Correlational Data Models from Social Media Event Timelines

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing technologies are limited in providing comprehensive decision-making assistance as they rely on specific, keyword-based searches of social media data, failing to leverage the vast, uncategorized experiential data from social media for broader decision-making support.

Innovation Solution

A system that extracts experiential social media posts from a large population, generates event timelines, constructs correlational data models by measuring correlations between events, and applies these models to decision-making calculations, enabling better-informed decisions based on collective user experiences.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If simple keyword-based search queries are used to parse social media data, then the search process is simple and easy to operate, but the scope of data available for review is narrowed and limited

Engineering Contradiction:

Improvesimplicity of search processVSAvoidscope of data available for review

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The system segments social media data into structured event timelines, separating raw data from processed insights. This allows users to query specific events while the system handles the complexity of data parsing and correlation in the background, maintaining ease of use while expanding data scope.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system introduces an intermediary layer of event timelines and correlational data models between raw social media data and user queries. This mediator processes and structures the vast uncategorized data, making it accessible through simple searches while preserving comprehensive data scope.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If existing technologies provide specific empirical data for decision-making, then the data is reliable and targeted, but the technology cannot leverage the vast uncategorized experiential data from social media

Engineering Contradiction:

Improvespecificity of decision-making dataVSAvoidvolume of experiential data utilized

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The system performs preliminary processing of social media data by extracting and structuring event timelines before they are queried. This advance preparation organizes the vast uncategorized data into usable formats, enabling both reliability through structured data and quantity through comprehensive coverage.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system transforms raw social media data parameters into structured event timeline formats and correlational models. This parameter transformation preserves the richness and volume of experiential data while organizing it into reliable, queryable structures that maintain specificity for decision-making.

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If social media data is extracted and processed to create event timelines and correlational models, then comprehensive decision-making support is achieved, but the system complexity increases

Engineering Contradiction:

Improvecomprehensiveness of decision-making supportVSAvoidcomplexity of data processing system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system divides the complex data processing task into segmented modules: data extraction, event timeline generation, correlational model construction, and query processing. This segmentation manages system complexity by breaking down complex operations into manageable, independent components.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system employs automated processes where the correlational data models self-update and refine based on incoming social media data. This self-service mechanism reduces the need for manual system configuration and maintenance, managing complexity through automation while maintaining comprehensive decision-making support.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS10037367B2Modeling actions, consequences and goal achievement from social media and other digital traces

Publication Date: 2018.07.31 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Systems, methods, and computer storage media are provided for analyzing a large amount of social media data from a large population of social media users and constructing correlational data models between one or more events that occur within each user's timeline. Social media posts directed to personal experiences of a large number of social media users are extracted. Event timelines are generated for each of the social media users, based on their personal experiences. The event timelines are analyzed with a particular event of interest to measure correlations between events occurring within the timelines and the particular event of interest. Using the measured correlations, a correlational data model is thereby constructed. The correlational data model may be used for application to decision-making calculations by one or more systems in an active or passive manner.