An automatic framework to continuously monitor multi-platform information spread

Identifying and tracking the proliferation of misinformation, or fake news, poses unique challenges to academic researchers and online social networking platforms. Fake news increasingly traverses multiple platforms, posted on one platform and then re-shared on another, making it difficult to manually track the spread of individual messages. Also, the prevalence of fake news cannot be measured by a single indicator, but requires an ensemble of metrics that quantify information spread along multiple dimensions. To address these issues, we propose a framework called Information Tracer, that can (1) track the spread of news URLs over multiple platforms, (2) generate customizable metrics, and (3) enable investigators to compare, calibrate, and identify possible fake news stories. We implement a system that tracks URLs over Twitter, Facebook and Reddit and operationalize three impact indicators - Total Interaction, Breakout Scale and Coefficient of Traffic Manipulation - to quantify news spread patterns. Using a collection of human-verified false URLs, we show that URLs from different origins have different propensities to spread to multiple platforms, cover different topics, while exhibit similar retweet patterns. We also demonstrate how our system can discover URLs whose spread pattern deviate from the norm, and be used to coordinate human fact-checking of news domains. Our framework provides a readily usable solution for researchers to trace information across multiple platforms, to experiment with new indicators, and to discover low-quality news URLs in near real-time.