Biblio

We analyze partner health apps compatible with the Fitbit fitness tracker, and record what third parties they are talking to. We focus on the ten partner Android applications that have more than 50,000 downloads and are fitness-related. Our results show that most of the them contact “unexpected” third parties. Such third parties include social networks; analytics and advertisement services; weather APIs. We also investigate what information is shared by the partner apps with these unexpected entities. Our findings suggest that in many cases personal information of users might be shared, including the phone model; location and SIM carrier; email and connection history.

Over the past few years, we have been witnessing the rise of misinformation on the Internet. People fall victims of fake news continuously, and contribute to their propagation knowingly or inadvertently. Many recent efforts seek to reduce the damage caused by fake news by identifying them automatically with artificial intelligence techniques, using signals from domain flag-lists, online social networks, etc. In this work, we present Check-It, a system that combines a variety of signals into a pipeline for fake news identification. Check-It is developed as a web browser plugin with the objective of efficient and timely fake news detection, while respecting user privacy. In this paper, we present the design, implementation and performance evaluation of Check-It. Experimental results show that it outperforms state-of-the-art methods on commonly-used datasets.