You Write like You Eat: Stylistic Variation as a Predictor of Social Stratification (P19-1)
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| Challenge: | In order to test whether and to what extent variations in writing style are influenced by socio-economic status, we used user-generated restaurant reviews on social media. |
| Approach: | They propose to use user-generated restaurant reviews to test whether and to what extent variations in writing style are influenced by socio-economic status. |
| Outcome: | The proposed model is based on user-generated restaurant reviews and user-created reviews. |
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| Challenge: | Structured social variation has been extensively studied, e.g., gender based variation, but little is known about how to characterize individual styles due to their idiosyncratic nature. |
| Approach: | They propose a method to study idiolects through a massive cross-author comparison to identify and encode stylistic features. |
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Thesis Proposal: Comparing Human and Model Perception of Writing Style under Controlled Perturbations (2026.eacl-srw)
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| Challenge: | This thesis examines how humans and models perceive writing style under controlled perturbations. |
| Approach: | They examine how humans and models perceive writing style under controlled perturbations . they also examine whether perturbations that reduce algorithmic recognition obscure stylistic identity . |
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Can You Make It Sound Like You? Post-Editing LLM-Generated Text for Personal Style (2026.acl-long)
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| Challenge: | Despite the growing use of large language models for writing tasks, it remains unclear whether users can effectively reshape LLM-generated text to reflect their personal style. |
| Approach: | They conduct an online study in which participants post-edit LLM-generated drafts for writing tasks where personal style matters to them. |
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An Empirical Analysis of the Writing Styles of Persona-Assigned LLMs (2024.emnlp-main)
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| Challenge: | Recent efforts to "personalize" large language models by assigning them specific personas are limited by current knowledge of how well they perform. |
| Approach: | They use a style embedding model to analyze writing styles of persona-assigned LLMs . they find significant style differences between personas using Kullback-Leibler divergence . |
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Analysis of Style-Shifting on Social Media: Using Neural Language Model Conditioned by Social Meanings (2023.findings-emnlp)
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Seiya Kawano, Shota Kanezaki, Angel Fernando Garcia Contreras, Akishige Yuguchi, Marie Katsurai, Koichiro Yoshino
| Challenge: | Using a personalized neural language model, we predict an individual’s conversational style based on surprisals predicted by a personal neural language modeling model. |
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Detecting, Generating, and Evaluating in the Writing Style of Different Authors (2025.naacl-srw)
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| Challenge: | In recent years, stylometry has been investigated in many different fields. |
| Approach: | They propose to use sentences from different books to generate and evaluate stylistic texts according to the authors' writing styles. |
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Whose Preferences? Differences in Fairness Preferences and Their Impact on the Fairness of AI Utilizing Human Feedback (2024.acl-long)
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| Challenge: | a growing body of work on learning from human feedback to align various aspects of machine learning systems with human values and preferences is focusing on the setting of fairness in content moderation. |
| Approach: | They propose to use human feedback to determine how two comments should be treated in content moderation to learn about human values and preferences. |
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Embedding Style Beyond Topics: Analyzing Dispersion Effects Across Different Language Models (2025.coling-main)
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| Challenge: | Using a literary corpus that alternates between topics and styles, we compare language models across French and English. |
| Approach: | They analyze how writing style affects embedding spaces across multiple language models . they use a literary corpus that alternates between topics and styles to compare their results . |
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Would you Rather? A New Benchmark for Learning Machine Alignment with Cultural Values and Social Preferences (2020.acl-main)
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| Challenge: | Existing studies on optimal decision-making are limited and only consider individuals in isolation. |
| Approach: | They propose a task and corpus for learning alignments between machine and human preferences based on a gamified voting game . |
| Outcome: | The proposed task and corpus show that current state-of-the-art NLP models still leave much room for improvement. |
Splits! Flexible Sociocultural Linguistic Investigation at Scale (2026.acl-long)
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| Challenge: | Variation in language use offers a rich lens into cultural perspectives, values, and opinions. |
| Approach: | They propose to construct a "sandbox" for systematic and flexible sociolinguistic research by splitting a reddit dataset into demographically/topically split SLPs. |
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