An audience agent-based video propagation effectiveness evaluation method and system

By constructing an audience intelligent agent system, combined with user profiles and small-world network models, the problems of audience differentiation modeling and social network simulation in video dissemination effectiveness evaluation are solved, enabling accurate prediction and multi-dimensional evaluation of video dissemination effectiveness, applicable to different platforms and thematic scenarios.

CN122336616APending Publication Date: 2026-07-03COMMUNICATION UNIVERSITY OF CHINA
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
COMMUNICATION UNIVERSITY OF CHINA
Filing Date
2026-01-21
Publication Date
2026-07-03

AI Technical Summary

Technical Problem

Existing technologies lack audience differentiation modeling in video dissemination effectiveness evaluation, do not incorporate social network structure simulation, have single evaluation indicators, and are difficult to accurately capture the cognitive preferences and dissemination behaviors of different groups, thus failing to recreate the real social dissemination path.

Method used

A communication effectiveness evaluation system based on audience intelligent agents is constructed. Through user profile construction, audience intelligent agent generation and social network simulation, combined with a small-world network model, multi-agent interaction and communication path simulation are realized, and a large language model is used for content understanding and evaluation.

Benefits of technology

It enables accurate prediction and evaluation of video dissemination effectiveness, can predict dissemination effects before release, provides multi-dimensional evaluation results, is applicable to different platforms and thematic scenarios, and improves the accuracy and interpretability of the evaluation.

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Abstract

The application discloses a video propagation efficiency evaluation method and system based on audience agents, and belongs to the field of information propagation. The method limits the evaluation range through theme and platform double constraints, synchronously collects high propagation volume videos and low propagation volume videos and associated comments and fan data, constructs a multi-dimensional user portrait containing general dimensions and theme personalized dimensions, identifies similar user groups through unsupervised clustering after semantic vectorization conversion, generates audience agents with proportionally matched real user distribution based on group cognitive characteristics, and constructs an agent relationship network by adopting a small-world network model. Then, the original video is converted into a structured script to drive the agent network to carry out multi-round propagation simulation, and finally, comprehensive evaluation results containing quantitative indicators and descriptive indicators are output. The application solves the problems of lack of audience differentiation modeling, non-recovery of social network structure and single evaluation indicator, realizes pre-position prediction of propagation efficiency, and improves the authenticity and interpretability of the evaluation results.
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