Social Media Influencer Recommendation Fact Verification Dataset
#fact verification #overgeneralization detection #evidence-based classification #influencer marketing compliance #social media recommendation verification #advertising claim assessment
- 5000
- 0.2GB
- JSON
- CATL
MOBIUSI INC
Updated:2026-10-19
AI Analysis & Value Prop
This dataset features synthetic recommendation copy modeled on social media influencer reviews, user testimonials, and native ads, paired with evidence that supports or challenges related claims. It covers personal experiences, objective product attributes, and generalized conclusions, with structured annotations for claim units, evidence support, and improper generalization. It supports fact verification training, advertising content review, and recommendation copy risk assessment.
Dataset Insights
Sample Examples
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| claim_units | array | Verifiable claims extracted from the recommendation copy, including their subject scope, category, and corresponding evidence references. |
| analysis_rationale | string | Explains the basis for the judgment by describing the relationships among personal experience, product attributes, generalized conclusions, and supporting evidence. |
| verification_label | string | The final classification of the recommendation copy's factual risk and any extension of personal experience. |
| recommendation_text | string | Complete recommendation copy from an influencer review, user testimonial, or native ad. |
| supporting_evidence | string | Evidence text used to support or challenge claims about effects, experiences, product attributes, or generalized conclusions. |
| evidence_support_level | string | Assesses how completely and adequately the supporting evidence covers the recommendation claims. |
| improper_generalization | boolean | Indicates whether personal experience or limited evidence is extended into a general fact without sufficient support. |
| generalization_direction | string | Describes the scope from which a claim is generalized and the scope to which it is extended. |
| product_attribute_mentions | array | Product features, ingredients, performance, or other objective attributes identified in the recommendation copy. |
| personal_experience_present | boolean | Indicates whether the copy describes a publisher's or specific user's experience, impressions, or results from using the product. |
| generalized_conclusion_present | boolean | Indicates whether the copy presents an individual or limited observation as a general conclusion applicable to most or all people. |
Compliance Statement
| Authorization Type | Proprietary - Commercial AI Training License (No Redistribution) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What does the dataset contain?
- It includes synthetic recommendation copy modeled on influencer reviews, user testimonials, and native ads, along with evidence that supports or challenges the claims. Structured annotations cover claim units, product attribute mentions, personal experiences, and generalization judgments.
- Which tasks can the dataset support?
- It can support fact verification, claim extraction, evidence support assessment, and improper generalization detection. Relevant applications include social media ad review, recommendation copy analysis, and compliance risk assessment.
- Are the examples real user reviews or advertisements?
- The copy is synthetic and is intended for model training, evaluation, and review workflow design. It should not be treated as authentic user testimony, verified product experience, or evidence of real-world advertising performance.
- How can I access the data, and what are the licensing terms?
- Follow the download or access instructions on the current product page. Before use, review the applicable license, commercial use conditions, and any restrictions on redistribution.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Social Media Influencer Recommendation Fact Verification Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/93518df0f2cdbf698cb288e725a172e0?dataset_scene_id=17},
urldate={2026-10-14},
keywords={social media fact verification,influencer ad review,user testimonial analysis,recommendation copy dataset,personal experience generalization,advertising claim verification,evidence support assessment,synthetic text training data},
version={1.0}
}Using this in research? Please cite us.
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