GroupUs: Smartphone Proximity Data and Human Interaction Type Mining

Tri, Trinh Minh; Gática-Pérez, Daniel

doi:10.1109/iswc.2011.28

Cited by 59 publications

(34 citation statements)

References 21 publications

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“…Recently, they have been found to be useful tools in the domain of activity modeling [10], particularly for mining wearable sensor data, such as location [7] and physical proximity data [1,4,8]. First we will describe the basic functionality of topic models in terms of text, and then introduce our approach for interpreting them in the context of human activity.…”

Section: Probabilistic Topic Modelsmentioning

confidence: 99%

Extracting Mobile Behavioral Patterns with the Distant N-Gram Topic Model

Farrahi

Gática-Pérez

2012

2012 16th International Symposium on Wearable Computers

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Mining patterns of human behavior from large-scale mobile phone data has potential to understand certain phenomena in society. The study of such human-centric massive datasets requires new mathematical models. In this paper, we propose a probabilistic topic model that we call the distant n-gram topic model (DNTM) to address the problem of learning long duration human location sequences. The DNTM is based on Latent Dirichlet Allocation (LDA). We define the generative process for the model, derive the inference procedure and evaluate our model on real mobile data. We consider two different real-life human datasets, collected by mobile phone locations, the first considering GPS locations and the second considering cell tower connections. The DNTM successfully discovers topics on the two datasets. Finally, the DNTM is compared to LDA by considering log-likelihood performance on unseen data, showing the predictive power of the model on unseen data. We find that the DNTM consistantly outperforms LDA as the sequence length increases.

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Section: Probabilistic Topic Modelsmentioning

confidence: 99%

Extracting Mobile Behavioral Patterns with the Distant N-Gram Topic Model

Farrahi

Gática-Pérez

2012

2012 16th International Symposium on Wearable Computers

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“…Our model was originally proposed in Do and GaticaPerez [8] and validated on a dataset with 40 users. In this paper, we discuss it in more details, evaluate it thoroughly, and also include additional nearby Bluetooth devices in the analysis.…”

Section: Related Workmentioning

confidence: 99%

“…= i=|t| t=1..T φ 1tūi φ 2tvi φ 3tci θs it (8) wheret denote the interaction type assignment for the set of test links. Since t is unknown, we take the sum over all possible assignmentst.…”

Section: Predictive Performancementioning

confidence: 99%

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Human interaction discovery in smartphone proximity networks

Minh

Gática-Pérez

2011

Pers Ubiquit Comput

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Since humans are fundamentally social beings and interact frequently with others in their daily life, understanding social context is of primary importance in building context-aware applications. In this paper, using smartphone Bluetooth as a proximity sensor to create social networks, we present a probabilistic approach to mine human interaction types in real life. Our analysis is conducted on Bluetooth data continuously sensed with smartphones for over one year from 40 individuals who are professionally or personally related. The results show that the model can automatically discover a variety of social contexts. We objectively validated our model by studying its predictive and retrieval performance.

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“…The Group Discovery Co-location (GDC) algorithm [12], was developed to combine user meeting frequency and duration for group detection and was validated on one month of smart phone data carried by 141 students. GroupUS [1] is a probabilistic relational model based on Latent Dirichlet Allocation for group detection, more specifically, interaction type and social context detection. This model was validated on a set of 40 individuals over the course of a year.…”

Section: Related Workmentioning

confidence: 99%

Socio-Technical Network Analysis from Wearable Interactions

Farrahi

Emonet

Ferscha

2012

2012 16th International Symposium on Wearable Computers

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Wearable sensing platforms like modern smartphones have proven to be effective means in the complexity and computational social sciences. This paper draws from explicit (phone calls, SMS messaging) and implicit (proximity sensing based on Bluetooth radio signals) interaction patterns collected via smartphones and reality mining techniques to explain the dynamics of personal interactions and relationships.We consider three real human to human interaction networks, namely physical proximity, phone communication and instant messaging. We analyze a real undergraduate community's social circles and consider various topologies, such as the interaction patterns of users with the entire community, and the interaction patterns of users within their own community. We fit distributions of various interactions, for example, showing that the distribution of users that have been in physical proximity but have never communicated by phone fits a gaussian. Finally, we consider five types of relationships, for example friendships, to see whether significant differences exist in their interaction patterns. We find statistically significant differences in the physical proximity patterns of people who are mutual friends and people who are non-mutual (or asymmetric) friends, though this difference does not exist between mutual friends and never friends, nor does it exist in their phone communication patterns.Our findings impact a wide range of data-driven applications in socio-technical systems by providing an overview of community interaction patterns which can be used for applications such as epidemiology, or in understanding the diffusion of opinions and relationships.

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GroupUs: Smartphone Proximity Data and Human Interaction Type Mining

Cited by 59 publications

References 21 publications

Extracting Mobile Behavioral Patterns with the Distant N-Gram Topic Model

Extracting Mobile Behavioral Patterns with the Distant N-Gram Topic Model

Human interaction discovery in smartphone proximity networks

Socio-Technical Network Analysis from Wearable Interactions

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