@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}
}
@phdthesis{Richter2020,
  author    = {Richter, Christian},
  title     = {Fernsehen - Netflix - YouTube},
  series = {Metabasis},
  journal   = {Metabasis},
  number    = {58},
  publisher = {transcript},
  address   = {Bielefeld},
  isbn      = {978-3-8376-5481-3},
  school      = {Universit{\"a}t Potsdam},
  pages     = {370},
  year      = {2020},
  abstract  = {Um ihre {\"a}sthetischen und strukturellen {\"A}hnlichkeiten zum Fernsehprogramm aufzudecken, analysiert Christian Richter ausf{\"u}hrlich mediale Inszenierungen von Netflix und YouTube. Die Schlagworte »Flow«, »Serialit{\"a}t«, »Liveness« und »Adressierung« dienen dabei als zentrale Orientierungshilfen. Antworten liefern etablierte Fernsehtheorien ebenso wie facettenreiche und triviale Beispiele. Diese reichen vom ZDF-Fernsehgarten und alten Horrorfilmen {\"u}ber den SuperBowl und einsame Bahnfahrten durch Norwegen bis zu BibisBeautyPalace und House of Cards. Am Ende sch{\"a}lt sich ein Zustand von FERNSEHEN heraus, der als eine neue Version aufgefasst werden kann.},
  language  = {de}
}