organize analytics Figure 1: Study flow showing the two phases of user studies: the Requirements Study, followed by the Ontology Study and their sub-steps.

Clickbait headlines work through superlatives and intensifiers, creating information gaps to increase the relevance of their associated links that direct users to time-wasting and sometimes even malicious websites. This approach can be amplified using targeted clickbait that takes publicly available information from social media to align clickbait to users’ preferences and beliefs. In this work, we first conducted preliminary studies to understand the influence of targeted clickbait on users’ clicking behavior. Based on our findings, we involved 24 users in the participatory design of story-based warnings against targeted clickbait. Our analysis of user-created warnings led to four design variations, which we evaluated through an online survey over Amazon Mechanical Turk. Our findings show the significance of integrating information with persuasive narratives to create effective warnings against targeted clickbait. Overall, our studies provide valuable insights into understanding users’ perceptions and behaviors towards targeted clickbait, and the efficacy of story-based interventions.

The evolving landscape of manipulated media, including the threat of deepfakes, has made information verification a daunting challenge for journalists. Technologists have developed tools to detect deepfakes, but these tools can sometimes yield inaccurate results, raising concerns about inadvertently disseminating manipulated content as authentic news. This study examines the impact of unreliable deepfake detection tools on information verification. We conducted role-playing exercises with 24 US journalists, immersing them in complex breaking-news scenarios where determining authenticity was challenging. Through these exercises, we explored questions regarding journalists’ investigative processes, use of a deepfake detection tool, and decisions on when and what to publish. Our findings reveal that journalists are diligent in verifying information, but sometimes rely too heavily on results from deepfake detection tools. We argue for more cautious release of such tools, accompanied by proper training for users to mitigate the risk of unintentionally propagating manipulated content as real news.

Deepfakes can be used maliciously to sway public opinion, defame an individual, or commit fraud. Hence, it is vital for journalists and social media platforms, as well as the general public, to be able to detect deepfakes. Existing deepfake detection methods, while highly accurate on datasets they have been trained on, falter in open-world scenarios due to different deepfake generations algorithms, video formats, and compression levels. In this paper, we seek to address this by building on the XceptionNet-based deepfake detection technique that utilizes convolutional latent representations with recurrent structures. In particular, we explore how to leverage a combination of visual frames, edge maps, and dense optical flow maps together as inputs to this architecture. We evaluate these techniques using the FaceForensics++ and DFDC-mini datasets. We also perform extensive studies to evaluate the robustness of our network against adversarial post-processing as well as the generalization capabilities to out-of-domain datasets and manipulation strategies. Our methods, which we call XceptionNet*, achieve 100% accuracy on the popular Face-Forensics-s+ dataset and set new benchmark standards on the difficult DFDC-mini dataset. The XceptionNet* models are shown to exhibit superior performance on cross-domain testing and demonstrate surprising resilience to adversarial manipulations.

There is heightened concern over deliberately inaccurate news. Recently, so-called deepfake videos and images that are modified by or generated by artificial intelligence techniques have become more realistic and easier to create. These techniques could be used to create fake announcements from public figures or videos of events that did not happen, misleading mass audiences in dangerous ways. Although some recent research has examined accurate detection of deepfakes, those methodologies do not generalize well to real-world scenarios and are not available to the public in a usable form. In this project, we propose a system that will robustly and efficiently enable users to determine whether or not a video posted online is a deepfake. We approach the problem from the journalists' perspective and work towards developing a tool to fit seamlessly into their workflow. Results demonstrate accurate detection on both within and mismatched datasets.