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1. A Look into User Privacy and Third-party Applications in Facebook

   Seng, Sovantharith; Al-Ameen, Mahdi Nasrullah; Wright, Matthew (July 2021, Information and computer security)

   Furnell, Steven (Ed.)

   A huge amount of personal and sensitive data is shared on Facebook, which makes it a prime target for attackers. Adversaries can exploit third-party applications connected to a user’s Facebook profile (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. We address this challenge in our work. We conducted a lab study with 31 participants, where we received data on how they share information in 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. We 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. Our analysis reveals significant mismatches between users’ privacy perceptions and reality (i.e., privacy policies of Facebook apps), where we identified over-optimism not only in users’ perceptions of information collection, but also on their self-efficacy in protecting their information in Facebook despite experiencing negative incidents in the past. To the best of our knowledge, this is the first study on the gap between users’ privacy perceptions around Facebook apps and the reality. The findings from this study offer directions 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. 

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2. Bias in AI Autocomplete Suggestions Leads to Attitude Shift on Societal Issues

   https://doi.org/10.31234/osf.io/mhjn6  

   Williams-Ceci, Sterling; Jakesch, Maurice; Bhat, Advait; Kadoma, Kowe; Zalmanson, Lior; Naaman, Mor (March 2024, PsyArXiv Preprints)

   AI technologies such as Large Language Models (LLMs) are increasingly used to make suggestions to autocomplete text as people write. Can these suggestions impact people’s writing and attitudes? In two large-scale preregistered experiments (N=2,582), we expose participants who are writing about important societal issues to biased AI-generated suggestions. The attitudes participants expressed in their writing and in a post-task survey converged towards the AI’s position. Yet, a majority of participants were unaware of the AI suggestions’ bias and their influence. Further, awareness of the task or of the AI’s bias, e.g. warning participants about potential bias before or after exposure to the treatment, did not mitigate the influence effect. Moreover, the AI’s influence is not fully explained by the additional information provided by the suggestions. 

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3. AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances

   Agarwal, Dhruv; Naaman, Mor; Vashistha, Aditya (September 2024, arXiv)

   Large language models (LLMs) are being increasingly integrated into everyday products and services, such as coding tools and writing assistants. As these embedded AI applications are deployed globally, there is a growing concern that the AI models underlying these applications prioritize Western values. This paper investigates what happens when a Western-centric AI model provides writing suggestions to users from a different cultural background. We conducted a cross-cultural controlled experiment with 118 participants from India and the United States who completed culturally grounded writing tasks with and without AI suggestions. Our analysis reveals that AI provided greater efficiency gains for Americans compared to Indians. Moreover, AI suggestions led Indian participants to adopt Western writing styles, altering not just what is written but also how it is written. These findings show that Western-centric AI models homogenize writing toward Western norms, diminishing nuances that differentiate cultural expression. 

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4. User Authentication by Fusion of Mouse Dynamics and Widget Interactions: Two Experiments with PayPal and Facebook

   https://doi.org/10.1109/CCNC51644.2023.10059968  

   Khan, Simon; Hou, Daqing (January 2023, 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC))

   Utilization of Internet in everyday life has made us vulnerable in terms of security and privacy of our data and systems. For example, large-scale data breaches have occurred at Yahoo and Equifax because of lacking of robust and secure data protection within systems. Therefore, it is imperative to find solutions to further boost data security and protect privacy of our systems. To this end, we propose to authenticate users by utilizing score-level fusions based on mouse dynamics (e.g., mouse movement on a screen) and widget interactions (e.g., when clicking or hovering over different icons on a screen) on two novel datasets. In this study, we focus on two common applications, PayPal (a money transaction website) and Facebook (a social media platform). Though we fuse the same modalities for both applications, the purpose of investigating PayPal is to demonstrate how we can authenticate users when the users interact with the app for only a short period of time, while the purpose of investigating Facebook is to authenticate users based on social media browsing activities. We have a total of 10 users for PayPal with an average of 12 minutes of data per user and a total of 15 users for Facebook with an average of 2 hours of data per user. By fusing a single mouse trajectory with the associated widget interactions that occur during the trajectory, our mean EERs (Equal Error Rates) with a score-level fusion of mouse dynamics and widget interactions are 7.64% (SVM-rbf) and 3.25% (GBM), for PayPal, and 5.49% (SVM-rbf) and 2.54% (GBM), for Facebook. To further improve the performance of our fusion, we combine decision scores from multiple consecutive trajectories, which yields a 0% mean EER after 11 decision scores across all the users for both PayPal and Facebook. 

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5. HyperSoRec: Exploiting Hyperbolic User and Item Representations with Multiple Aspects for Social-aware Recommendation

   https://doi.org/10.1145/3463913  

   Wang, Hao; Lian, Defu; Tong, Hanghang; Liu, Qi; Huang, Zhenya; Chen, Enhong (April 2022, ACM Transactions on Information Systems)

   Social recommendation has achieved great success in many domains including e-commerce and location-based social networks. Existing methods usually explore the user-item interactions or user-user connections to predict users’ preference behaviors. However, they usually learn both user and item representations in Euclidean space, which has large limitations for exploring the latent hierarchical property in the data. In this article, we study a novel problem of hyperbolic social recommendation, where we aim to learn the compact but strong representations for both users and items. Meanwhile, this work also addresses two critical domain-issues, which are under-explored. First, users often make trade-offs with multiple underlying aspect factors to make decisions during their interactions with items. Second, users generally build connections with others in terms of different aspects, which produces different influences with aspects in social network. To this end, we propose a novel graph neural network (GNN) framework with multiple aspect learning, namely, HyperSoRec. Specifically, we first embed all users, items, and aspects into hyperbolic space with superior representations to ensure their hierarchical properties. Then, we adapt a GNN with novel multi-aspect message-passing-receiving mechanism to capture different influences among users. Next, to characterize the multi-aspect interactions of users on items, we propose an adaptive hyperbolic metric learning method by introducing learnable interactive relations among different aspects. Finally, we utilize the hyperbolic translational distance to measure the plausibility in each user-item pair for recommendation. Experimental results on two public datasets clearly demonstrate that our HyperSoRec not only achieves significant improvement for recommendation performance but also shows better representation ability in hyperbolic space with strong robustness and reliability. 

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