Dong Wang

TL;DR: The approach is shown to outperform the state of the art fact-finding heuristics, as well as simple baselines such as majority voting, and to offer the first optimal solution to the above truth discovery problem.

TL;DR: The contribution of this paper lies in developing a model that considers the impact of information sharing on the analytical foundations of reliable sensing, and embed it into a tool called Apollo that uses Twitter as a “sensor network” for observing events in the physical world.

TL;DR: Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources.

TL;DR: A streaming fact-finder that recursively updates previous estimates based on new data that solves an expectation maximization (EM) problem to determine the odds of correctness of different observations and is described as the first fact- Finder developed with explicit consideration to the continuous update needs of crowd-sourcing applications.

TL;DR: In this article, the authors present challenges for researchers at the intersection of computer science and the social sciences in online social media, where the exploitation of this lens, termed social sensing, presents challenges.