Method Research: Uncovering Reddit Discourse Insights on U.S. Tariffs by Combining LDA Topic Modeling with Kamada–Kawai Graph Layout
Link to detailed methodology: https://www.researchgate.net/publication/392512276_Uncovering_Reddit_Discourse_Insights_on_US_Tariffs_by_Combining_LDA_Topic_Modeling_with_Kamada-Kawai_Graph_Layout
Link to GitHub (requires some cleaning): https://github.com/glombardo/Research/blob/main/LDA_plus_Kamada_Kawai_Network_Modeling.ipynb
Understanding the Public Conversation on U.S. Tariffs through Reddit
Social media has transformed how we discuss economic policies. Platforms like Reddit have become vibrant forums where users share their thoughts on complex topics such as U.S. tariff policies. This project explores Reddit discussions to uncover insights about how these conversations evolve, highlighting distinct communities and their interactions.
The interesting outcomes from this methods include:
The ability to concisely visualize whether a subgroup is “speaking the same language” with other subgroups and “the degree of commonality” between the subgroup conversation and others. In this case, we focus on subreddits (subgroups) that discuss the topic of Tariffs (please note that “Tariffs” is a Subreddit by itself, but a lot other subreddits discuss the topic!)
The ability to be deployed and scaled to include even more groups, social networks, and anything with rich conversational data.
Method uses LDA generated topic keywords to link nodes in a Kamada-Kawai network layout
From Reddit API to final Working File
Background
Initially, Reddit comments related to tariffs closely mirrored real-world policy cycles. But a significant spike in discussion occurred in April 2025, showing heightened public interest due to renewed tariff actions.
What exactly were users discussing, and how did the conversations differ across various communities?
Tariff-related comments across relevant subreddits
To answer this, I used two methods that even though relatively simple by themselves, combined proved to be powerful.
Specifically Latent Dirichlet Allocation (LDA), a way to extract underlying topics from massive text data, combined with Kamada-Kawai graph layouts. Deciding on Kamada-Kawai became clear after understanding the underlying methodology as well as its differences with other network layouts:
The analysis revealed several fascinating insights:
Central Political Cluster: Subreddits focused on politics, regardless of their ideological differences, often shared similar vocabularies. They discussed tariffs primarily as partisan and ideological issues, frequently mentioning political figures and concepts like fairness and responsibility.
Market-Focused Cluster: Financial subreddits, such as those dedicated to investing or stock market discussions, formed their own distinct group. Their conversations emphasized market behaviors, investment strategies, and financial impacts, clearly separate from the politically charged discussions.
Peripheral Practical Cluster: Small business-oriented and Q&A subreddits were positioned further from the main conversations, often isolated because their discussions revolved around practical, day-to-day issues such as pricing strategies, supply chain management, and navigating bureaucracy.
Visuals generated from these analyses clearly depicted these clusters, highlighting how closely related or divergent subreddit communities were based on their shared vocabularies and thematic content.
Notably, some subreddits acted as important bridges between these distinct clusters, serving as translators of discussions between politically motivated conversations and more neutral economic analyses. For example, the subreddit r/Economics uniquely connected political arguments with financial insights.
The implications of these findings are significant:
Fragmented Conversations: Communities discuss economic policies in different silos, each focusing on specific aspects, suggesting traditional "one-size-fits-all" communications about tariffs will not be effective.
Potential for Depolarization: Identifying and supporting bridging subreddits could foster understanding across divided communities, potentially reducing polarization and misinformation.
Ultimately, understanding these distinct community conversations allows policymakers and communicators to better tailor their outreach strategies, addressing each group in their own language and terms. This nuanced approach could lead to more informed public discourse on economic policy issues like tariffs.
