Correlating Social Network Connections with Article Links to Detect Spam
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Solution Overview
Problem
Online advertising and search engines are susceptible to spamming, where webmasters artificially inflate ad click-through rates and search engine rankings by colluding to create unnatural links, which undermines the integrity of ad revenue models and search result relevance.
Innovation Solution
A system that correlates user connections in social networks with article links to determine the independence of these links, using personalization information from social networks to identify and store user connections, and then correlating this information with article administrators and links to assess the authenticity of link associations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If webmasters collaborate to create excessive links between websites, then search engine rankings and ad click-through rates are artificially inflated, but the integrity of search results and ad revenue models deteriorates
Solution Approach 1:
The patent introduces social network connection data as an intermediary verification layer between web link structures and search ranking algorithms. By using socially verified user connections as a mediator, the system can distinguish between genuine user relationships and artificial link-building schemes, thereby improving ranking accuracy while combating search spamming
Solution Approach 2:
The system implements feedback mechanisms that correlate social network connection patterns with web link structures. By continuously analyzing whether link patterns align with genuine social connections, the system provides feedback to adjust search rankings and identify spamming activities, improving measurement precision while reducing harmful factors
2Quantity of substance
If webmasters excessively click on ads to increase click-through rates, then ad revenue is artificially inflated, but the reliability of ad revenue models deteriorates
Solution Approach 1:
The patent uses social network connection verification as an intermediary to authenticate ad click sources. By checking whether clicking users have genuine social connections to the ad content or website, the system can distinguish between legitimate user interest and artificial click farming, thereby maintaining ad revenue model integrity while preserving genuine click volume
Solution Approach 2:
The system replaces purely mechanical click-counting mechanisms with a socio-technical verification system that incorporates social network relationship validation. This substitution allows the system to maintain reliable measurement of genuine user engagement while filtering out artificial clicks, preserving both quantity quality and model reliability
3Adaptability or versatility
If social networks do not communicate with search engines and advertisers, then user connection data remains unused, but the ability to identify spamming activities is limited
Solution Approach 1:
The patent merges previously siloed data systems by integrating social network connection data with search engine indexing and advertising analytics. This combination creates a unified data ecosystem where user connection information enhances spamming detection capabilities across multiple domains, increasing both data utilization flexibility and detection effectiveness
Solution Approach 2:
The system implements multi-functional use of social network data, where the same connection verification mechanism serves multiple purposes: improving search ranking accuracy, validating ad click authenticity, and detecting various forms of spamming. This universal approach maximizes data versatility while enhancing detection capabilities across different applications
Data Source
AI summary
Methods and systems for correlating connections between users and links between articles to identify search and/or ad spamming are disclosed. Social networks can be used to identify connections between users for correlation with links between articles, which can be identified through searches of article contents and/or back tracing accesses to articles. One disclosed method comprises identifying first associations between a plurality of users in a network of associated users; identifying second associations between one or more users and one or more articles; identifying third associations between at least some of the articles or between some of the users and access to some of the articles; and determining at least one of the third associations is correlated with one or more of the first associations.