The Theory, Practice, and Ethical Challenges of Designing a Diversity-Aware Platform for Social Relations

Schelenz, Laura; Bison, Ivano; Busso, Matteo; Götzen, Amalia de; Gática-Pérez, Daniel; Giunchiglia, Fausto; Meegahapola, Lakmal; Ruíz-Correa, Salvador

doi:10.1145/3461702.3462595

Cited by 21 publications

(10 citation statements)

References 38 publications

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“…Hence, it is worth pointing out that this study regarding the drinking behavior of young adults in Switzerland is exploratory, and the results cannot be directly assumed as being representative of the drinking behavior in other countries. Recent work has highlighted the importance of considering diversity-awareness when building social platforms using machine learning models and mobile sensing data [42,95].…”

Section: Discussionmentioning

confidence: 99%

Examining the Social Context of Alcohol Drinking in Young Adults with Smartphone Sensing

Meegahapola,

Labhart,

Phan

et al. 2021

Preprint

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According to prior work, the type of relationship between a person consuming alcohol and others in the surrounding (friends, family, spouse, etc.), and the number of those people (alone, with one person, with a group) are related to many aspects of alcohol consumption, such as the drinking amount, location, motives, and mood. Even though the social context is recognized as an important aspect that influences the drinking behavior of young adults in alcohol research, relatively little work has been conducted in smartphone sensing research on this topic. In this study, we analyze the weekend nightlife drinking behavior of 241 young adults in a European country, using a dataset consisting of self-reports and passive smartphone sensing data over a period of three months. Using multiple statistical analyses, we show that features from modalities such as accelerometer, location, application usage, bluetooth, and proximity could be informative about different social contexts of drinking. We define and evaluate seven social context inference tasks using smartphone sensing data, obtaining accuracies of the range 75%-86% in four two-class and three three-class inferences. Further, we discuss the possibility of identifying the sex composition of a group of friends using smartphone sensor data with accuracies over 70%. The results are encouraging towards supporting future interventions on alcohol consumption that incorporate users' social context more meaningfully and reducing the need for user self-reports when creating drink logs for self-tracking tools and public health studies. CCS Concepts: • Human-centered computing → Empirical studies in HCI; Empirical studies in ubiquitous and mobile computing; Smartphones; Mobile phones; Mobile devices; Empirical studies in collaborative and social computing; • Computer systems organization → Sensors and actuators; • Applied computing → Consumer health; Health informatics; Sociology; Psychology.

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Section: Discussionmentioning

confidence: 99%

Examining the Social Context of Alcohol Drinking in Young Adults with Smartphone Sensing

Meegahapola,

Labhart,

Phan

et al. 2021

Preprint

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“…Second, it is elementary that those who collect the data be involved in the research, analysis, and publication. Therefore, in our case, all project partners went to great lengths to ensure that joint research was conducted with the collected data, resulting in joint publications (Schelenz et al, 2021;Meegahapola et al, 2021Meegahapola et al, , 2022Assi et al, 2023). This ensures that all stakeholders benefit from the data they help produce and that the data are used in ways that are relevant to different local contexts.…”

Section: Confronting the Two Sides Of The Neocolonial Circle: Extract...mentioning

confidence: 99%

Diversity and neocolonialism in Big Data research: Avoiding extractivism while struggling with paternalism

Helm,

de Götzen,

Cernuzzi

et al. 2023

Big Data & Society

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The extractive logic of Big Data-driven technology and knowledge production has raised serious concerns. While most criticism initially focused on the impacts on Western societies, attention is now increasingly turning to the consequences for communities in the Global South. To date, debates have focused on private-sector activities. In this article, we start from the conviction that publicly funded knowledge and technology production must also be scrutinized for their potential neocolonial entanglements. To this end, we analyze the dynamics of collaboration in an European Union-funded research project that collects data for developing a social platform focused on diversity. The project includes pilot sites in China, Denmark, the United Kingdom, India, Italy, Mexico, Mongolia, and Paraguay. We present the experience at four field sites and reflect on the project’s initial conception, our collaboration, challenges, progress, and results. We then analyze the different experiences in comparison. We conclude that while we have succeeded in finding viable strategies to avoid contributing to the dynamics of unilateral data extraction as one side of the neocolonial circle, it has been infinitely more difficult to break through the much more subtle but no less powerful mechanisms of paternalism that we find to be prevalent in data-driven North–South relations. These mechanisms, however, can be identified as the other side of the neocolonial circle.

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“…The results can not be assumed to represent the eating behavior of other age groups from the same country or people from other countries. Prior work has highlighted the importance of considering diversityawareness in social platforms that use mobile sensing and machine learning [76], [77]. Hence, future work needs to be carried out for different age groups and countries.…”

Section: E: Other Informative Featuresmentioning

confidence: 99%

Sensing Eating Events in Context: A Smartphone-Only Approach

Bangamuarachchi,

Chamantha,

Meegahapola

et al. 2022

Preprint

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While the task of automatically detecting eating events has been examined in prior work using various wearable devices, the use of smartphones as standalone devices to infer eating events remains an open issue. This paper proposes a framework that infers eating vs. non-eating events from passive smartphone sensing and evaluates it on a dataset of 58 college students. First, we show that time of the day and features from modalities such as screen usage, accelerometer, app usage, and location are indicative of eating and noneating events. Then, we show that eating events can be inferred with an AUROC (area under the receiver operating characteristics curve) of 0.65 using subject-independent machine learning models, which can be further improved up to 0.81 for subject-dependent and 0.81 for hybrid models using personalization techniques. Moreover, we show that users have different behavioral and contextual routines around eating episodes requiring specific feature groups to train fully personalized models. These findings are of potential value for future mobile food diary apps that are context-aware by enabling scalable sensing-based eating studies using only smartphones; detecting under-reported eating events, thus increasing data quality in self report-based studies; providing functionality to track food consumption and generate reminders for on-time collection of food diaries; and supporting mobile interventions towards healthy eating practices.

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The Theory, Practice, and Ethical Challenges of Designing a Diversity-Aware Platform for Social Relations

Cited by 21 publications

References 38 publications

Examining the Social Context of Alcohol Drinking in Young Adults with Smartphone Sensing

Examining the Social Context of Alcohol Drinking in Young Adults with Smartphone Sensing

Diversity and neocolonialism in Big Data research: Avoiding extractivism while struggling with paternalism

Sensing Eating Events in Context: A Smartphone-Only Approach

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