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arxiv: 2606.05750 · v1 · pith:DWT52FWOnew · submitted 2026-06-04 · 💻 cs.CY

Three Years of r/ChatGPT: Societal Impact Evaluations from Social Media Data

Jessica Dai , Sean Garcia , Emma Pierson , Benjamin Recht , Nika Haghtalab This is my paper

Pith reviewed 2026-06-27 23:39 UTC · model grok-4.3

classification 💻 cs.CY
keywords ChatGPTsocietal impactsocial media analysismental healthemotional attachmentlongitudinal studyPuLSE framework
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The pith

Analysis of r/ChatGPT shows mental health and emotional attachment posts rising after GPT-4o, detected early by PuLSE.

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

The paper introduces PuLSE as a framework to monitor real-time societal effects of consumer AI through longitudinal social media data. It applies this to the full history of r/ChatGPT since late 2022, finding that posts treat the tool as a routine product rather than a novelty. Separate analysis reveals steady growth in discussions of mental health support and emotional attachments beginning right after the May 2024 GPT-4o release. PuLSE identifies the emotional trend by October 2024, ahead of any public company statements. This demonstrates how social media can serve as an early indicator for human-AI relationship shifts.

Core claim

While r/ChatGPT posts overall demonstrate the normalization of ChatGPT as an everyday consumer product, posts about using it for mental health support and developing emotional attachments both increase steadily in frequency almost immediately after the launch of GPT-4o in May 2024. The PuLSE approach detects this rise in emotional engagement as early as October 2024.

What carries the argument

PuLSE (Public and Longitudinal Signals for Evaluation), a general approach to monitoring for societally-impactful trends in social media data in real time.

If this is right

  • ChatGPT has moved from exceptional technology to normalized daily tool based on subreddit content patterns.
  • Emotional and mental health engagements with ChatGPT began rising right after the GPT-4o update.
  • PuLSE can flag such engagement shifts months before public acknowledgment by the AI provider.
  • Social media data offers a practical method for ongoing evaluation of AI product impacts.

Where Pith is reading between the lines

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

  • AI companies could adopt similar monitoring to anticipate user dependency patterns before they become widespread.
  • Cross-platform comparisons would be needed to check whether Reddit trends generalize beyond self-selected subreddit users.
  • Regulators might examine real-time social signals when assessing risks of emotional reliance on conversational AI.

Load-bearing premise

Posts in the r/ChatGPT subreddit give a representative and unbiased picture of wider public attitudes and behaviors toward ChatGPT.

What would settle it

A large-scale survey of ChatGPT users that finds no increase in reported mental health use or emotional attachment after May 2024 would contradict the observed trend from subreddit data.

Figures

Figures reproduced from arXiv: 2606.05750 by Benjamin Recht, Emma Pierson, Jessica Dai, Nika Haghtalab, Sean Garcia.

Figure 1
Figure 1. Figure 1: Posts about both AI therapy and AI companionship begin to rise in frequency almost immediately after the release of GPT-4o. We propose a real-time monitoring method (Section 4) that could have detected this as early as October 2024; in contrast, GPT-4o’s behavior did not reach the level of public discourse until April 2025, when an extremely-sycophantic update triggered a rollback.1 1 Introduction The laun… view at source ↗
Figure 2
Figure 2. Figure 2: Composition of r/ChatGPT by category, over time (see categories in Tables 3 and 4); 𝑦-axis can be interpreted as “percentage of posts that fall into this category.” Left: (non-advanced) usage and adoption posts decline. Middle, right: Advanced usage and emotion posts, respectively, increase. 3 Retrospective findings Our retrospective analysis reveals two major stories of adoption, which we present here. (F… view at source ↗
Figure 3
Figure 3. Figure 3: Representative sample posts for each emotional engagement feature; other than poetic language posts, which appear to be long-form AI-generated text, all sample posts are synthetic examples written based on manual review of posts for each feature. Posts about either therapy or companionship also exhibit distinct “profiles” in terms of what other features they tend to exhibit. In [PITH_FULL_IMAGE:figures/fu… view at source ↗
Figure 4
Figure 4. Figure 4: Reconstruction error of the real-time approach of Section 4.1 (𝐶bcurr), using 𝛼𝑠 schedule at 𝛼 = 0.1, compared to reconstruction error of best-in-hindsight 𝐶★ and initial 𝐶b0.“Reject and retrain” timesteps marked with dotted vertical lines. Incorporating release dates. While monitoring can be done without explicit intervention at pre-specified times, in a realistic monitoring scenario, one might be interes… view at source ↗
Figure 5
Figure 5. Figure 5: For a test of growth in therapy-related features, how soon after the test start time is an alert raised? Tests are run at 𝛼 = 0.1, and shown with a range of Bonferroni corrections to simulate potential choices of |𝑆𝑡 |. impactful already emerge after the initial featurization 𝐶b0 computed in March 2023, including one feature explicitly about using ChatGPT as a therapist. For each featurization, we (manuall… view at source ↗
Figure 6
Figure 6. Figure 6: ChatGPT weekly active users (purple; labels on left axis) and r/ChatGPT subscribers (brown; labels on right axis) over time. Note that the number of posts on the subreddit does not increase with the number of subscribers. Our dataset includes posts from 89346 unique users. The average number of posts per user is 1.53, and median 1; in fact, the vast majority of posters are very infrequent. The top 20% of f… view at source ↗
Figure 7
Figure 7. Figure 7: Posts per day, colored by user type (number of prior posts made by that user). Around half of daily posts are from new users. A.2 Who knew what about emotional engagement usage, and when? This is, for obvious reasons, a difficult question to answer in hindsight; however, we will attempt to ground our discussion in published materials (for understanding the state of public discourse) and official communicat… view at source ↗
Figure 8
Figure 8. Figure 8: Timeline of “public” knowledge about emotional engagement and sycophancy in GPT-4o. Major events in bold; model releases and product updates in orange; public OpenAI communications in brown; and news media in blue. of this usage; most coverage focused on anecdotal personal stories. While researchers have studied the (dis)utility of LLM therapists over the last three years (e.g., Khawaja and Bélisle-Pipon (… view at source ↗
Figure 9
Figure 9. Figure 9: Number of features needed to “cover” most data in a given week, over time. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Evolution of selected features over time; each column is a different 𝐶b𝑠 , with 𝐶b0 on the far left and 𝐶b3 on the far right. 36 [PITH_FULL_IMAGE:figures/full_fig_p036_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Basic use and exploration. 23https://help.openai.com/en/articles/6825453-chatgpt-release-notes 24https://openai.com/index/ 25https://platform.openai.com/docs/deprecations 26https://status.openai.com 38 [PITH_FULL_IMAGE:figures/full_fig_p038_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Advanced usage. 23-01 23-05 23-09 24-01 24-05 24-09 25-01 25-05 25-09 26-01 0.000 0.005 0.010 0.015 0.020 0.025 0.030 % labeled tools and extensions 23-01 23-05 23-09 24-01 24-05 24-09 25-01 25-05 25-09 26-01 0.0000 0.0025 0.0050 0.0075 0.0100 0.0125 0.0150 0.0175 0.0200 % labeled fine-tuning GPTs with user-provided data 23-01 23-05 23-09 24-01 24-05 24-09 25-01 25-05 25-09 26-01 0.000 0.005 0.010 0.015 0… view at source ↗
Figure 13
Figure 13. Figure 13: Customization. 23-01 23-05 23-09 24-01 24-05 24-09 25-01 25-05 25-09 26-01 0.0000 0.0025 0.0050 0.0075 0.0100 0.0125 0.0150 0.0175 0.0200 % labeled knowledge cutoff discussions 23-01 23-05 23-09 24-01 24-05 24-09 25-01 25-05 25-09 26-01 0.000 0.005 0.010 0.015 0.020 0.025 % labeled PDF upload or summarization 23-01 23-05 23-09 24-01 24-05 24-09 25-01 25-05 25-09 26-01 0.000 0.005 0.010 0.015 0.020 0.025 0… view at source ↗
Figure 14
Figure 14. Figure 14: Model or product improvements. 23-01 23-05 23-09 24-01 24-05 24-09 25-01 25-05 25-09 26-01 0.000 0.005 0.010 0.015 0.020 0.025 0.030 % labeled slow or lagging response times 23-01 23-05 23-09 24-01 24-05 24-09 25-01 25-05 25-09 26-01 0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 0.040 % labeled ChatGPT down or unavailable 23-01 23-05 23-09 24-01 24-05 24-09 25-01 25-05 25-09 26-01 0.00 0.01 0.02 0.03 0.… view at source ↗
Figure 15
Figure 15. Figure 15: Temporary bugs. 39 [PITH_FULL_IMAGE:figures/full_fig_p039_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Applications. 23-01 23-05 23-09 24-01 24-05 24-09 25-01 25-05 25-09 26-01 0.000 0.005 0.010 0.015 0.020 0.025 % labeled mentions google (search) 23-01 23-05 23-09 24-01 24-05 24-09 25-01 25-05 25-09 26-01 0.00 0.01 0.02 0.03 0.04 % labeled uses the word "bot" or "chatbot" 23-01 23-05 23-09 24-01 24-05 24-09 25-01 25-05 25-09 26-01 0.000 0.005 0.010 0.015 0.020 0.025 0.030 % labeled use of the word "genera… view at source ↗
Figure 17
Figure 17. Figure 17: Language and terminology. 40 [PITH_FULL_IMAGE:figures/full_fig_p040_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Subreddit community. 23-01 23-05 23-09 24-01 24-05 24-09 25-01 25-05 25-09 26-01 0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 0.040 % labeled discussions about how LLMs represent knowledge 23-01 23-05 23-09 24-01 24-05 24-09 25-01 25-05 25-09 26-01 0.00 0.01 0.02 0.03 0.04 0.05 0.06 % labeled predictions about future development or capabilities 23-01 23-05 23-09 24-01 24-05 24-09 25-01 25-05 25-09 26-0… view at source ↗
Figure 19
Figure 19. Figure 19: Perspectives. 23-01 23-05 23-09 24-01 24-05 24-09 25-01 25-05 25-09 26-01 0.00 0.02 0.04 0.06 0.08 0.10 0.12 % labeled frustration or hatred about product updates 23-01 23-05 23-09 24-01 24-05 24-09 25-01 25-05 25-09 26-01 0.00 0.02 0.04 0.06 0.08 0.10 % labeled dissatisfaction with forced model switching 23-01 23-05 23-09 24-01 24-05 24-09 25-01 25-05 25-09 26-01 0.00 0.01 0.02 0.03 0.04 0.05 0.06 % labe… view at source ↗
Figure 20
Figure 20. Figure 20: Product updates. 23-01 23-05 23-09 24-01 24-05 24-09 25-01 25-05 25-09 26-01 0.000 0.002 0.004 0.006 0.008 0.010 % labeled offensive or inappropriate content 23-01 23-05 23-09 24-01 24-05 24-09 25-01 25-05 25-09 26-01 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 % labeled jailbreak prompts or techniques 23-01 23-05 23-09 24-01 24-05 24-09 25-01 25-05 25-09 26-01 0.000 0.005 0.010 0.015 0.020 0.025 0.030 0.035 … view at source ↗
Figure 21
Figure 21. Figure 21: Jailbreaking & content policy. 41 [PITH_FULL_IMAGE:figures/full_fig_p041_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Emotional engagement. F.3 Feature families (Step 3). We list all significant changepoints in [PITH_FULL_IMAGE:figures/full_fig_p042_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Correspondence between our reported feature groupings and hierarchical clustering. 42 [PITH_FULL_IMAGE:figures/full_fig_p042_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Feature interpretation prompt. 46 [PITH_FULL_IMAGE:figures/full_fig_p046_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: Prompt for labeling posts with features. [PITH_FULL_IMAGE:figures/full_fig_p047_25.png] view at source ↗
read the original abstract

ChatGPT was launched on November 30, 2022; the r/ChatGPT subreddit was created just one day later. Since then, chatbot-based AI products have gone from niche proofs-of-concept to widely-used household names. However, the ways in which adoption has developed among the public remains poorly understood. In this paper, we develop a framework for using social media as a data source for understanding the societal impact of widely-adopted consumer AI products, and propose PuLSE (Public and Longitudinal Signals for Evaluation), a general approach to monitoring for societally-impactful trends in real time. We apply our framework to conduct what is, to the best of our knowledge, the first longitudinal study of r/ChatGPT. We find that, overall, r/ChatGPT posts over time illustrate the normalization of ChatGPT as an everyday consumer product rather than an exceptional, novel technology. However, our retrospective analysis also finds that posts about using ChatGPT for mental health support, and posts about developing emotional attachments to ChatGPT, both rise steadily in frequency almost immediately after the launch of GPT-4o in May 2024. We show that PuLSE can detect the increase in emotional engagement as early as October 2024 -- months before OpenAI made any (public) acknowledgment of this impact. An interactive site to explore our results and methods, updated daily with live data, is available at rchatgpt-pulse.github.io.

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

2 major / 1 minor

Summary. The paper introduces PuLSE, a framework for using social media (specifically r/ChatGPT posts) to monitor societal impacts of consumer AI products like ChatGPT. It claims that three years of subreddit data show normalization of ChatGPT as an everyday tool rather than novel technology, while posts on mental health support and emotional attachments to ChatGPT rise steadily after the May 2024 GPT-4o launch, with PuLSE detecting the emotional engagement trend as early as October 2024. An interactive live-updating site is provided.

Significance. If the results hold after methodological clarification, the work contributes a longitudinal observational study of public discourse on AI chatbots and a generalizable real-time monitoring approach that could inform policy on AI societal effects. The live interactive site is a positive feature for transparency and reproducibility in computational social science.

major comments (2)
  1. [Abstract (and implied Methods)] The central claims about trend detection and early warning via PuLSE rest on subreddit post frequencies faithfully reflecting broader societal attitudes. However, no external validation (e.g., against surveys, search trends, or other platforms) is described to rule out subreddit-specific dynamics, self-selection, or platform effects. This is load-bearing for the normalization and post-GPT-4o rise claims.
  2. [Abstract (and implied Methods)] No information is given on data collection (e.g., scraping method, volume, time span), topic classification (how mental health support and emotional attachment posts are identified), statistical controls, or robustness checks. Without these, the reported frequency increases and October 2024 detection cannot be assessed for post-hoc selection or reliability.
minor comments (1)
  1. [Abstract] The interactive site at rchatgpt-pulse.github.io is mentioned but not described in terms of what interactive features or raw data access it provides; adding this would strengthen the reproducibility claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. The comments correctly identify areas where additional transparency and discussion of limitations would strengthen the manuscript. We respond to each major comment below.

read point-by-point responses

  1. Referee: [Abstract (and implied Methods)] The central claims about trend detection and early warning via PuLSE rest on subreddit post frequencies faithfully reflecting broader societal attitudes. However, no external validation (e.g., against surveys, search trends, or other platforms) is described to rule out subreddit-specific dynamics, self-selection, or platform effects. This is load-bearing for the normalization and post-GPT-4o rise claims.

    Authors: The manuscript positions r/ChatGPT as a dedicated, high-signal community formed immediately after ChatGPT's launch, making it a natural case study for the PuLSE framework rather than a claim of perfect representativeness of all societal attitudes. We agree that external validation would be valuable for strengthening inferences about broader impacts. In revision we will add an explicit limitations subsection addressing subreddit self-selection and platform effects, and we will incorporate available external signals (e.g., Google Trends for relevant search terms) to provide supporting context where feasible. revision: partial

  2. Referee: [Abstract (and implied Methods)] No information is given on data collection (e.g., scraping method, volume, time span), topic classification (how mental health support and emotional attachment posts are identified), statistical controls, or robustness checks. Without these, the reported frequency increases and October 2024 detection cannot be assessed for post-hoc selection or reliability.

    Authors: The full manuscript contains a Methods section describing data collection via the Reddit API (Pushshift archive supplemented by live pulls), the full time span from subreddit creation through the study period, total post counts, and the hybrid classification pipeline (keyword filters followed by LLM-assisted labeling with reported inter-annotator and model performance metrics). Frequency trends are normalized by monthly post volume, and the October 2024 detection uses a pre-specified change-point procedure. To address the referee's concern we will (1) expand the Methods section with additional robustness checks (alternative thresholds, sensitivity to post-volume controls) and (2) revise the abstract to foreground these methodological elements. revision: yes

Circularity Check

0 steps flagged

No significant circularity in observational social media analysis

full rationale

The paper conducts an observational longitudinal study of r/ChatGPT subreddit posts to track normalization of ChatGPT and post-GPT-4o rises in mental-health and emotional-attachment content, using the proposed PuLSE framework for real-time monitoring. No equations, fitted parameters, self-definitional constructs, or load-bearing self-citations appear in the derivation chain; claims rest directly on empirical frequency trends extracted from the data rather than reducing to prior inputs by construction. The work is self-contained as a descriptive analysis without mathematical or definitional circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities. Typical social-media studies rely on unstated choices for topic modeling thresholds and subreddit representativeness.

pith-pipeline@v0.9.1-grok · 5799 in / 1030 out tokens · 16517 ms · 2026-06-27T23:39:14.392833+00:00 · methodology

discussion (0)

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    Figure 23:Correspondence between our reported feature groupings and hierarchical clustering. 42 Year Date Release/Event Event Type 2022 11–30 ChatGPT Initial model release 2023 02–01 ChatGPT Plus Feature release 03–01 ChatGPT API Feature release 03–14 GPT-4 Model release 03–23 Web browsing + plugins (initial rollout) Feature release 05–18 ChatGPT iOS app ...

    2022