Enterprise AI Analysis
Unlocking Narrative Intelligence: Contextual Reasoning with Social Story Frames
Our innovative framework deepens understanding of reader response across diverse online communities.
Executive Impact
Quantifiable results demonstrating the power of contextual narrative understanding.
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Plausible Inferences
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Jaccard Index (IAA)
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Social Media Stories Analyzed
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Understanding Narrative Functions
The research explores how stories serve various social and communicative functions in online communities.
- Social Story Frames (SSF) introduces a 10-dimensional reader response taxonomy, including author intent, causal inferences, and affective responses, grounded in narrative theory, pragmatics, and psychology.
- The framework is operationalized with SSF-GENERATOR for inference generation and SSF-CLASSIFIER for mapping inferences to fine-grained taxonomy subcategories, validated through human surveys and expert annotations.
- The approach emphasizes contextual reasoning, incorporating community and conversational contexts to capture nuanced social dynamics of online storytelling.
Understanding Narrative Functions
The research explores how stories serve various social and communicative functions in online communities.
- A corpus of 6,140 stories (SSF-CORPUS) from Reddit is curated, filtering for storytelling content and removing toxic/explicit texts, with careful anonymization.
- Context summarization involves GPT-40 to distill community and conversational contexts (initial post, ancestral chain, preceding peers) into concise inputs for inference generation.
- Model distillation trains smaller Llama3.1-8B-Instruct models (SSF-GENERATOR and SSF-CLASSIFIER) on GPT-40 generated outputs, balancing performance with accessibility.
Understanding Narrative Functions
The research explores how stories serve various social and communicative functions in online communities.
- Plausibility ratings for SSF-GENERATOR show that >94% of generated inferences are deemed plausible by human annotators, with >78% rated as 'very' or 'somewhat likely'.
- SSF-CLASSIFIER achieves strong inter-annotator agreement (mean Jaccard Index of 0.732) and approaches GPT-4.1 level performance across most taxonomy dimensions.
- Narrative intents like 'justify/challenge a belief' (40%) and 'emotional release' (14%) are prevalent. 'Providing emotional support' is strongly associated with 'conveying a similar experience' (NPMI: 0.35).
- SSF-Sim, a narrative similarity measure, reveals that topically distinct communities can exhibit similar storytelling patterns, demonstrating its ability to capture pragmatic and interpretive dimensions beyond surface semantics.
- Analysis of community narrative diversity highlights varying authorial intents and reader responses across subreddits, e.g., r/Frugal and r/techsupport show low diversity, while r/Fitness shows high author but low reader diversity.
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of Generated Inferences Deemed Plausible
Enterprise Process Flow
Social Media Story & Context
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SSF-GENERATOR (Inference Generation)
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SSF-CLASSIFIER (Taxonomy Mapping)
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Plausible Reader Responses
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Community Narrative Diversity: r/MakeupAddiction vs. r/buildapc
Our SSF-Sim metric revealed surprising similarities between r/MakeupAddiction and r/buildapc in their storytelling patterns, despite their vastly different topical content. This indicates that communities can share underlying communicative functions and reader responses, even if the subject matter is unrelated. For instance, a post seeking product reassurance in r/MakeupAddiction functioned similarly to a troubleshooting post in r/buildapc in terms of author intent and reader engagement. This highlights SSF-Sim's ability to uncover shared pragmatic and interpretive dimensions beyond mere semantic overlap.
Calculate Your Potential AI Impact
Estimate the annual savings and reclaimed hours for your enterprise.
Annual Cost Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A phased approach to integrate Social Story Frames into your enterprise workflows.
Phase 1: Discovery & Strategy
Identify key narrative challenges and define specific AI objectives with our expert team. Tailor the SSF framework to your organizational context.
Phase 2: Data Integration & Model Training
Integrate your internal data sources. Leverage SSF-GENERATOR and SSF-CLASSIFIER with your datasets for contextual inference. Validate models with internal stakeholders.
Phase 3: Pilot Deployment & Optimization
Deploy SSF models in a pilot program. Gather feedback, refine outputs, and optimize for accuracy and relevance within your specific use cases.
Phase 4: Full-Scale Integration & Monitoring
Integrate SSF-powered insights across relevant platforms. Implement continuous monitoring and iterative improvements to ensure sustained value and performance.
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