Millions of posts are being generated in real-time by users in social networking services, such as Twitter. However, a considerable number of those posts are mundane posts that are of interest to the authors and possibly their friends only. This paper investigates the problem of automatically discovering valuable posts that may be of potential interest to a wider audience. Specifically, we model the structure of Twitter as a graph consisting of users and posts as nodes and retweet relations between the nodes as edges. We propose a variant of the HITS algorithm for producing a static ranking of posts. Experimental results on real world data demonstrate that our method can achieve better performance than several baseline methods.

Opinion retrieval involves the measuring of opinion score of a document about the given topic. We propose a new method, namely sentiment-relevance flow, that naturally unifies the topic relevance and the opinionated nature of a document. Experiments conducted over a large-scaled Web corpus show that the proposed approach improves performance of opinion retrieval in terms of precision at top ranks.

Most traditional ranking models roughly score the relevance of a given document by observing simple term statistics, such as the occurrence of query terms within the document or within the collection. Intuitively, the relative importance of query terms with regard to other individual non-query terms in a document can also be exploited to promote the ranks of documents in which the query is dedicated as the main topic. In this paper, we introduce a simple technique named intra-document term ranking, which involves ranking all the terms in a document according to their relative importance within that particular document. We demonstrate that the information regarding the rank positions of given query terms within the intra-document term ranking can be useful for enhancing the precision of top-retrieved results by traditional ranking models. Experiments are conducted on three standard TREC test collections.