Abstract
Big data is a popular term used to describe a massive volume of data, which is a key component of the current information age. Such data is complex and difficult to understand, and therefore, may be not useful for users in that state. News extraction, aggregation, clustering, news topic detection and tracking, and social network analysis are some of the several attempts that have been made to manage the massive data in social media. Current visualization tools are difficult to adapt to the constant growth of big data, specifically in online news articles. Therefore, this paper proposes Interactive Big Data Visualization Model Based on Hot Issues (IBDVM). IBDVM can be used to visualize hot issues in daily news articles. It is based on textual data clusters in textual databases that improve the performance, accuracy, and quality of big data visualization. This model is useful for online news reader, news agencies, editors, and researchers who involve in textual documents domains.
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