Hello,
The next Research Showcase will be live-streamed on Wednesday, December 16, at 9:30 AM PST/17:30 UTC, and will be on the theme of disinformation and reliability of sources in Wikipedia. In the first talk, Włodzimierz Lewoniewski will present recent work around multilingual approaches for the assessment of content quality and reliability of sources in Wikipedia leveraging machine learning algorithms. In the second talk, Diego Saez-Trumper will give an overview of ongoing work on fighting disinformation in Wikipedia; specifically, the development of tools and datasets aimed at supporting the discovery of suspicious content and improving verifiability.
Youtube stream: https://www.youtube.com/watch?v=v9Wcc-TeaEY
As usual, you can join the conversation on IRC at #wikimedia-research. You can also watch our past research showcases here: https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase
Talk 1
Speaker: Włodzimierz Lewoniewski (Poznań University of Economics and Business, Poland)
Title: Quality assessment of Wikipedia and its sources
Abstract: Information in Wikipedia can be edited in over 300 languages independently. Therefore often the same subject in Wikipedia can be described differently depending on language edition. In order to compare information between them one usually needs to understand each of considered languages. We work on solutions that can help to automate this process. They leverage machine learning and artificial intelligence algorithms. The crucial component, however, is assessment of article quality therefore we need to know how to define and extract different quality measures. This presentation briefly introduces some of the recent activities of Department of Information Systems at Poznań University of Economics and Business related to quality assessment of multilingual content in Wikipedia. In particular, we
demonstrate some of the approaches for the reliability assessment of sources in Wikipedia articles. Such solutions can help to enrich various language editions of Wikipedia and other knowledge bases with information of better quality.
Talk 2
Speaker: Diego Saez-Trumper (Research, Wikimedia Foundation)
Title: Challenges on fighting Disinformation in Wikipedia: Who has the (ground-)truth?
Abstract: Different from the major social media websites where the fight against disinformation mainly refers to preventing users to massively replicate fake content, fighting disinformation in Wikipedia requires tools that allows editors to apply the content policies of: verifiability, non-original research, and neutral point of view. Moreover, while other platforms try to apply automatic fact checking techniques to verify content, the ground-truth for such verification is done based on Wikipedia, for obvious reasons we can't follow the same pipeline for fact checking content on Wikipedia. In this talk we will explain the ML approach we are developing to build tools to efficiently support wikipedians to discover suspicious content and how we collaborate with external researchers on this task. We will also describe a group of datasets we are preparing to share with the research community in order to produce state-of-the-art algorithms to improve the verifiability of content on Wikipedia.
Just a reminder that this Showcase will be happening on Wednesday.
On Fri, Dec 11, 2020 at 11:22 AM Janna Layton jlayton@wikimedia.org wrote:
Hello,
The next Research Showcase will be live-streamed on Wednesday, December 16, at 9:30 AM PST/17:30 UTC, and will be on the theme of disinformation and reliability of sources in Wikipedia. In the first talk, Włodzimierz Lewoniewski will present recent work around multilingual approaches for the assessment of content quality and reliability of sources in Wikipedia leveraging machine learning algorithms. In the second talk, Diego Saez-Trumper will give an overview of ongoing work on fighting disinformation in Wikipedia; specifically, the development of tools and datasets aimed at supporting the discovery of suspicious content and improving verifiability.
Youtube stream: https://www.youtube.com/watch?v=v9Wcc-TeaEY
As usual, you can join the conversation on IRC at #wikimedia-research. You can also watch our past research showcases here: https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase
Talk 1
Speaker: Włodzimierz Lewoniewski (Poznań University of Economics and Business, Poland)
Title: Quality assessment of Wikipedia and its sources
Abstract: Information in Wikipedia can be edited in over 300 languages independently. Therefore often the same subject in Wikipedia can be described differently depending on language edition. In order to compare information between them one usually needs to understand each of considered languages. We work on solutions that can help to automate this process. They leverage machine learning and artificial intelligence algorithms. The crucial component, however, is assessment of article quality therefore we need to know how to define and extract different quality measures. This presentation briefly introduces some of the recent activities of Department of Information Systems at Poznań University of Economics and Business related to quality assessment of multilingual content in Wikipedia. In particular, we
demonstrate some of the approaches for the reliability assessment of sources in Wikipedia articles. Such solutions can help to enrich various language editions of Wikipedia and other knowledge bases with information of better quality.
Talk 2
Speaker: Diego Saez-Trumper (Research, Wikimedia Foundation)
Title: Challenges on fighting Disinformation in Wikipedia: Who has the (ground-)truth?
Abstract: Different from the major social media websites where the fight against disinformation mainly refers to preventing users to massively replicate fake content, fighting disinformation in Wikipedia requires tools that allows editors to apply the content policies of: verifiability, non-original research, and neutral point of view. Moreover, while other platforms try to apply automatic fact checking techniques to verify content, the ground-truth for such verification is done based on Wikipedia, for obvious reasons we can't follow the same pipeline for fact checking content on Wikipedia. In this talk we will explain the ML approach we are developing to build tools to efficiently support wikipedians to discover suspicious content and how we collaborate with external researchers on this task. We will also describe a group of datasets we are preparing to share with the research community in order to produce state-of-the-art algorithms to improve the verifiability of content on Wikipedia.
-- Janna Layton (she/her) Administrative Associate - Product & Technology Wikimedia Foundation https://wikimediafoundation.org/
This will be starting in about 30 minutes.
On Mon, Dec 14, 2020 at 11:01 AM Janna Layton jlayton@wikimedia.org wrote:
Just a reminder that this Showcase will be happening on Wednesday.
On Fri, Dec 11, 2020 at 11:22 AM Janna Layton jlayton@wikimedia.org wrote:
Hello,
The next Research Showcase will be live-streamed on Wednesday, December 16, at 9:30 AM PST/17:30 UTC, and will be on the theme of disinformation and reliability of sources in Wikipedia. In the first talk, Włodzimierz Lewoniewski will present recent work around multilingual approaches for the assessment of content quality and reliability of sources in Wikipedia leveraging machine learning algorithms. In the second talk, Diego Saez-Trumper will give an overview of ongoing work on fighting disinformation in Wikipedia; specifically, the development of tools and datasets aimed at supporting the discovery of suspicious content and improving verifiability.
Youtube stream: https://www.youtube.com/watch?v=v9Wcc-TeaEY
As usual, you can join the conversation on IRC at #wikimedia-research. You can also watch our past research showcases here: https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase
Talk 1
Speaker: Włodzimierz Lewoniewski (Poznań University of Economics and Business, Poland)
Title: Quality assessment of Wikipedia and its sources
Abstract: Information in Wikipedia can be edited in over 300 languages independently. Therefore often the same subject in Wikipedia can be described differently depending on language edition. In order to compare information between them one usually needs to understand each of considered languages. We work on solutions that can help to automate this process. They leverage machine learning and artificial intelligence algorithms. The crucial component, however, is assessment of article quality therefore we need to know how to define and extract different quality measures. This presentation briefly introduces some of the recent activities of Department of Information Systems at Poznań University of Economics and Business related to quality assessment of multilingual content in Wikipedia. In particular, we
demonstrate some of the approaches for the reliability assessment of sources in Wikipedia articles. Such solutions can help to enrich various language editions of Wikipedia and other knowledge bases with information of better quality.
Talk 2
Speaker: Diego Saez-Trumper (Research, Wikimedia Foundation)
Title: Challenges on fighting Disinformation in Wikipedia: Who has the (ground-)truth?
Abstract: Different from the major social media websites where the fight against disinformation mainly refers to preventing users to massively replicate fake content, fighting disinformation in Wikipedia requires tools that allows editors to apply the content policies of: verifiability, non-original research, and neutral point of view. Moreover, while other platforms try to apply automatic fact checking techniques to verify content, the ground-truth for such verification is done based on Wikipedia, for obvious reasons we can't follow the same pipeline for fact checking content on Wikipedia. In this talk we will explain the ML approach we are developing to build tools to efficiently support wikipedians to discover suspicious content and how we collaborate with external researchers on this task. We will also describe a group of datasets we are preparing to share with the research community in order to produce state-of-the-art algorithms to improve the verifiability of content on Wikipedia.
-- Janna Layton (she/her) Administrative Associate - Product & Technology Wikimedia Foundation https://wikimediafoundation.org/
-- Janna Layton (she/her) Administrative Associate - Product & Technology Wikimedia Foundation https://wikimediafoundation.org/
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