Correlating Social Network Connections with Article Links to Detect Spam
Find Innovative SolutionsGenerate Solutions
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 from 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 indices to assess the authenticity of links and ad clicks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If webmasters collaborate to create excessive links between websites, then search engine rankings and ad click-through rates are artificially inflated, but the integrity and reliability of search results and ad revenue models deteriorate
Solution Approach 1:
The patent introduces social network connection data as an intermediary layer between traditional link analysis and spam detection. By correlating social network associations with web link structures, the system can identify when links between websites do not correspond to genuine social connections, thereby detecting spamming activities. This intermediary approach allows the system to differentiate between legitimate collaborative content creation and artificial link-building schemes.
2Ease of operation
If search engines rely on link structures for ranking, then information retrieval is simplified, but the system becomes susceptible to manipulation through collaborative linking
Solution Approach 1:
The patent implements a feedback mechanism where social network connection data continuously informs and adjusts the search ranking process. The system correlates social associations with link structures, and when discrepancies are detected (indicating potential spamming), the search engine can adjust rankings accordingly. This feedback loop maintains the simplicity of link-based ranking while protecting against manipulation by incorporating additional verification layers.
3Productivity
If advertisers pay for click-throughs, then ad revenue is generated, but the system is vulnerable to artificial inflation through coordinated clicking
Solution Approach 1:
The patent uses social network connection data as an intermediary verification layer for ad click validation. By checking whether users who click on ads have genuine social connections to the content or advertisers, the system can distinguish between legitimate advertising engagement and artificial click inflation. This maintains ad revenue generation while improving the precision of click-through rate measurements.
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.