Social Ranking Techniques for the Web

TL;DR

This paper explores combining web page content and social media network data to develop personalized social ranking techniques, enhancing information organization based on social relationships.

Contribution

It introduces a novel approach that integrates web and social network data to create personalized, socially-aware web ranking methods.

Findings

Social media data can effectively influence web page ranking.

Personalized rankings improve information relevance for users.

Validation shows promising results for social ranking methods.

Abstract

The proliferation of social media has the potential for changing the structure and organization of the web. In the past, scientists have looked at the web as a large connected component to understand how the topology of hyperlinks correlates with the quality of information contained in the page and they proposed techniques to rank information contained in web pages. We argue that information from web pages and network data on social relationships can be combined to create a personalized and socially connected web. In this paper, we look at the web as a composition of two networks, one consisting of information in web pages and the other of personal data shared on social media web sites. Together, they allow us to analyze how social media tunnels the flow of information from person to person and how to use the structure of the social network to rank, deliver, and organize information specifically for each individual user. We validate our social ranking concepts through a ranking experiment conducted on web pages that users shared on Google Buzz and Twitter.