannettou, Savvas and Blackburn, Jeremy and Kourtellis, Nicolas and Leontiadis, Ilias and Stringhini, Gianluca and Sirivianos, Michael}, booktitle={14th International AAAI Conference on Web and Social Media}, year={2020}, organization={AAAI} }

Acknowledgments: This project has received funding from the European Union's Horizon 2020 Research and Innovation program under the Marie Skłodowska-Curie ENCASE project (Grant Agreement No. 691025) and from the National Science Foundation under grant CNS-1942610.

Keywords

ElsaGate, Deep Learning, YouTube, Disturbing Videos Classifier, YouTube Videos Detection, Toddler-oriented Disturbing Videos

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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