Understanding Group Event Scheduling via the OutWithFriendz Mobile Application

Zhang, Shuo; Alanezi, Khaled; Gartrell, Mike; Han, Richard; Lv, Qin; Mishra, Shivakant

doi:10.1145/3161200

Cited by 6 publications

(3 citation statements)

References 17 publications

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Section: User Behaviormentioning

confidence: 99%

“…There is a rich history of UbiComp/IMWUT research concentrating on user behavior analysis [63]. In recent years, with the increasing importance of user experiences in system design, we are witnessing an increasing number of research studies in the IMWUT community [2,24,26,27,33,37,40,45,47,63,64] to understand user behavior. Zhang et al [63] tried to understand user behavior in group event decisions based on data collected from a mobile application, and they finally provided detailed novel insights in the event scheduling process of social groups.…”

Section: User Behaviormentioning

confidence: 99%

“…In recent years, with the increasing importance of user experiences in system design, we are witnessing an increasing number of research studies in the IMWUT community [2,24,26,27,33,37,40,45,47,63,64] to understand user behavior. Zhang et al [63] tried to understand user behavior in group event decisions based on data collected from a mobile application, and they finally provided detailed novel insights in the event scheduling process of social groups. Srinivasan et al [45] performed a qualitative user study in order to understand user behaviors on using an interactive rule selection tool, and some insights were gained for designing future rule selection systems.…”

Section: User Behaviormentioning

confidence: 99%

See 2 more Smart Citations

Understanding User Behavior in Car Sharing Services Through The Lens of Mobility

Wang

Vaish

Sun

et al. 2020

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Qualitative and quantitative user studies can reveal valuable insights into user behavior, which in turn can assist system designers in providing better user experiences. Car sharing (e.g., Zipcar and car2go), as an emerging App-based online shared mobility mode, has been increasing dramatically worldwide in recent years. However, to date, comprehensive user behavior in car sharing systems has not been investigated, which is essential for understanding their characteristics and promotion roadblocks. With the goal of understanding various facets of user behavior in online car sharing systems, in this paper, we performed a qualitative and quantitative user study by adopting a mixed-methods approach. We first designed an attitude-aware online survey with a set of qualitative questions to perceive people's subjective attitudes to online car sharing, where a total of 185 participants (68 females) completed the survey. Next, we quantitatively analyzed a one-year real-world car sharing operation dataset collected from the Chinese city Beijing, which involves over 68,000 unique users and over 587,850 usage records. We dissected this attitude-free dataset to understand the objective car sharing user behavior from different dimensions, e.g., spatial, temporal, and demographic. Furthermore, we conducted a comparative study by utilizing one-year data from other two representative Chinese city Fuzhou and Lanzhou to show if the obtained findings from Beijing data may be generalizable to other cities having different urban features, e.g., different city size, population density, wealth, and climate conditions. We also do a case study by designing a user behavior-aware usage prediction model (i.e., BeXGBoost) based on findings from our user study (e.g., unbalanced spatiotemporal usage patterns, weekly regularity, demographic-related usage difference, and low-frequency revisitation), which is the basis for car sharing service station deployment and vehicle rebalancing. Finally, we summarize a set of findings obtained from our study about the unique user behavior in online car sharing systems, combined with some detailed discussions about implications for design.

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Section: User Behaviormentioning

confidence: 99%

Section: User Behaviormentioning

confidence: 99%

Section: User Behaviormentioning

confidence: 99%

See 1 more Smart Citation

Understanding User Behavior in Car Sharing Services Through The Lens of Mobility

Wang

Vaish

Sun

et al. 2020

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

View full text Add to dashboard Cite

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Anomaly Detection in Crowds Using Multi Sensory Information

Irfan

Tokarchuk

Regazzoni

2018

2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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Understanding Event Organization at Scale in Event-Based Social Networks

Zhang

2019

ACM Trans. Intell. Syst. Technol.

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Understanding real-world event participation behavior has been a subject of active research and can offer valuable insights for event-related recommendation and advertisement. The emergence of event-based social networks (EBSNs), which attracts online users to host/attend offline events, has enabled exciting new research in this domain. However, most existing works focus on understanding or predicting individual users’ event participation behavior or recommending events to individual users. Few studies have addressed the problem of event popularity from the event organizer’s point of view. In this work, we study the latent factors for determining event popularity using large-scale datasets collected from the popular Meetup.com EBSN in five major cities around the world. We analyze and model four contextual factors: spatial factor using location convenience, quality, popularity density, and competitiveness; group factor using group member entropy and loyalty; temporal factor using temporal preference and weekly event patterns; and semantic factor using readability, sentiment, part of speech, and text novelty. In addition, we have developed a group-based social influence propagation network to model group-specific influences on events. By combining the COntextual features and Social Influence NEtwork, our integrated prediction framework COSINE can capture the diverse influential factors of event participation and can be used by event organizers to predict/improve the popularity of their events. Detailed evaluations demonstrate that our COSINE framework achieves high accuracy for event popularity prediction in all five cities with diverse cultures and user event behaviors.

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Understanding Group Event Scheduling via the OutWithFriendz Mobile Application

Cited by 6 publications

References 17 publications

Understanding User Behavior in Car Sharing Services Through The Lens of Mobility

Understanding User Behavior in Car Sharing Services Through The Lens of Mobility

Anomaly Detection in Crowds Using Multi Sensory Information

Understanding Event Organization at Scale in Event-Based Social Networks

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