Search

In the current information era over the internet, social media has become one of the essential information sources for users. While the text is the primary information representation, finding relevant information is a challenging mission for researchers due to its nature (e.g., short length, sparseness). Acquiring high-quality search results from massive data, such as social media needs a set of representative query terms that are not always available. In this paper, we propose a novel query-based unsupervised learning model to represent the implicit relationships in the short text from social media. This bridges the gap of the lack of word co-occurrences without requiring many parameters to be estimated and external evidence to be collected. To confirm the proposed model effectiveness, we compare the proposed model with state-of-the-art lexical, topic model and temporal models on the large-scale TREC microblog 2011-2014 collections. The experimental results show that the proposed model significantly improved overall state-of-the-art lexical, topic model and temporal models with the maximum percentage of increase reaching 33.97% based on MAP value and 21.38% based on Precision at top 30 documents. The proposed model can improve the social media search effectiveness in potential closely retrieval tasks, such as question answering and timeline summarisation. [ABSTRACT FROM AUTHOR]

Although topic detection and tracking techniques have made great progress, most of the researchers seldom pay more attention to the following two aspects. First, the construction of a topic model does not take the characteristics of different topics into consideration. Second, the factors that determine the formation and development of hot topics are not further analyzed. In order to correctly extract news blog hot topics, the paper views the above problems in a new perspective based on the W2T (Wisdom Web of Things) methodology, in which the characteristics of blog users, context of topic propagation and information granularity are investigated in a unified way. The motivations and features of blog users are first analyzed to understand the characteristics of news blog topics. Then the context of topic propagation is decomposed into the blog community, topic network and opinion network, respectively. Some important factors such as the user behavior pattern, opinion leader and network opinion are identified to track the development trends of news blog topics. Moreover, a blog hot topic detection algorithm is proposed, in which news blog hot topics are identified by measuring the duration, topic novelty, attention degree of users and topic growth. Experimental results show that the proposed method is feasible and effective. These results are also useful for further studying the formation mechanism of opinion leaders in blogspace. [ABSTRACT FROM AUTHOR]