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“Broadcast your gender.”
(2022)
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.
Content: 1 The Development of the Estonian Gender Policy Machinery 1.1 Initiation of Institutionalisation as a Result of International Commitments 1.2 Institutional Measures Facilitating EU Membership 1.3 Assessment of the Gender Equality Machinery 2 Conditions for Gender Mainstreaming in Estonia 2.1 Social Conditions 2.2 Administrative Conditions 3 Gender Mainstreaming Activities in the Estonian Public Administration 3.1 The Legal Foundations 3.2 Inter-ministerial Cooperation 3.3 Gender Mainstreaming Training 3.4 Knowledge Basis 3.5 Lack of Standards for data and Statistics 3.6 Non-adminsitrative Liaisons 4 Conclusion