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arxiv: 2603.19747 · v2 · submitted 2026-03-20 · 💻 cs.HC

ConSearcher: Supporting Conversational Information Seeking in Online Communities with Member Personas

Shiwei Wu , Xinyue Chen , Yuheng Liu , Xingbo Wang , Qingyu Guo , Longfei Chen , Chuhan Shi , Zhenhui Peng This is my paper

Pith reviewed 2026-05-15 08:44 UTC · model grok-4.3

classification 💻 cs.HC
keywords conversational searchonline communitiesmember personasLLM toolsinformation seekinguser engagementpersonalization
0
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The pith

ConSearcher improves conversational search in online communities by generating member personas from queries to surface diverse perspectives.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents ConSearcher as an LLM-powered system that creates simulated member personas based on a user's query to support information seeking inside online communities. Users can inspect what a similar simulated member might ask and receive answers framed from multiple member viewpoints. Two studies, one exploratory and one within-subjects with 27 participants, show that this approach produces measurably higher information-seeking outcomes and engagement than baseline conversational search interfaces. The work also surfaces user concerns that heavy reliance on personalized personas can lead to over-personalization.

Core claim

ConSearcher dynamically generates member personas from user queries so that people can clarify their interests by checking what a simulated similar member might ask and can obtain responses that reflect diverse community perspectives, yielding significantly higher information-seeking outcomes and user engagement than two standard conversational search baselines.

What carries the argument

Dynamically generated member personas based on user queries, which simulate community members' perspectives to let users explore interests and receive viewpoint-diverse answers within a conversational interface.

If this is right

  • Users reach better information-seeking outcomes when they can preview questions from simulated similar members.
  • Engagement rises when responses are framed through multiple member personas rather than a single generic chat.
  • Over-personalization becomes a visible risk that can distort the range of perspectives users encounter.
  • The same persona-generation approach can be applied to other community topics such as travel planning or product advice.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Replacing LLM personas with real-time member data could reduce hallucination risks while preserving the engagement gains.
  • Over-personalization may create echo-chamber effects if users repeatedly interact only with personas that match their initial queries.
  • Scaling the system to very large communities would require testing whether persona quality remains stable across thousands of members.

Load-bearing premise

LLM-generated personas based on queries will accurately and helpfully represent real community members' perspectives without introducing systematic biases or hallucinations.

What would settle it

A direct comparison study in which participants rate ConSearcher responses against actual replies posted by real community members on the same queries, measuring accuracy, bias, and perceived helpfulness.

Figures

Figures reproduced from arXiv: 2603.19747 by Chuhan Shi, Longfei Chen, Qingyu Guo, Shiwei Wu, Xingbo Wang, Xinyue Chen, Yuheng Liu, Zhenhui Peng.

Figure 1
Figure 1. Figure 1: BaseAgent used in the exploratory study: (A) community interface with posts, comments, and a search box; (B) BaseAgent that B1) answers user queries with links to relevant community content and B2) recommends follow-up questions based on dialog history; (C) pop-up menu that allows users to highlight selected content or prompt BaseAgent to summarize it. search on the left felt like I was blindly going throu… view at source ↗
Figure 2
Figure 2. Figure 2: A walkthrough of the ConSearcher interface using the ’Japan Travel’ task. After an initial query (A1), the system visualizes key dimensions in the Factor Map (B), where circle sizes indicate post volume. Users can then select a Seeker Persona (C) to open a detailed profile (D), which includes the persona’s background (D1), situations for focused factors (D2), and tailored suggested queries generated based … view at source ↗
Figure 3
Figure 3. Figure 3: Computational workflow of ConSearcher’s persona-driven conversational search. In the user study, BaseAgent does not have the modules of (A) seeker personas and (B) provider personas, while the BaseSearcher does not have the module of (B) provider personas. Before conducting tasks Exploratory search tasks (within-subjects, N=27) Counterbalance with Latin Square Topics Japan travel Digital education Ph.D. Sy… view at source ↗
Figure 4
Figure 4. Figure 4: User study procedure. The tasks and systems ( [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The experiment’s statistical results about [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The statistical results about each item on user engagement. All items are using one-way repeated measures ANOVA [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The statistical results about each item on NASA task workload. All items are using one-way repeated measures ANOVA [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗
read the original abstract

Many people browse online communities to learn from others' experiences and opinions, e.g., for constructing travel plans. Conversational search powered by large language models (LLMs) could ease this information-seeking task, but it remains under-investigated within the online community. In this paper, we first conducted an exploratory study (N=10) that indicated the helpfulness of a classic conversational search tool and identified room for improvement. Then, we proposed ConSearcher, an LLM-powered tool with dynamically generated member personas based on user queries to facilitate conversational search in the community. In ConSearcher, users can clarify their interests by checking what a simulated member similar to them may ask and get responses from diverse members' perspectives. A within-subjects study (N=27) showed that compared to two conversational search baselines, ConSearcher led to significantly higher information-seeking outcome and user engagement but raised concerns about over-personalization. We discuss implications for supporting conversational information seeking in online communities.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper introduces ConSearcher, an LLM-powered conversational search tool for online communities that dynamically generates member personas based on user queries to provide diverse perspectives. Following an exploratory study (N=10) highlighting needs, a within-subjects study (N=27) demonstrates that ConSearcher yields significantly higher information-seeking outcomes and user engagement compared to two baselines, though it raises issues of over-personalization.

Significance. If the persona mechanism can be shown to drive the gains without artifacts from LLM generation, this could advance design of multi-perspective conversational tools in online communities, with implications for HCI and CSCW. The two user studies provide empirical grounding for the claims.

major comments (1)
  1. [N=27 within-subjects study] N=27 within-subjects study: The attribution of significantly higher information-seeking outcomes and engagement to the member personas rests on the untested assumption that LLM-generated personas faithfully represent real community members' perspectives. No ground-truth validation (e.g., overlap with actual posts, expert fidelity ratings, or hallucination audit) is reported, so measured gains could arise from generation artifacts rather than the intended multi-perspective mechanism.
minor comments (2)
  1. [Abstract] Abstract: The claim of 'significantly higher' outcomes omits any mention of the statistical tests, effect sizes, measures, or controls, which would allow immediate assessment of evidence strength.
  2. [Method] Method: More detail on the implementation of the two conversational search baselines would aid replication and clarify what aspects of ConSearcher drive the differences.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback. We address the major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [N=27 within-subjects study] N=27 within-subjects study: The attribution of significantly higher information-seeking outcomes and engagement to the member personas rests on the untested assumption that LLM-generated personas faithfully represent real community members' perspectives. No ground-truth validation (e.g., overlap with actual posts, expert fidelity ratings, or hallucination audit) is reported, so measured gains could arise from generation artifacts rather than the intended multi-perspective mechanism.

    Authors: We thank the referee for this observation. Both baselines in the N=27 within-subjects study rely on the identical LLM backbone, so any general generation artifacts are controlled for; the only systematic difference is the dynamic member-persona mechanism. The exploratory study (N=10) also provided qualitative support that users perceived the personas as reflecting plausible community viewpoints. We nevertheless agree that explicit fidelity validation (e.g., overlap metrics with real posts or expert ratings) is absent. In the revised manuscript we will expand the Limitations section to acknowledge this gap and outline concrete future validation steps, including expert fidelity audits and post-hoc comparison against community archives. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper is an empirical HCI contribution that proposes ConSearcher and evaluates it via two independent user studies (exploratory N=10 and within-subjects N=27). No equations, parameter fitting, or derivation chain exist; the central claims rest on measured outcomes from participant interactions rather than any self-referential reduction. Self-citations, if present, are not load-bearing for the reported results, which are externally falsifiable through the study protocols.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical HCI design paper with no mathematical model, free parameters, axioms, or invented entities; claims rest entirely on the two reported user studies.

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