Multi-scale Pattern Identification in Social Media Data
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Solution Overview
Problem
Current methods for analyzing social dynamics in social networks are limited in identifying structures and computational efficiency, and lack a rigorous approach for engineering outcome dynamics, as they fail to consider intervention in predictive models.
Innovation Solution
A computer-implemented method using a deep-learning algorithm to analyze time-series social media data for patterns across multiple time scales, combined with a generative model to engineer outcome dynamics by determining an influence indicator for influencing the outcome of a dynamic system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep-learning algorithms are used to analyze time-series social media data for patterns across multiple time scales, then pattern identification quality is improved, but computational complexity increases
Solution Approach 1:
The deep-learning algorithm is segmented into multiple processing layers that operate at different time scales. Each layer focuses on specific temporal patterns, dividing the complex analysis task into manageable segments that can be processed independently and then integrated, thereby maintaining high pattern identification quality while reducing overall computational complexity.
Solution Approach 2:
The algorithm processes social media data across multiple time scale dimensions simultaneously rather than analyzing each time scale separately. This multi-dimensional approach allows the system to identify patterns across different temporal resolutions in a single computational pass, improving pattern identification quality without linearly increasing computational complexity.
2Loss of information
If predictive models are used for social dynamics analysis, then understanding social structures is improved, but ability to support intervention in dynamics engineering is reduced
Solution Approach 1:
The system incorporates feedback mechanisms that allow it to not only predict social dynamics but also evaluate the potential impact of interventions. By continuously comparing predicted outcomes with actual outcomes and adjusting the model accordingly, the system maintains accurate social structure understanding while gaining the adaptability needed to support intervention in dynamics engineering.
Solution Approach 2:
The algorithm performs preliminary analysis of potential intervention scenarios by simulating their effects on social dynamics before actual implementation. This allows the system to maintain its predictive modeling strength while preparing intervention strategies in advance, thereby supporting dynamics engineering applications without compromising social structure understanding.
Data Source
AI summary
In some aspects, computer-implemented methods of identifying patterns in time-series social-media data. In an embodiment, the method includes applying a deep-learning methodology to the time-series social-media data at a plurality of temporal resolutions to identify patterns that may exist at and across ones of the temporal resolutions. A particular deep-learning methodology that can be used is a recursive convolutional Bayesian model (RCBM) utilizing a special convolutional operator. In some aspects, computer-implemented methods of engineering outcome-dynamics of a dynamic system. In an embodiment, the method includes training a generative model using one or more sets of time-series data and solving an optimization problem composed of a likelihood function of the generative model and a score function reflecting a utility of the dynamic system. A result of the solution is an influence indicator corresponding to intervention dynamics that can be applied to the dynamic system to influence outcome dynamics of the system.