RedditPersona: A Modular Framework for Community-Conditioned LLM Adaptation from Reddit

arXiv:2606.06027 · cs.AI, cs.CL, cs.LG, cs.SI · Submitted 2026-06-04 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: We're looking at the paper: "RedditPersona: A Modular Framework for Community-Conditioned LLM Adaptation from Reddit".

Jane: Here's the gist of the paper. The gist The RedditPersona framework provides a modular system for standardizing data collection, community definition, model adaptation, and evaluation when applying large language models to community-conditioned tasks.

Tom: Framework Overview RedditPersona is designed as a modular resource that transforms raw Reddit posts and comments into reusable community-conditioned language model adapters along with metrics for comparison The framework is designed around a simple principle: the same raw social activity should support multiple downstream definitions of “community” and should remain reusable until the fine-tuning-ready stage.

Jane: It collects posts and comments, profiles active users, constructs activity and interaction artifacts, applies multiple community grouping strategies, analyzes the resulting partitions with shared metrics, emits instruction-tuning data in a portable chat format, and trains parameter-efficient adapters per grouping strategy.

Tom: Data Collection and Profiling Phase 1 involves collecting Reddit posts and comments from a user-specified set of subreddits.

Title and authors: Tom: So we're looking at this paper today about RedditPersona: A Modular Framework for Community-Conditioned LLM Adaptation from Reddit. It sounds like they’re trying to take all the messy choices people make when they try to define a "community" on Reddit and put them into one standardized system.

Jane: That's right, Tom. The core idea is that instead of every single study having to reinvent how they collect data, define a community, or evaluate the results from scratch, this framework gives you reusable scripts for all those steps. It aims to make comparing different ways of grouping users much easier and more systematic.

Lu: What's really interesting here is the modularity they're proposing. They aren't just suggesting one way to do everything; they are building a pipeline where you can swap out the data collection, change how you define a community, or use different evaluation metrics independently.

Meng: From my side, I’m focused on what this means for deployment. The paper suggests you can train adapters for different community strategies using QLoRA on any existing instruction-tuned LLM, which sounds portable across models like Llama or Mistral because they encode the community identity in the system prompt.

Lalam: And what's good about that portability is that it means you can take a single raw social activity and generate multiple, distinct model adapters, each tailored to a different way of seeing that community.

Tom: Exactly. They start by collecting Reddit posts and comments from specified subreddits and then profiling active users to build these activity artifacts before they even get into the grouping strategies. That's the initial data heavy lifting for you.

Jane: Right, so after collecting all those posts, they move into phase three where they apply five different ways of partitioning that corpus—subreddit baseline, a graph strategy, a semantic strategy, and two hybrid or interaction strategies.

Lu: They’re testing these five distinct community detection strategies against each other using shared metrics to see which one works best for identifying communities. They even have this post-processing step where they consolidate the results from those long-tailed partitions into top K groups plus one other class to make them comparable in size.

Meng: That sounds like a lot of computational overhead just for grouping, but if it helps you compare assumptions systematically, I can see the value in that. It helps you see how much difference there is between a community defined by subreddit rules versus one defined by user interactions or text similarity.

Title and authors: Lalam: And they do compute these metrics—like community-size distribution and Gini—for every strategy, which gives you concrete numbers to compare their quality before you even start training models.

Tom: Moving into the adaptation part, phase four involves fine-tuning parameter-efficient adapters using QLoRA. They mention training data is emitted in an OpenAI messages format so it’s compatible with various model families like Qwen or Gemma.

Jane: It’s smart they are doing that for portability; you don't want your work locked into just one architecture when you can potentially swap out the base LLM later. The key here is encoding that community identity right in the system prompt during training so the adapter knows which context it’s operating in.

Lu: The paper provides a dedicated community analyzer to compute nine metrics for evaluation, including PPL, Dist-one/two and Comm-F1. These metrics help quantify how good each resulting adapter is at representing its intended community versus how well it mimics the original text distribution.

Meng: I’m interested in the evaluation side—the Comm-F1 metric specifically—because it seems to be a way to probe behavioral identifiability, which is something we need when we move beyond just looking at text similarity.

Lalam: And they find that this Comm-F1 ranking across all five strategies mirrors the NMI ranking of the grouping strategies versus the subreddit baseline, which suggests that agreement with the subreddit identity actually predicts how identifiable those communities are behaviorally.

Tom: That’s a key finding because it grounds the abstract community detection results in something behavioral, showing that structural similarity to a Reddit community translates into actual observable differences in how an AI model behaves when prompted about it.

Jane: And looking at the trade-offs they found, they point out that there's always a balance between how identifiable a group is and how much it looks like natural text. Subreddit-conditioned adapters are the most identifiable but least natural, whereas semantic adapters are the most natural but least identifiable.

Lu: That’s a very clear finding about the trade-off they observed across all five strategies. The study shows that you can't always have perfect identifiability and perfect distributional similarity to real text simultaneously.

Meng: From a deployment standpoint, if you need high fidelity to the actual language used by the community, semantic grouping might be better, even if it’s less immediately recognizable as a 'Reddit community'. If you prioritize strong behavioral signals for classification tasks, sticking with the subreddit baseline seems to give you that edge.

Lalam: And they found that the subreddit-based strategy actually has the lowest perplexity at thirty-eight point three and the lowest sentiment-JSD at zero point zero six seven, suggesting it’s a very strong signal for those specific measures of text quality.

Title and authors: Tom: Those numbers are solid because they come from analyzing their urban wellbeing subreddits, which is a useful domain for testing these different grouping assumptions about social factors. The paper shows that the subreddit condition actually provides the highest community classifiability with a Comm-F1 score of zero point two six two, which is over two times the chance you'd get if you had ten classes.

Jane: So, to summarize, RedditPersona gives us a standardized way to move from raw Reddit data through five different community definitions and into fine-tuned AI adapters with clear evaluation metrics that show us how well those communities are defined behaviorally.

Lu: The paper provides reusable scripts including configuration and anonymization stages which is a big deal for making this whole process accessible to other researchers without having to build the entire pipeline from scratch.

Meng: It’s useful because it standardizes the choices, so instead of every study having to guess whether a graph or a semantic approach is right, you can just compare them using these shared metrics.

Lalam: And they offer those reusable scripts as part of their third contribution, which means you can use the framework to compare community-conditioned LLM adaptation in many different contexts going forward.

Tom: So for anyone interested in how we condition LLMs on social context, this RedditPersona paper gives you a concrete blueprint for structuring that entire process. It’s about taking the complexity of community definition and making it manageable through a modular pipeline.

Jane: It really shows that standardizing these choices allows us to answer fundamental methodological questions about which unit—is it the subreddit, the interaction graph, or the text embedding cluster—that actually makes sense for defining a community context.

Lu: The paper confirms that this choice directly predicts behavioral identifiability in how well an AI model can recognize and classify its origin community.

Meng: And it shows you that there’s always a trade-off between how much the model looks like the original text versus how easy it is to tell which community it belongs to.

Lalam: It's useful because this framework moves the discussion away from just creating one model and toward creating multiple, comparable models based on different definitions of community.

Tom: So we’ve looked at RedditPersona today and seen how they standardize a whole complex process into a pipeline that lets us test different assumptions about community structure systematically. That should give you some good direction for your own research into community-conditioned AI.

The paper's summary: Tom: So, RedditPersona is basically this framework that lets you take raw Reddit posts and comments and turn them into reusable tools for fine-tuning AI models specifically for different communities, without having to start from scratch every single time.

Jane: Right, so instead of you building a whole new dataset and training a new model just because you want to study how an AI talks like a specific subreddit, you can use this framework to standardize the whole process. It’s like a kit for community-conditioned adaptation.

Lu: The core idea is modularity, which means you can swap out the data collection part, or change how you define what a "community" is—like using graph analysis versus text embeddings—and still get comparable results for training these adapters.

Meng: From my side, I’m thinking about the deployment. If I have ten different ways to define a community and I can train an adapter for each one using QLoRA on my existing Llama model, that sounds like a lot of flexibility when you're trying to ship something reliable.

Lalam: The paper sets up this pipeline where it collects the posts, profiles every active user to get their activity history and text corpus, then runs five different ways of grouping those users into communities.

Tom: And that grouping part is crucial because they run strategies like using just the subreddit itself as the community definition, or using a graph based on who replies to whom. It’s testing which way of seeing the social structure gives you better results.

Jane: They also do this consolidation step where they take all those different community partitions and boil them down into a few top communities plus one other class so everyone is comparing apples to apples when it comes to the final training data.

Lu: The interesting part they found is that there’s always a trade-off, you can’t have perfect behavioral identifiability and perfect similarity to real text at the same time across all five methods. Subreddit-conditioned adapters are the most identifiable but least natural sounding, while semantic ones are the most natural but less identifiable.

Meng: So if I want an AI that sounds super authentic to a specific niche, I might lean toward a semantic grouping strategy because it keeps the language grounded in how people actually talk there, even if it’s harder to tell it apart from other communities.

Lalam: They also gave us these nine evaluation metrics—like PPL and Comm-F1—to measure how good each resulting adapter actually is at capturing that community identity and text distribution.

Tom: And the biggest finding they show is that the ranking of these adapters by their identification score perfectly matches the ranking of their community detection strategies compared to just using a simple subreddit baseline. That links structure directly to behavior.

Jane: It means if you pick a certain way to define what a community looks like—say, based on user interactions instead of just which subreddit they belong to—you can predict how much better the AI will be at behaving exactly like that community.

Lu: The implications for me is that this framework gives us a clear path forward for testing different social theories. We can use it to see if graph-based definitions are actually more predictive of real-world behavioral differences than simple text clustering or subreddit membership.

Meng: For production, it confirms that we need to be very careful about what signal we choose—is the signal structural identity, or is it semantic fluency? That’s a design choice that matters for deployment.

Tom: So, this paper provides the blueprint for systematically testing those choices across multiple communities using standardized tools. Next up, we’re going to look at how they actually set up this whole data collection and profiling phase from scratch before they start grouping.

The paper's improvements: Tom: So, we’ve seen how RedditPersona sets up this whole system for taking raw data and creating multiple community-specific AI adapters using five different grouping strategies, and now we're looking at what they suggest improving next.

Jane: The paper points out that while the framework is modular, it could be even more robust by explicitly studying the limitations of those different grouping methods themselves.

Lu: They mention that since the strategies are so different—like using a simple subreddit baseline versus a complex interaction graph—we need better ways to compare them than just relying on those nine metrics they calculated.

Meng: I mean, if you’re deploying this, you want to know which strategy is actually stable under real-world noise, not just which one scores highest in an idealized benchmark.

Tom: Exactly. They suggest that the next step is to look at how these community definitions hold up when the underlying Reddit data itself changes or gets messy.

Jane: And they also propose looking at more complex ways to define those communities beyond just five categories, maybe finding new hybrid strategies that blend graph and semantic similarity in a different way than what they already tested.

Lu: I think that’s where the real ambition lies—moving past the current fixed set of strategies to see if we can discover entirely new structural patterns in user behavior on Reddit.

Meng: From an engineering standpoint, that means building tools to automate the discovery of these better groupings, instead of just testing a few pre-defined ones.

Tom: They also talk about extending this framework beyond Reddit itself and seeing if these community-conditioning ideas still hold up when you apply them to completely different social data sources.

Jane: That would be really important because it tests whether the core idea—that context dictates how an AI behaves—is universal across all kinds of human interaction.

Lu: And they also suggest contrasting these community-level adapters against individual-level finetuning, which is a big theoretical move to quantify the actual value of abstracting a community versus tuning for one specific person.

Meng: That’s interesting because individual tuning is much more expensive to manage at scale than having just one adapter per strategy.

Tom: So they are pushing toward understanding whether you should be managing one massive, highly specialized model, or a set of smaller adapters that you can dynamically switch between based on the context.

Jane: It really highlights that the value isn't just in making an AI sound right for one community, but in having a toolkit to manage multiple community personas efficiently.

Lu: The future work seems focused on pushing the boundaries of what these grouping strategies can discover and how those discovered patterns translate into useful, deployable AI components.

Conclusion: Tom: So to wrap up, RedditPersona gives us a standardized way to move from raw data collection through five different community definitions and into fine-tuned AI adapters with clear evaluation metrics that show how well those communities are defined behaviorally.

Lu: And it confirms that this choice directly predicts behavioral identifiability in how well an AI model can recognize and classify its origin community.

Meng: So we have this reusable pipeline that handles the heavy lifting of data organization, grouping strategies, and adapter training using QLoRA on any model architecture without locking us into a specific setup.

Lalam: I’m confident that this framework provides a solid foundation for anyone trying to build models conditioned on social context because it tests different structural assumptions head-to-head.

Tom: It’s about taking the complexity of community definition and making it manageable through a modular pipeline, which is something we need when we are actually shipping these things to production systems.

Jane: This standardization means researchers can focus more on the social science questions they want to ask, instead of spending all their time figuring out how to collect and process the raw Reddit data.

Lu: The real value here is in being able to swap components, so if a future study uses a different set of subreddits or a different grouping logic, we don't have to rebuild the whole infrastructure.

Meng: For us at the startup level, having those reusable scripts means we can prototype and iterate on community-conditioned adapters much faster without having to re-engineer the entire data processing stack every time.

Tom: It’s a solid piece of work because it takes a messy, subjective concept like "community" and turns it into an objective, measurable pipeline for AI training.

Jane: So this RedditPersona framework is a great tool for anyone interested in community-conditioned LLM adaptation, and it definitely sets the stage for us to test more complex social theories with AI.

Lu: The next step is really about pushing those boundaries—seeing if we can discover new grouping strategies that are even more insightful than the five they tested here.

Meng: And I’m still keen on seeing how this works when you apply it to different types of data, not just Reddit, because that’s where the real scaling challenges usually pop up.

Lalam: I think it will be useful because it gives us a concrete way to compare different ways of framing social context for models.

Tom: So that's our look at RedditPersona: A Modular Framework for Community-Conditioned LLM Adaptation from Reddit. Thanks for tuning in and sticking with us.

Amirhossein Ghaffari, Ali Goodarzi, Huong Nguyen, Simo Hosio, Lauri Lovén, Ekaterina Gilman

University of Oulu · Centre for Applied Computing

cs.AI, cs.CL, cs.LG, cs.SI

Submitted: 2026-06-04

Updated: 2026-08-12

Comments: Accepted manuscript (CC BY 4.0). To appear in ACM CIKM 2026, Rome, Italy, Nov 7-11, 2026. DOI: https://doi.org/10.1145/3799682.3840182

Code: https://github.com/Ahghaffari/redditpersona

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 85/100

The gist: The gist The RedditPersona framework provides a modular system for standardizing data collection, community definition, model adaptation, and evaluation when applying large language models to

Key concepts

RedditPersona Framework: A modular system designed to standardize the entire process of adapting large language models (LLMs) to specific online communities. It handles data collection, user profiling, multiple community grouping strategies, LLM fine-tuning via QLoRA, and evaluation metrics in a reusable pipeline.
Community Grouping Strategies: Five different mathematical and structural methods used to partition Reddit users into distinct groups. These include using the subreddit itself (S1), analyzing user interaction graphs (S2), clustering based on text similarity (S3), blending graph and text similarity (S4), or focusing on reply patterns (S5).
QLoRA Fine-tuning: A parameter-efficient fine-tuning technique used to adapt pre-trained LLMs for each community. It uses 4-bit quantization and LoRA adapters, allowing researchers to train a specific model adapter for one community while keeping the base model frozen. This process is portable across different LLM architectures.
Comm-F1 Metric: A discriminative metric used in evaluation to measure behavioral identifiability. It assesses how accurately a classifier can predict the original community label based on the text generated by an adapted LLM, confirming whether the model successfully captures the community's unique style.

Terminology

Adaptation — 2× in this paper · explained in 1 episode
Distributional Alignment — 1× in this paper · explained in 2 episodes
Fidelity — 1× in this paper · explained in 2 episodes

Summary

The gist The RedditPersona framework provides a modular system for standardizing data collection, community definition, model adaptation, and evaluation when applying large language models to community-conditioned tasks.

Framework Overview

RedditPersona is designed as a modular resource that transforms raw Reddit posts and comments into reusable community-conditioned language model adapters along with metrics for comparison The framework is designed around a simple principle: the same raw social activity should support multiple downstream definitions of “community” and should remain reusable until the fine-tuning-ready stage. It collects posts and comments, profiles active users, constructs activity and interaction artifacts, applies multiple community grouping strategies, analyzes the resulting partitions with shared metrics, emits instruction-tuning data in a portable chat format, and trains parameter-efficient adapters per grouping strategy.

Data Collection and Profiling

Phase 1 involves collecting Reddit posts and comments from a user-specified set of subreddits. A verification step filters out private, Not Safe For Work (NSFW), or low-subscriber communities before collection begins. An AsyncPRAW-based scraper can then mix hot, top, and new sort orders, apply exponential back-off on the rate limits, and checkpoints every N submissions to allow resumption. Phase 2 builds artifacts for every user passing a configurable minimum activity filter. A single-pass streaming aggregator builds three artifacts for every user passing a configurable minimum activity filter: a structured profile (subreddit histogram, first/last activity, totals), an activity matrix slice, and a concatenated text corpus written through a bounded LRU pool of file handles so that large user populations can be processed without exhausting RAM or file descriptors.

Community Grouping Strategies

Phase 3 involves partitioning the corpus using five community detection strategies. These strategies share a uniform interface, including (S1) the subreddit baseline, where each subreddit is its own multi-membership community; (S2) a graph strategy that projects the bipartite user-subreddit graph into a sparse user-user similarity graph and partitions it with Leiden [33] (Louvain [3] fallback); (S3) a semantic strategy that embeds each user’s text corpus and clusters via K-means [25] with a silhouette [8] over configurable K; (S4) a hybrid strategy that linearly blends graph and cosine semantic similarity on existing edges and re-applies Leiden (Louvain fallback); and (S5) an interaction strategy that runs Leiden [33] on the reply graph with a Louvain fallback. Since Louvain and Leiden produce long-tailed partitions, a shared post-processing step consolidates each strategy’s output into its top-K largest communities plus an other class, yielding comparable cardinalities across strategies.

LLM Adaptation and Evaluation

Phase 4 involves fine-tuning the LLMs using QLoRA. For each grouping strategy, any HuggingFace instruction-tuned LLM can be fine-tuned with QLoRA (4-bit NF4 quantization with double-quant, fp16 compute, and LoRA adapters on attention projections) driven by TRL’s SFTTrainer. Training data is emitted in the OpenAI messages format, with the community identity encoded in the system prompt; the tokenizer’s own chat template is automatically applied at training time, making the same training script portable across model families (Qwen, Llama, Mistral, Gemma, etc.). One community-pooled adapter is trained per strategy, plus a strategyagnostic baseline all adapter. Phase 6 involves evaluation and analysis. This phase computes nine metrics defined in Phase 6 (Section 3): PPL, Dist-1/2, Vocab-Jacc, Topic-KL, Sent-JSD, BERTScore-F1, MAUVE, and Comm-F1.

Key Findings

The experimental evaluation reveals that the Comm-F1 ranking of adapters mirrors the NMI ranking of grouping strategies versus the subreddit baseline This confirms that partition quality predicts behavioral identifiability. A consistent trade-off between identifiability and distributional similarity to real text holds across all five strategies. Specifically, subredditconditioned adapters are the most identifiable but least natural, while semantic adapters are the most natural but least identifiable. The strongest persona signal comes from S1 (subreddit), which attains the lowest perplexity (38.3), the lowest sentiment-JSD (0.067), and the highest community classifiability (Comm-F1 = 0.262, 2.6× chance over 10 classes). Crucially, the Comm-F1 ranking across strategies (S2 > S4 > S5 > S3) mirrors the NMI-vs-baseline ranking in Table 2, providing downstream confirmation that agreement with subreddit identity predicts behavioral identifiability.

Future Work

Several directions remain open. Immediate research applications include studying discourse norms across domains beyond urban well-being, generating community-conditioned training data for social science simulations, and benchmarking community grouping assumptions in polarization or public health settings. Extending the framework to other model families would test whether the metrics hold across architectures. Richer hybridization schemes beyond linear graph-semantic blending, as well as the design of new grouping strategies, are major directions for future work. Finally, contrasting community-level adapters with individual-level finetuning would directly quantify the practical value of community abstraction for persona simulation research.

The RedditPersona framework successfully standardizes the complex choices involved in creating community-conditioned LLM adaptations by providing an end-to-end pipeline from raw data collection to comparative evaluation. This standardization is critical because current research often makes data construction, community definition, model adaptation, and evaluation as tightly coupled choices made for one study. By offering a configurable framework that allows researchers to re-run individual phases or swap components, the paper addresses the difficulty in answering basic methodological questions regarding the most appropriate unit for community analysis or user reorganization. The framework’s modularity ensures that a single raw social activity can support multiple downstream definitions of “community” while remaining reusable until the fine-tuning stage.

How it works

The framework operates through six phases, each exposed as an independent sub-command of a single CLI and configurable through a layered configuration mechanism. The process starts with Phase 1, Data collection from Reddit, where the user supplies a list of subreddits. This phase uses an AsyncPRAW-based scraper to stream posts and comments to per-subreddit JSONL files while materializing two compact relational artifacts on the fly: a sparse user × subreddit activity matrix and a directed user → user interaction graph of reply edges weighted by frequency.

Phase 2, User profiling construction, aggregates data for every active user passing a configurable minimum activity filter. This phase builds three artifacts for every qualifying user: a structured profile (subreddit histogram, first/last activity, totals), an activity matrix slice, and a concatenated text corpus written through a bounded LRU pool of file handles so that large user populations can be processed without exhausting RAM or file descriptors.

Phase 3, Community grouping, applies five community detection strategies to partition the corpus. These strategies include (S1) the subreddit baseline, (S2) a graph strategy projecting the bipartite user-subreddit graph into a sparse user-user similarity graph and partitioning it with Leiden [33], (S3) a semantic strategy that embeds each user’s text corpus and clusters via K-means [25] with a silhouette [8] over configurable K, (S4) a hybrid strategy that linearly blends graph and cosine semantic similarity on existing edges and re-applies Leiden (Louvain fallback), and (S5) an interaction strategy that runs Leiden [33] on the reply graph with a Louvain fallback. A shared post-processing step consolidates each strategy’s output into its top-K largest communities plus an other class, yielding comparable cardinalities across strategies.

Phase 4, LLM adaptation (fine-tuning), trains parameter-efficient adapters per grouping strategy. For each grouping strategy, any HuggingFace instruction-tuned LLM can be fine-tuned with QLoRA (4-bit NF4 quantization with double-quant, fp16 compute, and LoRA adapters on attention projections) driven by TRL’s SFTTrainer. Training data is emitted in the OpenAI messages format, with the community identity encoded in the system prompt; the tokenizer’s own chat template is automatically applied at training time, making the same training script portable across model families (Qwen, Llama, Mistral, Gemma, etc.). One community-pooled adapter is trained per strategy.

Evaluation Metrics

Phase 6 evaluates the generated text using nine metrics defined in Phase 6 (Section 3). These include per-token perplexity (PPL) using the same chat template as training, and a set of metrics combining lexical, semantic, distributional, and discriminative measures.

Improvements for AI systems

Modular Framework for Community-Conditioned LLM Adaptation: The RedditPersona framework allows researchers to standardize data collection, community definition, model adaptation, and evaluation across different studies by providing reusable artifacts such as reusable scripts, including configuration, anonymization, fine-tuning, and an evaluation benchmark.
Comparative Analysis of Community Grouping Strategies: The unified interface for comparing grouping strategies enables researchers to test whether a subreddit boundary, an interaction graph, or a semantic cluster is the most appropriate unit by analyzing community-size distribution and Gini coefficient across different partitions like S1 (subreddit baseline) and S5 (interaction strategy).
Quantification of Identifiability vs. Distributional Similarity Trade-off: The framework reveals that a consistent trade-off between identifiability and distributional similarity to real text holds across all five strategies, specifically showing that subredditconditioned adapters are the most identifiable but least natural, while semantic adapters are the most natural but least identifiable.
Community-Conditioned LLM Adaptation via QLoRA: Researchers can fine-tune any HuggingFace instruction-tuned LLM with QLoRA to create community-specific adapters, ensuring that the same training script is portable across model families (Qwen, Llama, Mistral, Gemma, etc.) by encoding the community identity encoded in the system prompt.
Evaluation of Behavioral Identifiability: The Comm-F1 probe provides a discriminative metric that can be used to determine behavioral identifiability by training a classifier on generated text to recover the originating community label, allowing researchers to confirm that agreement with subreddit identity predicts behavioral identifiability.

Abstract

Community-conditioned language model adaptation needs choices about data collection, community definition, and evaluation that are currently made independently in each study, making it hard to compare assumptions or reuse artifacts. We present RedditPersona, a modular framework that standardizes these choices: it collects Reddit posts and comments, profiles active users, partitions them under five grouping strategies (subreddit-based, graph-structural, semantic, hybrid, and interaction-based), trains a parameter-efficient adapter per strategy via QLoRA, and evaluates them under a shared metric suite spanning fluency, fidelity, distributional alignment, and community identifiability. Applied to 112 subreddits in the urban well-being domain (301,429 user profiles, 16M+ comments), we find that adapters' behavioral identifiability tracks each strategy's agreement with the subreddit baseline, and that a consistent trade-off between identifiability and distributional similarity to real text holds across all five strategies. The code and configuration files are available at: https://github.com/Ahghaffari/redditpersona.