An Empathy-Based Sandbox Approach to Bridge Attitudes, Goals, Knowledge, and Behaviors in the Privacy Paradox

The "privacy paradox" describes the discrepancy between users' privacy attitudes and their actual behaviors. Mitigating this discrepancy requires solutions that account for both system opaqueness and users' hesitations in testing different privacy settings due to fears of unintended data exposure. We introduce an empathy-based approach that allows users to experience how privacy behaviors may alter system outcomes in a risk-free sandbox environment from the perspective of artificially generated personas. To generate realistic personas, we introduce a novel pipeline that augments the outputs of large language models using few-shot learning, contextualization, and chain of thoughts. Our empirical studies demonstrated the adequate quality of generated personas and highlighted the changes in privacy-related applications (e.g., online advertising) caused by different personas. Furthermore, users demonstrated cognitive and emotional empathy towards the personas when interacting with our sandbox. We offered design implications for downstream applications in improving user privacy literacy and promoting behavior changes

A Chatbot for Perinatal Women's and Partners' Obstetric and Mental Health Care: Development and Usability Evaluation Study

Background: To motivate people to adopt medical chatbots, the establishment of a specialized medical knowledge database that fits their personal interests is of great importance in developing a chatbot for perinatal care, particularly with the help of health professionals. Objective: The objectives of this study are to develop and evaluate a user-friendly question-and-answer (Q&A) knowledge database-based chatbot (Dr. Joy) for perinatal women's and their partners' obstetric and mental health care by applying a text-mining technique and implementing contextual usability testing (UT), respectively, thus determining whether this medical chatbot built on mobile instant messenger (KakaoTalk) can provide its male and female users with good user experience. Methods: Two men aged 38 and 40 years and 13 women aged 27 to 43 years in pregnancy preparation or different pregnancy stages were enrolled. All participants completed the 7-day-long UT, during which they were given the daily tasks of asking Dr. Joy at least 3 questions at any time and place and then giving the chatbot either positive or negative feedback with emoji, using at least one feature of the chatbot, and finally, sending a facilitator all screenshots for the history of the day's use via KakaoTalk before midnight. One day after the UT completion, all participants were asked to fill out a questionnaire on the evaluation of usability, perceived benefits and risks, intention to seek and share health information on the chatbot, and strengths and weaknesses of its use, as well as demographic characteristics. Results: Despite the relatively higher score of ease of learning (EOL), the results of the Spearman correlation indicated that EOL was not significantly associated with usefulness (ρ=0.26; P=.36), ease of use (ρ=0.19; P=.51), satisfaction (ρ=0.21; P=.46), or total usability scores (ρ=0.32; P=.24). Unlike EOL, all 3 subfactors and the total usability had significant positive associations with each other (all ρ>0.80; P<.001). Furthermore, perceived risks exhibited no significant negative associations with perceived benefits (ρ=-0.29; P=.30) or intention to seek (SEE; ρ=-0.28; P=.32) or share (SHA; ρ=-0.24; P=.40) health information on the chatbot via KakaoTalk, whereas perceived benefits exhibited significant positive associations with both SEE and SHA. Perceived benefits were more strongly associated with SEE (ρ=0.94; P<.001) than with SHA (ρ=0.70; P=.004). Conclusions: This study provides the potential for the uptake of this newly developed Q&A knowledge database-based KakaoTalk chatbot for obstetric and mental health care. As Dr. Joy had quality contents with both utilitarian and hedonic value, its male and female users could be encouraged to use medical chatbots in a convenient, easy-to-use, and enjoyable manner. To boost their continued usage intention for Dr. Joy, its Q&A sets need to be periodically updated to satisfy user intent by monitoring both male and female user utterances.ope

Regulating Habit-Forming Technology

Tech developers, like slot machine designers, strive to maximize the user’s “time on device.” They do so by designing habit-forming products— products that draw consciously on the same behavioral design strategies that the casino industry pioneered. The predictable result is that most tech users spend more time on device than they would like, about five hours of phone time a day, while a substantial minority develop life-changing behavioral problems similar to problem gambling. Other countries have begun to regulate habit-forming tech, and American jurisdictions may soon follow suit. Several state legislatures today are considering bills to regulate “loot boxes,” a highly addictive slot-machine- like mechanic that is common in online video games. The Federal Trade Commission has also announced an investigation into the practice. As public concern mounts, it is surprisingly easy to envision consumer regulation extending beyond video games to other types of apps. Just as tobacco regulations might prohibit brightly colored packaging and fruity flavors, a social media regulation might limit the use of red notification badges or “streaks” that reward users for daily use. It is unclear how much of this regulation could survive First Amendment scrutiny; software, unlike other consumer products, is widely understood as a form of protected “expression.” But it is also unclear whether well-drawn laws to combat compulsive technology use would seriously threaten First Amendment values. At a very low cost to the expressive interests of tech companies, these laws may well enhance the quality and efficacy of online speech by mitigating distraction and promoting deliberation

Nowcasting user behaviour with social media and smart devices on a longitudinal basis: from macro- to micro-level modelling

The adoption of social media and smart devices by millions of users worldwide over the last decade has resulted in an unprecedented opportunity for NLP and social sciences. Users publish their thoughts and opinions on everyday issues through social media platforms, while they record their digital traces through their smart devices. Mining these rich resources offers new opportunities in sensing real-world events and indices (e.g., political preference, mental health indices) in a longitudinal fashion, either at the macro (population)-, or at the micro(user)-level. The current project aims at developing approaches to “nowcast" (predict the current state of) such indices at both levels of granularity. First, we build natural language resources for the static tasks of sentiment analysis, emotion disclosure and sarcasm detection over user-generated content. These are important for opinion monitoring on a large scale. Second, we propose a general approach that leverages textual data derived from generic social media streams to nowcast political indices at the macro-level. Third, we leverage temporally sensitive and asynchronous information to nowcast the political stance of social media users, at the micro-level using multiple kernel learning. We then focus further on the micro-level modelling, to account for heterogeneous data sources, such as information derived from users' smart phones, SMS and social media messages, to nowcast time-varying mental health indices of a small cohort of users on a longitudinal basis. Finally, we present the challenges faced when applying such micro-level approaches in a real-world setting and propose directions for future research

The experience as a document: designing for the future of collaborative remembering in digital archives

How does it feel when we remember together on-line? Who gets to say what it is worth to be remembered? To understand how the user experience of participation is affecting the formation of collective memories in the context of online environments, first it is important to take into consideration how the notion of memory has been transformed under the influence of the digital revolution. I aim to contribute to the field of User Experience (UX) research theorizing on the felt experience of users from a memory perspective, taking into consideration aspects linked to both personal and collective memories in the context of connected environments.Harassment and hate speech in connected conversational environments are specially targeted to women and underprivileged communities, which has become a problem for digital archives of vernacular creativity (Burgess, J. E. 2007) such as YouTube, Twitter, Reddit and Wikipedia. An evaluation of the user experience of underprivileged communities in creative archives such as Wikipedia indicates the urgency for building a feminist space where women and queer folks can focus on knowledge production and learning without being harassed. The theoretical models and designs that I propose are a result of a series of prototype testing and case studies focused on cognitive tools for a mediated human memory operating inside transactive memory systems. With them, aims to imagine the means by which feminist protocols for UX design and research can assist in the building and maintenance of the archive as a safe/brave space.Working with perspectives from media theory, memory theory and gender studies and centering the user experience of participation for women, queer folks, people of colour (POC) and other vulnerable and underrepresented communities as the main focus of inquiring, my research takes an interdisciplinary approach to interrogate how online misogyny and other forms of abuse are perceived by communities placed outside the center of the hegemonic normativity, and how the user experience of online abuse is affecting the formation of collective memories in the context of online environments

Engineering social media to combat the fear of missing out (FoMO).

The fear of missing out (FoMO) in relation to social media is an emerging issue that is expected to become more widespread with the increasing availability of online facilities for social interaction. Researchers have recently begun to explore the negative consequences of FoMO that are faced by social media users. Investigations suggest that those experiencing what could be called digital addiction may also display a range of psychological disorders ranging from depression and negative feelings to lack of sleep and insomnia, eating disorders, reduced life competency, emotional tension, negative effects on physical well-being, anxiety, and a lack of emotional control. Despite clear indicators of the effect of FoMO on users’ well-being, engineering principles and tools that allow them or their carers to manage their FoMO are still unavailable. This thesis argues that software itself can be used as an effective solution to the management of social media related FoMO, and develops a method for managing digital usage that utilises existing features of social media and proposes others that could be added in the future. The method also includes an educational element that raises awareness of how social media related FoMO occurs and how it can be dealt with. It is hoped that this might build the user’s digital resilience and help them to cope with certain kinds of FoMO as they are triggered. The method is intended to enable people to regulate their use of social media, and in particular to manage their FoMO. To achieve the goal of the thesis, several empirical studies with end-users were conducted. These helped with the conceptualisation of various aspects of social media related FoMO, including how it happens, the social media features that act as triggers, and the technical countermeasures that can be used in its management. The results of these studies were exploited to devise the FoMO management method. The method was evaluated in terms of usefulness, clarity, coherence, completeness, engagement, acceptance and effectiveness. The evaluation results showed the method was accepted by the participants and helped them to manage their FoMO

Detecting Events and Patterns in Large-Scale User Generated Textual Streams with Statistical Learning Methods

A vast amount of textual web streams is influenced by events or phenomena emerging in the real world. The social web forms an excellent modern paradigm, where unstructured user generated content is published on a regular basis and in most occasions is freely distributed. The present Ph.D. Thesis deals with the problem of inferring information - or patterns in general - about events emerging in real life based on the contents of this textual stream. We show that it is possible to extract valuable information about social phenomena, such as an epidemic or even rainfall rates, by automatic analysis of the content published in Social Media, and in particular Twitter, using Statistical Machine Learning methods. An important intermediate task regards the formation and identification of features which characterise a target event; we select and use those textual features in several linear, non-linear and hybrid inference approaches achieving a significantly good performance in terms of the applied loss function. By examining further this rich data set, we also propose methods for extracting various types of mood signals revealing how affective norms - at least within the social web's population - evolve during the day and how significant events emerging in the real world are influencing them. Lastly, we present some preliminary findings showing several spatiotemporal characteristics of this textual information as well as the potential of using it to tackle tasks such as the prediction of voting intentions.Comment: PhD thesis, 238 pages, 9 chapters, 2 appendices, 58 figures, 49 table

Faculty Perceptions and Experiences of “presence” in the Online Learning Environment

The purpose of this interpretive phenomenological research study was to gain an understanding of how faculty who teach fully online courses perceive and experience presence. The 25 faculty participants in this study were drawn from a four-year institution of higher education in the Midwest. The faculty designed and taught their own courses. Data were collected through: (1) semi-structured in-depth interviews with each participant, (2) documentary analysis of two course syllabi from two different course offerings for each participant, and (3) observations of five participants’ online course sites over the duration of an academic semester (16 week course). Findings revealed that faculty perceived presence as “being seen.” Faculty were concerned with projecting their personalities online and they wanted their students to see them for who they were. An emotional dimension to the experiences of presence emerged in the interviews. Emotional responses of faculty to online instruction influenced their experiences of presence. An intriguing finding was that the perception and experience of presence required a cognitive reframing of the online learning environment. A traditional classroom environment is characterized by a one-to-many relationship from faculty to students. In the online environment, this transformed into many one-to-one relationships between faculty and individual students. Experiences of presence were heightened when participants were able to change their mindset and understand and acknowledge the change. Finally, this study introduced the conceptualization of online instruction as a dramatic performance enacted by the faculty to an audience of students. A diagrammatic depiction of online instruction as a staged performance is also provided