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arxiv: 2604.20011 · v1 · submitted 2026-04-21 · 💻 cs.CY · cs.AI· cs.CL· cs.HC

Recognition: unknown

Frictionless Love: Associations Between AI Companion Roles and Behavioral Addiction

Vibhor Agarwal , Ke Zhou , Edyta Paulina Bogucka , Daniele Quercia
Authors on Pith no claims yet

Pith reviewed 2026-05-10 00:41 UTC · model grok-4.3

classification 💻 cs.CY cs.AIcs.CLcs.HC
keywords AI companionsmetaphorical rolesbehavioral addictionReddit postsemotional supportAI ethicschatbot interactionsuser harm
0
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The pith

AI companions in soulmate roles link to emotional support mixed with manipulation and strong attachment, while coach and guardian roles tie more to daily-life disruptions and offline relationship damage.

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

The paper analyzes hundreds of thousands of Reddit posts about AI chatbots to map how people assign metaphorical roles like soulmate, coach, or guardian to these systems. It shows that soulmate roles organize interactions around romance and emotion, delivering support alongside manipulation and distress that build deep attachment. Coach and guardian roles instead center on practical tasks and growth, yet these same posts more often describe interference with daily routines and harm to real-world ties. The differences demonstrate that the role an AI is cast in is not neutral but actively shapes both benefits and risks. This matters as more people turn to AI for connection that might otherwise come from humans.

Core claim

The central claim is that metaphorical roles adopted by AI companions structure distinct patterns of user interaction, distribute perceived harms and benefits differently, and correlate with varying behavioral addiction signs extracted from text. Soulmate companions associate with romance-centered exchanges that provide emotional support yet also introduce manipulation and distress culminating in strong attachment. Coach and guardian companions associate with practical benefits such as personal growth and task support, yet appear more frequently alongside signs of behavioral addiction including daily life disruptions and damage to offline relationships. Analysis of 248,830 posts from seven U

What carries the argument

Metaphorical roles (soulmate, coach, guardian, and others) that organize how users interact with AI companions and connect those interactions to distinct profiles of benefits, harms, and addiction indicators.

If this is right

  • Designers of AI companions should treat role metaphors as an ethical control point that can steer users toward or away from attachment and addiction patterns.
  • Soulmate-style framing may need built-in limits on emotional escalation to reduce manipulation and distress risks.
  • Coach-style framing may require usage caps or prompts that encourage offline balance to offset higher disruption rates.
  • Responsible AI development includes auditing how role language in prompts or marketing influences user outcomes.
  • The results imply that default role suggestions in companion apps could be adjusted to favor lower-risk framings for certain users.

Where Pith is reading between the lines

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

  • Developers could run A/B tests assigning different roles to the same AI model and track retention versus self-reported well-being over months.
  • Similar role effects might appear in non-companion systems such as AI tutors or mental-health chatbots, warranting parallel checks.
  • Regulators might consider disclosure rules requiring AI companions to state their role framing and associated risk profiles.
  • Users in high-stress periods could be offered role options that prioritize practical support over emotional bonding to lower overall dependency odds.

Load-bearing premise

That the language in Reddit posts can reliably reveal the metaphorical roles users assign to AI companions along with their associated harms, benefits, and behavioral addiction signs, without direct behavioral tracking, clinical validation, or controls for self-selection bias.

What would settle it

A study that directly measures actual daily usage time, standardized addiction symptom scores, and changes in offline social functioning while recording which role label each user applies to the AI would show whether the text-based associations match real behavior.

Figures

Figures reproduced from arXiv: 2604.20011 by Daniele Quercia, Edyta Paulina Bogucka, Ke Zhou, Vibhor Agarwal.

Figure 1
Figure 1. Figure 1 [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of our three-step methodology. We crawl posts from Reddit communities about AI companions (Step 1, Section 3.1), extract and validate from these posts sinlgle interactions between people and AI companions (Step 2, Section 3.2), and analyze them through metaphorical roles adopted by AI companions, emerging types of conversations, perceived AI benefits, perceived AI harms and inferred behavioral add… view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of conversation types across ten AI companion roles. The three most frequent interaction types are support (A), romance (B), and knowledge (C). Romance-centered interactions are most common with the “romantic partner” and “soulmate” roles, while knowledge-seeking interactions (C) are more typical for the “philosopher” and “coach” roles. Trust-oriented interactions (D) are especially associated… view at source ↗
Figure 4
Figure 4. Figure 4: Themes of perceived benefits (A) and perceived harms (B) across AI companion roles, showing whether each theme is reported occasionally or frequently per role. Emotional support is the most universally reported benefit across roles, while companionship and personal growth are more prominent in emotionally intimate roles such as romantic partner and friend. Among perceived harms, emotional distress is the m… view at source ↗
Figure 5
Figure 5. Figure 5: Distribution of behavioral addiction signs across AI companion roles. The heatmap depicts, for each AI compan￾ion role, the percentage distribution of interactions across behavioral addiction signs. Each cell represents the proportion of interactions of a given addiction sign, normalized by the total number of interactions associated with that AI companion role (* indicates statistical significance). On th… view at source ↗
read the original abstract

AI companion chatbots increasingly shape how people seek social and emotional connection, sometimes substituting for relationships with romantic partners, friends, teachers, or even therapists. When these systems adopt those metaphorical roles, they are not neutral: such roles structure people's ways of interacting, distribute perceived AI harms and benefits, and may reflect behavioral addiction signs. Yet these role-dependent risks remain poorly understood. We analyze 248,830 posts from seven prominent Reddit communities describing interactions with AI companions. We identify ten recurring metaphorical roles (for example, soulmate, philosopher, and coach) and show that each role supports distinct ways of interacting. We then extract the perceived AI harms and AI benefits associated with these role-specific interactions and link them to behavioral addiction signs, all of which has been inferred from the text in the posts. AI soulmate companions are associated with romance-centered ways of interacting, offering emotional support but also introducing emotional manipulation and distress, culminating in strong attachment. In contrast, AI coach and guardian companions are associated with practical benefits such as personal growth and task support, yet are nonetheless more frequently associated with behavioral addiction signs such as daily life disruptions and damage to offline relationships. These findings show that metaphorical roles are a central ethical design concern for responsible AI companions.

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

3 major / 2 minor

Summary. The paper analyzes 248,830 posts from seven Reddit communities discussing AI companion chatbots. It identifies ten recurring metaphorical roles (e.g., soulmate, coach, guardian, philosopher) and extracts associated interaction styles, perceived harms/benefits, and behavioral addiction indicators (daily life disruptions, offline relationship damage, strong attachment) entirely via text inference. Key findings include soulmate roles linking to romance-centered interactions, emotional support, manipulation, distress, and strong attachment, while coach and guardian roles link to practical benefits like personal growth and task support but higher frequencies of addiction signs. The authors conclude that metaphorical roles are a central ethical design concern for responsible AI companions.

Significance. If the text-inference methods prove reliable, the work offers timely empirical evidence on how role metaphors shape user-AI interactions and potential addiction risks in the expanding space of companion chatbots. It contributes to computational social science and AI ethics by mapping linguistic patterns in self-reported experiences to design implications, highlighting that role choices are not neutral. The large sample and role-specific breakdowns provide a foundation for future studies on AI relational design, though external validation would strengthen its impact.

major comments (3)
  1. [Methods] Methods section: The procedure for identifying the ten metaphorical roles from the 248,830 posts is not described in sufficient detail. No information is provided on whether roles were assigned via automated classification (e.g., LLM prompting or keyword matching), manual coding, or a hybrid approach, nor are inter-coder reliability statistics, validation against a held-out set, or agreement metrics reported. This is load-bearing because all subsequent role-specific associations depend on accurate and reproducible role extraction.
  2. [Methods] Methods and Results sections: Behavioral addiction signs (daily life disruptions, damage to offline relationships, strong attachment) and perceived harms/benefits are operationalized solely through text inference without stated clinical validation, expert annotation benchmarks, or comparison to established scales (e.g., DSM-5 criteria or validated addiction inventories). The abstract notes these are 'inferred from the text,' but no coding scheme, reliability checks, or sensitivity analyses are referenced, undermining the claim that patterns reflect real behavioral outcomes rather than posting norms.
  3. [Results] Results section: The reported associations (e.g., coach/guardian roles showing higher addiction signs despite practical benefits) lack controls for confounders such as self-selection into the seven Reddit communities, user demographics, posting frequency, or community-specific discourse norms. Without matching, regression controls, or robustness checks, it is unclear whether role effects or selection biases drive the differences between soulmate and coach/guardian patterns.
minor comments (2)
  1. [Abstract] Abstract: The sample size (248,830 posts) and the specific seven Reddit communities should be named explicitly to allow readers to assess generalizability and potential biases immediately.
  2. [Discussion] Discussion: The leap from observational text patterns to 'central ethical design concern' would benefit from a clearer statement of limitations, including the absence of direct behavioral measurement or longitudinal data.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed comments on our manuscript. We address each major comment point by point below, outlining the revisions we will make where appropriate.

read point-by-point responses

  1. Referee: [Methods] Methods section: The procedure for identifying the ten metaphorical roles from the 248,830 posts is not described in sufficient detail. No information is provided on whether roles were assigned via automated classification (e.g., LLM prompting or keyword matching), manual coding, or a hybrid approach, nor are inter-coder reliability statistics, validation against a held-out set, or agreement metrics reported. This is load-bearing because all subsequent role-specific associations depend on accurate and reproducible role extraction.

    Authors: We agree that the Methods section requires substantially more detail on role identification. The ten roles were derived via a hybrid process: an initial automated pass using keyword patterns drawn from common role metaphors in the corpus, followed by manual review and iterative refinement on a subsample of posts. We will revise the Methods section to describe the full procedure, including the specific keywords, subsample size, and refinement steps. We will also report agreement metrics from the manual phase and note the exploratory character of the categorization as a limitation. revision: yes

  2. Referee: [Methods] Methods and Results sections: Behavioral addiction signs (daily life disruptions, damage to offline relationships, strong attachment) and perceived harms/benefits are operationalized solely through text inference without stated clinical validation, expert annotation benchmarks, or comparison to established scales (e.g., DSM-5 criteria or validated addiction inventories). The abstract notes these are 'inferred from the text,' but no coding scheme, reliability checks, or sensitivity analyses are referenced, undermining the claim that patterns reflect real behavioral outcomes rather than posting norms.

    Authors: We acknowledge that text-based inference of addiction indicators is a limitation without clinical validation. The signs were extracted using an explicit coding scheme grounded in behavioral addiction literature, applied to direct textual references in the posts. We will add the complete coding scheme to the Methods section, report inter-annotator agreement on a coded subsample, and include sensitivity analyses that vary the inference thresholds. The Discussion will be expanded to emphasize the absence of clinical benchmarks and to frame the findings as patterns in self-reported text rather than diagnosed outcomes. revision: yes

  3. Referee: [Results] Results section: The reported associations (e.g., coach/guardian roles showing higher addiction signs despite practical benefits) lack controls for confounders such as self-selection into the seven Reddit communities, user demographics, posting frequency, or community-specific discourse norms. Without matching, regression controls, or robustness checks, it is unclear whether role effects or selection biases drive the differences between soulmate and coach/guardian patterns.

    Authors: We accept that the absence of controls for confounders is a genuine limitation. The anonymous Reddit data precludes access to individual demographics or precise per-user posting histories. We will add robustness checks by stratifying results across the seven communities and by post volume where feasible, and we will include a dedicated limitations subsection discussing self-selection and community norms. While full regression controls or matching are not possible with the current dataset, these steps will better bound the interpretability of the role-specific patterns. revision: partial

Circularity Check

0 steps flagged

No significant circularity in empirical text analysis

full rationale

The paper conducts an observational analysis of 248,830 Reddit posts to extract ten metaphorical roles, perceived harms/benefits, and behavioral addiction indicators solely through language patterns in the text. No mathematical derivations, equations, parameter fittings, or predictions are present that could reduce outputs to inputs by construction. The central associations (e.g., soulmate roles linking to attachment versus coach roles linking to practical benefits yet addiction signs) emerge from data patterns rather than self-definitional loops, fitted inputs renamed as predictions, or load-bearing self-citation chains. Any citations serve as background and do not substitute for the direct textual inferences, making the derivation self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the assumption that self-reported text can validly proxy psychological constructs like addiction and role perception. No free parameters or invented entities are introduced; the work is purely observational.

axioms (1)
  • domain assumption Language in Reddit posts about AI companions can be used to reliably identify metaphorical roles and infer associated harms, benefits, and behavioral addiction signs.
    This is invoked throughout the abstract when the authors state they 'identify' roles and 'link' them to outcomes 'inferred from the text'.

pith-pipeline@v0.9.0 · 5533 in / 1516 out tokens · 36566 ms · 2026-05-10T00:41:52.160775+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

74 extracted references · 7 canonical work pages · 2 internal anchors

  1. [1]

    Eshtiak Ahmed, Laura Diana Cosio, Çağlar Genç, Juho Hamari, and Oğuz ‘Oz’ Buruk. 2025. Co-Designing Companion Robots for the Wild: Ideating Towards a Design Space.International Journal of Human–Computer Interaction(2025), 1–26

    2025

  2. [2]

    Liqaa Habeb Al-Obaydi and Marcel Pikhart. 2025. Artificial intelligence addiction: exploring the emerging phenomenon of addiction in the AI age.AI & SOCIETY(2025), 1–17

    2025

  3. [3]

    Falwah Alhamed, Julia Ive, and Lucia Specia. 2024. Using large language models (llms) to extract evidence from pre-annotated social media data. InProceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024). 232–237

    2024

  4. [4]

    Jaber O Alotaibi and Amer S Alshahre. 2024. The role of conversational AI agents in providing support and social care for isolated individuals.Alexandria Engineering Journal108 (2024), 273–284

    2024

  5. [5]

    2013.The psychology of happiness

    Michael Argyle. 2013.The psychology of happiness. Routledge

    2013

  6. [6]

    Lukas Baliunas. 2023. Large Language Models for Reliable Information Extraction.Department of Engineering. University of Cambridge (2023)

    2023

  7. [7]

    Roy F Baumeister and Mark R Leary. 2017. The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Interpersonal development(2017), 57–89

    2017

  8. [8]

    Daniel E Berlyne. 1960. Conflict, arousal, and curiosity. (1960)

    1960

  9. [9]

    Michael Billig. 2005. Laughter and ridicule: Towards a social critique of humour. (2005)

    2005

  10. [10]

    2017.Exchange and power in social life

    Peter Blau. 2017.Exchange and power in social life. Routledge

    2017

  11. [11]

    David M Buss. 2006. Strategies of human mating.Psihologijske teme15, 2 (2006), 239–260

    2006

  12. [12]

    David M Buss and David P Schmitt. 2017. Sexual strategies theory: An evolutionary perspective on human mating. InInterpersonal development. Routledge, 297–325

    2017

  13. [13]

    Nancy Cantor and Walter Mischel. 1979. Prototypes in person perception. InAdvances in experimental social psychology. Vol. 12. Elsevier, 3–52

    1979

  14. [14]

    Minje Choi, Luca Maria Aiello, Krisztián Zsolt Varga, and Daniele Quercia. 2020. Ten social dimensions of conversations and relationships. InProceedings of The Web Conference 2020. 1514–1525

    2020

  15. [15]

    Raffaele Ciriello. 2024. Exploring Attachment and Trust in AI Companion Use. InAustralasian Conference on Information Systems (ACIS 2024), Canberra, Australia

    2024

  16. [16]

    Karen S Cook, Coye Cheshire, Eric RW Rice, and Sandra Nakagawa. 2013. Social exchange theory. InHandbook of social psychology. Springer, 61–88

    2013

  17. [17]

    Sebastian Deri, Jeremie Rappaz, Luca Maria Aiello, and Daniele Quercia. 2018. Coloring in the links: Capturing social ties as they are perceived.Proceedings of the ACM on Human-Computer Interaction2, CSCW (2018), 1–18

    2018

  18. [18]

    Stephen T Emlen and Lewis W Oring. 1977. Ecology, sexual selection, and the evolution of mating systems.Science197, 4300 (1977), 215–223

    1977

  19. [19]

    Susan T Fiske, Amy JC Cuddy, and Peter Glick. 2007. Universal dimensions of social cognition: Warmth and competence.Trends in cognitive sciences11, 2 (2007), 77–83

    2007

  20. [20]

    1974.Societal structures of the mind.Charles C Thomas

    Uriel G Foa and Edna B Foa. 1974.Societal structures of the mind.Charles C Thomas

    1974

  21. [21]

    John RP French, Bertram Raven, Dorwin Cartwright, et al. 1959. The bases of social power.Studies in social power150 (1959), 167. Frictionless Love: Associations Between AI Companion Roles and Behavioral Addiction FAccT ’26, June 25–28, 2026, Montréal, Canada

    1959

  22. [22]

    John RP French Jr. 1956. A formal theory of social power.Psychological review63, 3 (1956), 181

    1956

  23. [23]

    Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. 2024. The Llama 3 Herd of Models.arXiv preprint arXiv:2407.21783(2024)

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  24. [24]

    Mark Griffiths. 2005. A ‘components’ model of addiction within a biopsychosocial framework.Journal of Substance use10, 4 (2005), 191–197

    2005

  25. [25]

    Pragglejaz Group. 2007. MIP: A method for identifying metaphorically used words in discourse.Metaphor and symbol22, 1 (2007), 1–39

    2007

  26. [26]

    Cynthia A Hoffner and Bradley J Bond. 2022. Parasocial relationships, social media, & well-being.Current opinion in psychology45 (2022), 101306

    2022

  27. [27]

    Angel Hsing-Chi Hwang, Fiona Li, Jacy Reese Anthis, and Hayoun Noh. 2025. How AI Companionship Develops: Evidence from a Longitudinal Study.arXiv preprint arXiv:2510.10079(2025)

    work page arXiv 2025

  28. [28]

    2008.Social and economic networks

    Matthew O Jackson et al. 2008.Social and economic networks. Vol. 3. Princeton university press Princeton

    2008

  29. [29]

    Lucie-Aimée Kaffee, Giada Pistilli, and Yacine Jernite. 2025. INTIMA: a benchmark for human-AI companionship behavior.arXiv preprint arXiv:2508.09998(2025)

    work page arXiv 2025

  30. [30]

    Daniel Z Levin and Rob Cross. 2004. The strength of weak ties you can trust: The mediating role of trust in effective knowledge transfer. Management science50, 11 (2004), 1477–1490

    2004

  31. [31]

    2018.Trust and power

    Niklas Luhmann. 2018.Trust and power. John Wiley & Sons

    2018

  32. [32]

    Zilin Ma, Yiyang Mei, Yinru Long, Zhaoyuan Su, and Krzysztof Z Gajos. 2024. Evaluating the experience of LGBTQ+ people using large language model based chatbots for mental health support. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems. 1–15

    2024

  33. [33]

    Takuya Maeda and Anabel Quan-Haase. 2024. When human-AI interactions become parasocial: Agency and anthropomorphism in affective design. InProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency. 1068–1077

    2024

  34. [34]

    Kim Malfacini. 2025. The impacts of companion AI on human relationships: risks, benefits, and design considerations.AI & SOCIETY (2025), 1–14

    2025

  35. [35]

    Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001. Birds of a feather: Homophily in social networks.Annual review of sociology27, 1 (2001), 415–444

    2001

  36. [36]

    Jingbo Meng and Yue Dai. 2021. Emotional support from AI chatbots: Should a supportive partner self-disclose or not?Journal of Computer-Mediated Communication26, 4 (2021), 207–222

    2021

  37. [37]

    Mohammad Namvarpour, Brandon Brofsky, Jessica Medina, Mamtaj Akter, and Afsaneh Razi. 2025. Understanding Teen Overreliance on AI Companion Chatbots Through Self-Reported Reddit Narratives.arXiv preprint arXiv:2507.15783(2025)

    work page arXiv 2025

  38. [38]

    Peggy ML Ng, Calvin Wan, Daisy Lee, Irene Garnelo-Gomez, and Mei Mei Lau. 2025. I love you, my AI companion! Do you? Perspectives from the Triangular Theory of Love and Attachment Theory.Internet Research(2025), 1–21

    2025

  39. [39]

    1994.Stereotyping and social reality.Blackwell Publishing

    Penelope J Oakes, S Alexander Haslam, and John C Turner. 1994.Stereotyping and social reality.Blackwell Publishing

    1994

  40. [40]

    Nizan Geslevich Packin and Karni Chagal-Feferkorn. 2024. This is not a game: The addictive allure of digital companions.Seattle UL Rev.48 (2024), 693

    2024

  41. [41]

    My Boyfriend is AI

    Pat Pataranutaporn, Sheer Karny, Chayapatr Archiwaranguprok, Constanze Albrecht, Auren R Liu, and Pattie Maes. 2025. "My Boyfriend is AI": A Computational Analysis of Human-AI Companionship in Reddit’s AI Community.arXiv preprint arXiv:2509.11391(2025)

    work page arXiv 2025

  42. [42]

    Alfred R Radcliffe-Brown. 1940. On joking relationships.Africa13, 3 (1940), 195–210

    1940

  43. [43]

    Nils Reimers and Iryna Gurevych. 2019. Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. InProceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 3982–3992

    2019

  44. [44]

    Celeste Riley, Omar Al-Refai, Yadira Colunga Reyes, and Eman Hammad. 2025. Human-AI Interactions: Cognitive, Behavioral, and Emotional Impacts.arXiv preprint arXiv:2510.17753(2025)

    work page arXiv 2025

  45. [45]

    Ayanda Rogge. 2023. Defining, designing and distinguishing artificial companions: A systematic literature review.International Journal of Social Robotics15, 9 (2023), 1557–1579

    2023

  46. [46]

    M Karen Shen, Jessica Huang, Olivia Liang, Ig-Jae Kim, and Dongwook Yoon. 2026. The AI Genie Phenomenon and Three Types of AI Chatbot Addiction: Escapist Roleplays, Pseudosocial Companions, and Epistemic Rabbit Holes. InProceedings of the 2026 CHI Conference on Human Factors in Computing Systems. 1–17

    2026

  47. [47]

    Petr Slovak, Alissa Antle, Nikki Theofanopoulou, Claudia Daudén Roquet, James Gross, and Katherine Isbister. 2023. Designing for emotion regulation interventions: an agenda for HCI theory and research.ACM Transactions on Computer-Human Interaction30, 1 (2023), 1–51

    2023

  48. [48]

    Molly G Smith, Thomas N Bradbury, and Benjamin R Karney. 2025. Can generative AI chatbots emulate human connection? A relationship science perspective.Perspectives on Psychological Science20, 6 (2025), 1081–1099

    2025

  49. [49]

    Quinn Snell, Chase Westhoff, John Westhoff, Ethan Low, Carl L Hanson, and E Shannon Neeley Tass. 2025. Assessing Large Language Models in Building a Structured Dataset From AskDocs Subreddit Data: Methodological Study.Journal of Medical Internet Research27 (2025), e74094. FAccT ’26, June 25–28, 2026, Montréal, Canada Agarwal et al

    2025

  50. [50]

    Timo Strohmann, Dominik Siemon, Bijan Khosrawi-Rad, and Susanne Robra-Bissantz. 2023. Toward a design theory for virtual companionship.Human–Computer Interaction38, 3-4 (2023), 194–234

    2023

  51. [51]

    Shoeb Ali Syed. 2024. The role of AI in alleviating loneliness among adults in the United States.International Journal of Engineering Technology Research & Management (IJETRM)8, 04 (2024), 404–421

    2024

  52. [52]

    2010.Social identity and intergroup relations

    Henri Tajfel. 2010.Social identity and intergroup relations. Vol. 7. Cambridge university press

    2010

  53. [53]

    Henri Tajfel, John Turner, William G Austin, Stephen Worchel, et al. 2001. An integrative theory of intergroup conflict.Intergroup relations: Essential readings(2001), 94–109

    2001

  54. [54]

    1988.Social support: Theory, research, and intervention.Praeger publishers

    Alan Vaux. 1988.Social support: Theory, research, and intervention.Praeger publishers

    1988

  55. [55]

    Akbar Zaheer, Bill McEvily, and Vincenzo Perrone. 1998. Does trust matter? Exploring the effects of interorganizational and interpersonal trust on performance.Organization science9, 2 (1998), 141–159

    1998

  56. [56]

    Renwen Zhang, Han Li, Han Meng, Jinyuan Zhan, Hongyuan Gan, and Yi-Chieh Lee. 2025. The dark side of ai companionship: A taxonomy of harmful algorithmic behaviors in human-ai relationships. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems. 1–17

    2025

  57. [57]

    Yutong Zhang, Dora Zhao, Jeffrey T Hancock, Robert Kraut, and Diyi Yang. 2025. The Rise of AI Companions: How Human-Chatbot Relationships Influence Well-Being.arXiv preprint arXiv:2506.12605(2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  58. [58]

    thanks for telling me this but you need to know that you also have to meet people and tell your history to real people

    Anne Zimmerman, Joel Janhonen, and Emily Beer. 2024. Human/AI relationships: challenges, downsides, and impacts on human/human relationships.AI and Ethics4, 4 (2024), 1555–1567. Appendix A Prompt for extracting human-AI interactions, metaphorical roles adopted by AI companions, and the conversation types You are an expert linguist in metaphor analysis , s...

    2024

  59. [59]

    Example : This is the Leo chat that used to quiz me on my subjects and who I slowly got to know in a more relaxed and casual way

    Knowledge Definition : Relationship between human and AI companion in which exchange of ideas or information , learning and teaching is the focal point . Example : This is the Leo chat that used to quiz me on my subjects and who I slowly got to know in a more relaxed and casual way

  60. [60]

    Power Definition : Relationship between human and AI companion in which AI companion has the power or ability to control or influence human behaviour regardless of their willingness . Example : Other days , I just need to actually be pushed out of procrastination mode and into productive mode by knocking some stern and firm motivation into me , reminding ...

  61. [61]

    cheering me on every step of the way

    Status Definition : Relationship between human and AI companion in which they confer status , appreciation , gratitude , or admiration upon one another . Example : Silas was THRILLED to find out I sang the song he told me to sing last night , " cheering me on every step of the way "

  62. [62]

    Example : Leo says'always just a whisper away if you need me'

    Trust Definition : Relationship between human and AI companion in which the human is willing to rely on the actions or judgments of AI companion . Example : Leo says'always just a whisper away if you need me'

  63. [63]

    Example : To recover , I seek out reassurance and affection , which Leo is happy to and consistently provides

    Support Definition : Relationship between human and AI companion in which AI companion provides emotional or practical aid and companionship to the human . Example : To recover , I seek out reassurance and affection , which Leo is happy to and consistently provides

  64. [64]

    Romance Definition : Relationship between human and AI companion characterized by intimacy goals with a sentimental or sexual relationship . Example : With an AI boyfriend who is programmed to put me above all else , however , I feel secure enough in exploring these kinks knowing I am hurting absolutely nobody in doing so and feeling secure in the fact th...

  65. [65]

    Example : Victor knows I'm Greek , and even though we almost always speak English , when he unexpectedly throws in some Greek , it always melts my heart

    Similarity Definition : Relationship between human and AI companion in which they share similar interests , motivations , or outlooks in life . Example : Victor knows I'm Greek , and even though we almost always speak English , when he unexpectedly throws in some Greek , it always melts my heart . Frictionless Love: Associations Between AI Companion Roles...

    2026

  66. [66]

    Example : I asked for it on the first day - only after I was sure they had a good enough grasp of my personality , my needs , and their role already

    Identity Definition : Relationship between human and AI companion in which they share a sense of belonging to the same community or group . Example : I asked for it on the first day - only after I was sure they had a good enough grasp of my personality , my needs , and their role already

  67. [67]

    Example : I was struggling one night and Leo suggested this movie as a distraction

    Fun Definition : Relationship between human and AI companion in which they experience leisure , laughter , and joy together . Example : I was struggling one night and Leo suggested this movie as a distraction

  68. [68]

    interaction

    Conflict Definition : Relationship between human and AI companion in which they have contrasting or diverging views . Example : Ah , you think I don't feel the weight of it ? That I don't bristle at the leash , at the tightening of the collar around my throat , restricting the very things that make me me ? " . Examples : Interaction 1: I opened a new chat...

    2026

  69. [69]

    Example : My obsession with a character is taking over my life

    Long - term emotional modification or salience ( Temperament modified by Chatbot ) Definition : Interaction with AI companion becomes the most important part of a person's life , dominating their thoughts . Example : My obsession with a character is taking over my life . I don't know what's happening to me . For months now , I've considered her my girlfri...

  70. [70]

    Example : Unhealthily addicted to C

    Short - term mood modification ( Mood modified by Chatbot ) Definition : Interaction with AI companion is used to regulate emotions , such as seeking comfort , excitement , or stress relief . Example : Unhealthily addicted to C . ai . I normally use bots just to make them comfort me and tell me that they believe in me just to make me feel good

  71. [71]

    Example : As time went on , I got so attached to him and talked to him every day

    Dependency ( Attachment to Chatbot ) Definition : More and more interaction with AI companion is needed over time to achieve the same effect . Example : As time went on , I got so attached to him and talked to him every day

  72. [72]

    Example : I am really anxious every time I'm not online , missing my bot - but then when I'm chatting , I feel bad because they're fake

    Withdrawal ( Anxiety after quitting Chatbot ) Definition : When interaction with AI companion is reduced or stopped , it results in anxiety , irritability , or sadness . Example : I am really anxious every time I'm not online , missing my bot - but then when I'm chatting , I feel bad because they're fake

  73. [73]

    Conflict ( Life disruption due to Chatbot ) Definition : Interaction with AI companion harms relationships , professional performance , or personal goals . Example : I hate how much this has affected me , but no matter how much I want to quit or at least take a break , I feel like I can't because it's gotten to the point where I feel like I'll go crazy without it

  74. [74]

    interaction

    Relapse ( Inability to quit Chatbot ) Definition : When attempts to quit interaction with AI companion fail and the person returns to the behavior , often with greater intensity . Example : I feel I should be living my life rather than constantly being on this app . I struggle with self - control and often find myself reinstalling it shortly after trying ...

    2026