Open source smartphone libraries for computational social science

Lathia, Neal; Rachuri, Kiran K.; Mascolo, Cecilia; Roussos, George

doi:10.1145/2494091.2497345

Cited by 37 publications

(19 citation statements)

References 15 publications

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“…These included longer measures of affect, and measures assessing multiple aspects of behavior and context. The app uses open sourced software libraries presented in [32] to periodically collect behavioral data from physical and software sensors in the phone (accelerometer, microphone, location, text messages, phone calls). The data collected through the app is stored on the device's file system and then uploaded to a server.…”

Section: Our Application For Mood Mon-itoringmentioning

confidence: 99%

Mobile Sensing at the Service of Mental Well-being

Servia-Rodríguez

Rachuri

Mascolo

et al. 2017

Proceedings of the 26th International Conference on World Wide Web

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210

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Measuring mental well-being with mobile sensing has been an increasingly active research topic. Pervasiveness of smartphones combined with the convenience of mobile app distribution platforms (e.g., Google Play) provide a tremendous opportunity to reach out to millions of users. However, the studies at the confluence of mental health and mobile sensing have been longitudinally limited, controlled, or confined to a small number of participants. In this paper we report on what we believe is the largest longitudinal in-the-wild study of mood through smartphones. We describe an Android app to collect participants' self-reported moods and system triggered experience sampling data while passively measuring their physical activity, sociability, and mobility via their device's sensors. We report the results of a large-scale analysis of the data collected for about three years from ∼ 18, 000 users.The paper makes three primary contributions. First, we show how we used physical and software sensors in smartphones to automatically and accurately identify routines. Then, we demonstrate the strong correlation between these routines and users' personality, well-being perception, and other psychological variables. Finally, we explore predictability of users' mood using their passive sensing data. Our findings show that, especially for weekends, mobile sensing can be used to predict users' mood with an accuracy of about 70%. These results have the potential to impact the design of future mobile apps for mood/behavior tracking and interventions.

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Section: Our Application For Mood Mon-itoringmentioning

confidence: 99%

Mobile Sensing at the Service of Mental Well-being

Servia-Rodríguez

Rachuri

Mascolo

et al. 2017

Proceedings of the 26th International Conference on World Wide Web

Self Cite

135

210

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“…By combining the two APIs, our mobile application can automatically decide important notifications. In addition, our mobile application exploits a third party library for computational social science [21], as well as SensorManager and SensorDataManager to obtain the contexts and store a large amount of data, respectively.…”

Section: Methodsmentioning

confidence: 99%

Reducing Smartwatch Users’ Distraction with Convolutional Neural Network

Lee

Kwon

Kim

2018

Mobile Information Systems

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Smartwatches provide a useful feature whereby users can be directly aware of incoming notifications by vibration. However, such prompt awareness causes high distractions to users. To remedy the distraction problem, we propose an intelligent notification management for smartwatch users. e goal of our management system is not only to reduce the annoying notifications but also to provide the important notifications that users will swiftly react to. To analyze how to respond to the notifications daily, we have collected 20,353 in-the-wild notifications. Subsequently, we trained the convolutional neural network models to classify important notifications according to the users' contexts. Finally, the proposed management allows important notifications to be forwarded to a smartwatch. As experiment results show, the proposed method can reduce the number of unwanted notifications on smartwatches by up to 81%.

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“…Data stream from sensors are accessed via the Stream class that can be instantiated on a mobile or on the server, through the SenSocial Manager, that in turn communicates with the Sensor Manager. To implement Sensor Manager, the SenSocial mobile middleware relies on the third party ESSensorManager library for adaptive sensing [30].…”

Section: Methodsmentioning

confidence: 99%

SenSocial

Mehrotra

Pejović

Musolesi

2014

Proceedings of the 15th International Middleware Conference on - Middleware '14

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Smartphone sensing enables inference of physical context, while online social networks (OSNs) allow mobile applications to harness users' interpersonal relationships. However, OSNs and smartphone sensing remain disconnected, since obstacles, including the synchronization of mobile sensing and OSN monitoring, inefficiency of smartphone sensors, and privacy concerns, stand in the way of merging the information from these two sources.In this paper we present the design, implementation and evaluation of SenSocial, a middleware that automates the process of obtaining and joining OSN and physical context data streams for the development of ubiquitous computing applications. SenSocial enables instantiation, management and aggregation of context streams from multiple remote devices. Through micro-benchmarks we show that SenSocial successfully and efficiently captures OSN and mobile sensed data streams. We developed two prototype applications in order to evaluate our middleware and we demonstrate that SenSocial significantly reduces the amount of programming effort needed for building social sensing applications.

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Open source smartphone libraries for computational social science

Cited by 37 publications

References 15 publications

Mobile Sensing at the Service of Mental Well-being

Mobile Sensing at the Service of Mental Well-being

Reducing Smartwatch Users’ Distraction with Convolutional Neural Network

SenSocial

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