SociRank: Identifying and Ranking Prevalent News Topics Using Social Media Factors
Mass media sources, specifically the news media, have traditionally informed us of daily events. In modern times, social media services such as Twitter provide an enormous amount of user-generated data, which have great potential to contain informative news-related content. For these resources to be useful, we must find a way to filter noise and only capture the content that, based on its similarity to the news media, is considered valuable. 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, information must be ranked in order of estimated importance considering three factors. First, the temporal prevalence of a particular topic in the news media is a factor of importance, and can be considered the media focus (MF) of a topic. Second, the temporal prevalence of the topic in social media indicates its user attention (UA). 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—SociRank—which identifies news topics prevalent in both social media and the news media, and then ranks them by relevance using their degrees of MF, UA, and UI. Our experiments show that SociRank improves the quality and variety of automatically identified news topics.
Historically, knowledge that apprises the general public of daily events has been provided by mass media sources, specifically the news media. The news media presents professionally verified occurrences or events, while social media presents the interests of the audience in these areas, and may thus provide insight into their popularity. Unfortunately, filter noise and only capture the content that, based on its news media, and social media is very difficult. However, even after noise is removed, information overload may still exist in the remaining data—hence, it is difficult to prioritize.
Disadvantages: 1. Hard to find a way to filter news from noisy. 2. High computational demand to prioritize.
We propose an unsupervised system SociRank which effectively identifies news topics that are prevalent in both social media and the news media, and then ranks them by relevance using their degrees of MF, UA, and UI. Even though this paper focuses on news topics. News media sources are considered reliable because they are published by professional journalists, who are held accountable for their content. On the other hand, the Internet, being a free and open forum for information exchange, has recently seen a fascinating phenomenon known as social media. In social media, regular, non-journalist users are able to publish unverified content and express their interest in certain events. Consolidated, filtered, and ranked news topics from both professional news providers and individuals have several benefits. The most evident use is the potential to improve the quality and coverage of news recommender systems or Web feeds, adding user popularity feedback.
Advantages: 1. We can find a way to filter noise and only capture the news. 2. We can filter the news based on topic. 3. Main use potential to improve the quality and coverage of news recommender systems.
SYSTEM REQUIREMENTS HARDWARE REQUIREMENTS:
Hardware : Pentium Speed : 1.1 GHz RAM : 1GB Hard Disk : 20 GB
Development team :