Article:
Describing Gender Equality in French Audiovisual Streams with a Deep Learning Approach

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dc.creatorDoukhan, David
dc.creatorPoels, GĂ©raldine
dc.creatorRezgui, Zohra
dc.creatorCarrive, Jean
dc.date.accessioned2020-08-24T13:55:01Z
dc.date.available2020-08-24T13:55:01Z
dc.date.issued2018-12-31
dc.description.abstractA 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.en
dc.identifier.doi10.18146/2213-0969.2018.jethc156
dc.identifier.doihttp://dx.doi.org/10.25969/mediarep/14757
dc.identifier.urihttps://mediarep.org/handle/doc/15737
dc.languageeng
dc.publisherNetherlands Institute for Sound and Vision
dc.publisher.placeHilversum
dc.relation.isPartOfissn:2213-0969
dc.relation.ispartofseriesVIEW Journal of European Television History and Culture
dc.rightsCreative Commons Attribution Share Alike 4.0 Generic
dc.rights.urihttps://creativecommons.org/licenses/by-sa/4.0
dc.subjectFernsehende
dc.subjectDigital Humanitiesen
dc.subjectMachine Learningen
dc.subjectopen-dataen
dc.subjectGender Equalityen
dc.subjectSpeaker Gender Detectionen
dc.subject.ddcddc:070
dc.subject.ddcddc:791
dc.titleDescribing Gender Equality in French Audiovisual Streams with a Deep Learning Approachen
dc.typearticle
dc.type.statuspublishedVersion
dspace.entity.typeArticleen
local.coverpage2021-05-29T06:13:29
local.identifier.firstpublishedhttps://doi.org/10.18146/2213-0969.2018.jethc156
local.source.epage122
local.source.issue14
local.source.spage103
local.source.volume7

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2018 | 14 | Audiovisual Data in Digital Humanities