An Empirical Study on Learning to Rank of Tweets

Twitter, as one of the most popular micro-blogging services, provides large quantities of fresh information including real-time news, comments, conversation, pointless babble and advertisements. Twitter presents tweets in chronological order. Recently, Twitter introduced a new ranking strategy that considers popularity of tweets in terms of number of retweets. This ranking method, however, has not taken into account content relevance or the twitter account. Therefore a large amount of pointless tweets inevitably flood the relevant tweets. This paper proposes a new ranking strategy which uses not only the content relevance of a tweet, but also the account authority and tweet-specific features such as whether a URL link is included in the tweet. We employ learning to rank algorithms to determine the best set of features with a series of experiments. It is demonstrated that whether a tweet contains URL or not, length of tweet and account authority are the best conjunction.1

• Type: Journal
• Year: 2010
• Where: COLING
• Authors: Yajuan Duan, Long Jiang, Tao Qin, Ming Zhou, Heung-Yeung Shum
• Keywords: COLING 2010, Computational Linguistics, Content Relevance, Tweet, Twitter