Yasmeen Abdrabou (University of the Bundeswehr Munich), Elisaveta Karypidou (LMU Munich), Florian Alt (University of the Bundeswehr Munich), Mariam Hassib (University of the Bundeswehr Munich)

We propose an approach to identify users’ exposure to fake news from users’ gaze and mouse movement behavior. Our approach is meant as an enabler for interventions that make users aware of engaging with fake news while not being consciously aware of this. Our work is motivated by the rapid spread of fake news on the web (in particular, social media) and the difficulty and effort required to identify fake content, either technically or by means of a human fact checker. To this end, we set out with conducting a remote online study (N = 54) in which participants were exposed to real and fake social media posts while their mouse and gaze movements were recorded. We identify the most predictive gaze and mouse movement features and show that fake news can be predicted with 68.4% accuracy from users’ gaze and mouse movement behavior. Our work is complemented by discussing the implications of using behavioral features for mitigating the spread of fake news on social media.

View More Papers

BinaryInferno: A Semantic-Driven Approach to Field Inference for Binary...

Jared Chandler (Tufts University), Adam Wick (Fastly), Kathleen Fisher (DARPA)

Read More

A Case Study on Fuzzing Satellite Firmware

Tobias Scharnowski and Felix Buchmann (Ruhr-Universitat Bochum), Simon Woerner and Thorsten Holz (CISPA Helmholtz Center for Information Security) Presenter: Tobias Scharnowski

Read More

Will They Share? Predicting Location Sharing Behaviors of Smartphone...

Muhammad Irtaza Safi, Abhiditya Jha (University of Central Florida); Malak Eihab Aly (New York University); Xinru Page (Bentley University); Sameer Patil (Indiana University); Pamela Wisniewski (University of Central Florida)

Read More