Searching for superspreaders of information in real-world social media

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for "viral" information dissemination in relevant applications.Comment: 12 pages, 7 figure

Why Is This First? Understanding and Analyzing Internet Search Results

Primarily due to their convenience, online search engines such as Google and Bing are becoming a central location for obtaining information. As a result, societies give search engines tremendous control over the spread of information to the public. Through a high-school-level sample lesson plan, the article was written to promote dialogue with teachers on the importance of teaching the intricacies of search engines. The sample lesson plan begins with fundamental knowledge on the functionality of search engines with emphasis on algorithms. With this instruction, students can understand not only search engines, but also their manipulation potential, which leads to ramifications. Using the manipulation potential as a catalyst, many societal concerns are raised, such as spread of misinformation, oppression of certain groups, and impact on behavior. Through this instruction and dialogue, practitioners will have a resource to integrate search engines into their curriculum in response to this new concern

A PAGERANK-BASED MINING ALGORITHM FOR USER INFLUENCES ON MICRO-BLOGS

With the development of Web technology, the Micro-Blog has become one of the most popular social platforms, and calculating and ranking the influences of the users on Micro-Blogs has been issuing an important research problem. Through improving the traditional the PageRank model, this paper presents a called PR4MB (PageRank for Micro-Blog) algorithm, which can obviously improve mining precisions for evaluating user influences on a Micro-Blog. While considering user link relations like the PageRank method, the PR4MB algorithm also takes attention to the activity, quality and credibility of a user on a Micro-Blog, so it constructs a dynamic mining model for user influences on a Micro-Blog by evaluation user online behaviors. The experimental results show that PR4MB algorithm, in comparing with the traditional PageRank algorithm, can more truly reflects the actual influences of different users on a Micro-Blog