Microcontent NLP for Social Stream Aggregation and Filtering

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Solution Overview

Problem

Users face information overload due to the fragmentation of social media messages across different networks, leading to redundancy and inefficiency in content digestion and engagement, as they need to navigate multiple platforms to keep up with various social streams.

Innovation Solution

A social intelligence system that employs natural language processing optimized for microcontent, enabling efficient message filtering, metadata enrichment, and stream ranking to provide unified and relevant content across networks, using a decentralized architecture and client-side processing to reduce CPU burden and store only actionable data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If users monitor multiple social networks separately, then they can access all social streams, but they experience information overload and spend excessive time navigating between platforms

Engineering Contradiction:

Improvecompleteness of social stream accessVSAvoidtime spent navigating between platforms

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The patent combines multiple social network streams into a single unified interface that displays content from all connected networks simultaneously. This merging eliminates the need for users to switch between platforms while maintaining access to all social streams, directly resolving the contradiction between complete information access and time efficiency.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system creates a universal platform that handles multiple social networks through a single application. This multi-functional approach allows users to monitor Twitter, Facebook, and other networks through one unified interface, reducing navigation time while preserving access to all streams.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Loss of information

If the system processes and stores all social media messages, then it provides comprehensive content analysis, but it creates excessive computational burden and storage requirements

Engineering Contradiction:

Improvecompleteness of content analysisVSAvoidCPU burden and storage resources

Core Design Contradiction:

Loss of informationVSUse of energy by moving object

Solution Approach 1:

The system extracts only the most relevant and actionable information from social media streams using natural language processing and metadata analysis. Instead of storing and processing all messages, it identifies and retains only significant content, reducing computational burden while maintaining comprehensive analysis of important topics.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system applies different processing depths to different types of content based on their importance. High-priority messages receive full NLP analysis and are stored, while lower-priority content receives minimal processing or is discarded entirely. This localized quality approach optimizes resource usage while preserving essential information.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS8938450B2Natural language processing optimized for micro content

Publication Date: 2015.01.20 BLAZENT INC

AI summary

A system and a method for microcontent natural language processing are presented. The method comprising steps of receiving a microcontent message from a social networking server, tokenizing the microcontent message into one or more text tokens, detecting the language of the microcontent message and selecting the property dictionary for part-of-speech tag, part-of-speech tagging the microcontent message to identify related pronouns and nouns based on the selected dictionary, and extracting topics form the microcontent messages and assigning confidence values to the topics.