Describing Gender Equality in French Audiovisual Streams with a Deep Learning Approach
A large-scale description of men and women speaking-time in media is presented, based on the analysis of about 700.000 hours of French audiovisual documents, broadcasted from 2001 to 2018 on 22 TV channels and 21 radio stations. Speaking-time is described using Women Speaking Time Percentage (WSTP),...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Netherlands Institute for Sound and Vision
2018-12-01
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| Series: | VIEW Journal of European Television History and Culture |
| Online Access: | https://www.viewjournal.eu/article/10.18146/2213-0969.2018.jethc156/ |
| _version_ | 1811320369163796480 |
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| author | Doukhan, David Poels, Géraldine Rezgui, Zohra Carrive, Jean |
| author_facet | Doukhan, David Poels, Géraldine Rezgui, Zohra Carrive, Jean |
| author_sort | Doukhan, David |
| collection | DOAJ |
| description | A large-scale description of men and women speaking-time in media is presented, based on the analysis of about 700.000 hours of French audiovisual documents, broadcasted from 2001 to 2018 on 22 TV channels and 21 radio stations. Speaking-time is described using Women Speaking Time Percentage (WSTP), which is estimated using automatic speaker gender detection algorithms, based on acoustic machine learning models. WSTP variations are presented across channels, years, hours, and regions. Results show that men speak twice as much as women on TV and on radio in 2018, and that they used to speak three times longer than women in 2004. We also show only one radio station out of the 43 channels considered is associated to a WSTP larger than 50%. Lastly, we show that WSTP is lower during high-audience time-slots on private channels. This work constitutes a massive gender equality study based on the automatic analysis of audiovisual material and offers concrete perspectives for monitoring gender equality in media.The software used for the analysis has been released in open-source, and the detailed results obtained have been released in open-data. |
| first_indexed | 2024-04-13T12:58:37Z |
| format | Article |
| id | doaj.art-78afc00a6b414a759b0cbc4781f5a105 |
| institution | Directory Open Access Journal |
| issn | 2213-0969 |
| language | English |
| last_indexed | 2024-04-13T12:58:37Z |
| publishDate | 2018-12-01 |
| publisher | Netherlands Institute for Sound and Vision |
| record_format | Article |
| series | VIEW Journal of European Television History and Culture |
| spelling | doaj.art-78afc00a6b414a759b0cbc4781f5a1052022-12-22T02:45:58ZengNetherlands Institute for Sound and VisionVIEW Journal of European Television History and Culture2213-09692018-12-0171410310.18146/2213-0969.2018.jethc156Describing Gender Equality in French Audiovisual Streams with a Deep Learning ApproachDoukhan, DavidPoels, GéraldineRezgui, ZohraCarrive, JeanA large-scale description of men and women speaking-time in media is presented, based on the analysis of about 700.000 hours of French audiovisual documents, broadcasted from 2001 to 2018 on 22 TV channels and 21 radio stations. Speaking-time is described using Women Speaking Time Percentage (WSTP), which is estimated using automatic speaker gender detection algorithms, based on acoustic machine learning models. WSTP variations are presented across channels, years, hours, and regions. Results show that men speak twice as much as women on TV and on radio in 2018, and that they used to speak three times longer than women in 2004. We also show only one radio station out of the 43 channels considered is associated to a WSTP larger than 50%. Lastly, we show that WSTP is lower during high-audience time-slots on private channels. This work constitutes a massive gender equality study based on the automatic analysis of audiovisual material and offers concrete perspectives for monitoring gender equality in media.The software used for the analysis has been released in open-source, and the detailed results obtained have been released in open-data.https://www.viewjournal.eu/article/10.18146/2213-0969.2018.jethc156/ |
| spellingShingle | Doukhan, David Poels, Géraldine Rezgui, Zohra Carrive, Jean Describing Gender Equality in French Audiovisual Streams with a Deep Learning Approach VIEW Journal of European Television History and Culture |
| title | Describing Gender Equality in French Audiovisual Streams with a Deep Learning Approach |
| title_full | Describing Gender Equality in French Audiovisual Streams with a Deep Learning Approach |
| title_fullStr | Describing Gender Equality in French Audiovisual Streams with a Deep Learning Approach |
| title_full_unstemmed | Describing Gender Equality in French Audiovisual Streams with a Deep Learning Approach |
| title_short | Describing Gender Equality in French Audiovisual Streams with a Deep Learning Approach |
| title_sort | describing gender equality in french audiovisual streams with a deep learning approach |
| url | https://www.viewjournal.eu/article/10.18146/2213-0969.2018.jethc156/ |
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