Social Media Data Mining for Targeted Advertising

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Solution Overview

Problem

Existing systems face challenges in accurately analyzing and monetizing social media data due to privacy concerns and the vast amount of demographic information hidden within, leading to inaccurate predictions and inefficient advertising strategies.

Innovation Solution

A system that filters and visualizes social media data to create personalized advertisements by identifying demographics and preferences, using a probabilistic classifier and Monte Carlo method to generate prediction sets for targeted advertising, while ensuring privacy through opt-in options.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If demographic information is collected and analyzed from social media data, then advertising accuracy and brand value increase, but user privacy is compromised

Engineering Contradiction:

Improvedemographic analysis accuracyVSAvoiduser privacy exposure

Core Design Contradiction:

Measurement precisionVSObject-affected harmful factors

Solution Approach 1:

The patent introduces an intermediary processing layer that analyzes social media data without directly accessing or storing personal identifiers. The system uses natural language processing and machine learning to extract demographic patterns from public posts while maintaining an intermediary barrier that prevents direct exposure of user identities, thus resolving the contradiction between accurate demographic analysis and privacy protection

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system transforms raw social media data into aggregated demographic parameters through probabilistic classification. By changing the data representation from individual-level personal information to population-level statistical patterns, the system achieves accurate demographic insights while inherently protecting individual privacy through parameter transformation

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If vast amounts of social media data are processed and stored, then demographic insights accuracy improves, but data management complexity and costs increase

Engineering Contradiction:

Improvedemographic insights accuracyVSAvoiddata management complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent extracts only the essential demographic features needed for advertising purposes from the vast social media data corpus. Rather than storing and processing all raw data, the system identifies and extracts key demographic parameters (age groups, gender, interests) using natural language processing, thereby reducing data management complexity while maintaining insight accuracy

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system segments the vast social media data into manageable categories and dimensions (demographic attributes, behavioral patterns, content themes). This segmentation allows the system to process and analyze data in organized chunks using distributed computing, reducing overall management complexity while improving analysis accuracy through structured processing

Inventive Principle:

Principle #1Segmentation

3Productivity

If traditional simplistic assumptions are used for demographic predictions, then processing speed increases, but prediction accuracy decreases

Engineering Contradiction:

Improveprediction processing speedVSAvoidprediction accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system performs preliminary processing of social media data by pre-tagging and categorizing posts with demographic attributes using trained machine learning models. This preliminary action creates pre-processed data structures that can be quickly queried and analyzed, maintaining high processing speed while enabling accurate predictions through pre-computed demographic indicators

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces traditional mechanical demographic assumption methods with intelligent systems using natural language processing and machine learning algorithms. These intelligent systems automatically infer demographic characteristics from content analysis, achieving superior prediction accuracy while maintaining efficiency through automated processing rather than manual or rule-based approaches

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS11748646B2Database query and data mining in intelligent distributed communication networks

Publication Date: 2023.09.05 ZOOMPH INC

AI summary

A system includes a processor and a memory device communicatively coupled to the processor. The system also includes a database communicatively coupled to the processor. The database is configured to store a first plurality of prediction sets. Each prediction set is associated with a respective individual within a first population. Each prediction set comprises a plurality of prediction results, and each prediction result corresponds to a selected one of a plurality of features. The processor is configured to receive a request for a distribution value associated with a selected feature and one or more parameters. The distribution value indicate how often the selected feature appears in a second population of individuals, the second population being defined by the one or more parameters.