TrendsSummary: a platform for retrieving and summarizing trendy multimedia contents

• Published in: Multimedia tools and applications 2014-11, Vol.73 (2), p.857-872
• Main Authors: Kim, Daehoon, Kim, Daeyong, Jun, Sanghoon, Rho, Seungmin, Hwang, Eenjun
• Format: Article
• Language: English
• Subjects: Bayesian analysis; Classification; Computer Communication Networks; Computer Science; Content management; Data Structures and Information Theory; Filtering; Floods; Internet; Keywords; Media; Multimedia; Multimedia communications; Multimedia Information Systems; Platforms; Popularity; Queuing theory; Recommender systems; Searching; Smartphones; Social networks; Special Purpose and Application-Based Systems; Studies; Trends; User generated content; Websites
• ISSN: 1380-7501; 1573-7721
• DOI: 10.1007/s11042-013-1547-0

With the flood and popularity of various multimedia contents on the Internet, searching for appropriate contents and representing them effectively has become an essential part for user satisfaction. So far, many contents recommendation systems have been proposed for this purpose. A popular approach is to select hot or popular contents for recommendation using some popularity metric. Recently, various social network services (SNSs) such as Facebook and Twitter have become a widespread social phenomenon owing to the smartphone boom. Considering the popularity and user participation, SNS can be a good source for finding social interests or trends. In this study, we propose a platform called TrendsSummary for retrieving trendy multimedia contents and summarizing them. To identify trendy multimedia contents, we select candidate keywords from raw data collected from Twitter using a syntactic feature-based filtering method. Then, we merge various keyword variants based on several heuristics. Next, we select trend keywords and their related keywords from the merged candidate keywords based on term frequency and expand them semantically by referencing portal sites such as Wikipedia and Google. Based on the expanded trend keywords, we collect four types of relevant multimedia contents—TV programs, videos, news articles, and images—from various websites. The most appropriate media type for the trend keywords is determined based on a naïve Bayes classifier. After classification, appropriate contents are selected from among the contents of the selected media type. Finally, both trend keywords and their related multimedia contents are displayed for effective browsing. We implemented a prototype system and experimentally demonstrated that our scheme provides satisfactory results.