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  • 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/
    Authors: Martin Radenz; Johannes Bühl;

    Identify coherent cloud objects in the Cloudnet classification and extract characteristic properties from layered mixed-phase clouds. The structure of the folders is as following: sniffer_code the cloud sniffer is originally based on Bühl [2016 ACP] adapted for the new datasets, additinal measurements, new server and especially larda3. cloud_properties .dat files produced by python3 cc_sniffer_ac.py --campaign lacros_dacapo --date 20181128 for a single day. Contains all the features (range chunks of profiles) connected into clouds. cloud_collections Statistics of the single cloud features for the full campaign in a .csv table. Created by python3 cc_collector_ac.py --campaign lacros_dacapo --date 20181128 The cloud collections for the LACROS campaigns at Leipzig, Limassol and Punta Arenas are included in the zenodo repository, but not in the github repository. To obtain those git clone this repository and then get the .csv files from the zenodo repository. analysis_code Collection of ipython notebooks to generate the analysis and the plots used in the recent publication. References Bühl, J., Seifert, P., Myagkov, A., and Ansmann, A.: Measuring ice- and liquid-water properties in mixed-phase cloud layers at the Leipzig Cloudnet station, Atmos. Chem. Phys., 16, 1060-10620, https://doi.org/10.5194/acp-16-10609-2016, 2016. Radenz, M., Bühl, J., Seifert, P., Baars, H., Engelmann, R., Barja González, B., Mamouri, R.-E., Zamorano, F., and Ansmann, A.: Hemispheric contrasts in ice formation in stratiform mixed-phase clouds: Disentangling the role of aerosol and dynamics with ground-based remote sensing, Atmos. Chem. Phys. Discuss. [accepted for publication], https://doi.org/10.5194/acp-2021-360, 2021.

    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/ ZENODOarrow_drop_down
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    ZENODO
    Software . 2021
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    ZENODO
    Software . 2021
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    ZENODO
    Software . 2021
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      ZENODO
      Software . 2021
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      Software . 2021
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  • 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/
    Authors: Papadamou, Kostantinos; Zannettou, Savvas; Blackburn, Jeremy; De Cristofaro, Emiliano; +2 Authors

    Abstract: The role played by YouTube's recommendation algorithm in unwittingly promoting misinformation and conspiracy theories is not entirely understood. Yet, this can have dire real-world consequences, especially when pseudoscientific content is promoted to users at critical times, such as the COVID-19 pandemic. In this paper, we set out to characterize and detect pseudoscientific misinformation on YouTube. We collect 6.6K videos related to COVID-19, the Flat Earth theory, as well as the anti-vaccination and anti-mask movements. Using crowdsourcing, we annotate them as pseudoscience, legitimate science, or irrelevant and train a deep learning classifier to detect pseudoscientific videos with an accuracy of 0.79. We quantify user exposure to this content on various parts of the platform and how this exposure changes based on the user's watch history. We find that YouTube suggests more pseudoscientific content regarding traditional pseudoscientific topics (e.g., flat earth, anti-vaccination) than for emerging ones (like COVID-19). At the same time, these recommendations are more common on the search results page than on a user's homepage or in the recommendation section when actively watching videos. Finally, we shed light on how a user's watch history substantially affects the type of recommended videos. What do we offer in this software? We make publicly available to the research community, as well as the open-source community, the following tools, and libraries: The codebase of a Deep Learning Classifier for pseudoscientific videos detection on YouTube, and examples on how to train and test it; A library that simplifies the usage of the trained classifier and implements all the required tasks for the classification of YouTube videos; An open-source library that provides a unified framework for assessing the effects of personalization on YouTube video recommendations in multiple parts of the platform: a) the homepage; b) the search results page; and c) the video recommendations section (recommendations when watching videos). The codebase is also available on GitHub. If you make use of any modules available in this codebase in your work, please cite the following paper: @article{papadamou2020just, title={"It is just a flu": Assessing the Effect of Watch History on YouTube's Pseudoscientific Video Recommendations}, author={Papadamou, Kostantinos and Zannettou, Savvas and Blackburn, Jeremy and De Cristofaro, Emiliano and Stringhini, Gianluca and Sirivianos, Michael}, journal={arXiv preprint arXiv:2010.11638}, year={2020} } Acknowledgments: This project has received funding from the European Union's Horizon 2020 Research and Innovation program under the CONCORDIA project (Grant Agreement No. 830927), and from the Innovation and Networks Executive Agency (INEA) under the CYberSafety II project (Grant Agreement No. 1614254). This work reflects only the authors' views; the funding agencies are not responsible for any use that may be made of the information it contains.

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    ZENODO
    Software . 2021
    License: CC BY
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    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 . 2021
    License: CC BY
    Data sources: Datacite
    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 . 2021
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      ZENODO
      Software . 2021
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      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 . 2021
      License: CC BY
      Data sources: Datacite
      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
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  • 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/
    Authors: Papadamou, Kostantinos; Papasavva, Antonis; Zannettou, Savvas; Blackburn, Jeremy; +4 Authors

    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.

    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/ ZENODOarrow_drop_down
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    ZENODO
    Software . 2019
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    Data sources: Datacite
    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: ZENODO
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      ZENODO
      Software . 2019
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      Software . 2019
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  • 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/
    Authors: Martin Radenz; Johannes Bühl;

    Identify coherent cloud objects in the Cloudnet classification and extract characteristic properties from layered mixed-phase clouds. The structure of the folders is as following: sniffer_code the cloud sniffer is originally based on Bühl [2016 ACP] adapted for the new datasets, additinal measurements, new server and especially larda3. cloud_properties .dat files produced by python3 cc_sniffer_ac.py --campaign lacros_dacapo --date 20181128 for a single day. Contains all the features (range chunks of profiles) connected into clouds. cloud_collections Statistics of the single cloud features for the full campaign in a .csv table. Created by python3 cc_collector_ac.py --campaign lacros_dacapo --date 20181128 The cloud collections for the LACROS campaigns at Leipzig, Limassol and Punta Arenas are included in the zenodo repository, but not in the github repository. To obtain those git clone this repository and then get the .csv files from the zenodo repository. analysis_code Collection of ipython notebooks to generate the analysis and the plots used in the recent publication. References Bühl, J., Seifert, P., Myagkov, A., and Ansmann, A.: Measuring ice- and liquid-water properties in mixed-phase cloud layers at the Leipzig Cloudnet station, Atmos. Chem. Phys., 16, 1060-10620, https://doi.org/10.5194/acp-16-10609-2016, 2016. Radenz, M., Bühl, J., Seifert, P., Baars, H., Engelmann, R., Barja González, B., Mamouri, R.-E., Zamorano, F., and Ansmann, A.: Hemispheric contrasts in ice formation in stratiform mixed-phase clouds: Disentangling the role of aerosol and dynamics with ground-based remote sensing, Atmos. Chem. Phys. Discuss. [accepted for publication], https://doi.org/10.5194/acp-2021-360, 2021.

    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/ ZENODOarrow_drop_down
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    ZENODO
    Software . 2021
    Data sources: Datacite
    ZENODO
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    Software . 2021
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      ZENODO
      Software . 2021
      Data sources: Datacite
      ZENODO
      Software . 2021
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      ZENODO
      Software . 2021
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  • 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/
    Authors: Papadamou, Kostantinos; Zannettou, Savvas; Blackburn, Jeremy; De Cristofaro, Emiliano; +2 Authors

    Abstract: The role played by YouTube's recommendation algorithm in unwittingly promoting misinformation and conspiracy theories is not entirely understood. Yet, this can have dire real-world consequences, especially when pseudoscientific content is promoted to users at critical times, such as the COVID-19 pandemic. In this paper, we set out to characterize and detect pseudoscientific misinformation on YouTube. We collect 6.6K videos related to COVID-19, the Flat Earth theory, as well as the anti-vaccination and anti-mask movements. Using crowdsourcing, we annotate them as pseudoscience, legitimate science, or irrelevant and train a deep learning classifier to detect pseudoscientific videos with an accuracy of 0.79. We quantify user exposure to this content on various parts of the platform and how this exposure changes based on the user's watch history. We find that YouTube suggests more pseudoscientific content regarding traditional pseudoscientific topics (e.g., flat earth, anti-vaccination) than for emerging ones (like COVID-19). At the same time, these recommendations are more common on the search results page than on a user's homepage or in the recommendation section when actively watching videos. Finally, we shed light on how a user's watch history substantially affects the type of recommended videos. What do we offer in this software? We make publicly available to the research community, as well as the open-source community, the following tools, and libraries: The codebase of a Deep Learning Classifier for pseudoscientific videos detection on YouTube, and examples on how to train and test it; A library that simplifies the usage of the trained classifier and implements all the required tasks for the classification of YouTube videos; An open-source library that provides a unified framework for assessing the effects of personalization on YouTube video recommendations in multiple parts of the platform: a) the homepage; b) the search results page; and c) the video recommendations section (recommendations when watching videos). The codebase is also available on GitHub. If you make use of any modules available in this codebase in your work, please cite the following paper: @article{papadamou2020just, title={"It is just a flu": Assessing the Effect of Watch History on YouTube's Pseudoscientific Video Recommendations}, author={Papadamou, Kostantinos and Zannettou, Savvas and Blackburn, Jeremy and De Cristofaro, Emiliano and Stringhini, Gianluca and Sirivianos, Michael}, journal={arXiv preprint arXiv:2010.11638}, year={2020} } Acknowledgments: This project has received funding from the European Union's Horizon 2020 Research and Innovation program under the CONCORDIA project (Grant Agreement No. 830927), and from the Innovation and Networks Executive Agency (INEA) under the CYberSafety II project (Grant Agreement No. 1614254). This work reflects only the authors' views; the funding agencies are not responsible for any use that may be made of the information it contains.

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    ZENODO
    Software . 2021
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    ZENODO
    Software . 2021
    License: CC BY
    Data sources: Datacite
    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 . 2021
    License: CC BY
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      ZENODO
      Software . 2021
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    Authors: Papadamou, Kostantinos; Papasavva, Antonis; Zannettou, Savvas; Blackburn, Jeremy; +4 Authors

    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.

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