Information Dissemination Heat Degree Prediction Using Multi-Level Sharing Data
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Solution Overview
Problem
Existing methods for determining the heat degree of information dissemination are inadequate as they rely solely on click rates, which are backward indicators and fail to predict the dissemination heat degree at the initial stage, leading to confusion in identifying valuable information amidst vast amounts of network information.
Innovation Solution
A method that analyzes multi-level sharing data to calculate dissemination heat degree based on dissemination speed and intensity parameters, using sharing time averages and numbers, and applies weight factors to enhance accuracy, allowing for prediction of information popularity during subsequent dissemination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the heat degree of information is determined based on click rate, then the measurement is simple and backward verification is achieved, but the ability to predict dissemination heat degree at the initial stage is lost and valuable information cannot be identified amidst spam
Solution Approach 1:
The patent applies preliminary action by calculating the dissemination heat degree at the initial stage of information propagation using sharing data from early sharing levels (level 1 and level 2). This allows the system to predict which information will become popular before it achieves widespread dissemination, enabling users to identify valuable information early rather than waiting for click rate data to accumulate. The formula uses sharing time averages T1, T2 and sharing numbers P1, P2 from these early levels to compute S = (T2-T1)/(T1*T2) + (P2-P1)/(P1*P2), providing forward-looking prediction capability.
2Reliability
If multi-level sharing data analysis is used to determine dissemination heat degree, then prediction accuracy is improved, but the complexity of data analysis and parameter calculation increases
Solution Approach 1:
The patent applies segmentation by dividing the information dissemination process into distinct sharing levels (level 0 for original release, level 1 for first sharing, level 2 for second sharing, etc.). Each level is analyzed separately to extract specific parameters: sharing time average (Tj) and sharing number (Pj) for each level j. This segmentation allows the system to capture the propagation dynamics at different stages, with particular emphasis on early levels for prediction, while maintaining manageable data collection and analysis requirements through structured parameter extraction from each segment.
Data Source
AI summary
The present disclosure provides a method, an apparatus, and a computing device for determining dissemination heat degree of information. The method for determining dissemination heat degree of information includes analyzing N levels of sharing data of specific network information to determine parameters related to each sharing level of the specific network information, where N is a natural number greater than 1; and calculating a dissemination heat degree S of the specific network information based on the parameters related to the sharing of the specific network information. The dissemination heat degree obtained according to the disclosed method, apparatus, and computing device can be used to predict the popular level of the specific network information during the subsequent dissemination process.