Mailing Archived Emails as Postcards: Probing the Value of Virtual
Collections
Physical and Digital Collections
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Gerritsen, David B.
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Tasse, Dan
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Olsen, Jennifer K.
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Vlahovic, Tatiana A.
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Gulotta, Rebecca
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Odom, William
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Wiese, Jason
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Zimmerman, John
Proceedings of the ACM CHI'16 Conference on Human Factors in Computing
Systems
2016-05-07
v.1
p.1187-1199
© Copyright 2016 ACM
Summary: People accumulate huge assortments of virtual possessions, but it is not yet
clear how systems and system designers can help people make meaning from these
large archives. Early research in HCI has suggested that people generally
appear to value their virtual things less than their material things, but
theory on material possessions does not entirely explain this difference. To
investigate if changes to the form and behavior of virtual things may surface
valued elements of a virtual archive, we designed a technology probe that
selected snippets from old emails and mailed them as physical postcards to
participating households. The probe uncovered features of emails that trigger
meaningful reflection, and how contextual information can help people engage in
reminiscence. Our study revealed insights about how materializing virtual
possessions influences factors shaping how people draw on, understand, and
value those possessions. We conclude with implication and strategies for aimed
at supporting people in having more meaningful interactions and experiences
with their virtual possessions.
Zensors: Adaptive, Rapidly Deployable, Human-Intelligent Sensor Feeds
Understanding Crowdwork in Many Domains
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Laput, Gierad
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Lasecki, Walter S.
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Wiese, Jason
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Xiao, Robert
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Bigham, Jeffrey P.
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Harrison, Chris
Proceedings of the ACM CHI'15 Conference on Human Factors in Computing
Systems
2015-04-18
v.1
p.1935-1944
© Copyright 2015 ACM
Summary: The promise of "smart" homes, workplaces, schools, and other environments
has long been championed. Unattractive, however, has been the cost to run wires
and install sensors. More critically, raw sensor data tends not to align with
the types of questions humans wish to ask, e.g., do I need to restock my
pantry? Although techniques like computer vision can answer some of these
questions, it requires significant effort to build and train appropriate
classifiers. Even then, these systems are often brittle, with limited ability
to handle new or unexpected situations, including being repositioned and
environmental changes (e.g., lighting, furniture, seasons). We propose Zensors,
a new sensing approach that fuses real-time human intelligence from online
crowd workers with automatic approaches to provide robust, adaptive, and
readily deployable intelligent sensors. With Zensors, users can go from
question to live sensor feed in less than 60 seconds. Through our API, Zensors
can enable a variety of rich end-user applications and moves us closer to the
vision of responsive, intelligent environments.
"You Never Call, You Never Write": Call and SMS Logs Do Not Always Indicate
Tie Strength
My Mobile, My Friends
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Wiese, Jason
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Min, Jun-Ki
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Hong, Jason I.
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Zimmerman, John
Proceedings of ACM CSCW 2015 Conference on Computer-Supported Cooperative
Work and Social Computing
2015-02-28
v.1
p.765-774
© Copyright 2015 ACM
Summary: How effective are call and SMS logs in modeling tie strength? Frequency and
duration of communication has long been cited as a major aspect of tie
strength. Intuitively, this makes sense: people communicate with those that
they feel close to. Highly cited research papers have pushed this idea further,
using communication as a direct proxy for tie strength. However, this
operationalization has not been validated. Our work evaluates this assumption.
We collected call and SMS logs and ground truth relationship data from 36
participants. Consistent with theory, we found that frequent or long-duration
communication likely indicates a strong tie. However, the use of call and SMS
logs produced many errors in separating strong and weak ties, suggesting this
approach is incomplete. Follow-up interviews indicate fundamental challenges
for inferring tie strength from communication logs.
Challenges and opportunities in data mining contact lists for inferring
relationships
Mobile-social
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Wiese, Jason
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Hong, Jason I.
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Zimmerman, John
Proceedings of the 2014 International Joint Conference on Pervasive and
Ubiquitous Computing
2014-09-13
v.1
p.643-647
© Copyright 2014 ACM
Summary: The smartphone contact list has the potential to be a valuable source of
data about personal relationships. To understand how we might data mine the
information that people store in their contact lists, we collected the contact
lists of 54 participants. Initially we found that the majority of contact list
features were unused. However, a further examination of the "name" field
revealed a broad variety of contact-naming behaviors. We observed contact
"name" fields that included affiliations, relationship role labels, multiple
names, phone types, and references to companies/services/places. People's
appropriation and usage of contact lists have implications for automated
attempts to merge or mine contact lists that assume people use the features and
structure of the contact list tool as intended. They also offer new
opportunities for data mining to better describe relationships between users
and their contacts.
Toss 'n' turn: smartphone as sleep and sleep quality detector
Activity recognition
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Min, Jun-Ki
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Doryab, Afsaneh
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Wiese, Jason
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Amini, Shahriyar
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Zimmerman, John
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Hong, Jason I.
Proceedings of ACM CHI 2014 Conference on Human Factors in Computing Systems
2014-04-26
v.1
p.477-486
© Copyright 2014 ACM
Summary: The rapid adoption of smartphones along with a growing habit for using these
devices as alarm clocks presents an opportunity to use this device as a sleep
detector. This adds value to UbiComp and personal informatics in terms of user
context and new performance data to collect and visualize, and it benefits
healthcare as sleep is correlated with many health issues. To assess this
opportunity, we collected one month of phone sensor and sleep diary entries
from 27 people who have a variety of sleep contexts. We used this data to
construct models that detect sleep and wake states, daily sleep quality, and
global sleep quality. Our system classifies sleep state with 93.06% accuracy,
daily sleep quality with 83.97% accuracy, and overall sleep quality with 81.48%
accuracy. Individual models performed better than generally trained models,
where the individual models require 3 days of ground truth data and 3 weeks of
ground truth data to perform well on detecting sleep and sleep quality,
respectively. Finally, the features of noise and movement were useful to infer
sleep quality.
Enabling an ecosystem of personal behavioral data
Adjunct 3: doctoral consortium/symposium submissions
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Wiese, Jason
Adjunct Proceedings of the 2013 ACM Symposium on User Interface Software and
Technology
2013-10-08
v.2
p.41-44
© Copyright 2013 ACM
Summary: Almost every computational system a person interacts with keeps a detailed
log of that person's behavior. The possibility of this data promises a breadth
of new service opportunities for improving people's lives through deep
personalization, tools to manage aspects of their personal wellbeing, and
services that support identity construction. However, the way that this data is
collected and managed today introduces several challenges that severely limit
the utility of this rich data.
This thesis maps out a computational ecosystem for personal behavioral data
through the design, implementation, and evaluation of Phenom, a web service
that factors out common activities in making inferences from personal
behavioral data. The primary benefits of Phenom include: a structured process
for aggregating and representing user data; support for developing models based
on personal behavioral data; and a unified API for accessing inferences made by
models within Phenom. To evaluate Phenom for ease of use and versatility, an
external set of developers will create example applications with it.
Phoneprioception: enabling mobile phones to infer where they are kept
Papers: mobile interaction
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Wiese, Jason
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Saponas, T. Scott
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Brush, A. J. Bernheim
Proceedings of ACM CHI 2013 Conference on Human Factors in Computing Systems
2013-04-27
v.1
p.2157-2166
© Copyright 2013 ACM
Summary: Enabling phones to infer whether they are currently in a pocket, purse or on
a table facilitates a range of new interactions from placement-dependent
notifications setting to preventing "pocket dialing". We collected data from
693 participants to understand where people keep their phone in different
contexts and why. Using this data, we identified three placement personas:
Single Place Pat, Consistent Casey, and All-over Alex. Based on these results,
we collected two weeks of labeled accelerometer data in-situ from 32
participants. We used this data to build models for inferring phone placement,
achieving an accuracy of approximately 85% for inferring whether the phone is
in an enclosed location and for inferring if the phone is on the user. Finally,
we prototyped a capacitive grid and a multispectral sensor and collected data
from 15 participants in a laboratory to understand the added value of these
sensors.
ZoomBoard: a diminutive qwerty soft keyboard using iterative zooming for
ultra-small devices
Papers: mobile text entry
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Oney, Stephen
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Harrison, Chris
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Ogan, Amy
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Wiese, Jason
Proceedings of ACM CHI 2013 Conference on Human Factors in Computing Systems
2013-04-27
v.1
p.2799-2802
© Copyright 2013 ACM
Summary: The proliferation of touchscreen devices has made soft keyboards a routine
part of life. However, ultra-small computing platforms like the Sony SmartWatch
and Apple iPod Nano lack a means of text entry. This limits their potential,
despite the fact they are quite capable computers. In this work, we present a
soft keyboard interaction technique called ZoomBoard that enables text entry on
ultra-small devices. Our approach uses iterative zooming to enlarge otherwise
impossibly tiny keys to comfortable size. We based our design on a QWERTY
layout, so that it is immediately familiar to users and leverages existing
skill. As the ultimate test, we ran a text entry experiment on a keyboard
measuring just 16 x 6mm -- smaller than a US penny. After eight practice
trials, users achieved an average of 9.3 words per minute, with accuracy
comparable to a full-sized physical keyboard. This compares favorably to
existing mobile text input methods.
Mining smartphone data to classify life-facets of social relationships
Mining social media data
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Min, Jun-Ki
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Wiese, Jason
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Hong, Jason I.
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Zimmerman, John
Proceedings of ACM CSCW'13 Conference on Computer-Supported Cooperative Work
2013-02-23
v.1
p.285-294
© Copyright 2013 ACM
Summary: People engage with many overlapping social networks and enact diverse social
roles across different facets of their lives. Unfortunately, many online social
networking services reduce most people's contacts to "friend". A richer
computational model of relationships would be useful for a number of
applications such as managing privacy settings and organizing communications.
In this paper, we take a step towards a richer computational model by using
call and text message logs from mobile phones to classifying contacts according
to life facet (family, work, and social). We extract various features such as
communication intensity, regularity, medium, and temporal tendency, and
classify the relationships using machine-learning techniques. Our experimental
results on 40 users showed that we could classify life facets with up to 90.5%
accuracy. The most relevant features include call duration, channel selection,
and time of day for the communication.
The post that wasn't: exploring self-censorship on Facebook
Understanding people's practices in social networks
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Sleeper, Manya
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Balebako, Rebecca
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Das, Sauvik
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McConahy, Amber Lynn
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Wiese, Jason
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Cranor, Lorrie Faith
Proceedings of ACM CSCW'13 Conference on Computer-Supported Cooperative Work
2013-02-23
v.1
p.793-802
© Copyright 2013 ACM
Summary: Social networking site users must decide what content to share and with
whom. Many social networks, including Facebook, provide tools that allow users
to selectively share content or block people from viewing content. However,
sometimes instead of targeting a particular audience, users will self-censor,
or choose not to share. We report the results from an 18-participant user study
designed to explore self-censorship behavior as well as the subset of unshared
content participants would have potentially shared if they could have
specifically targeted desired audiences. We asked participants to report all
content they thought about sharing but decided not to share on Facebook and
interviewed participants about why they made sharing decisions and with whom
they would have liked to have shared or not shared. Participants reported that
they would have shared approximately half the unshared content if they had been
able to exactly target their desired audiences.
Are you close with me? are you nearby?: investigating social groups,
closeness, and willingness to share
How close?
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Wiese, Jason
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Kelley, Patrick Gage
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Cranor, Lorrie Faith
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Dabbish, Laura
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Hong, Jason I.
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Zimmerman, John
Proceedings of the 2011 International Conference on Ubiquitous Computing
2011-09-17
p.197-206
© Copyright 2011 ACM
Summary: As ubiquitous computing becomes increasingly mobile and social, personal
information sharing will likely increase in frequency, the variety of friends
to share with, and range of information that can be shared. Past work has
identified that whom you share with is important for choosing whether or not to
share, but little work has explored which features of interpersonal
relationships influence sharing. We present the results of a study of 42
participants, who self-report aspects of their relationships with 70 of their
friends, including frequency of collocation and communication, closeness, and
social group. Participants rated their willingness to share in 21 different
scenarios based on information a UbiComp system could provide. Our findings
show that (a) self-reported closeness is the strongest indicator of willingness
to share, (b) individuals are more likely to share in scenarios with common
information (e.g. we are within one mile of each other) than other kinds of
scenarios (e.g. my location wherever I am), and (c) frequency of communication
predicts both closeness and willingness to share better than frequency of
collocation.
Beyond 'yesterday's tomorrow': towards the design of awareness technologies
for the contemporary worker
Work and security
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Wiese, Jason
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Biehl, Jacob T.
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Turner, Thea
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van Melle, William
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Girgensohn, Andreas
Proceedings of the 13th Conference on Human-computer interaction with mobile
devices and services
2011-08-30
p.455-464
© Copyright 2011 ACM
Summary: Modern office work practices increasingly breach traditional boundaries of
time and place, increasing breakdowns workers encounter when coordinating
interactions with colleagues. We conducted interviews with 12 workers and
identified key problems introduced by these practices. To address these
problems we developed myUnity, a fully functional platform enabling rich
workplace awareness and coordination. myUnity is one of the first integrated
platforms to span mobile and desktop environments, both in terms of access and
sensing. It uses multiple sources to report user location, availability, tasks,
and communication channels. A pilot field study of myUnity demonstrated the
significant value of pervasive access to workplace awareness and communication
facilities, as well as positive behavioral change in day-to-day communication
practices for most users. We present resulting insights about the utility of
awareness technology in flexible work environments.
I'm the mayor of my house: examining why people use foursquare -- a
social-driven location sharing application
Location sharing
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Lindqvist, Janne
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Cranshaw, Justin
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Wiese, Jason
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Hong, Jason
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Zimmerman, John
Proceedings of ACM CHI 2011 Conference on Human Factors in Computing Systems
2011-05-07
v.1
p.2409-2418
© Copyright 2011 ACM
Summary: There have been many location sharing systems developed over the past two
decades, and only recently have they started to be adopted by consumers. In
this paper, we present the results of three studies focusing on the foursquare
check-in system. We conducted interviews and two surveys to understand, both
qualitatively and quantitatively, how and why people use location sharing
applications, as well as how they manage their privacy. We also document
surprising uses of foursquare, and discuss implications for design of mobile
social services.
