Since the growth of social media, news outrage over mass through social media has become evident and its control over all information sources has increased significantly. Social media services such as twitter collects enormous amounts of information and allows media companies to publish news related information as tweets. Every other big news company has its twitter account to public news in form of tweets. Beside news social media platforms has enormous amount of news attached to them as well. To make correct and better information reach to users we have to filter noise and segregate the content based on similarity and content’s respective value. Even after filtering noise, information payload exists in data so to prioritize information must be ranked in order of considered factors. In our proposed work, news are filtered and ranked based on three factors. First, media focus (MF) which tells the temporal prevalence of a particular topic in news media. Second, user attention (UA) which tells how mass is responding to the topic. Last, is the user interaction which tells how users are forming view over the topic. Our proposed work introduces an unsupervised machine learning framework which identifies news topics prevalent in both social media and the news media, and then ranks them ordering them using their degrees of MF, UA, and UI.

Modern scientific workflow systems lack strong support for protecting the scientific data and their provenance from being forged or altered. As a result, scientists may be misled into believing that they have found a specific result, but only to discover later that the data they used have been altered and should not be trusted. To address this limitation, we develop a new system called SciBlock that leverages recent advances in blockchain technology to provide a tamper-proof and non-repudiable storage for scientific workflow provenance. SciBlock provides primitives that allow users to query scientific workflow provenance data efficiently. Moreover, SciBlock offers the capability of invalidating wrong or outdated scientific workflow provenance data without removing them from the blockchain. We conducted extensive experiments to evaluate the performance and scalability of SciBlock. Our experimental results show that SciBlock offers a promising approach to enhancing scientific research integrity in a distributed collaborative environment.