Gender Identification From Community Question Answering Avatars

There are several reasons why gender recognition is vital for online social networks such as community Question Answering (cQA) platforms. One of them is progressing towards gender parity across topics as a means of keeping communities vibrant. More specifically, this demographic variable has shown...

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Autores principales:	Billy Peralta, Alejandro Figueroa, Orietta Nicolis, Alvaro Trewhela
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Community question answering image processing social computing user demographic analysis computers and information processing data systems Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/49f329fef00b4dd5923df310270617e5
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id	oai:doaj.org-article:49f329fef00b4dd5923df310270617e5
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spelling	oai:doaj.org-article:49f329fef00b4dd5923df310270617e52021-12-02T00:00:35ZGender Identification From Community Question Answering Avatars2169-353610.1109/ACCESS.2021.3130078https://doaj.org/article/49f329fef00b4dd5923df310270617e52021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9625020/https://doaj.org/toc/2169-3536There are several reasons why gender recognition is vital for online social networks such as community Question Answering (cQA) platforms. One of them is progressing towards gender parity across topics as a means of keeping communities vibrant. More specifically, this demographic variable has shown to play a crucial role in devising better user engagement strategies. For instance, by kindling the interest of their members for topics dominated by the opposite gender. However, in most cQA websites, the gender field is neither mandatory nor verified when submitting and processing enrollment forms. And as might be expected, it is left blank most of the time, forcing cQA services to infer this demographic information from the activity of their users on their platforms such as prompted questions, answers, self-descriptions and profile images. There is only a handful of studies dissecting automatic gender recognition across cQA fellows, and as far as we know, this work is the first effort to delve into the contribution of their profile pictures to this task. Since these images are an unconstrained environment, their multifariousness poses a particularly difficult and interesting challenge. With this mind, we assessed the performance of three state-of-art image processing techniques, namely pre-trained neural network models. In a nutshell, our best configuration finished with an accuracy of 81.68% (Inception-ResNet-50), and its corresponding Grad-Cam maps unveil that one of its principal focus of attention is determining silhouettes edges. All in all, we envisage that our findings are going to play a fundamental part in the design of efficient multi-modal strategies.Billy PeraltaAlejandro FigueroaOrietta NicolisAlvaro TrewhelaIEEEarticleCommunity question answeringimage processingsocial computinguser demographic analysiscomputers and information processingdata systemsElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 156701-156716 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Community question answering image processing social computing user demographic analysis computers and information processing data systems Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Community question answering image processing social computing user demographic analysis computers and information processing data systems Electrical engineering. Electronics. Nuclear engineering TK1-9971 Billy Peralta Alejandro Figueroa Orietta Nicolis Alvaro Trewhela Gender Identification From Community Question Answering Avatars
description	There are several reasons why gender recognition is vital for online social networks such as community Question Answering (cQA) platforms. One of them is progressing towards gender parity across topics as a means of keeping communities vibrant. More specifically, this demographic variable has shown to play a crucial role in devising better user engagement strategies. For instance, by kindling the interest of their members for topics dominated by the opposite gender. However, in most cQA websites, the gender field is neither mandatory nor verified when submitting and processing enrollment forms. And as might be expected, it is left blank most of the time, forcing cQA services to infer this demographic information from the activity of their users on their platforms such as prompted questions, answers, self-descriptions and profile images. There is only a handful of studies dissecting automatic gender recognition across cQA fellows, and as far as we know, this work is the first effort to delve into the contribution of their profile pictures to this task. Since these images are an unconstrained environment, their multifariousness poses a particularly difficult and interesting challenge. With this mind, we assessed the performance of three state-of-art image processing techniques, namely pre-trained neural network models. In a nutshell, our best configuration finished with an accuracy of 81.68% (Inception-ResNet-50), and its corresponding Grad-Cam maps unveil that one of its principal focus of attention is determining silhouettes edges. All in all, we envisage that our findings are going to play a fundamental part in the design of efficient multi-modal strategies.
format	article
author	Billy Peralta Alejandro Figueroa Orietta Nicolis Alvaro Trewhela
author_facet	Billy Peralta Alejandro Figueroa Orietta Nicolis Alvaro Trewhela
author_sort	Billy Peralta
title	Gender Identification From Community Question Answering Avatars
title_short	Gender Identification From Community Question Answering Avatars
title_full	Gender Identification From Community Question Answering Avatars
title_fullStr	Gender Identification From Community Question Answering Avatars
title_full_unstemmed	Gender Identification From Community Question Answering Avatars
title_sort	gender identification from community question answering avatars
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/49f329fef00b4dd5923df310270617e5
work_keys_str_mv	AT billyperalta genderidentificationfromcommunityquestionansweringavatars AT alejandrofigueroa genderidentificationfromcommunityquestionansweringavatars AT oriettanicolis genderidentificationfromcommunityquestionansweringavatars AT alvarotrewhela genderidentificationfromcommunityquestionansweringavatars
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Gender Identification From Community Question Answering Avatars

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