Multimodal Scoring Mechanism for Extremist Content Detection
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Solution Overview
Problem
Existing methods for identifying connections between trends, groups, and users on social media platforms are inefficient and biased, often relying on rigid approaches that fail to comprehensively utilize text, image, and location data, leading to incomplete understanding of user tendencies and missed connections.
Innovation Solution
A method that receives and processes data objects from various sources, identifying relevant data objects by comparing text, image, and location data to a predefined element, assigning scores based on these data types, and aggregating scores to identify users associated with specific groups or subject areas, using natural language processing and computer vision to improve accuracy and versatility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rigid approaches are used to identify connections between trends, groups, and users, then the method is simple to implement, but the identification accuracy and comprehensiveness deteriorate
Solution Approach 1:
The patent combines multiple data types (text data, image data, location data) and multiple analysis methods into a unified scoring system. The scoring mechanism integrates results from natural language processing, computer vision, and location-based analysis to generate comprehensive scores that accurately identify connections between users, groups, and trends while maintaining systematic complexity management.
Solution Approach 2:
The scoring mechanism serves multiple functions simultaneously: it identifies extremist content, detects connections between users and groups, analyzes trends, and provides comprehensive user behavior understanding. This multi-functional approach improves identification accuracy across different analysis objectives without requiring separate rigid methods for each purpose.
2Loss of information
If only text data is analyzed, then the processing speed is fast, but the understanding of user tendencies becomes incomplete
Solution Approach 1:
The patent segments the analysis process into distinct modules that handle different data types independently: text analysis module, image analysis module, and location analysis module. Each module processes its specific data type using appropriate techniques, then results are aggregated into comprehensive scores. This segmentation preserves processing efficiency while achieving complete information understanding.
Solution Approach 2:
The patent transitions from single-dimensional text analysis to multi-dimensional analysis by incorporating image data and location data as additional dimensions. This dimensional expansion provides comprehensive understanding of user tendencies through diverse data perspectives while maintaining processing efficiency through parallel module execution.
3Measurement precision
If manual review methods are used, then the accuracy of identifying extremist content is high, but the resource consumption and time required increase significantly
Solution Approach 1:
The scoring mechanism operates autonomously to identify and score extremist content without requiring continuous manual review. The system automatically processes data objects, calculates scores based on multiple data types, and identifies connections between users, groups, and trends independently, significantly reducing resource consumption while maintaining high identification accuracy.
Solution Approach 2:
The patent replaces manual review mechanisms with automated computational scoring. The scoring mechanism uses algorithmic processing of text, image, and location data to substitute human reviewers, achieving accurate extremist content identification while eliminating the high resource consumption and time requirements of manual methods.
4Reliability
If comprehensive multimodal data analysis is performed, then the identification of user connections becomes accurate, but the computational complexity increases
Solution Approach 1:
The scoring mechanism dynamically adjusts its analysis based on the specific data objects being processed. The system adapts its computational resources and analysis depth according to the characteristics of each data object, maintaining accurate user connection identification while optimizing computational complexity through dynamic resource allocation rather than static comprehensive processing.
Data Source
AI summary
A device may receive a plurality of data objects from a plurality of sources; identify text data, image data, and location data of the plurality of data objects; identify relevant data objects, of the plurality of data objects, based on the text data, and/or based on the image data, based on the location data, and/or based on comparing the text data, the image data, and the location data to a predefined element that identifies values relevant to a particular group or subject area; assign scores to the relevant data objects based on the text data, the image data, and the location data; aggregate the scores, as one or more aggregated scores, with regard to one or more users associated with the relevant data objects; and/or perform one or more actions based on the one or more aggregated scores associated with the one or more users.