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REVIEW 3 major objections 4 minor 90 references

Understanding and Supporting Co-viewing Comedy in VR with Embodied Expressive Avatars

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper claims that visible, user-triggered avatar laughter in VR changes co-viewing comedy from solo immersion into socially coordinated participation, amplifying emotional contagion and prompting shared norms about when to laugh.

desk verdict Solid mixed-methods study of VR co-viewing with a real confound between avatar presence and laughter cues; the causal claim about laughter cues needs either a neutral-avatar control or a narrower framing. read the letter →

arxiv 2505.20082 v1 pith:RP2IBCOL submitted 2025-05-26 cs.HC

Ryo Ohara , Chi-Lan Yang , Takuji Narumi , Hideaki Kuzuoka This is my paper · ORCID
classification cs.HC
keywords co-viewingvirtualrealityembodiedavatarslaughtercuesemotionalcontagionengagementexpressivenormscommunicationaccommodation
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper tries to establish that in virtual reality, seeing co-viewers' embodied laughter, not just hearing their voices, changes how people watch comedy together. In a within-subjects experiment with eight acquainted triads (N=24), the authors compared voice-only co-viewing with co-viewing in which participants were embodied as expressive avatars that displayed full-body and facial laughter cues the user could trigger. They found that visible laughter cues raised Involvement, Endurability, and Novelty scores and increased chained laughter events, while interview data showed attention shifting from the screen toward socially coordinated watching, with participants monitoring and adjusting their own laughter to fit the group. The authors interpret this as emotional contagion through visible mimicry and the emergence of expressive norms, with a real trade-off: participants sometimes felt social pressure and missed the freedom of laughing unobserved. If the claim holds, designers of remote co-viewing systems should treat visible embodied reactions as a way to foster shared emotional experiences, but must give users control over how visible those reactions are.

What carries the argument

The central object is the user-triggered embodied laughter cue: a button-pressed avatar animation combining whole-body seated-laughter motion with Facial Action Coding System (FACS) facial expressions, including cheek raising, lip-corner pulling, and lips parting, displayed on each participant's avatar and visible to the whole triad through a mirror above the shared screen. The mirror lets each viewer see both their own avatar's laughter and co-viewers' reactions, creating a self-observation and mutual-observation loop. This machinery carries the argument by being the only systematic difference between the two conditions, and it operationalizes emotional contagion as chained laughter events, a follow-up laughter cue triggered within five seconds of another participant's cue.

What would settle it

Run the same study with three conditions, voice only, an idling avatar that is present but never animates laughter, and the expressive avatar, and compare chained laughter, engagement scores, and interview reports of social pressure. If the idling-avatar condition behaves like voice-only, the laughter animation is the causal ingredient; if it behaves like the expressive condition, avatar presence itself is the cause.

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Extended reading notes

Core claim

The paper's central claim is that visible, user-triggered avatar laughter, not the voice channel alone, is what shifts triadic VR co-viewing of comedy from individual immersion to socially coordinated participation. In the comparison of Voice Only versus Voice + Laughter Cues, the expressive condition significantly increased self-reported Involvement, Endurability, and Novelty and significantly increased the rate of chained laughter events, with no significant change in focused attention or perceived usability. The qualitative analysis argues that these cues heighten self-awareness of one's own emotional expression, create emotional contagion through observable motor mimicry, and lead groups to develop normative expectations about when and how to laugh. The authors present the downside as integral to the finding: with visible expressive avatars, participants reported social pressure to conform, hesitation about initiating laughter, and occasional deliberate withholding of reactions to preserve personal boundaries.

Load-bearing premise

The load-bearing assumption is that the visible laughter animation, not simply having an avatar present, causes the observed shifts, because the expressive condition adds embodiment and visible expression together while the voice-only baseline has neither, and the authors explicitly chose not to include an idling-avatar control because mismatched audio and visual cues can undermine social presence.

Editorial extensions

If this is right

  • If the central claim is right, adding user-triggered embodied laughter cues to remote co-viewing will raise involvement, endurability, and novelty relative to voice-only co-viewing, without necessarily sharpening focused attention on the content.
  • Designers should expect visible expressive cues to generate social norms: viewers monitor co-viewers and accommodate the timing of their laughter, so systems need per-user and per-content control over how visible emotional expressions are.
  • Seeing one's own avatar laugh can intensify felt amusement, implying that avatar animation is not only a signal to others but also a way to shape the viewer's own emotional experience.
  • For content a viewer wants to enjoy privately or react to honestly, an invisible or low-expressivity mode is likely preferable, so co-viewing systems should support both visible and hidden expressive modes.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the same visible-cue mechanism should transfer to other emotions and genres if the avatar animation matches the target emotion; a horror or concert setting with user-triggered fearful or awe expressions would be a direct test.
  • Editorial inference: the five-second chained-laughter count likely undercounts contagion in the voice-only condition, where smiles and silent laughs can follow audio cues without a logged button press; adding head-tracking or vocal-pitch logs would sharpen the comparison.
  • Editorial inference: the tension between connection and conformity suggests a tunable emotional-visibility control as a design direction, with the prediction that users will choose different visibility settings for close ties versus strangers and for comedy versus sensitive content.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. This paper reports a within-subjects VR experiment (N=24 in eight triads) comparing voice-only co-viewing with a condition in which participants were embodied in expressive avatars with manually triggered laughter animations. Through questionnaires, system logs of chained laughter, and post-hoc interviews, the authors argue that visible embodied laughter cues shifted engagement from individual immersion to socially coordinated participation, heightened self-awareness and emotional contagion, and fostered expressive norms. The paper also derives design implications for balancing personal immersion with interpersonal emotional accommodation when co-viewing in social VR.

Significance. The topic is timely and the mixed-method design is well suited to generating rich, ecologically grounded observations about avatar-mediated co-viewing. The authors include an a priori power analysis, counterbalancing, deception to reduce demand characteristics, and a thematic analysis with saturation checks, and they describe the implemented VRChat world in enough detail for replication. The qualitative materials and interview quotes are valuable for hypothesis generation. However, the central causal claim is not identified by the experimental contrast, and the quantitative engagement results are weak under multiple-testing correction. The paper's contribution would be stronger as an exploratory account of avatar-mediated co-viewing than as a demonstration that laughter cues specifically cause the observed shifts.

major comments (3)
  1. [Section 3.1 and abstract] The VO condition has no avatars at all, while the VLC condition adds full-body avatars, a mirror, and laughter animations simultaneously. The contrast therefore cannot identify 'embodied laughter cues' as the active ingredient; any difference could be due to having an avatar, seeing co-viewers' avatars, or seeing one's own avatar. The authors' stated rationale for omitting an idling-avatar condition explains a design constraint but does not provide the needed control. Participants' own quotes (P01, P05, P20) attribute togetherness to 'Having avatars', 'being embodied in an avatar', and 'With avatars present', not specifically to laughter animations. In addition, in VO the laughter trigger produced no visible feedback, so the two conditions also differ in whether the trigger affords visible expression. Please either add an idling-avatar control or reframe the central claim from 'embodied laughter cues' to 'embodied expressive avatars' and qualify the causal attribution throughout the paper.
  2. [Section 4.1.4 and Table 2] Three of five engagement subscales are nominally significant (Endurability p=.014, Novelty p=.021, Involvement p=.011) with small effects (r=.18 to .32). With five related subscales, a Bonferroni correction sets the threshold at .01, and none of these p-values survives; the Involvement result (p=.011) is close but still above the corrected threshold. The paper should report adjusted p-values or a multivariate/omnibus test, and the text should not describe these results as 'significant' without qualification. The qualitative findings can stand independently, but the quantitative support for RQ1 is thinner than the current wording implies.
  3. [Section 3.5.3 and Section 4.2.2] The chained-laughter measure relies on an ad-hoc 5-second window and assumes that a cue following within that window is caused by the preceding cue. Simultaneous or video-triggered laughter could produce the same pattern, and no sensitivity analysis is reported for the window choice. The p=.006 result (V=0 over eight triads) is driven by a strong within-group difference, but the operational definition should be validated (e.g., with shorter/longer windows or a permutation baseline) before being used as the main quantitative evidence for emotional contagion. Moreover, in VO participants could press the same trigger but with no visible avatar, so the functional meaning of a 'chained laughter' event in VO is unclear and not comparable to VLC.
minor comments (4)
  1. [Section 3.3] The compensation text contains a typo: 'Amaazon' should be 'Amazon'.
  2. [Section 4.3.2] In the P01 quote, 'press [the the laughter] button' contains a duplicated 'the' and should be corrected.
  3. [Section 5.3.1 and reference [81]] The word 'accomodative' in Section 5.3.1 and 'Accomodation' in reference [81] should be spelled 'accommodative' and 'Accommodation', respectively.
  4. [Table 2] The table would benefit from confidence intervals for the effect sizes, and the text should clarify whether the chained-laughter counts are aggregated at the group level or the individual level.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the paper's claims rest on its own measured self-reports and system logs; the only self-citation supports the animation design, not the conclusions.

full rationale

This is an empirical user study with no formal derivation chain, no fitted parameters, and no predictions that reduce by construction to the inputs. The central findings (shift from individual immersion to socially coordinated participation, heightened self-awareness, emotional contagion, and expressive norms) are supported by the paper's own data: semi-structured interviews analyzed thematically, an engagement questionnaire, and system-logged chained-laughter counts (e.g., Table 2, V=0, p=.006). No equation or definition makes an outcome equal to an input. The chained-laughter operationalization (a 5-second window chosen to match the 4-second animation length) is a self-authored measurement convention, not a circular prediction. The only self-citation is the authors' prior work [62], used in Section 3.2.1 to justify the avatar laughter animations ('based on established designs from prior work [62]') and to note that avatar-based visual laughter cues were shown effective previously; this is a stimulus-design reference and does not carry the paper's conclusions, which stand on the present experiment's measurements. The VO vs. VLC contrast changes avatar presence and laughter cues simultaneously, but that is an internal-validity (confound) concern, not circularity, and per the scoring rules belongs under correctness risk rather than inflating the circularity score.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The central claim rests on four unverified domain assumptions and one hand-selected threshold. No free parameters are fitted to data in the mathematical sense, and no invented theoretical entities are introduced. The most consequential assumption is that the voice-only baseline isolates the effect of visible laughter cues despite the absence of an idling-avatar control.

free parameters (1)
  • Chained laughter time window = 5 seconds
    Hand-selected threshold for defining a 'chain' in RQ2 analysis; authors align it with the 4-second animation but provide no sensitivity analysis. The significant chained-laughter result depends on this operationalization.
assumptions (4)
  • domain assumption Manual button presses faithfully represent experienced laughter/amusement
    The study treats self-initiated laughter cue triggers as a valid measure of emotional expression and as the basis for chained-laughter counts. The authors cite Nomura and Maruno [60] for consistency between self-reported humor and sensitivity, but do not validate this mapping in VR co-viewing.
  • ad hoc to paper A laughter cue followed within 5 seconds by another participant's cue is caused by the first cue (contagion)
    The 5-second window is an author-defined threshold, not an externally established measure. It can include coincidental or voice-driven reactions, especially since voice laughter is present in both conditions.
  • domain assumption Voice-only is an adequate baseline for isolating the effect of visible laughter cues; idling avatars would introduce audiovisual mismatch
    The authors deliberately omit an avatar-without-laughter control, citing Bailenson et al. [5] about mismatched cues. This assumption is load-bearing for attributing differences to laughter cues rather than to avatar presence.
  • domain assumption Participants did not guess the actual research focus despite the cover story
    The study relies on a cover story ('identify humorous segments') to prevent demand characteristics. No manipulation check is reported, and participants experienced both conditions, so awareness of the avatar manipulation is plausible.

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Cite this review

Pith. "Pith review of Understanding and Supporting Co-viewing Comedy in VR with Embodied Expressive Avatars." pith.science (2026). https://pith.science/paper/RP2IBCOL

@misc{pith2026250520082,
  author       = {Pith},
  title        = {Pith review of: Understanding and Supporting Co-viewing Comedy in VR with Embodied Expressive Avatars},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RP2IBCOL}},
  note         = {Machine review of arXiv:2505.20082}
}
read the original abstract

Co-viewing videos with family and friends remotely has become prevalent with the support of communication channels such as text messaging or real-time voice chat. However, current co-viewing platforms often lack visible embodied cues, such as body movements and facial expressions. This absence can reduce emotional engagement and the sense of co-presence when people are watching together remotely. Although virtual reality (VR) is an emerging technology that allows individuals to participate in various social activities while embodied as avatars, we still do not fully understand how this embodiment in VR affects co-viewing experiences, particularly in terms of engagement, emotional contagion, and expressive norms. In a controlled experiment involving eight triads of three participants each (N=24), we compared the participants' perceptions and reactions while watching comedy in VR using embodied expressive avatars that displayed visible laughter cues. This was contrasted with a control condition where no such embodied expressions were presented. With a mixed-method analysis, we found that embodied laughter cues shifted participants' engagement from individual immersion to socially coordinated participation. Participants reported heightened self-awareness of emotional expression, greater emotional contagion, and the development of expressive norms surrounding co-viewers' laughter. The result highlighted the tension between individual engagement and interpersonal emotional accommodation when co-viewing with embodied expressive avatars.

Figures

Figures reproduced from arXiv: 2505.20082 by the authors.

Figure 1
Figure 1. Co-viewing comedy in VR under two conditions. (A) [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Implementation overview. (A) We provided six default avatars from ReadyPlayerMe for participants to [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Themes mapped to our three research questions. First, participants’ [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗

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Reference graph

Works this paper leans on

90 extracted references · 45 canonical work pages

  1. [1]

    Masaki Abe, Kohei Okuoka, and Masahiko Osawa. 2021. Examining the Factors that Make Co-Watching with Agents Effective. InProceedings of the 9th International Conference on Human-Agent Interaction(Virtual Event, Japan)(HAI ’21). Association for Computing Machinery, New York, NY, USA, 354–357. doi:10.1145/3472307.3484654

    work page arXiv 2021

  2. [2]

    Saba Ahsan, Varun Singh, and Jörg Ott. 2014. Characterizing Internet Video for Large-scale Active Measurements. ArXivabs/1408.5777 (2014). https://api.semanticscholar.org/CorpusID:764721 18 Ohara et al

    work page Pith review arXiv 2014

  3. [3]

    Arthur Aron, Elaine Aron, and Danny Smollan. 1992. Inclusion of Other in the Self Scale and the Structure of Interpersonal Closeness.Journal of Personality and Social Psychology63 (10 1992), 596–612. doi:10.1037/0022- 3514.63.4.596

    doi:10.1037/0022- 1992

  4. [4]

    Jo-Anne Bachorowski and Michael J. Owren. 2001. Not All Laughs are Alike: Voiced but Not Unvoiced Laugh- ter Readily Elicits Positive Affect.Psychological Science12, 3 (2001), 252–257. doi:10.1111/1467-9280.00346 arXiv:https://doi.org/10.1111/1467-9280.00346 PMID: 11437310

    work page arXiv 2001

  5. [5]

    Bailenson, Kim Swinth, Crystal Hoyt, Susan Persky, Alex Dimov, and Jim Blascovich

    Jeremy N. Bailenson, Kim Swinth, Crystal Hoyt, Susan Persky, Alex Dimov, and Jim Blascovich. 2005. The Independent and Interactive Effects of Embodied-Agent Appearance and Behavior on Self-Report, Cognitive, and Behavioral Markers of Copresence in Immersive Virtual Environments.Presence14, 4 (Aug 2005), 379–393. doi:10.1162/105474605774785235

    work page doi:10.1162/105474605774785235 2005

  6. [6]

    Omotayo O Banjo, Zheng Wang, Osei Appiah, Christopher Brown, Whitney Walther-Martin, John Tchernev, Alexander Hedstrom, and Matthew Irwin. 2017. Experiencing racial humor with outgroups: A psychophysiological examination of co-viewing effects.Media Psychology20, 4 (2017), 607–631

    2017

  7. [7]

    Sigal G Barsade. 2002. The ripple effect: Emotional contagion and its influence on group behavior.Administrative science quarterly47, 4 (2002), 644–675

    2002

  8. [8]

    Ava Bartolome and Shuo Niu. 2023. A Literature Review of Video-Sharing Platform Research in HCI. InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems(Hamburg, Germany)(CHI ’23). Association for Computing Machinery, New York, NY, USA, Article 790, 20 pages. doi:10.1145/3544548.3581107

    arXiv 2023

Show all 90 references
  1. [9]

    Ursula Beermann, Fabian Gander, Damian Hiltebrand, Tobias Wyss, and Willibald Ruch. 2007. Laughing at oneself: Trait or State? doi:10.5167/uzh-20237

    2007 doi

  2. [10]

    Lindsay Blackwell, Nicole Ellison, Natasha Elliott-Deflo, and Raz Schwartz. 2019. Harassment in Social Virtual Reality: Challenges for Platform Governance.Proc. ACM Hum.-Comput. Interact.3, CSCW, Article 100 (Nov. 2019), 25 pages. doi:10.1145/3359202

    2019 doi

  3. [11]

    Virginia Braun and Victoria Clarke. 2006. Using thematic analysis in psychology.Qual- itative Research in Psychology3, 2 (2006), 77–101. doi:10.1191/1478088706qp063oa arXiv:https://www.tandfonline.com/doi/pdf/10.1191/1478088706qp063oa

    2006 doi

  4. [12]

    Di (Laura) Chen, Dustin Freeman, and Ravin Balakrishnan. 2019. Integrating Multimedia Tools to Enrich Interactions in Live Streaming for Language Learning. InProceedings of the 2019 CHI Conference on Human Factors in Computing Systems(Glasgow, Scotland Uk)(CHI ’19). Associatio...

    2019

  5. [13]

    I’d rather drink in VRChat

    Qijia Chen, Andrea Bellucci, and Giulio Jacucci. 2024. “I’d rather drink in VRChat”: Understanding Drinking in Social Virtual Reality. InProceedings of the 2024 CHI Conference on Human Factors in Computing Systems. 1–16

    2024

  6. [14]

    Sukyoung Choi and Eun-Mee Kim. 2021. Between Instagram browsing and subjective well-being: Social com- parison or emotional contagion?Media Psychology24, 6 (2021), 866–890. doi:10.1080/15213269.2020.1824120 arXiv:https://doi.org/10.1080/15213269.2020.1824120

    2021

  7. [15]

    Orchard Chris Fullwood and Sarah A

    Lisa J. Orchard Chris Fullwood and Sarah A. Floyd. 2013. Emoticon convergence in Internet chat rooms.Social Semiotics23, 5 (2013), 648–662. doi:10.1080/10350330.2012.739000 arXiv:https://doi.org/10.1080/10350330.2012.739000

    2013

  8. [16]

    Sarah Cosentino, Salvatore Sessa, and Atsuo Takanishi. 2016. Quantitative Laughter Detection, Measurement, and Classification—A Critical Survey.IEEE Reviews in Biomedical Engineering9 (2016), 148–162. doi:10.1109/RBME.2016. 2527638

    2016 doi

  9. [17]

    2009.The Social Neuroscience of Empathy

    Jean Decety and William Ickes. 2009.The Social Neuroscience of Empathy. The MIT Press. doi:10.7551/mitpress/ 9780262012973.001.0001

    2009 doi

  10. [18]

    Guillaume Dezecache and R.I.M. Dunbar. 2012. Sharing the joke: the size of natural laughter groups.Evolution and Human Behavior33, 6 (2012), 775–779. doi:10.1016/j.evolhumbehav.2012.07.002

    2012 doi

  11. [19]

    Xiaofeng Dou, Jiahe Dong, Shuhao Zhang, Qian Zhu, and Quan Li. 2025. Danmaku Avatar: Enabling Asynchronous Co-viewing Experiences in Virtual Reality via Danmaku. InProceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA ’25). A...

    2025

  12. [20]

    P Drack, T Huber, and Willibald Ruch. 2009. The apex of happy laughter: A FACS-study with actors. (01 2009). doi:10.5167/uzh-20236

    2009 doi

  13. [21]

    R. I. M. Dunbar, N. D. C. Duncan, and D. Nettle. 1995. Size and structure of freely forming conversational groups. Human Nature6, 1 (01 Mar 1995), 67–78. doi:10.1007/BF02734136

    1995 doi

  14. [22]

    Paul Ekman and Wallace V Friesen. 1978. Facial action coding system.Environmental Psychology & Nonverbal Behavior (1978)

    1978

  15. [23]

    Franz Faul, Edgar Erdfelder, Axel Buchner, and Albert-Georg Lang. 2009. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses.Behavior Research Methods41, 4 (01 Nov 2009), 1149–1160. doi:10. 3758/BRM.41.4.1149 Understanding and Supporting ...

    2009

  16. [24]

    Emilio Ferrara and Zeya Yang. 2015. Measuring Emotional Contagion in Social Media.PLoS ONE10 (2015). https: //api.semanticscholar.org/CorpusID:12174053

    2015

  17. [25]

    McNeese, and Kelsea Schulenberg

    Guo Freeman, Dane Acena, Nathan J. McNeese, and Kelsea Schulenberg. 2022. Working Together Apart through Embodiment: Engaging in Everyday Collaborative Activities in Social Virtual Reality.Proc. ACM Hum.-Comput. Interact.6, GROUP, Article 17 (Jan. 2022), 25 pages. doi:10.1145/3492836

    2022 doi

  18. [26]

    Guo Freeman and Divine Maloney. 2021. Body, Avatar, and Me: The Presentation and Perception of Self in Social Virtual Reality.Proc. ACM Hum.-Comput. Interact.4, CSCW3, Article 239 (Jan. 2021), 27 pages. doi:10.1145/3432938

    2021 doi

  19. [27]

    Ken Fujiwara, Rens Hoegen, Jonathan Gratch, and Norah E. Dunbar. 2022. Synchrony facilitates altruistic decision making for non-human avatars.Computers in Human Behavior128 (2022), 107079. doi:10.1016/j.chb.2021.107079

    2022

  20. [28]

    2015.Communication Accommodation Theory

    Cindy Gallois and Howard Giles. 2015.Communication Accommodation Theory. John Wi- ley & Sons, Ltd, 1–18. doi:10.1002/9781118611463.wbielsi066 arXiv:https://onlinelibrary-wiley- com.utokyo.idm.oclc.org/doi/pdf/10.1002/9781118611463.wbielsi066

    2015 doi

  21. [29]

    Edwards, and Joseph B

    Howard Giles, America L. Edwards, and Joseph B. Walther. 2023. Communication accommodation theory: Past accomplishments, current trends, and future prospects.Language Sciences99 (2023), 101571. doi:10.1016/j.langsci. 2023.101571

    2023

  22. [30]

    Gonzales, Jeffrey T

    Amy L. Gonzales, Jeffrey T. Hancock, and James W. Pennebaker. 2010. Language Style Matching as a Predictor of Social Dynamics in Small Groups.Communication Research37, 1 (2010), 3–19. doi:10.1177/0093650209351468 arXiv:https://doi.org/10.1177/0093650209351468

    2010 doi

  23. [31]

    Mar González-Franco, Daniel Pérez-Marcos, Bernhard Spanlang, and Mel Slater. 2010. The contribution of real-time mirror reflections of motor actions on virtual body ownership in an immersive virtual environment. In2010 IEEE Virtual Reality Conference (VR). 111–114. doi:10.1109...

    2010

  24. [32]

    Griffin, Min S.H

    Harry J. Griffin, Min S.H. Aung, Bernardino Romera-Paredes, Ciaran McLoughlin, Gary McKeown, William Curran, and Nadia Bianchi-Berthouze. 2013. Laughter Type Recognition from Whole Body Motion. In2013 Humaine Association Conference on Affective Computing and Intelligent Intera...

    2013 doi

  25. [33]

    Griffin, Min

    Harry J. Griffin, Min. S. Hane Aung, Bernadino Romera-Paredes, Ciaran McLoughlin, Gary McKeown, William Curran, and Nadia Bianchi-Berthouze. 2015. Perception and Automatic Recognition of Laughter from Whole-Body Motion: Continuous and Categorical Perspectives.IEEE Transactions...

    2015

  26. [34]

    Jiajing Guo and Susan R. Fussell. 2020. A Preliminary Study of Emotional Contagion in Live Streaming. InCompanion Publication of the 2020 Conference on Computer Supported Cooperative Work and Social Computing(Virtual Event, USA) (CSCW ’20 Companion). Association for Computing ...

    2020 doi

  27. [35]

    Haimson and John C

    Oliver L. Haimson and John C. Tang. 2017. What Makes Live Events Engaging on Facebook Live, Periscope, and Snapchat. InProceedings of the 2017 CHI Conference on Human Factors in Computing Systems(Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, ...

    2017

  28. [36]

    Hancock, Kailyn Gee, Kevin Ciaccio, and Jennifer Mae-Hwah Lin

    Jeffrey T. Hancock, Kailyn Gee, Kevin Ciaccio, and Jennifer Mae-Hwah Lin. 2008. I’m sad you’re sad: emotional contagion in CMC. InProceedings of the 2008 ACM Conference on Computer Supported Cooperative Work(San Diego, CA, USA)(CSCW ’08). Association for Computing Machinery, N...

    2008

  29. [37]

    Elaine Hatfield, Lisamarie Bensman, Paul D Thornton, and Richard L Rapson. 2014. New perspectives on emotional contagion: A review of classic and recent research on facial mimicry and contagion.Interpersona: An International Journal on Personal Relationships8, 2 (2014), 159–179

    2014

  30. [38]

    Cacioppo, and Richard L

    Elaine Hatfield, John T. Cacioppo, and Richard L. Rapson. 1993. Emotional Contagion.Current Directions in Psychological Science2, 3 (1993), 96–100. doi:10.1111/1467-8721.ep10770953 arXiv:https://doi.org/10.1111/1467-8721.ep10770953

    1993 doi

  31. [40]

    Carolina Herrando and Efthymios Constantinides. 2021. Emotional Contagion: A Brief Overview and Future Directions. Frontiers in Psychology12 (2021). doi:10.3389/fpsyg.2021.712606

    2021

  32. [41]

    Closer than Real

    Misato Hide, Yuji Hatada, Hideaki Kuzuoka, and Takuji Narumi. 2025. "Closer than Real": How Social VR Platform Features Influence Friendship Dynamics. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems (CHI ’25). Association for Computing Machinery,...

    2025

  33. [42]

    Yasuyuki Inoue and Michiteru Kitazaki. 2021. Virtual Mirror and Beyond: The Psychological Basis for Avatar Embodiment via a Mirror.Journal of Robotics and Mechatronics33, 5 (2021), 1004–1012. doi:10.20965/jrm.2021.p1004

    2021 doi

  34. [43]

    Henrik Jodén and Jacob Strandell. 2022. Building viewer engagement through interaction rituals on Twitch. tv. Information, Communication & Society25, 13 (2022), 1969–1986. 20 Ohara et al

    2022

  35. [44]

    Christian Keysers and Valeria Gazzola. 2010. Social Neuroscience: Mirror Neurons Recorded in Humans.Current Biology20, 8 (27 Apr 2010), R353–R354. doi:10.1016/j.cub.2010.03.013

    2010 doi

  36. [45]

    Adam D.I. Kramer. 2012. The spread of emotion via facebook. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems(Austin, Texas, USA)(CHI ’12). Association for Computing Machinery, New York, NY, USA, 767–770. doi:10.1145/2207676.2207787

    2012

  37. [46]

    Adam D. I. Kramer, Jamie E. Guillory, and Jeffrey T. Hancock. 2014. Experimental evidence of massive-scale emotional contagion through social networks.Proceedings of the National Academy of Sciences111, 24 (2014), 8788–8790. doi:10.1073/pnas.1320040111 arXiv:https://www.pnas.o...

    2014 doi

  38. [47]

    Junhyuk Lee, Jeonguk Hong, and Sangwon Lee. 2025. One Screen, Many Minds: A Smart TV Interface Development for Co-viewing through a Mental Model Framework. InProceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA ’25). Associa...

    2025

  39. [48]

    Chen Li. 2018. How danmaku influences emotional responses : exploring the effects of co-viewing and copresence. https://api.semanticscholar.org/CorpusID:210270879

    2018

  40. [49]

    Chen Li, Yixin Dai, Guang Chen, Jing Liu, Ping Li, and Horace Ho shing Ip. 2025. Avatar-mediated communication in collaborative virtual environments: A study on users’ attention allocation and perception of social interactions. Computers in Human Behavior167 (2025), 108598. do...

    2025

  41. [50]

    Anders Sundnes Løvlie, Lucas Schønrock, Jannik Bryld, and Louise Barkhuus. 2022. Designing for Shared Remote Video Experiences. InProceedings of the 25th International Academic Mindtrek Conference(Tampere, Finland)(Academic Mindtrek ’22). Association for Computing Machinery, N...

    2022

  42. [51]

    Xiaojuan Ma and Nan Cao. 2017. Video-based Evanescent, Anonymous, Asynchronous Social Interaction: Motivation and Adaption to Medium. InProceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing(Portland, Oregon, USA)(CSCW ’17). Associat...

    2017

  43. [52]

    Talking without a Voice

    Divine Maloney, Guo Freeman, and Donghee Yvette Wohn. 2020. "Talking without a Voice": Understanding Non-verbal Communication in Social Virtual Reality.Proc. ACM Hum.-Comput. Interact.4, CSCW2, Article 175 (Oct. 2020), 25 pages. doi:10.1145/3415246

    2020 doi

  44. [53]

    Katsutoshi Masai, Keisuke Morita, and Maki Sugimoto. 2024. Analysis of Co-viewing with Virtual Agent Towards Affectively Immersive Interaction in Virtual Spaces. In2024 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). 355–356. doi:10.1109/IS...

    2024

  45. [54]

    Takuto Matsuda and Yoshiko Arimoto. 2023. Detection of Laughter and Screaming Using the Attention and CTC Models. InInterspeech 2023. 1025–1029. doi:10.21437/Interspeech.2023-1412

    2023 doi

  46. [55]

    Mario Montagud, Jie Li, Gianluca Cernigliaro, Abdallah El Ali, Sergi Fernández, and Pablo Cesar. 2022. Towards socialVR: evaluating a novel technology for watching videos together.Virtual Reality26, 4 (01 Dec 2022), 1593–1613. doi:10.1007/s10055-022-00651-5

    2022 doi

  47. [56]

    Fares Moustafa and Anthony Steed. 2018. A longitudinal study of small group interaction in social virtual reality. InProceedings of the 24th ACM Symposium on Virtual Reality Software and Technology(Tokyo, Japan)(VRST ’18). Association for Computing Machinery, New York, NY, USA...

    2018

  48. [57]

    Koki Nagano, Jaewoo Seo, Jun Xing, Lingyu Wei, Zimo Li, Shunsuke Saito, Aviral Agarwal, Jens Fursund, and Hao Li

  49. [58]

    Thornton Nicolas Ducheneaut, Robert J

    Lora Oehlberg James D. Thornton Nicolas Ducheneaut, Robert J. Moore and Eric Nickell. 2008. Social TV: Designing for Distributed, Sociable Television Viewing.International Journal of Human–Computer Interaction24, 2 (2008), 136–154. doi:10.1080/10447310701821426

    2008 doi

  50. [59]

    Radosław Niewiadomski and Catherine Pelachaud. 2012. Towards Multimodal Expression of Laughter. InIntelligent Virtual Agents, Yukiko Nakano, Michael Neff, Ana Paiva, and Marilyn Walker (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 231–244

    2012

  51. [60]

    Ryota Nomura and Shunichi Maruno. 2009. The relationships between real-time rating scores of subjective humor experience and smile/laughter scores (in Japanese).Kyushu University Psychological Research(2009)

    2009

  52. [61]

    O’Brien and Elaine G

    Heather L. O’Brien and Elaine G. Toms. 2010. The development and evaluation of a survey to measure user engagement. Journal of the American Society for Information Science and Technology61, 1 (2010), 50–69. doi:10.1002/asi.21229 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10...

    2010 doi

  53. [62]

    Ryo Ohara, Chi-Lan Yang, Takuji Narumi, and Hideaki Kuzuoka. 2025. Embodied Laugh Track: Supporting VR Comedy Viewing Experience with Multimodal Laughter from Virtual Audience. InProceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems ...

    2025

  54. [63]

    Owren and Jo-Anne Bachorowski

    Michael J. Owren and Jo-Anne Bachorowski. 2003. Reconsidering the Evolution of Nonlinguistic Communication: The Case of Laughter.Journal of Nonverbal Behavior27, 3 (01 Sep 2003), 183–200. doi:10.1023/A:1025394015198

    2003 doi

  55. [64]

    Tekla S. Perry. 2016. Virtual reality goes social.IEEE Spectrum53, 1 (2016), 56–57. doi:10.1109/MSPEC.2016.7367470

    2016

  56. [65]

    Robert R. Provine. 1992. Contagious laughter: Laughter is a sufficient stimulus for laughs and smiles.Bulletin of the Psychonomic Society30, 1 (01 Jul 1992), 1–4. doi:10.3758/BF03330380

    1992 doi

  57. [66]

    Raquel Breejon Robinson, Ricardo Rheeder, Madison Klarkowski, and Regan L Mandryk. 2022. ”Chat Has No Chill”: A Novel Physiological Interaction For Engaging Live Streaming Audiences. InProceedings of the 2022 CHI Conference on Human Factors in Computing Systems(New Orleans, LA...

    2022

  58. [67]

    2001.The expressive pattern of laughter

    Willibald Ruch and Paul Ekman. 2001.The expressive pattern of laughter. World Scientific, 426–443. doi:10.1142/ 9789812810687_0033

    2001

  59. [68]

    David Saffo, Sara Di Bartolomeo, Caglar Yildirim, and Cody Dunne. 2021. Remote and Collaborative Virtual Reality Experiments via Social VR Platforms. InProceedings of the 2021 CHI Conference on Human Factors in Computing Systems(Yokohama, Japan)(CHI ’21). Association for Compu...

    2021

  60. [69]

    John Scott Siri Jr., Hamna Khalid, Luong Nguyen, and Donghee Yvette Wohn. 2018. Screen-viewing Practices in Social Virtual Reality. InCompanion of the 2018 ACM Conference on Computer Supported Cooperative Work and Social Computing(Jersey City, NJ, USA)(CSCW ’18 Companion). Ass...

    2018

  61. [70]

    Harrison Jesse Smith and Michael Neff. 2018. Communication Behavior in Embodied Virtual Reality. InProceedings of the 2018 CHI Conference on Human Factors in Computing Systems(Montreal QC, Canada)(CHI ’18). Association for Computing Machinery, New York, NY, USA, 1–12. doi:10.1...

    2018

  62. [71]

    Moria Smoski and Jo-Anne Bachorowski. 2003. Antiphonal laughter between friends and strangers.Cognition and Emotion17, 2 (2003), 327–340. doi:10.1080/02699930302296 PMID: 29715722

    2003 doi

  63. [72]

    Martin, and Sabine Stepper

    Fritz Strack, Leonard L. Martin, and Sabine Stepper. 1988. Inhibiting and facilitating conditions of the human smile: a nonobtrusive test of the facial feedback hypothesis.Journal of personality and social psychology54(5) (1988), 768–777. doi:10.1037//0022-3514.54.5.768

    1988 doi

  64. [73]

    Emily Sun, Rodrigo de Oliveira, and Joshua Lewandowski. 2017. Challenges on the Journey to Co-Watching YouTube. InProceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing(Portland, Oregon, USA)(CSCW ’17). Association for Computing Mach...

    2017 doi

  65. [74]

    Kata Szita, Wyatt Moss-Wellington, Xiaolin Sun, and Eugene Ch’ng. 2024. Going to the movies in VR: Virtual reality cinemas as alternatives to in-person co-viewing.Int. J. Hum.-Comput. Stud.181, C (Jan. 2024), 11 pages. doi:10.1016/j.ijhcs.2023.103150

    2024

  66. [76]

    Matilde Tassinari, Matthias Burkard Aulbach, and Inga Jasinskaja-Lahti. 2022. Investigating the Influence of Intergroup Contact in Virtual Reality on Empathy: An Exploratory Study Using AltspaceVR.Frontiers in PsychologyVolume 12 - 2021 (2022). doi:10.3389/fpsyg.2021.815497

    2022

  67. [77]

    Pei-Yun Tu, Mei-Ling Chen, Chi-Lan Yang, and Hao-Chuan Wang. 2016. Co-viewing room: Mobile TV content sharing in social chat. InProceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. 1615–1621

    2016

  68. [78]

    Stef van der Struijk, Hung-Hsuan Huang, Maryam Sadat Mirzaei, and Toyoaki Nishida. 2018. FACSvatar: An Open Source Modular Framework for Real-Time FACS based Facial Animation. InProceedings of the 18th International Conference on Intelligent Virtual Agents(Sydney, NSW, Austral...

    2018

  69. [79]

    Erik Van Haeringen and Charlotte Gerritsen. 2024. Emotion Contagion in Avatar-Mediated Group Interactions. In 2024 12th International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, 247–256

    2024

  70. [80]

    Radu-Daniel Vatavu. 2015. Audience Silhouettes: Peripheral Awareness of Synchronous Audience Kinesics for Social Television. InProceedings of the ACM International Conference on Interactive Experiences for TV and Online Video (Brussels, Belgium)(TVX ’15). Association for Compu...

    2015 doi

  71. [81]

    von der Pütten, Laura Hoffmann, Jennifer Klatt, and Nicole C

    Astrid M. von der Pütten, Laura Hoffmann, Jennifer Klatt, and Nicole C. Krämer. 2011. Quid Pro Quo? Reciprocal Self- disclosure and Communicative Accomodation towards a Virtual Interviewer. InIntelligent Virtual Agents, Hannes Högni Vilhjálmsson, Stefan Kopp, Stacy Marsella, a...

    2011

  72. [82]

    Cheng Yao Wang, Mose Sakashita, Upol Ehsan, Jingjin Li, and Andrea Stevenson Won. 2020. Again, Together: Socially Reliving Virtual Reality Experiences When Separated. InProceedings of the 2020 CHI Conference on Human Factors in Computing Systems(Honolulu, HI, USA)(CHI ’20). As...

    2020

  73. [83]

    Qi Wang, Xianghua Ding, Tun Lu, and Ning Gu. 2012. Digitality and materiality of new media: online TV watching in china. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems. 347–356

    2012

  74. [84]

    ES White. 1986. Interpersonal bias in television and interactive media.Inter/Media: Interpersonal communication in a media world3 (1986), 110–120

    1986

  75. [85]

    Qunfang Wu, Yisi Sang, and Yun Huang. 2019. Danmaku: A New Paradigm of Social Interaction via Online Videos. Trans. Soc. Comput.2, 2, Article 7 (jun 2019), 24 pages. doi:10.1145/3329485

    2019 doi

  76. [86]

    Nick Yee and Jeremy Bailenson. 2007. The Proteus Effect: The Effect of Transformed Self-Representation on Behavior. Human Communication Research33, 3 (07 2007), 271–290. doi:10.1111/j.1468-2958.2007.00299.x

    2007

  77. [87]

    Nick Yee, Jeremy N Bailenson, and Nicolas Ducheneaut. 2009. The Proteus effect: Implications of transformed digital self-representation on online and offline behavior.Communication Research36, 2 (2009), 285–312

    2009

  78. [88]

    Shigeo Yoshida, Tomohiro Tanikawa, Sho Sakurai, Michitaka Hirose, and Takuji Narumi. 2013. Manipulation of an emotional experience by real-time deformed facial feedback. InProceedings of the 4th Augmented Human International Conference(Stuttgart, Germany)(AH ’13). Association ...

    2013

  79. [89]

    Richard David Young and Margaret Frye. 1966. Some are Laughing; Some are Not—Why?Psychological Reports18, 3 (1966), 747–754. doi:10.2466/pr0.1966.18.3.747 arXiv:https://doi.org/10.2466/pr0.1966.18.3.747

    1966 doi

  80. [90]

    Qin Gao Yue Chen and Pei-Luen Patrick Rau. 2017. Watching a Movie Alone yet Together: Understanding Reasons for Watching Danmaku Videos.International Journal of Human–Computer Interaction33, 9 (2017), 731–743. doi:10.1080/ 10447318.2017.1282187 arXiv:https://doi.org/10.1080/10...

    2017

  81. [91]

    Jingyi Zhang and Joshua Juvrud. 2024. Gender expression and gender identity in virtual reality: avatars, role-adoption, and social interaction in VRChat.Frontiers in Virtual RealityVolume 5 - 2024 (2024). doi:10.3389/frvir.2024.1305758 A Interview Guide The semi-structured int...

    2024

  82. [2019]

    Graph.37 (2019), 258

    paGAN: real-time avatars using dynamic textures.ACM Trans. Graph.37 (2019), 258. https://api.semanticscholar. org/CorpusID:54134066

    2019

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.