Public interest analysis based on implicit feedback of IPTV users

Modern information systems make it increasingly easy to gain more insight into the public interest, which is becoming more and more important in diverse public and corporate activities and processes. The disadvantage of existing research that focuses on mining the information from social networks and online communities is that it does not uniformly represent all population groups and that the content can be subjected to self-censoring or curation. In this paper, we propose and describe a framework and a method for estimating public interest from the implicit negative feedback collected from the Internet protocol television (IPTV) audience. Our research focuses primarily on the channel change events and their match with the content information obtained from closed captions. The presented framework is based on concept modeling, viewership profiling, and combines the implicit viewer reactions (channel changes) into an interest score. The proposed framework addresses both above-mentioned disadvantages or concerns. It is able to cover a much broader population, and it can detect even minor variations in user behavior. We demonstrate our approach on a large pseudonymized real-world IPTV dataset provided by an ISP, and show how the results correlate with different trending topics and with parallel classical long-term population surveys.