Politics on YouTube: Detecting Online Group Polarization Based on News Videos’ Comments
<p dir="ltr">Technology-mediated group toxicity polarization is a major socio-technological issue of our time. For better large-scale monitoring of polarization among social media news content, we quantify the toxicity of news video comments using a Toxicity Polarization Score. For p...
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| مؤلفون آخرون: | , , , , |
| منشور في: |
2024
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إضافة وسم
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| _version_ | 1864513509777211392 |
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| author | Raghvendra Mall (581171) |
| author2 | Mridul Nagpal (19273783) Joni Salminen (7434770) Hind Almerekhi (7434776) Soon-gyo Jung (7434773) Bernard J. Jansen (7434779) |
| author2_role | author author author author author |
| author_facet | Raghvendra Mall (581171) Mridul Nagpal (19273783) Joni Salminen (7434770) Hind Almerekhi (7434776) Soon-gyo Jung (7434773) Bernard J. Jansen (7434779) |
| author_role | author |
| dc.creator.none.fl_str_mv | Raghvendra Mall (581171) Mridul Nagpal (19273783) Joni Salminen (7434770) Hind Almerekhi (7434776) Soon-gyo Jung (7434773) Bernard J. Jansen (7434779) |
| dc.date.none.fl_str_mv | 2024-05-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1177/21582440241256438 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Politics_on_YouTube_Detecting_Online_Group_Polarization_Based_on_News_Videos_Comments/26421625 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Data management and data science Human-centred computing social media toxicity group polarization machine learning media news |
| dc.title.none.fl_str_mv | Politics on YouTube: Detecting Online Group Polarization Based on News Videos’ Comments |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Technology-mediated group toxicity polarization is a major socio-technological issue of our time. For better large-scale monitoring of polarization among social media news content, we quantify the toxicity of news video comments using a Toxicity Polarization Score. For polarizing news videos, our premise is that the comments’ toxicity approximates either an “M” or “U” shaped distribution—that is, there is unevenly balanced toxicity among the comments. We evaluate our premises through a case study using a dataset of ~180,000 YouTube comments on ~3,700 real news videos from an international online news organization. Toward polarization-mitigating information systems, we build a predictive machine learning model to score the toxicity polarization of news content even when its comments are disabled or not available, as it is a current trend among news publishers to disable comments. Findings imply that the most engaging news content is also often the most polarizing, which we associate with increasing research on clickbait content and the detrimental effect of attention-based metrics on the health of online social media communities, especially news communities.</p><h2>Other Information</h2><p dir="ltr">Published in: Sage Open<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1177/21582440241256438" target="_blank">https://dx.doi.org/10.1177/21582440241256438</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_a60bb5749b7e4380fe0c2e9228e283fd |
| identifier_str_mv | 10.1177/21582440241256438 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26421625 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Politics on YouTube: Detecting Online Group Polarization Based on News Videos’ CommentsRaghvendra Mall (581171)Mridul Nagpal (19273783)Joni Salminen (7434770)Hind Almerekhi (7434776)Soon-gyo Jung (7434773)Bernard J. Jansen (7434779)Information and computing sciencesData management and data scienceHuman-centred computingsocial mediatoxicitygroup polarizationmachine learningmedianews<p dir="ltr">Technology-mediated group toxicity polarization is a major socio-technological issue of our time. For better large-scale monitoring of polarization among social media news content, we quantify the toxicity of news video comments using a Toxicity Polarization Score. For polarizing news videos, our premise is that the comments’ toxicity approximates either an “M” or “U” shaped distribution—that is, there is unevenly balanced toxicity among the comments. We evaluate our premises through a case study using a dataset of ~180,000 YouTube comments on ~3,700 real news videos from an international online news organization. Toward polarization-mitigating information systems, we build a predictive machine learning model to score the toxicity polarization of news content even when its comments are disabled or not available, as it is a current trend among news publishers to disable comments. Findings imply that the most engaging news content is also often the most polarizing, which we associate with increasing research on clickbait content and the detrimental effect of attention-based metrics on the health of online social media communities, especially news communities.</p><h2>Other Information</h2><p dir="ltr">Published in: Sage Open<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1177/21582440241256438" target="_blank">https://dx.doi.org/10.1177/21582440241256438</a></p>2024-05-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1177/21582440241256438https://figshare.com/articles/journal_contribution/Politics_on_YouTube_Detecting_Online_Group_Polarization_Based_on_News_Videos_Comments/26421625CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/264216252024-05-01T00:00:00Z |
| spellingShingle | Politics on YouTube: Detecting Online Group Polarization Based on News Videos’ Comments Raghvendra Mall (581171) Information and computing sciences Data management and data science Human-centred computing social media toxicity group polarization machine learning media news |
| status_str | publishedVersion |
| title | Politics on YouTube: Detecting Online Group Polarization Based on News Videos’ Comments |
| title_full | Politics on YouTube: Detecting Online Group Polarization Based on News Videos’ Comments |
| title_fullStr | Politics on YouTube: Detecting Online Group Polarization Based on News Videos’ Comments |
| title_full_unstemmed | Politics on YouTube: Detecting Online Group Polarization Based on News Videos’ Comments |
| title_short | Politics on YouTube: Detecting Online Group Polarization Based on News Videos’ Comments |
| title_sort | Politics on YouTube: Detecting Online Group Polarization Based on News Videos’ Comments |
| topic | Information and computing sciences Data management and data science Human-centred computing social media toxicity group polarization machine learning media news |