Clickbait, masked behind interesting headlines and thumbnails, is often used to spread misinformation and trick users into clicking on social media posts or links that direct them to malicious websites. To help users protect against clickbait, we examined interventions based on persuasion theories including designs that used social consequence, personal consequence, and badges. To this end, we first conducted a preliminary study to translate the participants’ feedback into improving our initial designs, followed by a lab study with 20 participants (60% Male, 40% Female; 18–44 years old) aimed at understanding their perceptions of the improved interventions; we further updated our designs based on their feedback. We then conducted an online study with 773 participants (56% Male, 42% Female; 18 to above 65 years old) over MTurk to evaluate the impact of persuasion techniques leveraged in our designs. Our findings suggest that persuasion can be an effective strategy to warn users against clickbait, specifically ones that use incentives such as revealing mystery of clickbait. Overall, our studies provide valuable insights into understanding users’ needs and expectations around interventions against clickbait, and offer guidelines for future research in these directions.

Purpose A huge amount of personal and sensitive data are shared on Facebook, which makes it a prime target for attackers. Adversaries can exploit third-party applications connected to a user’s Facebook profiles (i.e. Facebook apps) to gain access to this personal information. Users’ lack of knowledge and the varying privacy policies of these apps make them further vulnerable to information leakage. However, little has been done to identify mismatches between users’ perceptions and the privacy policies of Facebook apps. This paper aims to address this challenge in the work. Design/methodology/approach The authors conducted a lab study with 31 participants, where the authors received data on how they share information on Facebook, their Facebook-related security and privacy practices and their perceptions on the privacy aspects of 65 frequently-used Facebook apps in terms of data collection, sharing and deletion. The authors then compared participants’ perceptions with the privacy policy of each reported app. Participants also reported their expectations about the types of information that should not be collected or shared by any Facebook app. Findings The analysis reveals significant mismatches between users’ privacy perceptions and reality (i.e. privacy policies of Facebook apps), where the authors identified over-optimism not only in users’ perceptions of information collection but also in their self-efficacy in protecting their information in Facebook despite experiencing negative incidents in the past. Originality/value To the best of the knowledge, this is the first study on the gap between users’ privacy perceptions around Facebook apps and reality. The findings from this study offer direction for future research to address that gap through designing usable, effective and personalized privacy notices to help users to make informed decisions about using Facebook apps.

Facebook remains the largest social media platform on the Internet with over one billion active monthly users. A variety of personal and sensitive data is shared on the platform, which makes it a prime target for attackers. Increasingly, we see phishing attacks that take advantage of users' lack of security knowledge, deceiving victims by using fake or compromised accounts to share malicious posts. These attacks may slip undetected by the Facebook defense system, exposing users to potentially be phished or have their devices infected with drive-by downloads and malware. Only a few studies have been conducted to date to understand how users interact with attacks like this in Facebook. In our prior work, we conducted a study to address this challenge using a simulated interface and think-aloud protocol. In this study, we aim to make further progress in understanding the impact of different factors on users' clicking decision in social media through a vignette study that encourages participants to think about realistic scenarios that they might face.

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.