TY  - JOUR
A1  - Verwiebe, Roland
A1  - Wolf, Margarita
A1  - Seewann, Lena
T1  - Werte, Wertebildung und ihre interdisziplinäre Deutung
JF  - Werte und Wertebildung aus interdisziplinärer Perspektive
Y1  - 2019
SN  - 978-3-658-21975-8
SP  - 285
EP  - 307
PB  - Springer
CY  - Wiesbaden
ER  - 
TY  - JOUR
A1  - Verwiebe, Roland
A1  - Seewann, Lena
T1  - Werte und Wertebildung in der Einwanderungsgesellschaft
JF  - Werte und Wertebildung aus interdisziplinärer Perspektive
Y1  - 2019
SN  - 978-3-658-21975-8
SP  - 239
EP  - 264
PB  - Springer
CY  - Wiesbaden
ER  - 
TY  - JOUR
A1  - Seewann, Lena
A1  - Verwiebe, Roland
A1  - Buder, Claudia
A1  - Fritsch, Nina-Sophie
T1  - “Broadcast your gender.”
BT  - A comparison of four text-based classification methods of German YouTube channels
JF  - Frontiers in Big Data
N2  - 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.
KW  - text based classification methods
KW  - gender
KW  - YouTube
KW  - machine learning
KW  - authorship attribution
Y1  - 2022
U6  - https://doi.org/10.3389/fdata.2022.908636
SN  - 2624-909X
IS  - 5
PB  - Frontiers
CY  - Lausanne, Schweiz
ER  -