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Word embeddings provide an unsupervised way to understand differences in word usage between discursive communities. A number of recent papers have focused on identifying words that are used differently by two or more communities. But word embeddings are complex, high-dimensional spaces and a focus on identifying differences only captures a fraction of their richness. Here, we take a step towards leveraging the richness of the full embedding space, by using word embeddings to map out how words are used differently. Specifically, we describe the construction of dialectograms, an unsupervised way to visually explore the characteristic ways in which each community use a focal word. Based on these dialectograms, we provide a new measure of the degree to which words are used differently that overcomes the tendency for existing measures to pick out low frequent or polysemous words. We apply our methods to explore the discourses of two US political subreddits and show how our methods identify stark affective polarisation of politicians and political entities, differences in the assessment of proper political action as well as disagreement about whether certain issues require political intervention at all.

Ethnographic fieldnotes can contain richer and more thorough descriptions of social phenomena compared to other data sources. Their open-ended and flexible character makes them especially useful in explorative research. However, fieldnotes are typically highly unstructured and personalized by individual researchers, which make them harder to use as a method for data collection in collaborative and mixed methods research. More precisely, the unstructured nature of ethnographic fieldnotes presents three distinct challenges: 1) Organizability—it can be difficult to search and sort fieldnotes and thus to get an overview of them, 2) Integrability—it is difficult to meaningfully integrate fieldnotes with other more quantitative data types such as more such as surveys or geospatial data, and 3) Computational Processability—it is hard to process and analyze fieldnotes with computational methods such as topic models and network analysis. To solve these three challenges, we present a new digital tool, for the systematic collection, processing, and analysis of ethnographic fieldnotes. The tool is developed and tested as part of an interdisciplinary mixed methods pilot study on attention dynamics at a political festival in Denmark. Through case examples from this study, we show how adopting this new digital tool allowed our team to overcome the three aforementioned challenges of fieldnotes, while retaining the flexible and explorative character of ethnographic research, which is a key strength of ethnographic fieldwork.

Children’s media, such as Disney, present some of children’s first narratives about complex phenomena and cultural constructions in society through the fairy tales and trials of animated characters. The- ory of media representation claims how descriptive and symbolic rep- resentation matters and states how children can be affected by the values and beliefs depicted in media. Previous litteratur regarding samples of Disney films, mostly princess films, states how Disney films include traditional gender stereotypes. Though, no one has yet examined a broad sample of Disney films which make existing con- clusions about Disney’s general gender representation limited. Fur- ther, a focus on gendered speech in Disney films is also lacking in the literature. To address this, we have examined the representation and portrayals of gender in 62 Disney Classics and Disney Pixar films (from 1937-2021) through a threefold analysis of speech investigat- ing the distribution, syntax and semantics. We do so through a com- puter assisted approach to cultural analysis to not let the research be led by computational methods, but to let the methods assist in an understanding of speech in Disney films. Results suggest that Dis- ney underrepresents women, both descriptive and symbolic, thereby contributing to representations of gender inequality. Through time, we find equalitarian trends, though women characters of the Present period are still descriptive underrepresented in speech and consist de- pending on a man, thus operating in a man’s world.

Children’s media, such as Disney, present some of children’s first narratives about complex phenomena and cultural constructions in society through the fairy tales and trials of animated characters. The- ory of media representation claims how descriptive and symbolic rep- resentation matters and states how children can be affected by the values and beliefs depicted in media. Previous litteratur regarding samples of Disney films, mostly princess films, states how Disney films include traditional gender stereotypes. Though, no one has yet examined a broad sample of Disney films which make existing con- clusions about Disney’s general gender representation limited. Fur- ther, a focus on gendered speech in Disney films is also lacking in the literature. To address this, we have examined the representation and portrayals of gender in 62 Disney Classics and Disney Pixar films (from 1937-2021) through a threefold analysis of speech investigat- ing the distribution, syntax and semantics. We do so through a com- puter assisted approach to cultural analysis to not let the research be led by computational methods, but to let the methods assist in an understanding of speech in Disney films. Results suggest that Dis- ney underrepresents women, both descriptive and symbolic, thereby contributing to representations of gender inequality. Through time, we find equalitarian trends, though women characters of the Present period are still descriptive underrepresented in speech and consist de- pending on a man, thus operating in a man’s world.