Of Course it’s Political! A Critical Inquiry into Underemphasized Dimensions in Civic Text Visualization

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Eric P. S. Baumer, Mahmood Jasim, Ali Sarvghad, and Narges Mahyar. 2022. Of Course it’s Political! A Critical Inquiry into Underemphasized Dimensions in Civic Text Visualization. Computer Graphics Forum 41, 3 (EuroVis). preprint

Abstract

Recent developments in critical information visualization have brought the field’s attention to political, feminist, ethical, and rhetorical aspects of data visualization. However, less work has explored the interplay between design decisions and political ramifications—structures of authority, means of representation, etc. In this paper, we build upon these critical perspectives and highlight the political aspect of civic text visualization especially in the context of democratic decision-making. Based on a critical analysis of survey papers about text visualization in general, followed by a review on the status quo of text visualization in civics, we argue that civic text visualization inherits an exclusively analytic framing. This framing leads to a series of issues and challenges in the fundamentally political context of civics, such as misinterpretation of data, missing minority voices, and excluding the public from decision making processes. To span this gap between political context and analytic framing, we provide a series of two-pole conceptual dimensions, such as from singular user to multiple relationships, and from complexity to inclusivity of visualization design. For each dimension, we discuss how the tensions between these poles can help surface the political ramifications of design decisions in civic text visualization. These dimensions can thus help visualization researchers, designers, and practitioners attend more intentionally to these political aspects and inspire their design choices. We conclude by suggesting that these dimensions may be useful for visualization design across a variety of application domains, beyond civic text visualization.

Where Do Stories Come From? Examining the Exploration Process in Investigative Data Journalism

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Dilruba Showkat and Eric P. S. Baumer. 2021. Where Do Stories Come From? Examining the Exploration Process in Investigative Data Journalism. Proceedings of the ACM on Human-Computer Interaction 5, CSCW2: 390:1-390:31. https://doi.org/10.1145/3479534

Abstract

Investigative data journalists work with a variety of data sources to tell a story. Though prior work has indicated that there is a close relationship between journalists’ data work practices and that of data scientists. However, these relationships and data work practices are not empirically examined, and understanding them is crucial to inform the design of tools that are used by different groups of people including data scientists and data journalists. Thus, to bridge this gap, we studied investigative reporters’ data work practices with one non-profit investigative newsroom. Our study design includes two activities: 1) semi-structured interviews with journalists, and 2) a sketching activity allowing journalists to depict examples of their work practices. By analyzing these data and synthesizing them across related prior work, we propose the major phases in the data-driven investigative journalism story idea generation process. Our study findings show that the journalists employ a collection of multiple, iterative, cyclic processes to identify journalistically “interesting” story ideas. These processes both significantly resemble and show subtle nuanced differences with data science work practices identified in prior research. We further verified our proposal through a member check with key informants. This work offers three primary contributions. First, it provides a close glimpse into the main phases of investigative journalists’ data-driven story idea generation technique. Second, it complements prior work studying formal data science practices by examining data-driven investigative journalists, whose primary expertise lies outside computing. Third, it identifies particular points in the data exploration processes that would benefit from design interventions and suggests future research directions.

A Review on Strategies for Data Collection, Reflection, and Communication in Eating Disorder Apps

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Anjali Devakumar, Jay Modh, Bahador Saket, Eric P. S. Baumer, and Munmun De Choudhury. 2021. A Review on Strategies for Data Collection, Reflection, and Communication in Eating Disorder Apps. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI), 1–19. https://doi.org/10.1145/3411764.3445670

Abstract

Eating disorders (EDs) constitute a mental illness with the highest mortality. Today, mobile health apps provide promising means to ED patients for managing their condition. Apps enable users to monitor their eating habits, thoughts, and feelings, and offer analytic insights for behavior change. However, not only have scholars critiqued the clinical validity of these apps, their underlying design principles are not well understood. Through a review of 34 ED apps, we uncovered 11 different data types ED apps collect, and 9 strategies they employ to support collection and refection. Drawing upon personal health informatics and visualization frameworks, we found that most apps did not adhere to best practices on what and how data should be collected from and reflected to users, or how data-driven insights should be communicated. Our review offers suggestions for improving the design of ED apps such that they can be useful and meaningful in ED recovery.

Misfires, Missed Data, Misaligned Treatment: Disconnects in Collaborative Treatment of Eating Disorders

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Lauren C. Taylor, Kelsie Belan, Munmun De Choudhury, and Eric P. S. Baumer. 2021. Misfires, Missed Data, Misaligned Treatment: Disconnects in Collaborative Treatment of Eating Disorders. Proceedings of the ACM on Human-Computer Interaction 5, CSCW1: 31:1-31:28. https://doi.org/10.1145/3449105

Abstract

Technology bears important relationships to our health and wellness and has been utilized over the past two decades as an aid to support both self-management goals as well as collaboration among treatment teams. However, when chronic illnesses such as eating disorders (ED) are managed outside of institutionalized care settings, designing effective technology to support collaboration in treatment necessitates that we understand the relationships between patients, clinicians, and support networks. We conducted in-depth, semi-structured, interviews with 9 ED patients and 10 clinicians to understand the ED journey through the lens of collaborative efforts, technology use, and potential detriments. Based on our analysis of these 19 interviews, we present novel findings on various underlying disconnects within the collaborative ED treatment process – disconnects among clinicians, between treatment foci, among preferences in tracking, within support networks, and in patients’ own identities. Our findings highlight how these various disconnects are concomitant with and gaps can stem from a lack of collaboration between different stakeholders in the ED journey. We also identify methods of facilitating collaboration in these disconnects through technological mediators.

Who Has a Choice?: Survey-Based Predictors of Volitionality in Facebook Use and Non-use

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Patrick Skeba, Devansh Saxena, Shion Guha, and Eric P. S. Baumer. (2021). Who Has a Choice?: Survey-Based Predictors of Volitionality in Facebook Use and Non-use. Proceedings of the ACM on Human-Computer Interaction 5, GROUP: 223:1-223:25. https://doi.org/10.1145/3463935

Abstract

This paper examines volitionality of Facebook usage, that is, which individuals feel they have a choice about whether or not to use the site. It analyzes data from two large surveys, conducted three years apart. Across the two surveys, a variety of factors impacted whether or not respondents saw their Facebook usage as a matter of their own choice, such as engaging in non-use behaviors, measures of Facebook addiction, a sense of their own agency, and, across both studies, level of education. These results expand on prior literature around technology use and non-use, especially in terms of which populations may feel obligated to use, or be unwillingly prevented from using, social media such as Facebook. Furthermore, they provide potential implications both for future work and for technology policy.

Informational Friction as a Lens for Studying Algorithmic Aspects of Privacy

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Patrick Skeba and Eric P. S. Baumer. (2020). Informational Friction as a Lens for Studying Algorithmic Aspects of Privacy. Proceedings of the ACM on Human-Computer Interaction 4, CSCW.

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This paper addresses challenges in conceptualizing privacy posed by algorithmic systems that can infer sensitive information from seemingly innocuous data. This type of privacy is of imminent concern due to the rapid adoption of machine learning and artificial intelligence systems in virtually every industry. In this paper, we suggest informational friction, a concept from Floridi’s ethics of information, as a valuable conceptual lens for studying algorithmic aspects of privacy. Informational friction describes the amount of work required for one agent to access or alter the information of another. By focusing on amount of work, rather than the type of information or manner in which it is collected, informational friction can help to explain why automated analyses should raise privacy concerns independently of, and in addition to, those associated with data collection. As a demonstration, this paper analyze law enforcement use of facial recognition, andFacebook’s targeted advertising model using informational friction and demonstrate risks inherent to these systems which are not completely identified in another popular framework, Nissenbaum’s Contextual Integrity.The paper concludes with a discussion of broader implications, both for privacy research and for privacy regulation.

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Evaluating Design Fiction: The Right Tool for the Job

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Eric P. S. Baumer, Mark Blythe, and Theresa J. Tanenbaum. (2020). Evaluating Design Fiction: The Right Tool for the Job. in ACM Conference on Designing Interactive Systems (DIS). (Eindhoven, the Netherlands; held virtually). [24.0% acceptance rate; Honorable Mention Award]

Abstract

Design fiction has become so widely adopted that it regularly appears in contexts ranging from CEO speeches to dedicated tracks at academic conferences. However, evaluating this kind of work is difficult; it is not clear what good or bad design fiction is or what the judgment criteria should be. In this paper we assert that design fiction is a heterogeneous set of methods, and practices, able to produce a diversity of scholarly and design contributions. We argue locating these diverse practices under the single header of “design fiction” has resulted in epistemological confusion over the appropriate method of evaluation. We identify different traditions within the HCI literature—critical design; narratology and literary theory; studio-based design “crits”; user studies; scenarios and persona development; and thought experiments—to articulate a typology of evaluative frames. There is often a mismatch between the standards to which design fiction is held and the knowledge that speculative methods seek to produce. We argue that evaluating a given instance of design fiction requires us to properly select the right epistemological tool for the job.

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Methods for Generating Typologies of Non/use

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Devansh Saxena, Patrick Skeba, Shion Guha, and Eric P. S. Baumer. (2020). Methods for Generating Typologies of Non/use. Proceedings of the ACM on Human-Computer Interaction 3, CSCW.

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Prior studies of technology non-use demonstrate the need for approaches that go beyond a simple binary distinction between users and non-users. This paper proposes a set of two different methods by which researchers can identify types of non/use relevant to the particular sociotechnical settings they are studying. These methods are demonstrated by applying them to survey data about Facebook non/use. The results demonstrate that the different methods proposed here identify fairly comparable types of non/use. They also illustrate how the two methods make different trade offs between the granularity of the resulting typology and the total sample size. The paper also demonstrates how the different typologies resulting from these methods can be used in predictive modeling, allowing for the two methods to corroborate or disconfirm results from one another. The discussion considers implications and applications of these methods, both for research on technology non/use and for studying social computing more broadly.

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Topicalizer: Reframing Core Concepts in Machine Learning Visualization by Co-designing for Interpretivist Scholarship

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Eric P. S. Baumer, Drew Siedel, Lena McDonnell, Jiayun Zhong, Patricia Sittikul, and Micki McGee. (2020). Topicalizer: Reframing Core Concepts in Machine Learning Visualization by Co-designing for Interpretivist Scholarship. Human-Computer Interaction, Special Issue on Unifying Human Computer Interaction and Artificial Intelligence 35(5-6): 452-480.

Abstract

Computational algorithms can provide novel, compelling functionality for interactive systems. However, designing such systems for users whose expertise lies outside computer science poses novel and complex challenges. This paper focuses specifically on the domain of designing interactive topic modeling visualizations to support interpretivist scholars. It describes a co-design process that involved working directly with two such scholars across two different corpora. The resultant visualization has both several similarities and key differences with other topic modeling visualizations, illustrated here using both the final design and discarded prototypes. The paper’s core contribution is an attention to how our emphasis on interpretation played out, both in the design process and in the final visualization design. The paper concludes by discussing the kinds of issues and tensions that emerged in the course of this work, as well as the ways that these issues and tensions can apply to much broader contexts of designing interactive algorithmic systems.

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All Users are (Not) Created Equal: Predictors Vary for Different Forms of Facebook Non/use

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Eric P. S. Baumer, Patrick Skeba, Shion Guha, and Geri Gay. (2019). All Users are (Not) Created Equal: Predictors Vary for Different Forms of Facebook Non/use. Proceedings of the ACM Human-Computer Interaction 2, CSCW.

Abstract

Relatively little work has empirically examined use and non-use of social technologies as more than a dichotomous binary, despite increasing calls to do so. This paper compares three different forms of non/use that might otherwise fall under the single umbrella of Facebook “user”: (1) those who have a current active account; (2) those who have deactivated their account; and (3) those who have considered deactivating but not actually done so. A subset of respondents (N=256) from a larger, demographically representative sample of internet users completed measures for usage and perceptions of Facebook, Facebook addiction, privacy experiences and behaviors, and demographics. Multinomial logistic regression modeling shows four specific variables as most predictive of a respondent’s type: negative effects from “addictive” use, subjective intensity of Facebook usage, number of Facebook friends, and familiarity with or use of Facebook’s privacy settings. These findings both fill gaps left by, and help resolve conflicting expectations from, prior work. Furthermore, they demonstrate how valuable insights can be gained by disaggregating “users” based on different forms of engagement with a given technology.

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