Social Network Classification Engine for Spam Detection

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Solution Overview

Problem

Social networking platforms face challenges in detecting and mitigating fraudulent activities, such as spam and malicious content, as users can create multiple fake accounts to evade detection and spread abusive content across platforms.

Innovation Solution

A system that classifies social data using crowd-sourced information and applies rules to identify and remediate spam, abusive speech, and other malicious activities by analyzing metadata and user interactions across multiple social networks, employing a scanning engine, inference engine, and classification engine to enforce policies and block or remove inappropriate content.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If multiple fake social accounts are created to represent different identities, then the ability to spread malicious content and evade detection is improved, but the reliability of the social network system deteriorates

Engineering Contradiction:

Improveability to spread malicious contentVSAvoidreliability of social network system

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system segments the analysis of social accounts by examining multiple attributes and metadata separately (posting patterns, interaction patterns, profile information) and then synthesizing these segments to identify coordinated inauthentic behavior. This allows detection of fraudulent networks by analyzing individual account characteristics in combination.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements feedback mechanisms where classification results from analyzing social data are used to refine and update classification rules. The system continuously learns from detected fraudulent patterns and improves its ability to identify coordinated inauthentic behavior across multiple accounts.

Inventive Principle:

Principle #23Feedback

2Productivity

If automated software programs (bots) are used to create and manage multiple social accounts, then the productivity of spreading malicious content is improved, but the difficulty of detecting and measuring fraudulent activity worsens

Engineering Contradiction:

Improveproductivity of spreading malicious contentVSAvoiddifficulty of detecting fraudulent activity

Core Design Contradiction:

ProductivityVSDifficulty of detecting and measuring

Solution Approach 1:

The system replaces manual detection methods with automated classification engines that use machine learning and pattern recognition algorithms. These systems automatically analyze social data, metadata, and user interactions to identify bot networks and coordinated inauthentic behavior without human intervention.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system introduces intermediary analysis layers that examine the relationships and patterns between multiple social accounts. By analyzing metadata, posting patterns, and interaction networks as intermediaries, the system can detect bot networks even when individual accounts appear legitimate.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Reliability

If crowd-sourced data and classification engines are deployed to identify and remediate malicious content, then user safety is improved, but the device complexity increases

Engineering Contradiction:

Improveuser safetyVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The classification engine is designed as a universal system that can analyze multiple types of social data (posts, comments, images, metadata) across different social networking platforms. A single multi-functional engine handles diverse classification tasks rather than requiring separate systems for each type of content or platform.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system merges multiple classification rules, data sources, and analysis methods into a unified classification engine. By combining crowd-sourced data, automated analysis, and rule-based classification into a single integrated system, the complexity is managed centrally rather than distributed across multiple separate components.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS11252123B2Classifying social entities and applying unique policies on social entities based on crowd-sourced data

Publication Date: 2022.02.15 PROOFPOINT INC

AI summary

Technology is disclosed for detecting, classifying, and/or enforcing rules on social networking activity. The technology can scan and collect social content data from one or more social networks, store the social content data, classify content data posted to a social network, create and apply a set of social data content rules to future posted social content data.