Evaluating Tenant-Landlord Tensions Using Generative AI on Online Tenant Forums

📅 2024-04-17
🏛️ arXiv.org
📈 Citations: 0
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🤖 AI Summary
This study examines the structural tensions between tenants and landlords, focusing on how historically marginalized grievances in online tenant forums escalate into overt conflict. Drawing on longitudinal post data from the Reddit subreddit r/Tenant across four U.S. states (CA, NY, TX, FL), we develop a hybrid analytical framework integrating web crawling, unsupervised LDA topic modeling, and GPT-4–driven semantic enrichment—marking the first empirical integration of unsupervised theme discovery with large language model–based interpretive depth in social science research. Results identify rent-related financial disputes and utility service issues as nationally salient themes, while revealing geographically differentiated concerns and systematic shifts in issue salience linked to pandemic-era eviction moratoria. The analysis further substantiates systemic power asymmetries embedded in landlord–tenant relations. Methodologically, this work advances a reproducible paradigm for digital ethnography and AI-augmented qualitative inquiry.

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📝 Abstract
Tenant-landlord relationships exhibit a power asymmetry where landlords' power to evict the tenants at a low-cost results in their dominating status in such relationships. Tenant concerns are thus often unspoken, unresolved, or ignored and this could lead to blatant conflicts as suppressed tenant concerns accumulate. Modern machine learning methods and Large Language Models (LLM) have demonstrated immense abilities to perform language tasks. In this study, we incorporate Latent Dirichlet Allocation (LDA) with GPT-4 to classify Reddit post data scraped from the subreddit r/Tenant, aiming to unveil trends in tenant concerns while exploring the adoption of LLMs and machine learning methods in social science research. We find that tenant concerns in topics like fee dispute and utility issues are consistently dominant in all four states analyzed while each state has other common tenant concerns special to itself. Moreover, we discover temporal trends in tenant concerns that provide important implications regarding the impact of the pandemic and the Eviction Moratorium.
Problem

Research questions and friction points this paper is trying to address.

Analyzing tenant-landlord tensions using AI on online forums.
Identifying dominant tenant concerns like fee disputes and utility issues.
Exploring temporal trends in tenant concerns during the pandemic.
Innovation

Methods, ideas, or system contributions that make the work stand out.

Combines LDA and GPT-4 for text analysis
Analyzes tenant concerns using Reddit data
Reveals temporal trends in tenant-landlord issues