Digital Content Vernacular Analysis for Unfamiliar Token Comprehension
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Solution Overview
Problem
The rapid spread of digital content across diverse communities through the Internet leads to users encountering unfamiliar words, terms, and symbols, making it difficult for them to understand digital content from outside their familiar groups or interests.
Innovation Solution
A computer-implemented method that identifies a user's familiarity category and performs natural language processing to compute a content unfamiliarity index, generating supplemental definitions for digital content when the index exceeds a threshold, allowing users to access explanations for unfamiliar tokens, non-words, and symbols via mouseover or click gestures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If digital content is shared across diverse communities through the Internet, then content dissemination efficiency and accessibility are improved, but users encounter unfamiliar words, terms, and symbols that reduce comprehension
Solution Approach 1:
The system pre-identifies unfamiliar tokens, non-words, and symbols in digital content before user interaction, and prepares supplemental definitions in advance. When users encounter these elements, the explanations are already available for immediate display, eliminating comprehension barriers while maintaining efficient content delivery across diverse communities.
2Loss of information
If supplemental definitions are provided for all unfamiliar content, then user comprehension is improved, but system complexity and processing overhead increase
Solution Approach 1:
Instead of uniformly processing all content, the system applies different levels of analysis to different portions of digital content. It specifically targets unfamiliar tokens, non-words, and symbols for supplemental definition generation, while leaving familiar content unprocessed. This localized approach reduces overall system complexity while maintaining comprehension support where needed.
Solution Approach 2:
The system dynamically adjusts its processing based on the unfamiliarity index calculated for different content elements. By changing the parameter of definition provision from universal to selective based on measured unfamiliarity levels, the system optimizes the balance between comprehension support and processing efficiency.
3Loss of information
If the system tracks and analyzes non-word evolution and origins, then linguistic research value is improved, but computational resources and processing time increase
Solution Approach 1:
The system performs preliminary identification and tracking of non-words as they appear in digital content, building a database of non-word origins and evolution over time. This pre-processing enables future linguistic analysis without requiring intensive real-time computation, preserving linguistic information while managing processing time efficiently.
Data Source
AI summary
Disclosed embodiments provide techniques to identify the in-context meanings of natural language in order to decipher the evolution or creation of new vocabulary words and create a more holistic user experience. Thus, disclosed embodiments improve the technical field of digital content comprehension. In embodiments, machine learning is used to identify sentiment of text, perform entity detection to determine topics of text, and/or perform image analysis on images used in digital content. Words, symbols, and images that are determined to be potentially unfamiliar to a user are augmented with a supplemental definition indication. Invoking the supplemental definition indication enables rendering of additional definition information for the user. This serves to accelerate understanding of digital content such as webpages and social media posts.