Searching for Quality Microblog Posts: Filtering and Ranking Based on Content Analysis and Implicit Links

Abstract

Today, social networking has become a popular web activity, with a large amount of information created by millions of people every day. However, the study on effective searching of such social information is still in its infancy. In this paper, we focus on Twitter, a rapidly growing microblogging platform, which provides a large amount, diversity and varying quality of content. In order to provide higher quality content (e.g. posts mentioning news, events, useful facts or well-formed opinions) when a user searches for tweets on Twitter, we propose a new method to filter and rank tweets according to their quality. In order to model the quality of tweets, we devise a new set of link-based features, in addition to content-based features. We examine the implicit links between tweets, URLs, hashtags and users, and then propose novel metrics to reflect the popularity as well as quality-based reputation of websites, hashtags and users. We then evaluate both the content-based and link-based features in terms of classification effectiveness and identify an optimal feature subset that achieves the best classification accuracy. A detailed evaluation of our filtering and ranking models shows that the optimal feature subset outperforms traditional bag-of-words representation, while requiring significantly less computational time and storage. Moreover, we demonstrate that the proposed metrics based on implicit links are effective for determining tweets’ quality.