Improving News Ranking by Community Tweets

TL;DR

This paper introduces a Community Tweets Voting Model (CTVM) that leverages large-scale Twitter community data to improve news ranking accuracy, addressing limitations of traditional user-centric methods.

Contribution

The paper presents a novel re-ranking approach using community Twitter data, enhancing news search results without relying on individual user data.

Findings

CTVM outperforms baseline Google and Yahoo rankings for specific online communities.

Utilizes open social media data to improve personalization in news ranking.

Provides a scalable approach addressing privacy and data sparsity issues.

Abstract

Users frequently express their information needs by means of short and general queries that are difficult for ranking algorithms to interpret correctly. However, users' social contexts can offer important additional information about their information needs which can be leveraged by ranking algorithms to provide augmented, personalized results. Existing methods mostly rely on users' individual behavioral data such as clickstream and log data, but as a result suffer from data sparsity and privacy issues. Here, we propose a Community Tweets Voting Model (CTVM) to re-rank Google and Yahoo news search results on the basis of open, large-scale Twitter community data. Experimental results show that CTVM outperforms baseline rankings from Google and Yahoo for certain online communities. We propose an application scenario of CTVM and provide an agenda for further research.