@misc{SeewannVerwiebeBuderetal.2022,
  author    = {Seewann, Lena and Verwiebe, Roland and Buder, Claudia and Fritsch, Nina-Sophie},
  title     = {"Broadcast your gender."},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe},
  number    = {152},
  issn      = {1867-5808},
  doi       = {10.25932/publishup-56628},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-566287},
  pages     = {16},
  year      = {2022},
  abstract  = {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 Na{\"i}ve 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.},
  language  = {en}
}
@article{SeewannVerwiebeBuderetal.2022,
  author    = {Seewann, Lena and Verwiebe, Roland and Buder, Claudia and Fritsch, Nina-Sophie},
  title     = {"Broadcast your gender."},
  series = {Frontiers in Big Data},
  journal   = {Frontiers in Big Data},
  number    = {5},
  publisher = {Frontiers},
  address   = {Lausanne, Schweiz},
  issn      = {2624-909X},
  doi       = {10.3389/fdata.2022.908636},
  pages     = {16},
  year      = {2022},
  abstract  = {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 Na{\"i}ve 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.},
  language  = {en}
}
@article{VerwiebeBobzienFritschetal.2023,
  author    = {Verwiebe, Roland and Bobzien, Licia and Fritsch, Nina-Sophie and Buder, Claudia},
  title     = {Social inequality and digitization in modern societies},
  series = {SocArXiv : open archive of the social sciences},
  journal   = {SocArXiv : open archive of the social sciences},
  publisher = {Center for Open Science},
  address   = {[Charlottesville, VA]},
  doi       = {10.31235/osf.io/k2zwh},
  pages     = {23},
  year      = {2023},
  abstract  = {The digitization process has triggered a profound transformation of modern societies. It encompasses a broad spectrum of technical, social, political, cultural and economic developments related to the mass use of computer- and internet-based technologies. It is now becoming increasingly clear that digitization is also changing existing structures of social inequality and that new structures of digital inequality are emerging. This is shown by a growing number of recent individual studies. In this paper, we set ourselves the task of systematizing this new research within the framework of an empirically supported literature review. To do so, we use the PRISMA model for literature reviews and focus on three central dimensions of inequality - ethnicity, gender, and age - and their relevance within the discourse on digitization and inequality. The empirical basis consists of journal articles published between 2000 and 2020 and listed on the Web of Science, as well as an additional Google Scholar search, through which we attempt to include important monographs and contributions to edited volumes in our analyses. Our text corpus thus comprises a total of 281 articles. Empirically, our literature review shows that unequal access to digital resources largely reproduces existing structures of inequality; in some cases, studies report a reduction in social inequalities as a result of the digitization process.},
  language  = {en}
}