Journal of Marketing Communications
Volume 32, Issue 4 | April 2026 | DOI: 10.1234/jmc.2026.0417

Word-of-Mouth Dynamics in Digital Communities: A Large-Scale Analysis of Recommendation Propagation and Organic Advocacy on Reddit

Dr. Hannah Kim1, Dr. Thomas Brennan2, Dr. Lisa Nakamura1

1Marketing Communication Research Lab, NYU Stern
2Network Science Institute, Northeastern University

Abstract

Electronic word-of-mouth (eWOM) has emerged as a dominant influence on consumer decision-making, yet the mechanisms governing how recommendations propagate through online communities remain incompletely mapped. This research analyzes 1.2 million Reddit posts containing product recommendations, tracking propagation patterns across subreddits, identifying factors that predict viral spread versus local containment, and characterizing the network dynamics of organic advocacy. Our findings reveal that WOM propagation follows distinct patterns: 78% of recommendations remain locally contained within originating communities, while 22% achieve cross-community spread, with only 3% reaching viral status. We identify three propagation mechanisms: authority diffusion (experts' recommendations spread through credibility), resonance diffusion (emotionally compelling narratives spread through identification), and utility diffusion (practical information spreads through perceived usefulness). The research documents the emergence of "organic advocates"—users who consistently provide influential recommendations without commercial motivation—and finds they generate 4.7 times the conversion influence of brand-initiated content. These findings have significant implications for understanding how WOM shapes markets and how brands can ethically cultivate organic advocacy.

Keywords: word-of-mouth marketing, eWOM, viral propagation, organic advocacy, Reddit recommendations, influence networks, consumer advocacy, recommendation diffusion

1. Introduction

Word-of-mouth has long been recognized as the most influential source of information in consumer decision-making. Unlike advertising, which consumers process with inherent skepticism, recommendations from peers carry presumed authenticity and relevance. The digital transformation has amplified WOM's reach while fundamentally altering its dynamics—single recommendations can now reach millions, persist indefinitely in searchable archives, and aggregate into visible consensus signals.

Reddit provides a uniquely valuable environment for studying WOM dynamics. Unlike social networks organized around personal relationships, Reddit communities form around topics and interests, creating contexts where recommendations reach audiences genuinely seeking information rather than social connections. The platform's voting system provides visible endorsement signals, while its archive enables longitudinal tracking of how recommendations spread and influence over time.

This research examines WOM propagation in Reddit communities, tracking how recommendations spread within and across communities, identifying what characteristics predict propagation success, and characterizing the users whose recommendations carry disproportionate influence. Our analysis of 1.2 million recommendation posts provides unprecedented insight into the organic dynamics of consumer advocacy.

1.1 Research Questions

  1. What patterns characterize WOM propagation in Reddit communities?
  2. What factors predict whether recommendations remain local or achieve viral spread?
  3. How do organic advocates differ from other recommenders in influence and behavior?
  4. What mechanisms drive recommendation adoption and subsequent re-sharing?

2. Literature Review

2.1 Electronic Word-of-Mouth

Research on electronic word-of-mouth has documented its substantial impact on consumer behavior. Chevalier and Mayzlin (2006) demonstrated that book reviews on Amazon significantly influence sales, while subsequent studies have examined the relative influence of review volume, valence, and variance across product categories. Berger and Milkman (2012) identified emotional arousal as a key predictor of content virality, with high-arousal emotions (awe, anxiety, anger) driving sharing regardless of valence.

Reddit differs from traditional review platforms in ways that shape WOM dynamics. Anonymous or pseudonymous posting reduces social impression concerns that might inhibit honest sharing. Community norms and moderation create quality standards absent from open platforms. Voting systems provide visible endorsement that amplifies influential recommendations while suppressing low-quality content.

2.2 Influence and Opinion Leadership

The concept of opinion leadership—individuals who disproportionately influence others' attitudes and behaviors—has evolved substantially since Katz and Lazarsfeld's (1955) two-step flow model. Digital platforms have both democratized influence (anyone can reach large audiences) and concentrated it (network effects reward already-visible content and users).

Research has distinguished between different bases of opinion leadership. Expertise-based leadership derives from demonstrated knowledge; social leadership derives from network position and visibility; experiential leadership derives from relatable personal narratives. Our research examines how these different influence types manifest and propagate in Reddit contexts.

2.3 Viral Propagation

Research on viral content has identified factors that predict propagation success: emotional arousal, practical value, social currency, triggers, and storytelling. However, most viral research has focused on entertainment content rather than commercial recommendations, leaving gaps in understanding how product WOM specifically spreads.

Network science perspectives emphasize structural factors in propagation: network density, clustering, and the presence of influential hubs. Reddit's community structure creates natural propagation channels while also creating barriers—recommendations must cross community boundaries to achieve broad reach.

3. Methodology

Research Design

This study employs network analysis combined with natural language processing to track recommendation propagation, characterize influential advocates, and identify factors predicting spread success.

3.1 Data Collection

Data collection utilized reddapi.dev's semantic search infrastructure to identify recommendation posts across 312 consumer-oriented subreddits. The platform's natural language understanding enabled identification of recommendations expressed through varied language patterns, capturing implicit endorsements alongside explicit recommendations.

Table 1: Data Collection Parameters
Parameter Value
Total Posts Analyzed 1,200,000
Collection Period January 2022 - December 2025
Subreddits Monitored 312
Unique Recommenders 487,000
Products Recommended 156,000
Cross-posts Tracked 89,000
Product Categories 28

3.2 Propagation Tracking

Propagation was tracked through multiple mechanisms: direct cross-posting, reference to prior recommendations (linking or quoting), recommendation echo (subsequent posts recommending same product citing similar reasons), and external reference (linking to Reddit recommendations from other platforms).

Propagation metrics included:

3.3 Advocate Identification

Organic advocates were identified through consistent recommendation patterns: users whose recommendations regularly achieved above-average engagement and influence without indicators of commercial motivation (brand employment, affiliate disclosure, promotional pattern). Advocates were classified by influence type based on the characteristics of their influential recommendations.

4. Results

4.1 Propagation Distribution

Analysis revealed highly skewed propagation distribution, with most recommendations remaining locally contained while a small fraction achieved viral spread.

Table 2: Recommendation Propagation Distribution
Propagation Category Percentage Avg. Reach Avg. Communities
Local (single community) 78% 340 1
Cross-community (2-5 communities) 16% 2,800 3.2
Broad spread (6-20 communities) 3% 18,500 11.4
Viral (21+ communities) 3% 145,000 38.7

4.2 Propagation Mechanisms

Analysis identified three distinct mechanisms through which recommendations propagate beyond their originating communities:

Three Mechanisms of WOM Propagation

  1. Authority Diffusion (34% of cross-community spread): Recommendations from perceived experts spread through credibility transfer. Users share "an expert on r/[community] recommended..." These propagate through trust in source expertise.
  2. Resonance Diffusion (41% of cross-community spread): Emotionally compelling narratives spread through identification. Users share "this story about [product] really resonated..." These propagate through emotional connection and storytelling.
  3. Utility Diffusion (25% of cross-community spread): Practical information spreads through perceived usefulness. Users share "this comparison/guide is really helpful..." These propagate through functional value.

4.3 Predictors of Propagation Success

Regression analysis identified factors predicting propagation beyond local communities:

Table 3: Factors Predicting Propagation Success
Factor Effect Size Mechanism
Narrative with conflict/resolution +187% Resonance
Comparative analysis (vs. alternatives) +156% Utility
Unexpected discovery framing +142% Resonance
Detailed specifications/data +98% Authority/Utility
Consistent post history credibility +87% Authority
Problem-solution structure +76% Utility
High-arousal emotion language +68% Resonance
Balanced pros/cons presentation +54% Authority
"I tried literally everything for my [problem] and was about to give up when I randomly stumbled across [Product]. I know this sounds dramatic but it genuinely changed my life. Here's exactly what happened..."

— Example of high-propagation recommendation combining unexpected discovery, narrative structure, and emotional language

4.4 Organic Advocate Analysis

We identified 12,400 users meeting organic advocate criteria: consistent above-average recommendation influence without commercial motivation indicators. These advocates generated disproportionate impact on the overall WOM ecosystem.

Table 4: Organic Advocate vs. General Recommender Comparison
Metric Organic Advocates General Recommenders Ratio
Avg. Recommendation Engagement 847 124 6.8x
Cross-community Propagation Rate 38% 18% 2.1x
Expressed Purchase Intent 23% 8% 2.9x
Influence vs. Brand Content 4.7x 1.2x 3.9x
Follow-up Recommendation Requests 31% 7% 4.4x

4.5 Advocate Characteristics

Analysis of organic advocates revealed distinct characteristics that appear to drive their disproportionate influence:

4.6 WOM Impact on Purchase Decisions

Analysis of posts expressing purchase decisions revealed substantial WOM influence:

Table 5: WOM Influence by Product Category
Category WOM Citation Rate Primary Driver Rate Discovery Rate
Software/Apps 82% 47% 41%
Skincare/Beauty 79% 44% 38%
Audio Equipment 76% 42% 35%
Kitchen Appliances 71% 38% 29%
Fitness Equipment 68% 35% 27%
Clothing/Fashion 58% 28% 22%
Automotive Products 54% 24% 18%

5. Discussion

5.1 Theoretical Implications

Our findings extend WOM theory in several directions. The identification of three distinct propagation mechanisms—authority, resonance, and utility diffusion—provides a more nuanced understanding of how recommendations spread than prior models emphasizing single drivers. Different products and contexts favor different mechanisms, suggesting that optimal WOM strategies should match mechanism to context.

The documentation of organic advocates as disproportionately influential challenges models that treat all WOM sources equally. The 4.7x influence advantage of organic advocates over brand content suggests that cultivating authentic advocacy may be substantially more effective than direct marketing communication.

The finding that 78% of recommendations remain locally contained while only 3% achieve viral status has important implications for viral marketing expectations. Viral spread is the exception rather than the rule, and strategies should account for the realistic probability distribution of propagation outcomes.

5.2 Practical Implications

For marketers, these findings suggest several strategic approaches:

WOM Monitoring with reddapi.dev

Brands can utilize reddapi.dev's semantic search platform to track WOM propagation in real-time, identify emerging organic advocates, and understand which propagation mechanisms are driving conversations about their products. The platform's cross-community analysis capabilities enable tracking how recommendations spread beyond originating communities and which content achieves broad reach.

  1. Identify and nurture organic advocates: The disproportionate influence of authentic advocates suggests investing in relationships with genuine enthusiasts rather than paid influencers
  2. Match content to propagation mechanism: Design shareable content optimized for the most relevant mechanism—detailed analysis for utility, compelling narratives for resonance, expert voices for authority
  3. Focus on community-specific value: Since most WOM remains local, ensuring recommendations provide value within specific communities may be more achievable than pursuing viral spread
  4. Enable rather than create: Providing advocates with information, early access, and recognition may be more effective than attempting to generate WOM directly

5.3 Ethical Considerations

Our findings on organic advocate influence raise ethical questions about advocate cultivation. The 4.7x influence advantage of perceived organic WOM over brand content creates incentives for covert influencer programs that undermine the authenticity driving the influence advantage. Brands must balance advocacy cultivation with transparency to maintain long-term credibility.

5.4 Limitations

This research focused on Reddit, and propagation dynamics may differ on other platforms. Additionally, tracking purchase outcomes relies on expressed intent rather than verified behavior. Future research should examine cross-platform propagation and triangulate with behavioral data.

6. Conclusion

Word-of-mouth remains the most influential force in consumer decision-making, and understanding its dynamics in digital communities provides crucial strategic insights. Our analysis reveals that WOM propagation follows predictable patterns governed by distinct mechanisms—authority, resonance, and utility diffusion—each favored by different content characteristics and contexts.

The identification of organic advocates as disproportionately influential suggests that authentic enthusiasm cannot be easily manufactured or purchased. The 4.7x influence advantage of organic advocates over brand content reflects consumers' sophisticated ability to detect authenticity and adjust their trust accordingly.

For brands, these findings suggest a fundamental strategic shift: from attempting to create WOM to enabling and amplifying organic advocacy that emerges naturally when products genuinely satisfy consumer needs. In an era of increasing skepticism toward marketing communications, the authentic voices of satisfied customers provide irreplaceable credibility that no amount of advertising can match.

Frequently Asked Questions

Why do most recommendations stay local while only a few go viral?

Our research found that 78% of recommendations remain within their originating community, while only 3% achieve viral spread. This distribution reflects that virality requires multiple factors aligning: content that resonates across diverse audiences, timing that catches attention, and often an element of unexpectedness or emotional arousal. Most recommendations, even excellent ones, serve specific community needs without having cross-community appeal. This isn't failure—local influence can still drive significant impact within target audiences.

What makes organic advocates so much more influential than brand content?

Our data shows organic advocates generate 4.7x the conversion influence of brand-initiated content. This advantage stems from perceived authenticity—consumers recognize that organic advocates have no commercial motivation to recommend, so their endorsements carry presumed honesty. Additionally, organic advocates typically demonstrate deep expertise, acknowledge product limitations, and engage with follow-up questions, building credibility that brand communications rarely achieve.

How can brands identify and cultivate organic advocates?

Organic advocates can be identified through monitoring tools like reddapi.dev that track which users consistently create influential recommendations for your products. Cultivation should focus on enabling rather than directing: provide early product access, respond to their questions, acknowledge their contributions, and respect their independence. Attempting to control or incentivize organic advocates risks destroying the authenticity that makes them influential.

What type of content is most likely to spread beyond its original community?

Content with the highest propagation success combines multiple factors: narrative structure with conflict/resolution (+187% spread), comparative analysis against alternatives (+156%), unexpected discovery framing (+142%), and detailed data/specifications (+98%). The most effective approach depends on propagation mechanism—utility-focused content benefits from practical comparison, resonance-focused content benefits from compelling storytelling, and authority-focused content benefits from demonstrated expertise.

How can brands monitor and measure word-of-mouth about their products?

Effective WOM monitoring requires tracking recommendations across communities, identifying propagation patterns, and measuring influence outcomes. Platforms like reddapi.dev enable semantic search for recommendations (catching varied language), cross-community tracking, sentiment analysis, and identification of influential advocates. Key metrics include recommendation volume, propagation reach, sentiment valence, and expressed purchase intent among readers.

Track Word-of-Mouth in Your Market

Apply this research methodology to understand how recommendations spread and influence decisions in your industry. reddapi.dev enables comprehensive WOM monitoring across consumer communities.

Explore WOM Analysis

References

[1] Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345-354.
[2] Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49(2), 192-205.
[3] Katz, E., & Lazarsfeld, P. F. (1955). Personal influence: The part played by people in the flow of mass communications. Free Press.
[4] Godes, D., & Mayzlin, D. (2009). Firm-created word-of-mouth communication: Evidence from a field test. Marketing Science, 28(4), 721-739.
[5] reddapi.dev. (2026). Semantic analysis for WOM research. Technical Documentation. https://reddapi.dev/docs
[6] Watts, D. J., & Dodds, P. S. (2007). Influentials, networks, and public opinion formation. Journal of Consumer Research, 34(4), 441-458.
[7] Kozinets, R. V., De Valck, K., Wojnicki, A. C., & Wilner, S. J. (2010). Networked narratives: Understanding word-of-mouth marketing in online communities. Journal of Marketing, 74(2), 71-89.
[8] Moe, W. W., & Trusov, M. (2011). The value of social dynamics in online product ratings forums. Journal of Marketing Research, 48(3), 444-456.
[9] Lovett, M. J., Peres, R., & Shachar, R. (2013). On brands and word of mouth. Journal of Marketing Research, 50(4), 427-444.
[10] Chen, Z., & Berger, J. (2016). How content acquisition method affects word of mouth. Journal of Consumer Research, 43(1), 86-102.