Mass media sources such as news media used to inform us of daily events before. Now a day, unlike news media, social media services like Twitter provide a huge amount of user-generated data, which contain informative news-related content. For these resources to be useful, we need to find a way to filter the noise and capture only the content based on its similarity to the news media. However, even after noise is removed, information overload may still exist in the remaining data-hence, it is convenient to prioritize it for consumption. To achieve prioritization, the information must be ranked in order of estimated importance considering three factors. First, the media focus(MF) of a topic, the temporal prevalence of a particular topic in the news media. Second, user attention (UA), the temporal prevalence of the topic in social media. Last, the interaction between the social media users who mention this topic indicates the strength of the community discussing it, and can be regarded as the user interaction (UI) toward the topic. We propose an unsupervised framework—NewSociRank—which recognizes the news topics prevalent(common) in both social media and the news media, and then ranks them by relevance(popularity) using their degrees of MF, UA, and UI.
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