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arxiv: 2509.10957 · v2 · submitted 2025-09-13 · 💻 cs.HC

The Digital Landscape of God: Narrative, Visuals and Viewer Engagement of Religious Videos on YouTube

Rongyi Chen , Ziyan Xin , Qing Xiao , Ruiwei Xiao , Jingjia Xiao , Bingbing Zhang , Hong Shen , Zhicong Lu This is my paper

Pith reviewed 2026-05-18 16:25 UTC · model grok-4.3

classification 💻 cs.HC
keywords religious videosYouTubenarrative frameworksvisual elementsviewer engagementmixed-methodsAI-generated contentspiritual media
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The pith

Religious videos on YouTube mainly use lecture formats and salvation stories to guide viewers, with faith-specific visuals and measurable engagement links especially for AI content.

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

This paper studies how religious videos on YouTube combine stories and images to shape spiritual experiences and viewer responses across major faiths. The authors build taxonomies of narrative styles, visual choices, and interaction patterns through mixed-methods analysis of platform content. They find that most videos rely on lecture-style delivery by authority figures centered on salvation themes for persuasion and guidance. Visual preferences include bright lighting overall, with Buddhism leaning toward warm tones and symbols, Judaism toward indoor scenes, and Hinduism toward sacred objects. The work also maps how these features connect to emotional sharing and engagement metrics, offering practical directions for producing more effective spiritual media.

Core claim

Religious videos predominantly adopt lecture-style formats with authority-based persuasion strategies, using salvation narratives for guidance. All prefer bright lighting, with Buddhism favoring warm tones and prominent symbols, Judaism preferring indoor settings, and Hinduism emphasizing sacred objects. Differentiated patterns of emotional sharing among religious viewers were identified, along with significant correlations between content characteristics and engagement, particularly regarding AI-generated content.

What carries the argument

Taxonomies of narrative frameworks, visual elements, and viewer interaction, built via LLM-assisted mixed-methods analysis of YouTube videos from major religions.

If this is right

  • Creators can apply lecture formats and salvation narratives to strengthen guidance and viewer connection.
  • Matching visuals to each religion's preferences, such as symbols or sacred objects, supports better audience resonance.
  • AI-generated elements warrant special consideration due to their distinct ties to engagement levels.
  • Emotional sharing differences suggest faith-specific strategies for content design.
  • The taxonomies supply concrete guidance for producing inclusive spiritual videos on digital platforms.

Where Pith is reading between the lines

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

  • These patterns may influence how religious communities form and share beliefs over longer periods online.
  • Extending the same taxonomy approach to short-form platforms could expose sharper differences in engagement.
  • Platform design choices around recommendation could be informed by the observed links between content features and viewer responses.
  • The distinct role of AI content invites further checks on whether it affects viewer perceptions of authenticity in religious contexts.

Load-bearing premise

The study assumes that LLM-assisted coding can reliably extract narrative frameworks, visual elements, and engagement metrics from the video sample without substantial bias, with sufficient human validation to support the reported taxonomies and correlations.

What would settle it

A fully manual re-coding of the same or a larger sample of religious YouTube videos that finds no dominant lecture format, different visual patterns, or absent engagement correlations would undermine the central claims.

Figures

Figures reproduced from arXiv: 2509.10957 by Bingbing Zhang, Hong Shen, Jingjia Xiao, Qing Xiao, Rongyi Chen, Ruiwei Xiao, Zhicong Lu, Ziyan Xin.

Figure 1
Figure 1. Figure 1: Examples of video types: (a) Speaking-style, (b) Event recording, (c) Music, (d) Dance, and (e) Other. [PITH_FULL_IMAGE:figures/full_fig_p009_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Examples of religious images Scene selection is a key creative condition for constructing a video’s atmosphere and sense of place, a principle well￾established in film studies [38, 147]. Outdoor scenes encompass open spaces including natural environments, urban spaces, and historical sites (fig. 2(a)), accounting for 22.64% of religious videos, primarily emphasizing openness, universality, and naturalness.… view at source ↗
Figure 3
Figure 3. Figure 3: Examples of religious images Photography is the art of light, a golden phrase in film and television professions [56, 57]. Lighting is a key design element for creating atmosphere, guiding emotion, and expressing symbolic meaning. Adjusting light intensity and angles, like in Rembrandt lighting, can shape a character’s image and emotion. This lighting operation mainly appears in professional studios and in… view at source ↗
Figure 4
Figure 4. Figure 4: Examples of religious images Color tone is another key visual element [45]. Warm tones primarily include red, orange, yellow, and their variations (fig. 4(a)), with relatively longer wavelengths. Cool tones mainly include blue, green, purple, and their variations (fig. 4(b)), with relatively shorter wavelengths. Neutral tones refer to balanced color combinations with no obvious warm or cool tendencies in v… view at source ↗
Figure 5
Figure 5. Figure 5: Examples of religious images Manuscript submitted to ACM [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Examples of religious images 5.8 B-Roll Integration B-roll is an editing technique that uses auxiliary footage to create visual hierarchy and rhythm [60, 81]. It enhances narrative depth, maintains engagement, and offers editorial flexibility. In our coding process, we identified videos containing additional Manuscript submitted to ACM [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
read the original abstract

The digital transformation of religious practice has reshaped how billions of people engage with spiritual content, with video-sharing platforms becoming central to contemporary religious communication. Yet HCI research lacks systematic understanding of how narrative and visual elements create meaningful spiritual experiences and foster viewer engagement. We present a mixed-methods study of religious videos on YouTube across major religions, developing taxonomies of narrative frameworks, visual elements, and viewer interaction. Using LLM-assisted analysis, we studied relationships between content characteristics and viewer responses. Religious videos predominantly adopt lecture-style formats with authority-based persuasion strategies, using salvation narratives for guidance. All prefer bright lighting, with Buddhism favoring warm tones and prominent symbols, Judaism preferring indoor settings, and Hinduism emphasizing sacred objects. We identified differentiated patterns of emotional sharing among religious viewers while revealing significant correlations between content characteristics and engagement, particularly regarding AI-generated content. We provide evidence-based guidance for creating inclusive and engaging spiritual media.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper presents a mixed-methods study of religious videos on YouTube across major religions. It develops taxonomies of narrative frameworks, visual elements, and viewer interactions via LLM-assisted analysis of content and metrics. Central claims include predominant lecture-style formats using authority-based persuasion and salvation narratives, religion-specific visual preferences (e.g., bright lighting overall, warm tones for Buddhism), differentiated emotional sharing patterns, and significant correlations between content features and engagement, especially AI-generated content. The work concludes with guidance for inclusive spiritual media.

Significance. If the methodological gaps are addressed, the study could contribute to HCI by supplying empirical taxonomies and observed correlations that illuminate how narrative and visual strategies shape engagement with religious content on video platforms. This addresses an under-explored area of digital spiritual practice and could inform platform design or content guidelines, though the current lack of validation and statistical detail limits its immediate utility.

major comments (2)
  1. [Methods] Methods section: the LLM-assisted analysis for extracting narrative frameworks, visual elements, and engagement metrics reports no prompt details, sample size, video selection criteria, inter-rater reliability (e.g., Cohen’s kappa), disagreement resolution procedure, or quantified human validation/error rates. These omissions are load-bearing because the taxonomies, predominant-format claims, and correlations (including the AI-generated content finding) rest directly on the reliability of the automated coding.
  2. [Results] Results section: claims of 'significant correlations' between content characteristics and viewer engagement are stated without sample sizes, statistical tests, p-values, effect sizes, or error bars. This prevents assessment of whether the data support the reported patterns and the emphasis on AI-generated content.
minor comments (1)
  1. [Abstract] Abstract: the phrase 'significant correlations' is used without naming the statistical approach; adding a brief clause on the method would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our mixed-methods study of religious videos on YouTube. The comments highlight important areas for improving methodological transparency and statistical reporting, which we will address in the revision to strengthen the paper's rigor and utility for the HCI community.

read point-by-point responses

  1. Referee: [Methods] Methods section: the LLM-assisted analysis for extracting narrative frameworks, visual elements, and engagement metrics reports no prompt details, sample size, video selection criteria, inter-rater reliability (e.g., Cohen’s kappa), disagreement resolution procedure, or quantified human validation/error rates. These omissions are load-bearing because the taxonomies, predominant-format claims, and correlations (including the AI-generated content finding) rest directly on the reliability of the automated coding.

    Authors: We agree that additional details on the LLM-assisted analysis are necessary for readers to evaluate the reliability of the taxonomies and downstream claims. In the revised manuscript, we will expand the Methods section to specify the prompts used with the LLM, the sample size of videos analyzed, the video selection criteria, inter-rater reliability metrics (including Cohen’s kappa from human validation), the disagreement resolution procedure, and quantified human validation/error rates. These additions will directly support the credibility of the narrative frameworks, visual elements, and correlations reported. revision: yes

  2. Referee: [Results] Results section: claims of 'significant correlations' between content characteristics and viewer engagement are stated without sample sizes, statistical tests, p-values, effect sizes, or error bars. This prevents assessment of whether the data support the reported patterns and the emphasis on AI-generated content.

    Authors: We acknowledge that the Results section requires fuller statistical reporting to allow proper evaluation of the correlations. In the revision, we will include the sample sizes for each analysis, the specific statistical tests employed, associated p-values, effect sizes, and error bars or confidence intervals. This will provide a transparent basis for assessing the evidence, including the patterns observed with AI-generated content. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical claims derive from observed video data

full rationale

The paper conducts a mixed-methods empirical study analyzing YouTube religious videos across religions, developing taxonomies of narrative frameworks, visual elements, and engagement metrics via LLM-assisted coding followed by correlation analysis. All central claims—lecture-style formats, authority-based persuasion, salvation narratives, differentiated emotional sharing, and correlations with AI-generated content—rest on direct observation of sampled content and viewer metrics rather than any self-definitional reduction, fitted input renamed as prediction, or load-bearing self-citation chain. No equations, uniqueness theorems, or ansatzes are invoked that collapse back to the paper's own inputs by construction. The derivation chain is self-contained and externally falsifiable against the video corpus.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on the representativeness of the YouTube video sample and the accuracy of LLM-assisted content coding; both are domain assumptions without independent verification described in the abstract.

axioms (2)
  • domain assumption YouTube videos across major religions are representative of contemporary digital religious communication and viewer engagement
    The study draws broad conclusions about religious practice from the sampled videos without stating how the sample was constructed or validated.
  • domain assumption LLM-assisted analysis can accurately identify narrative frameworks, visual elements, and emotional patterns with minimal bias
    The abstract invokes LLM assistance for taxonomy development and correlation analysis but does not detail validation procedures.

pith-pipeline@v0.9.0 · 5707 in / 1452 out tokens · 53695 ms · 2026-05-18T16:25:49.928350+00:00 · methodology

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Reference graph

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