Multilingual Contextual Affective Analysis of LGBT People Portrayals in Wikipedia

TL;DR

This paper extends contextual affective analysis to multilingual Wikipedia biographies, revealing cultural differences and biases in LGBT portrayals across English, Russian, and Spanish, using a new corpus and multilingual model.

Contribution

It introduces a novel multilingual dataset and model for contextual affective analysis, enabling cross-cultural studies of narrative portrayals in Wikipedia.

Findings

Systematic differences in LGBT portrayals across languages

Cultural variations influence narrative and bias signals

Model can identify articles needing further analysis

Abstract

Specific lexical choices in narrative text reflect both the writer's attitudes towards people in the narrative and influence the audience's reactions. Prior work has examined descriptions of people in English using contextual affective analysis, a natural language processing (NLP) technique that seeks to analyze how people are portrayed along dimensions of power, agency, and sentiment. Our work presents an extension of this methodology to multilingual settings, which is enabled by a new corpus that we collect and a new multilingual model. We additionally show how word connotations differ across languages and cultures, highlighting the difficulty of generalizing existing English datasets and methods. We then demonstrate the usefulness of our method by analyzing Wikipedia biography pages of members of the LGBT community across three languages: English, Russian, and Spanish. Our results show systematic differences in how the LGBT community is portrayed across languages, surfacing cultural differences in narratives and signs of social biases. Practically, this model can be used to identify Wikipedia articles for further manual analysis -- articles that might contain content gaps or an imbalanced representation of particular social groups.