Web Content Credibility Scoring via ML Indicators
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Solution Overview
Problem
Existing systems for determining the credibility of web page content are not scalable and take too long to keep up with the increasing amount of misinformation from various websites, making them ineffective in combating misinformation effectively.
Innovation Solution
A computing system that uses machine learning models to analyze text from web pages by determining title topic indicators, sentiment indicators, and text subjectivity indicators, and applies these to generate credibility and bias scores, providing graphical representations to digital devices for immediate assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fact-checking systems are used to verify web content credibility, then accuracy in identifying misinformation is improved, but processing time increases and scalability deteriorates
Solution Approach 1:
The patent replaces manual fact-checking mechanisms with automated machine learning models that analyze linguistic patterns, sentiment, and subjectivity. This substitution enables real-time credibility assessment of web content without requiring human reviewers, thus improving processing speed while maintaining reasonable accuracy through algorithms trained on labeled datasets of credible and non-credible sources.
Solution Approach 2:
The system transforms the credibility assessment problem from verifying factual accuracy into measuring linguistic parameters such as sentiment intensity, subjectivity scores, and topic coherence. By changing the assessment parameters from fact-based verification to language-based metrics, the system achieves scalable automated evaluation that can process vast amounts of web content rapidly.
2Measurement precision
If manual fact-checking methods are employed to assess web content, then credibility determination accuracy is improved, but the system cannot keep up with the increasing volume of misinformation
Solution Approach 1:
The patent replaces manual fact-checking mechanisms with automated machine learning models that analyze linguistic patterns, sentiment, and subjectivity. This substitution enables real-time credibility assessment of web content without requiring human reviewers, thus improving processing speed while maintaining reasonable accuracy through algorithms trained on labeled datasets of credible and non-credible sources.
Solution Approach 2:
The system creates and applies pre-trained machine learning models that encapsulate credibility assessment knowledge. These models can be replicated and deployed across multiple systems simultaneously, enabling parallel processing of vast amounts of web content. The copied models maintain consistent assessment standards while scaling to handle increasing volumes of misinformation.
3Measurement precision
If comprehensive analysis of web content is performed to determine credibility, then assessment accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent divides the credibility assessment task into separate analytical components: topic modeling to identify subject matter, sentiment analysis to detect emotional bias, and subjectivity measurement to evaluate opinionated language. Each component is handled by specialized machine learning models that process specific linguistic features independently, reducing overall computational complexity while maintaining comprehensive assessment accuracy.
Data Source
AI summary
An example system may include instructions to control processor(s) to receive text from content of a first web page, determine, based on the content, a first title topic indicator, a first sentiment indicator, and a first text subjectivity indicator, apply the first title topic indicator, the first sentiment indicator, and the first text subjectivity indicator to a credibility machine learning model to generate a first content credibility score and a first content bias score for the text of the first web page, the credibility machine learning model being trained on text from other web pages using known title topic indicators, known sentiment indicators, and known text subjectivity indicators, and known credibility scores and bias scores, generate a first graphical representation for the first content credibility score and the first bias credibility score, and provide the graphical representation to a first digital device.