Exploring Relationship-aware Dynamic Message Screening for Mobile Messengers

Lee, Seung−Chul; Pushp, Saumay; Min, Chulhong; Song, Junehwa

doi:10.1145/3267305.3267673

Cited by 3 publications

(4 citation statements)

References 5 publications

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“…Location APIs (e.g., LocationManager, LocationProvider) and Google Location Services API are representative API frameworks mainly used to collect location context information in many existing studies (e.g., [20,17,101,110,102,37,103,104,23,16,15,27,68]). In terms of efficiency and accuracy, Google Location Services APIs are superior to the Location APIs.…”

Section: Location Context Apismentioning

confidence: 99%

“…• Notification types (i.e., TYPE_NOTIFICATION_STATE_CHANGED): 18 studies which were used notification data [22,21,102,24,23,16,27,30,28,29,26,34,36,33,35,31,50,85] • Exploration types (i.e., TOUCH_GESTURE_DETECTION_START/END): Rodrgues et al [140] and Alshayban et al [158] which were used gesture interaction data for enhancing the user interface of the people with disabilities in exploration mode.…”

Section: Types Of Touch Interaction Ref Nummentioning

confidence: 99%

“…• Detailed described studies as the data term level of the third layer in Figure 4 (e.g., "click", "scroll", "text changed", "notification time", "accelerometer", "gyro", "battery level"): [23,24,34,33,25,31,20,21,17,30,28,27,102,87,103,104,18,22,36,121,68,66,73,79,82,83,80,49,57] • Vaguely described studies as the data term level of the first or second layer in Figure 4 (e.g., "all UI events", "sensors", "sensor data", "event type", "user actions", "gesture action", "context events", "context logs"): [35,16,32,140,29,127,72,76] Furthermore, depending on the method of describing the data terms, there can be a difference in the degree of ease in terms of reproducibility for other researchers. The method of describing data terms in 110 number of papers can be divided into two types of studies: First type of studies are described by words or words with categorization, second type of studies are described by words in sentences.…”

Section: Data Term Issues Regarding Reproducibilitymentioning

confidence: 99%

“…• Understanding a user's smartphone usage pattern [17,20,21,22,18] • Identifying a user's notification checking factors, how the user is affected by the notification, and message management and scheduling [23,24,16,25,26,27,28,29,30,31,32,33,34,35,36].…”

Section: Introductionmentioning

confidence: 99%

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A Systematic Survey on Android API Usage for Data-Driven Analytics with Smartphones

Lee,

Park,

Lee

2021

Preprint

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Recently, there has been an increase in industrial and academic research on data-driven analytics with smartphones based on the collection of app usage patterns and surrounding context data. The Android mobile operating system utilizes Usage Statistics API (US API) and Accessibility Service API (AS API) as representative APIs to passively collect app usage data. These APIs are used for various research purposes as they can collect app usage patterns (e.g., app status, usage time, app name, user interaction state, and smartphone use state) and fine-grained data (e.g., user interface elements & hierarchy and user interaction type & target & time) of each application. In addition, other sensing APIs help to collect the user's surroundings context (location, network, ambient environment) and device state data, along with AS/US API. In this review, we provide insights on the types of mobile usage and sensor data that can be collected for each research purpose by considering Android built-in APIs and sensors (AS/US API, and other sensing APIs). Moreover, we classify the research purposes of the surveyed papers into four categories and 17 sub-categories, and create a hierarchical structure for data classification, comprising three layers. We present the important trends in the usage of Android's built-in APIs and sensors, including AS/US API, the types of data collected using the presented APIs, and discuss the utilization of mobile usage and sensor data in future research.

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Section: Location Context Apismentioning

confidence: 99%

Section: Types Of Touch Interaction Ref Nummentioning

confidence: 99%

Section: Data Term Issues Regarding Reproducibilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Systematic Survey on Android API Usage for Data-Driven Analytics with Smartphones

Lee,

Park,

Lee

2021

Preprint

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Notification privacy protection via unobtrusive gripping hand verification using media sounds

Huang

Wang

2021

Proceedings of the 27th Annual International Conference on Mobile Computing and Networking

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A Systematic Survey on Android API Usage for Data-driven Analytics with Smartphones

2022

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Recent industrial and academic research has focused on data-driven analytics with smartphones by collecting user interaction, context, and device systems data through Application Programming Interfaces (APIs) and sensors. The Android OS provides various APIs to collect such mobile usage and sensor data for third-party developers. Usage Statistics API (US API) and Accessibility Service API (AS API) are representative Android APIs for collecting app usage data and are used for various research purposes as they can collect fine-grained interaction data (e.g., app usage history, user interaction type). Furthermore, other sensor APIs help to collect a user’s context and device state data, along with AS/US APIs. This review investigates mobile usage and sensor data-driven research using AS/US APIs, by categorizing the research purposes and the data types. In this paper, the surveyed studies are classified as follows: five themes and 21 subthemes, and a four-layer hierarchical data classification structure. This allows us to identify a data usage trend and derive insight into data collection according to research purposes. Several limitations and future research directions of mobile usage and sensor data-driven analytics research are discussed, including the impact of changes in the Android API versions on research, the privacy and data quality issues, and the mitigation of reproducibility risks with standardized data typology.

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Exploring Relationship-aware Dynamic Message Screening for Mobile Messengers

Cited by 3 publications

References 5 publications

A Systematic Survey on Android API Usage for Data-Driven Analytics with Smartphones

A Systematic Survey on Android API Usage for Data-Driven Analytics with Smartphones

Notification privacy protection via unobtrusive gripping hand verification using media sounds

A Systematic Survey on Android API Usage for Data-driven Analytics with Smartphones

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