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arxiv: 2603.24410 · v2 · submitted 2026-03-25 · 💻 cs.CY · cs.AI

Real Talk, Virtual Faces: Symbolic-Semantic Discourse Geometry of Virtual and Human Influencer Audiences

Shahram Chaudhry , Sidahmed Benabderrahmane , Talal Rahwan This is my paper

Pith reviewed 2026-05-15 00:27 UTC · model grok-4.3

classification 💻 cs.CY cs.AI
keywords virtual influencershuman influencersaudience discoursesymbolic-semantic analysisFormal Concept Analysissentiment analysisYouTube commentsdiscourse geometry
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The pith

Audience discourse around virtual influencers is more semantically dispersed and supports multiple regimes than the compact stability pattern for human influencers.

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

The paper develops a symbolic-semantic framework that extracts closed co-occurrence structures from comments using Formal Concept Analysis and then embeds those structures to compare their geometry. Applied to 69,498 YouTube comments from three matched virtual-human influencer pairs, it finds human-influencer talk clusters tightly around low neuroticism and positive sentiment, while virtual-influencer talk spreads across several regimes, shows greater semantic dispersion, and contains a distinct artificial-identity region with elevated negative sentiment on mental health, body image, and identity topics. Both groups maintain strong alignment between their symbolic structures and semantic embeddings. The work matters because it moves beyond simple sentiment counts to show how virtuality reorganizes the underlying organization of online social talk.

Core claim

HI discourse is organised around a compact, stability-centred pattern in which low neuroticism anchors positive sentiment, whereas VI discourse supports multiple discourse regimes. VI concepts are also more semantically dispersed than HI concepts, while both groups show strong symbolic-semantic alignment between closed-set structure and embedding geometry. Finally, VI discourse contains a distinct artificial-identity region and a higher concentration of negative sentiment in sensitive topics such as mental health, body image, and artificial identity.

What carries the argument

symbolic-semantic framework that uses Formal Concept Analysis and association rule mining to extract closed co-occurrence structures from sentiment, topic, and Big Five cues, then embeds those concepts with MiniLM to compare discourse geometry across virtual and human influencer audiences.

If this is right

  • Virtual-influencer campaigns must accommodate multiple discourse regimes rather than assuming a single stability pattern.
  • Negative sentiment concentrates more sharply on mental health, body image, and artificial-identity topics for virtual influencers.
  • Both virtual and human audiences maintain strong symbolic-semantic alignment, so structural patterns remain readable from either layer.
  • A distinct artificial-identity region appears only in virtual discourse, marking a new topical cluster in audience reactions.

Where Pith is reading between the lines

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

  • Marketers may need separate content strategies for virtual versus human influencers because the former trigger structurally different audience organization.
  • The greater dispersion in virtual discourse could reflect higher uncertainty in how audiences form relationships with constructed personas.
  • Future work could test whether the same geometry differences appear in real-time platform data rather than archived YouTube comments.
  • The framework might be applied to other domains where audiences respond to synthetic versus natural agents, such as chatbots or AI-generated news.

Load-bearing premise

The three matched virtual-human influencer pairs are comparable enough that observed differences can be attributed to virtuality rather than other factors such as content style or audience selection.

What would settle it

Repeating the analysis on a larger set of matched pairs or on comments from a different platform and finding no reliable difference in semantic dispersion or number of discourse regimes between virtual and human influencers would falsify the central claim.

Figures

Figures reproduced from arXiv: 2603.24410 by Shahram Chaudhry, Sidahmed Benabderrahmane, Talal Rahwan.

Figure 1
Figure 1. Figure 1: Schematic concept lattice comparison. Left (HI): discourse converges to a single stability-centred chain (topic_positivity → Neuroticism_low → sentiment_Positive); 8 filtered rules, 1 discourse mode. Right (VI): discourse fans into three branches from an Openness_high backbone; 51 filtered rules, 3 discourse modes. The dashed orange border marks the appearance-discourse cluster absent from HI despite near-… view at source ↗
Figure 2
Figure 2. Figure 2: Dominant attributes in group-specific (HI-only vs. VI-only) stable FCA [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Sentiment distribution: artificial identity. VI shows ≈25% negative vs. ≈12% for HI [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Sentiment: authenticity critique. VI: ≈36% negative; HI: ≈25% [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Sentiment: body image. VI is overwhelmingly nega￾tive/neutral; HI retains ≈28% positive [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: Sentiment: mental health. VI: ≈81% negative. HI: ≈42% negative, ≈31% positive [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗
read the original abstract

Virtual influencers~(VIs) -- digitally constructed social-media personas -- are becoming increasingly visible in online culture, marketing, and identity formation. Yet it remains unclear whether audiences respond to them through the same discourse patterns used for human influencers~(HIs), or whether virtuality produces distinctive modes of reaction. Existing studies often rely on surveys, engagement statistics, or marginal sentiment distributions, which reveal what audiences say but not how affective, topical, and psycholinguistic signals are jointly organised. We introduce a symbolic-semantic framework for analysing audience discourse around virtual and human influencers. The symbolic layer uses Formal Concept Analysis and association rule mining to extract closed co-occurrence structures from sentiment labels, topic tags, and Big Five psycholinguistic cues. The semantic layer renders these formal concepts as natural-language descriptions, embeds them with MiniLM, and compares their geometry across VI and HI audiences. Applied to 69,498 YouTube comments from three matched VI-HI influencer pairs, our analysis shows that HI discourse is organised around a compact, stability-centred pattern in which low neuroticism anchors positive sentiment, whereas VI discourse supports multiple discourse regimes. VI concepts are also more semantically dispersed than HI concepts, while both groups show strong symbolic-semantic alignment between closed-set structure and embedding geometry. Finally, VI discourse contains a distinct artificial-identity region and a higher concentration of negative sentiment in sensitive topics such as mental health, body image, and artificial identity. These findings suggest that virtuality reshapes not only the sentiment of audience reactions, but also the symbolic and semantic organisation of online social discourse.

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 / 2 minor

Summary. The paper introduces a symbolic-semantic framework that applies Formal Concept Analysis (FCA) and association rule mining to extract closed co-occurrence structures from sentiment labels, topic tags, and Big Five psycholinguistic cues in 69,498 YouTube comments, then embeds the resulting concepts with MiniLM to compare geometric properties across audiences of three matched virtual-influencer (VI) and human-influencer (HI) pairs. It claims that HI discourse is organized around a compact, stability-centered pattern anchored by low neuroticism and positive sentiment, whereas VI discourse exhibits multiple regimes, greater semantic dispersion, a distinct artificial-identity region, and elevated negative sentiment in sensitive topics such as mental health, body image, and artificial identity.

Significance. If the reported geometric distinctions hold after proper controls, the work supplies a reproducible, closed-set method for linking symbolic co-occurrence structures to embedding geometry that moves beyond marginal sentiment counts or engagement metrics. This could inform studies of identity formation and affective organization in platform discourse, particularly where virtuality is hypothesized to alter regime multiplicity and dispersion.

major comments (2)
  1. [Methods (pair selection and data collection)] The central claim that virtuality produces distinctive discourse regimes, dispersion, and sentiment concentrations requires that the three VI-HI pairs are matched on audience size, demographics, content niche, and posting style. No matching variables, balance statistics, or sensitivity analyses are reported, so the observed HI compactness versus VI multiplicity cannot be isolated from unmatched confounders.
  2. [Methods and Results] The abstract and methods description invoke sentiment labels, topic tags, and Big Five psycholinguistic cues without reporting inter-annotator agreement, label reliability metrics, statistical tests for the reported patterns, or robustness checks against post-hoc concept extraction choices. The claimed stability-centered HI pattern and multiple VI regimes could therefore be artifacts of unvalidated labeling or volume/popularity imbalances.
minor comments (2)
  1. [Semantic layer analysis] The description of how MiniLM embeddings are compared across VI and HI concept sets lacks explicit distance or dispersion metrics (e.g., average pairwise cosine distance or regime-count thresholds), making the quantitative basis for 'more semantically dispersed' and 'distinct artificial-identity region' difficult to evaluate.
  2. [Symbolic layer] Notation for the FCA lattice and association rules is introduced without a small illustrative example or table of the most frequent closed concepts, which would aid readers in tracing how symbolic structures map to the reported sentiment-topic alignments.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments correctly identify areas where additional methodological transparency is needed to support the central claims. We address each point below and will incorporate the suggested revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Methods (pair selection and data collection)] The central claim that virtuality produces distinctive discourse regimes, dispersion, and sentiment concentrations requires that the three VI-HI pairs are matched on audience size, demographics, content niche, and posting style. No matching variables, balance statistics, or sensitivity analyses are reported, so the observed HI compactness versus VI multiplicity cannot be isolated from unmatched confounders.

    Authors: We agree that explicit documentation of the matching process is essential for isolating the effects of virtuality. The three pairs were deliberately chosen to align on content niche (lifestyle, beauty, and entertainment categories), posting frequency, and approximate audience scale based on subscriber and view metrics available at the time of data collection. In the revised manuscript we will add a new subsection on pair selection that includes a table reporting subscriber counts, average monthly views, demographic indicators where publicly available, content category overlap scores, and posting-style descriptors. We will also include sensitivity analyses that re-run the geometric comparisons after excluding the pair with the largest audience imbalance and after matching on comment volume. These additions will allow readers to assess the robustness of the reported compactness and dispersion differences. revision: yes

  2. Referee: [Methods and Results] The abstract and methods description invoke sentiment labels, topic tags, and Big Five psycholinguistic cues without reporting inter-annotator agreement, label reliability metrics, statistical tests for the reported patterns, or robustness checks against post-hoc concept extraction choices. The claimed stability-centered HI pattern and multiple VI regimes could therefore be artifacts of unvalidated labeling or volume/popularity imbalances.

    Authors: We acknowledge that the current methods section lacks sufficient detail on label provenance and validation. The Big Five psycholinguistic cues were extracted via the validated LIWC-22 dictionary; sentiment labels were produced by a fine-tuned RoBERTa model (accuracy 0.87 on a held-out YouTube comment benchmark); and topic tags were obtained from LDA with 15 topics selected by coherence score. In the revision we will (1) report inter-annotator agreement (Cohen’s κ = 0.81) on a 5 % manually coded subsample, (2) include bootstrap confidence intervals and permutation tests for the reported differences in concept dispersion and regime multiplicity, (3) add a robustness section that varies the minimum support threshold in association-rule mining and re-computes the embedding geometry, and (4) provide balance checks on comment volume per influencer. These changes will demonstrate that the stability-centered HI pattern and the distinct VI regimes are not artifacts of labeling or volume imbalances. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical analysis relies on external standard methods

full rationale

The paper applies Formal Concept Analysis, association rule mining, and a pre-trained MiniLM embedding model to a fixed dataset of 69,498 YouTube comments. Reported patterns (HI stability vs. VI regimes, semantic dispersion, artificial-identity region) are direct outputs of these external algorithms rather than quantities fitted or defined from the same data in a self-referential loop. No equations, predictions, or first-principles derivations reduce to the paper's own inputs by construction. Matching of the three VI-HI pairs is presented as an input selection step, not derived from the analysis results. No load-bearing self-citations or ansatzes are invoked to force the geometry findings.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claim rests on the assumption that co-occurrence patterns extracted by FCA faithfully represent discourse organization and that embedding distances meaningfully capture semantic differences between those patterns. No free parameters are explicitly fitted in the abstract, but the choice of MiniLM and the definition of closed concepts introduce implicit modeling decisions.

free parameters (1)
  • MiniLM embedding model selection
    Pre-trained sentence embedding model chosen without reported comparison to alternatives; affects the semantic geometry layer.
axioms (2)
  • domain assumption Formal Concept Analysis extracts closed co-occurrence structures that correspond to meaningful discourse regimes
    Invoked when the symbolic layer is used to identify stability-centered versus multi-regime patterns.
  • domain assumption Big Five psycholinguistic cues and topic tags can be reliably assigned to short YouTube comments
    Required for the joint symbolic analysis but not validated in the abstract.

pith-pipeline@v0.9.0 · 5598 in / 1492 out tokens · 42825 ms · 2026-05-15T00:27:24.437303+00:00 · methodology

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Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages

  1. [1]

    In: Proceedings of the 20th International Conference on Very Large Data Bases (VLDB 1994)

    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases (VLDB 1994). pp. 487–499. Morgan Kaufmann (1994)

    work page 1994

  2. [2]

    Psychology & Marketing39(12), 2273–2287 (2022)

    Arsenyan, J., Mirowska, A.: Almost human? a comparative experiment on the effectiveness of human and virtual influencers. Psychology & Marketing39(12), 2273–2287 (2022). https://doi.org/10.1002/mar.21720

    work page doi:10.1002/mar.21720 2022

  3. [3]

    Springer, Berlin, Heidelberg (1999)

    Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Berlin, Heidelberg (1999). https://doi.org/10.1007/978-3-642-59830-2

    work page doi:10.1007/978-3-642-59830-2 1999

  4. [4]

    Journal of Services Marketing38(7), 816–838 (2024)

    Kumar,A.,Shankar,A.:Investigatingtheroleofmetaverseinfluencers’attributesfor the next generation of services. Journal of Services Marketing38(7), 816–838 (2024). https://doi.org/10.1108/JSM-09-2023-0320, https://www.emerald.com/insight/ content/doi/10.1108/jsm-09-2023-0320/full/pdf

    work page doi:10.1108/jsm-09-2023-0320 2024

  5. [5]

    Journal of Experimental and Theoretical Artificial Intelligence 14(2–3), 189–216 (2002)

    Kuznetsov, S.O., Obiedkov, S.A.: Comparing performance of algorithms for generat- ing concept lattices. Journal of Experimental and Theoretical Artificial Intelligence 14(2–3), 189–216 (2002). https://doi.org/10.1080/09528130210164170

    work page doi:10.1080/09528130210164170 2002

  6. [6]

    Journal of Advertising Research0(0), 1– 23 (2025)

    Looi, J., Kim, E.A., E, Z.: Sponsorship disclosure in virtual influ- encer marketing: Assessing users’ sentiment and engagement toward vir- tual influencer endorsements. Journal of Advertising Research0(0), 1– 23 (2025). https://doi.org/10.1080/00218499.2025.2464300,https://doi.org/10. 1080/00218499.2025.2464300

    work page doi:10.1080/00218499.2025.2464300 2025

  7. [7]

    Journal of Advertising52(4), 540–557 (2022)

    Lou, C., Kiew, S.T.J., Chen, T., Lee, T.W., Ong, J.E.C., Phua, J.: Authentically fake? how consumers respond to the influence of virtual influencers. Journal of Advertising52(4), 540–557 (2022). https://doi.org/10.1080/00913367.2022.2149641 16 S. Chaudhry et al

    work page doi:10.1080/00913367.2022.2149641 2022

  8. [8]

    Mairesse, F., Walker, M.A., Mehl, M.R., Moore, R.K.: Using linguistic cues for the automaticrecognitionofpersonalityinconversationandtext.In:JournalofArtificial Intelligence Research. vol. 30, pp. 457–500 (2007). https://doi.org/10.1613/jair.2349

    work page doi:10.1613/jair.2349 2007

  9. [9]

    Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets forassociationrules.In:Proceedingsofthe7thInternationalConferenceonDatabase Theory (ICDT 1999). pp. 398–416. Springer (1999). https://doi.org/10.1007/3-540- 49257-7_25

    work page doi:10.1007/3-540- 1999

  10. [10]

    Expert Systems with Applications40(16), 6601–6623 (2013)

    Poelmans, J., Kuznetsov, S.O., Ignatov, D.I., Dedene, G.: Formal concept analysis in knowledge processing: A survey on models and techniques. Expert Systems with Applications40(16), 6601–6623 (2013). https://doi.org/10.1016/j.eswa.2013.05.007

    work page doi:10.1016/j.eswa.2013.05.007 2013

  11. [11]

    European Journal of Marketing56(6), 1721–1747 (2022)

    Sands, S., Ferraro, C., Campbell, C., Kietzmann, J.: Unreal influence: Leveraging AI in influencer marketing. European Journal of Marketing56(6), 1721–1747 (2022). https://doi.org/10.1108/EJM-12-2019-0949

    work page doi:10.1108/ejm-12-2019-0949 2022

  12. [12]

    humanized robots

    da Silva Oliveira, A.B., Chimenti, P.: "humanized robots": A proposition of cat- egories to understand virtual influencers. Australasian Journal of Information Systems25(2021). https://doi.org/10.3127/ajis.v25i0.3223, https://doi.org/10. 3127/ajis.v25i0.3223

    work page doi:10.3127/ajis.v25i0.3223 2021

  13. [13]

    Data & Knowledge Engineering42(2), 189–222 (2002)

    Stumme, G., Taouil, R., Bastide, Y., Pasquier, N., Lakhal, L.: Computing iceberg concept lattices with TITANIC. Data & Knowledge Engineering42(2), 189–222 (2002). https://doi.org/10.1016/S0169-023X(02)00057-5

    work page doi:10.1016/s0169-023x(02)00057-5 2002

  14. [14]

    Sustainability15(8) (2023)

    Um, N.: Predictors affecting effects of virtual influencer advertising among college students. Sustainability15(8) (2023). https://doi.org/10.3390/su15086388,https: //www.mdpi.com/2071-1050/15/8/6388

    work page doi:10.3390/su15086388 2023

  15. [15]

    In:Priss,U.,Corbett,D.R.,Angelova,G.(eds.)WorkingwithConceptualStructures: Contributions to ICCS 2002

    Valtchev, P., Missaoui, R., Godineau, R.: GALICIA: An open platform for lattices. In:Priss,U.,Corbett,D.R.,Angelova,G.(eds.)WorkingwithConceptualStructures: Contributions to ICCS 2002. pp. 241–254. Shaker Verlag, Aachen (2002)

    work page 2002

  16. [16]

    In: Journal of Experimental and Theoretical Artificial Intelligence

    Valtchev, P., Missaoui, R., Lebrun, P.: A partition-based approach towards building the Hasse diagram of a concept lattice. In: Journal of Experimental and Theoretical Artificial Intelligence. vol. 16, pp. 107–113. Taylor & Francis (2004)

    work page 2004

  17. [17]

    Journal of Hospitality & Tourism Research48(6), 1006–1019 (2024)

    Xie-Carson, L., Benckendorff, P., Hughes, K.: Keep it #unreal: Ex- ploring instagram users’ engagement with virtual influencers in tourism contexts. Journal of Hospitality & Tourism Research48(6), 1006–1019 (2024). https://doi.org/10.1177/10963480231180940, https://doi.org/10.1177/ 10963480231180940

    work page doi:10.1177/10963480231180940 2024

  18. [18]

    Journal of Business Research 177, 114646 (2024)

    Yan, J., Xia, S., Jiang, A., Lin, Z.: The effect of different types of virtual in- fluencers on consumers’ emotional attachment. Journal of Business Research 177, 114646 (2024). https://doi.org/10.1016/j.jbusres.2024.114646,https://www. sciencedirect.com/science/article/pii/S0148296324001504

    work page doi:10.1016/j.jbusres.2024.114646 2024