Real Talk, Virtual Faces: Symbolic-Semantic Discourse Geometry of Virtual and Human Influencer Audiences
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Proof-Carrying Neuro-Symbolic Code
This invited paper introduces the concept of "proof-carrying neuro-symbolic code" and explains its meaning and value, from both the "neural" and the "symbolic" perspectives. The talk outlines the first successes and chal…
ThingTalk: An Extensible, Executable Representation Language for Task-Oriented Dialogues
Task-oriented conversational agents rely on semantic parsers to translate natural language to formal representations. In this paper, we propose the design and rationale of the ThingTalk formal representation, and how the…
Semantic ParsingExpressive Talking Head Generation With Granular Audio-Visual Control
Generating expressive talking heads is essential for creating virtual humans. However, existing one- or few-shot methods focus on lip-sync and head motion, ignoring the emotional expressions that make talking faces r…
Talking Head GenerationEmoTalker: Emotionally Editable Talking Face Generation via Diffusion Model
In recent years, the field of talking faces generation has attracted considerable attention, with certain methods adept at generating virtual faces that convincingly imitate human expressions. However, existing methods f…
DenoisingFace GenerationTalking Face GenerationA Neurosymbolic Framework for Interpretable Cognitive Attack Detection in Augmented Reality
Augmented Reality (AR) enriches human perception by overlaying virtual elements onto the physical world. However, this tight coupling between virtual and real content makes AR vulnerable to cognitive attacks: manipulatio…