In the recent years, we have witnessed social networks blossom. Social networking reshaped worldwide communication significantly increased the speed of news spread, and connected the world stronger than ever. Although social networking has been such a revolutionary invention for the society, and many researchers have turned towards social media to explore trending topics, mainstream media still remains as the origin of the majority of the news discussed in social networking sites. Social stream mining to make video recommendations based on the trending topics has been an active direction in the research community. Understanding the trending topics and its impact on video sharing sites is very interesting for network traffic engineers. Quality of service can be significantly improved if we can predict what kind of video content will generate large traffic. The focus of this paper is to study which type of media, mainstream or social, can contribute better towards identifying trending topics. We present the experimental study of the story development process in mainstream and social media based on the real-world data. The study helps us properly identify which media source is more appropriate for the video recommendation and network traffic prediction systems. Through our findings, we discovered mainstream media could significantly improve the trend detection. Index Terms— Mainstream media, social network, topic model, popularity prediction, video recommendation.

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