Realtime Analysis of Information Diffusion for Trust and Relevance
Modern social media like Twitter or Facebook encompass a significant and growing share of the population, which is actively using it to share messages. Given this broad coverage of the world as well as its fast reaction times, social media acts as a powerful ”social sensor”, while activities originating on social media can also have significant impact on the physical world.
While scalable methods have been developed to detect trends and events in such media, assessing the relevance of social media messages and their trustworthiness is mostly driven by manual, after-the-fact checking. One of the most important factor for such an assessment is an understanding of the information diffusion. A typical use case for such analyses is online journalism, where journalists a) need to understand where information came from and how it reached them b) whom information published by themselves influences and in which way c) how the overall diffusion process is proceeding.
Yet, existing work on analyzing information diffusion is centered on complex models with offline computations, making them unsuitable for real-time, large-scale analyses. In this project we aim to develop algorithms and systems to trace the spreading of information in social media that produce large scale, rapid data. The results of these analysis are then used to assess trustworthiness; the results are compared against complex, state-of-the-art methods.
Funded by:DFG (Deutsche Forschungsgemeinschaft)
Local head of project:Prof. Dr. Peter M. Fischer
Local scientists:Prof. Dr. Peter M. Fischer