25,453 research outputs found
Effect of Values and Technology Use on Exercise: Implications for Personalized Behavior Change Interventions
Technology has recently been recruited in the war against the ongoing obesity
crisis; however, the adoption of Health & Fitness applications for regular
exercise is a struggle. In this study, we present a unique demographically
representative dataset of 15k US residents that combines technology use logs
with surveys on moral views, human values, and emotional contagion. Combining
these data, we provide a holistic view of individuals to model their physical
exercise behavior. First, we show which values determine the adoption of Health
& Fitness mobile applications, finding that users who prioritize the value of
purity and de-emphasize values of conformity, hedonism, and security are more
likely to use such apps. Further, we achieve a weighted AUROC of .673 in
predicting whether individual exercises, and we also show that the application
usage data allows for substantially better classification performance (.608)
compared to using basic demographics (.513) or internet browsing data (.546).
We also find a strong link of exercise to respondent socioeconomic status, as
well as the value of happiness. Using these insights, we propose actionable
design guidelines for persuasive technologies targeting health behavior
modification
DeepCity: A Feature Learning Framework for Mining Location Check-ins
Online social networks being extended to geographical space has resulted in
large amount of user check-in data. Understanding check-ins can help to build
appealing applications, such as location recommendation. In this paper, we
propose DeepCity, a feature learning framework based on deep learning, to
profile users and locations, with respect to user demographic and location
category prediction. Both of the predictions are essential for social network
companies to increase user engagement. The key contribution of DeepCity is the
proposal of task-specific random walk which uses the location and user
properties to guide the feature learning to be specific to each prediction
task. Experiments conducted on 42M check-ins in three cities collected from
Instagram have shown that DeepCity achieves a superior performance and
outperforms other baseline models significantly
Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty
With the rapid expansion of mobile phone networks in developing countries,
large-scale graph machine learning has gained sudden relevance in the study of
global poverty. Recent applications range from humanitarian response and
poverty estimation to urban planning and epidemic containment. Yet the vast
majority of computational tools and algorithms used in these applications do
not account for the multi-view nature of social networks: people are related in
myriad ways, but most graph learning models treat relations as binary. In this
paper, we develop a graph-based convolutional network for learning on
multi-view networks. We show that this method outperforms state-of-the-art
semi-supervised learning algorithms on three different prediction tasks using
mobile phone datasets from three different developing countries. We also show
that, while designed specifically for use in poverty research, the algorithm
also outperforms existing benchmarks on a broader set of learning tasks on
multi-view networks, including node labelling in citation networks
Knowledge is at the Edge! How to Search in Distributed Machine Learning Models
With the advent of the Internet of Things and Industry 4.0 an enormous amount
of data is produced at the edge of the network. Due to a lack of computing
power, this data is currently send to the cloud where centralized machine
learning models are trained to derive higher level knowledge. With the recent
development of specialized machine learning hardware for mobile devices, a new
era of distributed learning is about to begin that raises a new research
question: How can we search in distributed machine learning models? Machine
learning at the edge of the network has many benefits, such as low-latency
inference and increased privacy. Such distributed machine learning models can
also learn personalized for a human user, a specific context, or application
scenario. As training data stays on the devices, control over possibly
sensitive data is preserved as it is not shared with a third party. This new
form of distributed learning leads to the partitioning of knowledge between
many devices which makes access difficult. In this paper we tackle the problem
of finding specific knowledge by forwarding a search request (query) to a
device that can answer it best. To that end, we use a entropy based quality
metric that takes the context of a query and the learning quality of a device
into account. We show that our forwarding strategy can achieve over 95%
accuracy in a urban mobility scenario where we use data from 30 000 people
commuting in the city of Trento, Italy.Comment: Published in CoopIS 201
Evolution of Conversations in the Age of Email Overload
Email is a ubiquitous communications tool in the workplace and plays an
important role in social interactions. Previous studies of email were largely
based on surveys and limited to relatively small populations of email users
within organizations. In this paper, we report results of a large-scale study
of more than 2 million users exchanging 16 billion emails over several months.
We quantitatively characterize the replying behavior in conversations within
pairs of users. In particular, we study the time it takes the user to reply to
a received message and the length of the reply sent. We consider a variety of
factors that affect the reply time and length, such as the stage of the
conversation, user demographics, and use of portable devices. In addition, we
study how increasing load affects emailing behavior. We find that as users
receive more email messages in a day, they reply to a smaller fraction of them,
using shorter replies. However, their responsiveness remains intact, and they
may even reply to emails faster. Finally, we predict the time to reply, length
of reply, and whether the reply ends a conversation. We demonstrate
considerable improvement over the baseline in all three prediction tasks,
showing the significant role that the factors that we uncover play, in
determining replying behavior. We rank these factors based on their predictive
power. Our findings have important implications for understanding human
behavior and designing better email management applications for tasks like
ranking unread emails.Comment: 11 page, 24th International World Wide Web Conferenc
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