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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.
Value research has a long and extensive history of theoretical definitions and empirical investigations using large scale quantitative surveys. However, the way the general population understands, defines, and relates to the concept of values, and how these views vary across individuals is seldom addressed. The present study examined subjective interpretations of the term through focus group interviews, and reports on the development of a Value Conceptualisation Scale (VCS) that distinguishes six dimensions of different views on values: normativity, relevance, validity, stability, consistency, and awareness. Focus group interviews (n = 38) as well as several surveys (n = 100, n = 1519, n = 903, n = 94) were used to develop, refine, and test the scale in terms of response variety, temporal stability, as well as convergent and discriminant validity. These systematic results show that views on values do indeed vary significantly between participants. Correlations with dogmatism, preference for consistency, and metacognition were found for corresponding dimensions. The VCS provides an original measure, which enables future research to explore this variation on the conceptualisation of values.
The social stratification systems of major cities are transforming all around the globe. International research has been discussing this trend and focus on changing occupational classes. However, the precise effects on urban households, taking social welfare and different family arrangements into account, as well as the precise effects on people with a migration background, remain unclear. Using the example of Vienna, this article examines immigration as a key dimension for social stratification. Although household income structures in Austria have remained comparatively stable over the past two decades, the middle-income share in Vienna (as the sole metropolis in Austria) has dramatically decreased. This predominantly affects people from migrant backgrounds. Using a comprehensive dataset (two waves, N = 16,700 participants, including N = 4,500 migrants), we systematically examine the role of (a) migration-specific and (b) education- and employment-related factors to explain the decline of middle-income migrants. The results of multinomial logistic regression and decomposition analyses suggest that transformations in the labour market is the main driving force. Changing migrant characteristics have counteracted this process. If today's migrants displayed similar showed characteristics (e.g., origin and educational levels) to those prevalent in the past decade, the ethnic stratification disparities would have been even stronger.