{"id":"f79ac231-dd57-4cad-9f5c-29a46e355505","arxiv_id":"2502.02943","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"high","formal_verification":"none","parameter_count":3,"one_line_summary":"Reddit users' inferred posting policies reveal behavioral homophily that is partly independent of topical similarity, including a distinct 'Disagreer' persona.","lead":"This paper proposes a new way to measure homophily on Reddit: use inverse reinforcement learning to infer each user's posting and replying policy, then measure behavioral homophily as the similarity of those policies. The authors apply it to 662 users and 5.9 million interactions, finding behavioral personas such as 'Disagreers' and showing that users can behave alike while discussing unrelated topics.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Algorithm 1's soft Q-learning requires a transition kernel P, but Section 3.5 never specifies or estimates P; without P the inferred policies, SWKL homophily, and personas are not well-defined, so the central claim is unverified.","rationale":"The reader's weakest assumption identifies precisely the same load-bearing gap: Algorithm 1 requires a transition kernel P that is never specified or estimated. My stress-test confirms that this is not a minor implementation detail. Soft Q-learning cannot be executed without P or a sample-based substitute, so the policies returned by the algorithm are not defined. Since SWKL and the persona clusters are computed from these policies, the central claim that IRL-derived policies provide a valid behavioral homophily measure is not established. The state-action redundancy (same stance labels in states and actions) strengthens the concern by suggesting that the inferred policy might be recoverable from empirical transition counts alone, making the IRL machinery and its missing dynamics especially consequential. The paper's own validation is internal and does not address sensitivity to the transition model. The reader's REJECT verdict remains appropriate for the manuscript as written: the method is not reproducible and the main claim is not well-posed. If the authors specify P, estimate it from data, and demonstrate stability of the SWKL and persona results across reasonable transition specifications, a conditional accept could become defensible; as submitted, the central argument has not been established.","tokens_in":21079,"tokens_out":5412,"duration_ms":58160,"concrete_test":"Specify P empirically from the data: for each state-action context, estimate the empirical distribution of the next state (e.g., after 'Wait reply', the observed stance distribution of the first reply received). Re-run Algorithm 1 with this empirical P, and also with two alternative P choices: (1) uniform over the three GR stances, and (2) a no-stochastic-transition variant where GR stances are treated as deterministic from the last action. Compare the inferred 12x6 policy matrices, the SWKL heatmap in Fig. 3b, and the k=5 persona clusters in Figs. 3c/4. If all three outputs are materially unchanged across these P specifications, the missing P is not load-bearing; if they shift, the paper must supply a justified transition kernel and release code before the behavioral-homophily claim can be assessed.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim is that the SWKL divergence between inferred IRL policies is a valid behavioral homophily measure, and that the resulting personas (e.g., Disagreers) capture real user roles. For Algorithm 1 to solve a well-posed maximum-entropy deep IRL problem, the soft Q-learning step (line 5) must have a transition kernel P for the MDP. Section 3.5 defines the 12 states and 6 actions but never specifies P, nor does it describe estimating P from the observed trajectories. Some transitions are deterministic consequences of the user's own action (e.g., posting a reply with stance + plausibly leads to the ER+ state), but the 'Wait reply' action transitions to GR+/GR~/GR- states that depend on other users' responses, and those probabilities are never given. Without P, the soft Bellman backup cannot be computed, the reward gradient in Algorithm 1 is undefined, and every downstream object (policy matrix, SWKL, k-means personas, subreddit heatmaps) inherits the arbitrariness of the missing environmental model. The issue is compounded by state-action redundancy: the states IR+/ER+/GR+ and actions PR+/PR~/PR- share the same stance labels, so part of the inferred 'policy' may simply be a first-order Markov fingerprint of the user's own previous action rather than a recovered reward-driven policy. The validation in Appendix C.2 only compares each inferred policy against random policies, which does not test the transition model or the homophily measure. Thus the central claim is not supported as written: a different but equally unspecified choice of P could yield different policies and different substantive conclusions.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes a new measure of behavioral homophily on Reddit. It models each user as an agent in a Markov decision process, uses maximum-entropy deep inverse reinforcement learning to infer a policy from the user's observed interactions, and then defines behavioral homophily between two users via a symmetric weighted KL divergence between their inferred policies. The method is applied to 662 users and 5.9 million interactions across 15 subreddits, and the inferred policies are clustered into five personas, including \"Disagreers.\" The authors compare behavioral homophily with a BERTopic-based topical homophily baseline, report a soccer/League-of-Legends case where behaviors align despite topically distant communities, and examine temporal stability of the two homophily measures.","tokens_in":21360,"tokens_out":3513,"duration_ms":39341,"significance":"If the IRL inference is well-posed, the paper offers a novel and potentially valuable operationalization of homophily for platforms without explicit social ties, and it is grounded in an unusually large Reddit corpus. The authors make concrete falsifiable claims, such as the existence of a \"Disagreer\" persona and the behavioral similarity between soccer and e-sports users, and they explicitly contrast their measure with topic-based homophily. However, the central MDP specification is incomplete: the transition kernel required by the soft Q-learning step is never defined or estimated, so the policies, SWKL values, and persona clusters are not currently well-defined. The validation appendix also does not test the homophily measure against a meaningful baseline, which weakens the central claim as presented.","major_comments":[{"comment":"Section 3.5 defines the state space and action space but never specifies or estimates the transition kernel P of the MDP, even though Algorithm 1's soft Q-learning update requires P. Some transitions are deterministic consequences of the user's own action, but the \"Wait reply\" action transitions to GR+, GR~, and GR- states whose probabilities depend on other users' responses, and those probabilities are not given or estimated. Without P, the soft Bellman backup, the reward gradient, and the inferred policy are not defined, and every downstream object (SWKL homophily, k-means personas, subreddit heatmaps) inherits this arbitrariness. The authors should either specify a transition kernel estimated from the observed data, or explicitly adopt a model-free IRL/soft-Q variant and describe how the missing P is bypassed.","section":"Section 3.5 / Algorithm 1"},{"comment":"The stance labels appear both in the state space (IR+/IR~/IR-, ER+/ER~/ER-, GR+/GR~/GR-) and in the action space (PR+/PR~/PR-). Since posting a reply with a given stance transitions to an engaged-reply state with the same stance label, part of the inferred policy is an almost deterministic re-encoding of the user's own previous action rather than a learned reward-driven choice. For example, a high probability of PR- in state ER- may simply reflect a first-order Markov fingerprint of the user's own prior behavior. This complicates the interpretation of the \"Disagreer\" persona and the behavioral homophily measure. The authors should remove the redundancy, or include a first-order Markov baseline and show that the IRL policy adds signal beyond it.","section":"Section 3.5 (state-action redundancy)"},{"comment":"The validation in Appendix C.2 compares the log-likelihood of a user's trajectory under the inferred policy with the log-likelihood under random policies. This is a goodness-of-fit check on the same trajectories used to fit the policy; it does not validate the transition model, nor does it validate the SWKL homophily measure as a meaningful behavioral comparison. A random policy is a weak baseline because a policy learned by maximum-likelihood IRL will naturally assign high likelihood to its training data. The authors should compare against a first-order Markov policy computed directly from state-action frequencies, and preferably evaluate the homophily measure on held-out trajectories or against an external criterion, such as the propensity of policy-similar users to interact.","section":"Appendix C.2"}],"minor_comments":[{"comment":"The phrase \"compiled an preliminary user set\" should read \"compiled a preliminary user set.\"","section":"Section 3.2"},{"comment":"The neutral reply action appears as PR~ in the text and as \"PR□\" in the figure legend; the notation should be made consistent.","section":"Figure 3c"},{"comment":"The topic modeling step uses a minimum threshold of 1,000 documents per topic, but the paper does not state whether this threshold was chosen in advance or after inspecting the resulting number of topics; please clarify.","section":"Section 3.4 / Appendix A.3.2"},{"comment":"The paper does not state whether the data and code used for the IRL inference, clustering, and validation are available; providing an artifact or a detailed reproducibility statement would strengthen the contribution.","section":"General"}],"recommendation":"major_revision","confidential_remarks":"The strongest concern is the missing transition kernel, which is a correctness issue in the core method rather than a presentation issue. That said, the paper's overall direction is promising for the WWW audience, and the concern seems addressable: the authors can estimate P from the observed trajectories or adopt a model-free formulation and rerun the analysis. If they do so, the state-action redundancy and the weak validation baseline should also be addressed, as they currently affect the interpretability of the personas and the evidence for the homophily claim."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Short version: this paper has a genuinely useful idea—measuring homophily by comparing IRL-inferred policies instead of topic vectors—and a large, carefully assembled Reddit dataset. But the central algorithm as written is missing its transition kernel, and that makes the whole pipeline under-specified. The empirical findings are interesting enough that I'd send it to reviewers, but I'd expect a major revision.\n\nWhat's new: the SWKL divergence over policies is a proper extension of prior IRL work on social media (Das & Lavoie, Luceri et al., Hoiles et al.), and the hierarchical state/action design for Reddit is thoughtful. The persona decomposition (Thread Creators, Root Only, Balanced, Disagreers) is the kind of output that could be useful for moderation research. The observation that r/soccer and r/leagueoflegends users behave alike despite near-zero topical overlap is a nice example of what a behavioral measure can add.\n\nThe soft spots: the missing transition kernel is the real one. Algorithm 1 calls soft Q-learning, which needs P(s'|s,a), but Section 3.5 never gives it. Some transitions are deterministic given the user's action, but 'Wait reply' leads to GR+/GR-/GR~ states that depend on other users' responses, and those probabilities are nowhere specified or estimated. Without P, the soft Bellman backup, the reward gradient, and the resulting policies are not well-defined. The validation in Appendix C.2 only checks that a user's own trajectory is more likely under their own policy than under random policies—on the same trajectories used to fit those policies. That's a goodness-of-fit check, not a test of the homophily measure. The persona count k=5 is a compromise between a silhouette score that says 2 and a gap statistic that says 10, which is honest but post hoc.\n\nThe state/action stance labels do overlap, but less problematically than it might look: the state encodes the parent comment's stance, the action encodes the user's own reply stance. That's a distinction worth keeping. The paper should still spell out the transition model; a simple empirical estimate from the trajectory data would probably be enough.\n\nWho it's for: computational social scientists studying Reddit or platform behavior. The paper deserves a serious referee; the gap is fixable and the empirical findings are worth checking. If the authors supply P and a somewhat more convincing validation, this could be a solid contribution. As written, I'd hold off citing it until the method is pinned down.","headline":"Useful measurement idea and rich Reddit study, but the IRL core is under-specified: no transition kernel is given, so the policies and homophily scores aren't currently reproducible.","tokens_in":21948,"tokens_out":3074,"would_cite":false,"duration_ms":30141,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"IRL policies reveal Reddit's hidden 'disagreers'.","keywords":["homophily","inverse reinforcement learning","behavioral homophily","Reddit","SWKL","social media dynamics","online communities","disagreement detection"],"falsifier":"Re-run the policy inference twice: once with a uniform transition kernel and once with a transition kernel estimated from the observed reply patterns. If the SWKL persona clusters and the r/soccer-r/leagueoflegends anomaly change materially under the two kernels, then the behavioral-homophily result depends on an assumption the paper leaves unspecified; if they are unchanged, the measure is robust to that assumption.","tokens_in":20833,"feed_emoji":"💬","tokens_out":6531,"duration_ms":55671,"temperature":0.7,"pith_summary":"The paper claims that homophily on Reddit can be measured behaviorally rather than by shared topics or follower ties. It treats each user as an agent whose posting, replying, and waiting-for-reply choices are the output of an unknown reward function, infers that reward with maximum-entropy deep inverse reinforcement learning, and compares users by the similarity of the resulting policies. On a six-year sample of 662 users and 5.9 million interactions, the measure separates users into behavioral personas, including a class of 'Disagreers' whose main activity is posting disagreeing replies. The paper's central empirical claim is that this behavioral measure captures structure that topic-based homophily misses, for instance users in r/soccer and r/leagueoflegends who behave alike while discussing unrelated subjects.","feed_headline":"Behavior, not topics, reveals Reddit's 'disagreers'","feed_subtitle":"Users can behave alike while discussing different topics, and some Redditors exist just to disagree.","key_machinery":"The load-bearing object is the per-user policy matrix: a 12 x 6 table produced by maximum-entropy deep IRL from a trajectory of Reddit interactions. The 12 states encode conversation positions (thread creation, root comment, reply, receiving a reply, each split by agreement, neutrality, or disagreement), and the 6 actions are waiting for a reply, creating a thread, posting a root comment, or posting an agreeing, neutral, or disagreeing reply. The Symmetric Weighted Kullback-Leibler Divergence (SWKL) symmetrizes the KL divergence between two users' policies and weights each state by how often that user visits it, so the comparison is dominated by states that characterize each user. The machinery carries the argument because the paper's persona clusters and cross-subreddit comparisons are all computed from these policy matrices, not from the content of the comments.","core_discovery":"The central discovery the paper argues for is that a user's inferred IRL policy is a meaningful signature of that user's online behavior, and that the Symmetric Weighted Kullback-Leibler (SWKL) divergence between two policies is a valid measure of behavioral homophily. Two users count as behaviorally similar when their action distributions over the same conversation states are close, with rarely visited states down-weighted. Applied to Reddit, the approach finds that behavioral and topical homophily usually align, but not always: r/leagueoflegends and r/soccer users are far apart in topic but close in behavior, and political subreddits are rich in 'Disagreers' while meme and gaming communities have none. The paper therefore claims that behavior is an independent dimension of homophily, one that can be estimated for anonymous users on platforms without explicit social networks.","pith_inferences":["If the policy-based measure is taken seriously, a natural next test is whether SWKL-similar users actually form social ties or reciprocate replies; the paper does not test this directly.","The persona clustering suggests a platform-design lever: communities wanting substantive debate might try to shift users from the Disagreer pattern toward Balanced or Root Favored patterns, though the paper does not propose interventions.","Because the policy space is compact and label-free, the same IRL-then-SWKL pipeline could be applied to other hierarchical forums or to logs of conversational agents, not just Reddit.","A stricter robustness check would be to learn the environmental response distribution from data rather than assume it; if the soccer-esports anomaly and persona clusters survive that replacement, the measure stands on firmer ground."],"forward_implications":["Behavioral homophily can be computed for anonymous users on platforms with no follower graph or hashtag structure, since it needs only public action sequences.","Subreddit membership is not a reliable guide to behavior: users from different home subreddits can share a persona, and users within one subreddit can diverge.","A measurable class of users, the Disagreers, exists across political and news subreddits and is nearly absent from meme, gaming, and support communities.","Topic-based homophily and behavioral homophily are partially independent; agreement between them is the empirical rule, and the r/leagueoflegends-r/soccer pair is a documented exception."],"supporting_citations":[{"why":"Supplies the full Reddit interaction history from which user trajectories and the 5.9-million-interaction case study are drawn.","marker":"[3]"},{"why":"Defines the maximum-entropy IRL objective that the paper's reward inference is based on.","marker":"[47]"},{"why":"Provides the deep IRL extension used to parameterize the reward function with a neural network.","marker":"[45]"},{"why":"Provides the labeled comment-reply pairs used to fine-tune the agreement, neutrality, and disagreement classifier.","marker":"[35]"},{"why":"Provides the DeBERTaV3 model that performs the argumentation classification.","marker":"[19]"},{"why":"Provides BERTopic, which defines the 484 topics used for the topic-homophily baseline.","marker":"[18]"},{"why":"Defines the KL divergence that the SWKL behavioral-homophily measure symmetrizes and weights.","marker":"[24]"},{"why":"Establishes the inverse-reinforcement-learning problem of inferring rewards from observed behavior.","marker":"[32]"},{"why":"Demonstrates IRL on social media behavior, the application the paper extends to hierarchical Reddit data.","marker":"[27]"}],"fun_headline_variants":["IRL maps Reddit's behavioral tribes, independent of topics","Reddit has users who only exist to disagree, study finds","Behavioral homophily: How Reddit users act alike across topics","Inverse reinforcement learning uncovers Reddit's behavioral roles"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The argument assumes the Reddit environment has a known or learnable transition model P, describing how other users and threads respond to a user's action, even though the paper never specifies or estimates P; without it the inferred policies are not solutions to a well-posed inverse-reinforcement-learning problem.","fun_headline_variants_meta":{"raw":{"variants":["IRL maps Reddit's behavioral tribes, independent of topics","Reddit has users who only exist to disagree, study finds","Behavioral homophily: How Reddit users act alike across topics","Inverse reinforcement learning uncovers Reddit's behavioral roles"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000372,"raw_usage":{"total_tokens":2011,"prompt_tokens":986,"completion_tokens":1025,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":602,"completion_tokens_details":{"reasoning_tokens":953}},"tokens_in":602,"tokens_out":1025,"duration_ms":10261,"temperature":1.0,"reasoning_tokens":953,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-09T10:31:08.104210+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Re-run the policy inference twice: once with a uniform transition kernel and once with a transition kernel estimated from the observed reply patterns. If the SWKL persona clusters and the r/soccer-r/leagueoflegends anomaly change materially under the two kernels, then the behavioral-homophily result depends on an assumption the paper leaves unspecified; if they are unchanged, the measure is robust to that assumption.","supporting_citations":[{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Supplies the full Reddit interaction history from which user trajectories and the 5.9-million-interaction case study are drawn."},{"cited_title":"microsoft/deberta-v3-base","cited_arxiv_id":null,"evidence_quote":"Defines the maximum-entropy IRL objective that the paper's reward inference is based on."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Provides the labeled comment-reply pairs used to fine-tune the agreement, neutrality, and disagreement classifier."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Provides the DeBERTaV3 model that performs the argumentation classification."},{"cited_title":"Kullback and R","cited_arxiv_id":null,"evidence_quote":"Defines the KL divergence that the SWKL behavioral-homophily measure symmetrizes and weights."},{"cited_title":"Ng and Stuart J","cited_arxiv_id":null,"evidence_quote":"Establishes the inverse-reinforcement-learning problem of inferring rewards from observed behavior."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Demonstrates IRL on social media behavior, the application the paper extends to hierarchical Reddit data."}],"review_version":1}