In recent years, the smartphone boom has made various social media monitoring including news, blogs, Twitter, and Facebook in a social phenomenon. Twitter is an online social networking and microblogging service which has gained worldwide popularity with over 500 million active users as of 2012. Even though one tweet may contain at most 140 characters, the number of tweets that are generated daily is enormous and hence they, collectively, could give important clues to resolving several issues such as public opinion, current trend, and burst keywords. If detecting the information could be successfully done, various services related to the information, such as multimedia contents recommendation and trend contents visualization, can be provided. So far, many studies have been done to pursue these purposes. Therefore, we are looking for efficient and effective meaningful information such as topic and trend detecting/tracking algorithms on SNS (social network services) big data.

The goal of this special issue is to discover new efficient and effective topic detection and tracking algorithms and trend analysis techniques on SNS big data.

Potential topics include, but are not limited to:

• Algorithms and systems for social multimedia search
• Data mining for social multimedia
• Multimedia contents recommendation systems based on detected meaningful information
• Novel theoretical models and new computational models for social multimedia data
• Privacy and security issues for social multimedia data
• Topic detection and tracking (TDT) for social multimedia data
• Visualization analytics for social multimedia data