Chengcheng Shao

Hoaxy: A Platform for Tracking Online Misinformation

Massive amounts of misinformation have been observed to spread in uncontrolled fashion across social media. Examples include rumors, hoaxes, fake news, and conspiracy theories. At the same time, several journalistic organizations devote significant efforts to high-quality fact checking of online claims. The resulting information cascades contain instances of both accurate and inaccurate information, unfold over multiple time scales, and often reach audiences of considerable size. All these factors pose challenges for the study of the social dynamics of online news sharing. Here we introduce Hoaxy, a platform for the collection, detection, and analysis of online misinformation and its related fact-checking efforts. We discuss the design of the platform and present a preliminary analysis of a sample of public tweets containing both fake news and fact checking. We find that, in the aggregate, the sharing of fact-checking content typically lags that of misinformation by 10-20 hours. Moreover, fake news are dominated by very active users, while fact checking is a more grass-roots activity. With the increasing risks connected to massive online misinformation, social news observatories have the potential to help researchers, journalists, and the general public understand the dynamics of real and fake news sharing.

Anatomy of an online misinformation network

Massive amounts of fake news and conspiratorial content have spread over social media before and after the 2016 US Presidential Elections despite intense fact-checking efforts. How do the spread of misinformation and fact-checking compete? What are the structural and dynamic characteristics of the core of the misinformation diffusion network, and who are its main purveyors? How to reduce the overall amount of misinformation? To explore these questions we built Hoaxy, an open platform that enables large-scale, systematic studies of how misinformation and fact-checking spread and compete on Twitter. Hoaxy captures public tweets that include links to articles from low-credibility and fact-checking sources. We perform k-core decomposition on a diffusion network obtained from two million retweets produced by several hundred thousand accounts over the six months before the election. As we move from the periphery to the core of the network, fact-checking nearly disappears, while social bots proliferate. The number of users in the main core reaches equilibrium around the time of the election, with limited churn and increasingly dense connections. We conclude by quantifying how effectively the network can be disrupted by penalizing the most central nodes. These findings provide a first look at the anatomy of a massive online misinformation diffusion network.

SocAware: A Middleware for Social Applications in Online Social Networks

The popularity of online social network (OSN) services has given rise to a variety of social network applications. However these applications lack a common platform for information sharing and people interoperating. In this paper, we propose SocAware, a middleware designed for OSN services. SocAware extracts social relation from heterogeneous networks, and builds a uniform knowledge base to manage the social information. SocAware distinguishes itself from other OSN middlewares by analyzing the OSN activities to classify the social relations, and by calculating the strength of social relations to provide reusability between social applications. We also provide a set of API to facilitate third-party application development and the effective utilization of these relations. In order to validate SocAware, we developed two prototype applications above the middleware. The experimental results demonstrate the usability and expansibility of the middleware.

Social Authentication Identity: An Alternate to Internet Real Name System

Rumors and defamation are now becoming a main threat to Online Social Networks (OSNs). To prevent them, Real Name System (RNS) was proposed, but has been proved vulnerable by the data leakage in South Korea. In this paper, we propose a new identity model, Social Authentication Identity (SAI), to trace rumor-makers. In SAI, only a small number of users (called roots) are required to be authenticated by RNS. And the others are authenticated by vouching of friends, called social authentication. We evaluate factors that affect the efficiency of SAI. Results show that selecting roots in communities are the best strategy, comparing with random and maximum degree strategies. We also provide an social tracing mechanism to trace down rumor-makes. Analysis shows our social tracing is robust enough to defend Sybil attacks.