Computational Models of Identity Presentation in Language

Language use varies across demographic, social, and cultural distinctions among speakers. Consequently, many researchers in computational sociolinguistics have built models of how language use reflects latent, stable identities of language users. However, researchers in sociolinguistics, linguistic anthropology, and discourse analysis posit that language also constitutes these very identities. Identity, which we define as the positioning of self and others in interaction, is in part reproduced, challenged, and performed in language. This thesis attempts to apply this perspective in computational tools that investigate not only how the identity of language users affects language, but how language positions the identity of the speaker and others. We explore identity positioning in language along several dimensions, from the effects of explicit self-labeling on social media to the implicit framing of gender and sexuality in narrative. We investigate each of these with machine learning and methods drawn from computational linguistics for use on large datasets of linguistic and social interaction. First, we examine the social effects of self-presented identity labels on Tumblr, a social media site known for talk about identity. We extract both identity categories and specific labels presented by Tumblr users in free-text bio boxes and consider whether similarities or differences in self-presentation affect the propagation of content. To test this hypothesis, we use self-presentation features in a machine learning task predicting whether a user will share content from another user. We find that these identity features provide an informative signal often overlooked in previous work on content propagation. Interpreting the learned feature weights in a linear model, we find that alignment on different “levels” of identity self-presentation (broad categories or exact label matches) have differing effects on content propagation in a social network. Specific interactions between labels that indicate shared experience or values, such as conceptualizations of gender, are particularly informative. Though we cannot directly “observe” the construction of social solidarity or alignment that comes from self-presentation in language, in this way we are able to use computational tools to discover its effects. We then examine how identity positioning can take place more implicitly in representations of characters in narrative. For this we use a corpus of fanfiction, stories written by fans that expand or change original narratives from TV shows, comics, books and movies. We investigate a suite of computational approaches that use neural network-based word vectors to represent characters and relationships between them in narrative. In particular, we investigate the ability of such approaches to capture fanfiction writers changing the depiction of a relationship from its portrayal in the original, source narrative. To validate these approaches, we use a machine learning task predicting if fictional characters are paired in the same or different romantic relationships in a specific work of fanfiction compared to the original media on which it was based. We also construct visualizations of the learned representations of characters, and use these experiments and visualizations to analyze the extent to which contrasts in these representations reflect contrasts in the positioning December 2, 2019 DRAFT of characters between original and derived works. We find that these representations can capture elements such as the linguistic construction of a writer’s attitude toward a character, or gender and sexuality changes in portrayed characters or relationships. However, some difficulty in separating out social phenomena from more surface-level features in language such as genre persists. Taking this work a step further in a proposed project, we plan to examine changes in ideological positioning of identity types in fanfiction, specifically gender roles in relationships. Fanfiction has been considered a “queer space” for its considerable representation of same-sex relationships. But how are fanfiction writers depicting gender roles in both gay and straight relationships? Does the “queer space” of fanfiction transcend traditional gender roles in relationships, or does it redeploy heterosexual gender roles in gay and straight relationships? We seek to discover gender roles in relationships in fanfiction in this project. To do so without imposing a binary framework of gender inappropriate to the context, we first find roles played by characters in an unsupervised fashion, clustering text relevant to character portrayal across stories. We then quantify the effect of character gender on distributions of character roles–and how this varies from the original series to fanfiction, and across straight and queer relationships. Such computational tools allow us to study the extent to which fanfiction has created an alternative representation in language for gender along with sexuality–or discover that traditional gender roles are redeployed or even left intact in same-sex relationships. Finally, we attempt to learn flexible, relational representations for identity labels with notions of similarity and difference that are relevant to observed user behavior. We again use a machine learning prediction task predicting user content sharing behavior, but this time to supervise the construction of an embedding space that positions vector representations of identity labels. To evaluate this learned representation space of identity labels, we compare with a feature-based approach in the content propagation task. Meanings and values of identity labels are relative to the communities in which they are used. To evaluate if our learned identity representations are capturing community-specific meanings, we explore how this space corresponds to community-defining identity characteristics of communities defined by network structure.