Multiple Methods for Visualizing Human Language: A Tutorial for Social and Behavioural Scientists
Eijsbroek, V. C., Nilsson, A., Ackermann, L., Gu, Z., Wiebel, C., Ganesan, A. V., Kjell, K., Schwartz, H. A., & Kjell, O. N. E. (2026). Multiple Methods for Visualizing Human Language: A Tutorial for Social and Behavioural Scientists. PsyArXiv. https://doi.org/10.31234/osf.io/nxfvr_v3
@misc{eijsbroek2026multiple,
title = {{Multiple Methods for Visualizing Human Language: A Tutorial for Social and Behavioural Scientists}},
author = {Veerle C. Eijsbroek and August Nilsson and Leon Ackermann and Zhuojun Gu and Clara Wiebel and Adithya V. Ganesan and Katarina Kjell and H. Andrew Schwartz and Oscar N.E. Kjell},
year = {2026},
howpublished = {PsyArXiv},
doi = {10.31234/osf.io/nxfvr_v3},
url = {https://doi.org/10.31234/osf.io/nxfvr_v3}
}
Abstract
Humans use language to communicate psychological experiences. Advancements in Natural Language Processing have enabled researchers to quantitatively study and assess psychological states and behaviors through language (e.g., well-being and mental health). Insights from language analyses are often best conveyed through visualizations. Data-driven visualizations of how language statistically relates to psychological dimensions can help interpret assessment scores, uncover meaningful patterns, and understand the intricate relationships between language use and mental states. Visualizations can be used to differentiate between related constructs (e.g., How does language related to depression versus anxiety differ?), understand the validity of assessment tools (e.g., Does the language related to high depression severity match with depression theory?), and explain language-based assessments (e.g., Which language is indicative of higher depression severity in a language-based assessment?). This tutorial demonstrates how to transform language data into visual insights using the text and topics packages in R. We provide practical guidance on creating four types of visualizations based on different language analysis techniques, examining how language use patterns are statistically associated with a psychological construct. We visualize 1) individual words and phrases (n-grams), 2) topics (word clusters), 3) words in the word embedding space, and 4) relevant language examples. These different visualizations offer complementary perspectives, visualizing linguistic elements from individual words to overarching topics and text examples. The underlying methods range from correlations between individual words to more advanced techniques, including topic modeling and Large Language Models. By making these methods accessible, this tutorial aims to empower researchers to use language visualizations for meaningful, data-driven insights.
