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“Broadcast your gender.” A comparison of four text-based classification methods of German YouTube channels
Social media platforms provide a large array of behavioral data relevant to social scientific research. However, key information such as sociodemographic characteristics of agents are often missing. This paper aims to compare four methods of classifying social attributes from text. Specifically, we...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9515904/ https://www.ncbi.nlm.nih.gov/pubmed/36188727 http://dx.doi.org/10.3389/fdata.2022.908636 |
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author | Seewann, Lena Verwiebe, Roland Buder, Claudia Fritsch, Nina-Sophie |
author_facet | Seewann, Lena Verwiebe, Roland Buder, Claudia Fritsch, Nina-Sophie |
author_sort | Seewann, Lena |
collection | PubMed |
description | Social media platforms provide a large array of behavioral data relevant to social scientific research. However, key information such as sociodemographic characteristics of agents are often missing. This paper aims to compare four methods of classifying social attributes from text. Specifically, we are interested in estimating the gender of German social media creators. By using the example of a random sample of 200 YouTube channels, we compare several classification methods, namely (1) a survey among university staff, (2) a name dictionary method with the World Gender Name Dictionary as a reference list, (3) an algorithmic approach using the website gender-api.com, and (4) a Multinomial Naïve Bayes (MNB) machine learning technique. These different methods identify gender attributes based on YouTube channel names and descriptions in German but are adaptable to other languages. Our contribution will evaluate the share of identifiable channels, accuracy and meaningfulness of classification, as well as limits and benefits of each approach. We aim to address methodological challenges connected to classifying gender attributes for YouTube channels as well as related to reinforcing stereotypes and ethical implications. |
format | Online Article Text |
id | pubmed-9515904 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95159042022-09-29 “Broadcast your gender.” A comparison of four text-based classification methods of German YouTube channels Seewann, Lena Verwiebe, Roland Buder, Claudia Fritsch, Nina-Sophie Front Big Data Big Data Social media platforms provide a large array of behavioral data relevant to social scientific research. However, key information such as sociodemographic characteristics of agents are often missing. This paper aims to compare four methods of classifying social attributes from text. Specifically, we are interested in estimating the gender of German social media creators. By using the example of a random sample of 200 YouTube channels, we compare several classification methods, namely (1) a survey among university staff, (2) a name dictionary method with the World Gender Name Dictionary as a reference list, (3) an algorithmic approach using the website gender-api.com, and (4) a Multinomial Naïve Bayes (MNB) machine learning technique. These different methods identify gender attributes based on YouTube channel names and descriptions in German but are adaptable to other languages. Our contribution will evaluate the share of identifiable channels, accuracy and meaningfulness of classification, as well as limits and benefits of each approach. We aim to address methodological challenges connected to classifying gender attributes for YouTube channels as well as related to reinforcing stereotypes and ethical implications. Frontiers Media S.A. 2022-09-14 /pmc/articles/PMC9515904/ /pubmed/36188727 http://dx.doi.org/10.3389/fdata.2022.908636 Text en Copyright © 2022 Seewann, Verwiebe, Buder and Fritsch. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Big Data Seewann, Lena Verwiebe, Roland Buder, Claudia Fritsch, Nina-Sophie “Broadcast your gender.” A comparison of four text-based classification methods of German YouTube channels |
title | “Broadcast your gender.” A comparison of four text-based classification methods of German YouTube channels |
title_full | “Broadcast your gender.” A comparison of four text-based classification methods of German YouTube channels |
title_fullStr | “Broadcast your gender.” A comparison of four text-based classification methods of German YouTube channels |
title_full_unstemmed | “Broadcast your gender.” A comparison of four text-based classification methods of German YouTube channels |
title_short | “Broadcast your gender.” A comparison of four text-based classification methods of German YouTube channels |
title_sort | “broadcast your gender.” a comparison of four text-based classification methods of german youtube channels |
topic | Big Data |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9515904/ https://www.ncbi.nlm.nih.gov/pubmed/36188727 http://dx.doi.org/10.3389/fdata.2022.908636 |
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