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social media. Discovering keyword-based correlated networks of these large graphs is an important primitive in data analysis, from which users can pay more attention about their concerned information in the large graph. In this paper, we propose and define the problem of keyword-based correlated network computation over a
Social media keeps growing and providing us with rich sources of information to understand our everyday lives, customs, and culture in the form of periodic topics. This paper proposes a method of detecting periodic topics based on autocorrelation using the time series of the document frequencies of keywords. To deal
their historical and social context by understanding how the major topics associated with them have changed over time. Users can relate articles through time by examining the topical keywords that summarize a specific news event. By tracking the attention to a news article in the form of references in social media (such as
for vlogs. We extract informative keywords from both the target vlog itself and external resources which are semantically and visually relevant to it. We also present a new evaluation criterion, which assigns a score to an annotation according to its accuracy and completeness in representing the vlogpsilas semantics
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