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1. The TikTok Tradeoff: Compelling Algorithmic Content at the Expense of Personal Privacy

   https://doi.org/10.1145/3490632.3497864  

   De Los Santos, Maya; Klug, Daniel (January 2021, 20th International Conference on Mobile and Ubiquitous Multimedia (MUM 2021), December 5–8, 2021, Leuven, Belgium. ACM, New York, NY, USA)

   This paper presents the results of an interview study with twelve TikTok users to explore user awareness, perception, and experiences with the app’s algorithm in the context of privacy. The social media entertainment app TikTok collects user data to cater individualized video feeds based on users’ engagement with presented content which is regulated in a complex and overly long privacy policy. Our results demonstrate that participants generally have very little knowledge of the actual privacy regulations which is argued for with the benefit of receiving free entertaining content. However, participants experienced privacy-related downsides when algorithmically catered video content increasingly adapted to their biography, interests, or location and they in turn realized the detail of personal data that TikTok had access to. This illustrates the tradeoff users have to make between allowing TikTok to access their personal data and having favorable video consumption experiences on the app. 

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2. Design and Validation of a Stress Detection Model for Use with a VR Based Interview Simulator for Autistic Young Adults

   https://doi.org/10.1007/978-3-030-78092-0_40  

   Migovich, M; Korman, A; Wade, J; Sarkar, N. (July 2021, International Conference on Human-Computer Interaction (HCII))

   Antona, M; null (Ed.)

   Studies show that young autistic adults are under- or unemployed, with almost half never holding a paying job in their 20’s. Unemployment within this population leads to decreased personal growth and increased dependence on caregivers. Research suggests that the interview process is one of the largest barriers to employment for this population. Autistic individuals often struggle with emotion regulation, which can be exacerbated by the interview process. To address this, we propose the use of a stress detection model in conjunction with a virtual reality interview simulator. This combination will allow for the interview to adapt to the state of the participant to improve the skills and engagement of the user and positively influence their comfort level. Data regarding negative affective responses to categories of questions can also be used to inform employers on better interviewing techniques. A model was designed using data obtained from neurotypical participants completing a modified Computerized Paced Serial Addition Task (PASAT-C) and evaluated on a dataset obtained from Autistic participants who took part in a simulated interview. Agreement between the model and ground truth was compared based on Pearson correlation coefficients. It was found that was r(289) = 0.28, which was statistically significant (p < .001; CI: 0.17 to 0.38). Our preliminary results provide evidence for the validity of observer-based labeling of data captured using a wrist-worn physiological sensor. 

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3. Characterizing Exploratory Behaviors on a Personal Visualization Interface Using Interaction Logs

   Sukumar, P.T. (January 2020, Proc. of EuroVis)

   Personal visualizations present a separate class of visualizations where users interact with their own data to draw inferences about themselves. In this paper, we study how a realistic understanding of personal visualizations can be gained from analyzing user interactions. We designed an interface presenting visualizations of the personal data gathered in a prior study and logged interactions from 369 participants as they each explored their own data. We found that the participants spent different amounts of time in exploring their data and used a variety of physical devices which could have affected their engagement with the visualizations. Our findings also suggest that the participants made more comparisons between their data instances than with the provided baselines and certain interface design choices, such as the ordering of options, influenced their exploratory behaviors. 

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4. The Way it Makes you Feel Predicting Users’ Engagement during Interviews with Biofeedback and Supervised Learning

   https://doi.org/10.1109/RE48521.2020.00016  

   Girardi, Daniela; Ferrari, Alessio; Novielli, Nicole; Spoletini, Paola; Fucci, Davide; Huichapa, Thaide (August 2020, 2020 IEEE 28th International Requirements Engineering Conference (RE))

   null (Ed.)

   Capturing users' engagement is crucial for gathering feedback about the features of a software product. In a market-driven context, current approaches to collect and analyze users' feedback are based on techniques leveraging information extracted from product reviews and social media. These approaches are hardly applicable in bespoke software development, or in contexts in which one needs to gather information from specific users. In such cases, companies need to resort to face-to-face interviews to get feedback on their products. In this paper, we propose to utilize biofeedback to complement interviews with information about the engagement of the user on the discussed features and topics. We evaluate our approach by interviewing users while gathering their biometric data using an Empatica E4 wristband. Our results show that we can predict users' engagement by training supervised machine learning algorithms on the biometric data. The results of our work can be used to facilitate the prioritization of product features and to guide the interview based on users' engagement. 

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5. Who Am I?: A Design Probe Exploring Real-Time Transparency about Online and Offline User Profiling Underlying Targeted Ads

   https://doi.org/10.1145/3478122  

   Barbosa, Natã M.; Wang, Gang; Ur, Blase; Wang, Yang (September 2021, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   To enable targeted ads, companies profile Internet users, automatically inferring potential interests and demographics. While current profiling centers on users' web browsing data, smartphones and other devices with rich sensing capabilities portend profiling techniques that draw on methods from ubiquitous computing. Unfortunately, even existing profiling and ad-targeting practices remain opaque to users, engendering distrust, resignation, and privacy concerns. We hypothesized that making profiling visible at the time and place it occurs might help users better understand and engage with automatically constructed profiles. To this end, we built a technology probe that surfaces the incremental construction of user profiles from both web browsing and activities in the physical world. The probe explores transparency and control of profile construction in real time. We conducted a two-week field deployment of this probe with 25 participants. We found that increasing the visibility of profiling helped participants anticipate how certain actions can trigger specific ads. Participants' desired engagement with their profile differed in part based on their overall attitudes toward ads. Furthermore, participants expected algorithms would automatically determine when an inference was inaccurate, no longer relevant, or off-limits. Current techniques typically do not do this. Overall, our findings suggest that leveraging opportunistic moments within pervasive computing to engage users with their own inferred profiles can create more trustworthy and positive experiences with targeted ads. 

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