Presentation of an experimental mobile application, which allows users to add different descriptions of context information to their Facebook status updates. The meaningfulness and the usage of different context descriptions were evaluated in a two-week user trial. The results show that the most frequently used awareness cues in the test setting were location, surroundings, friends and activity. The results also indicate that user-defined semantic abstractions of context items (e.g. “home”, “work”) were often more informative and useful than more accurate indicators (e.g. the address or the name of the place). We also found out that using shared context from friends in vicinity (e.g. identifying the people around) needs careful design to overcome the extended privacy implications.

1. ContextCapture: Exploring the Usage of
Context-based Awareness Cues in
Informal Information Sharing
Ville Antila, Jussi Polet, Minna Isomursu
VTT Technical Research Centre, Oulu
Ari-Heikki Sarjanoja, Petri Saarinen
Nokia Research Centre, Oulu & Tampere

2. Background – SmarcoS project
• Smarcos creates solutions to allow devices and services to
communicate in UI level, exchange context information, user
actions, and semantic data
• It allows applications to follow the user's actions, predict
needs and react appropriately to unexpected actions
• Partners from
– Netherlands, UK, Finland,
Belgium, Czech Rep., Italy
and Spain
www.smarcos-project.eu

3. Outline
1. Introduction
2. Research approach
3. ContextCapture –application
4. User study
5. Findings
6. Discussion
7. Conclusions
8. Lessons learned (application and user study)
– Demo @ UbiComp 2011, Beijing

4. Introduction
• Smartphones are equipped with sensors and
communication tools, which can provide a wide
range of awareness and presence information

5. Introduction
• Information from the physical world is increasingly
“digitalized” and shared
– Photos tagged, presence in IM, location check-ins, sports
tracking, informal awareness cues in Facebook and Twitter

6. Challenges
• Context information is often ambiguous or too low-
level to be meaningful (e.g. Raw sensor data, GPS
coordinates, or just free text)
– Also this information contains a lot of noise
• We propose to add (some) structure to the data
1. Provide abstracted, ”story-like” context data to social
networks (motivation for the user)
2. Gather structured data about these user-defined
abstractions in order to label context data (and
eventually learn from these associations to provide
better abstractions)

7. Research approach
• Approach:
1. We developed an experimental mobile application, which
allows users to add different types of contextual
information to their Facebook status updates in a format
of a “story” or a narrative of the situation
2. We developed a semantic database which links the
abstract, user-defined context labels to the low-level
sensor data
3. Conducted a two-week user trial exploring the
meaningfulness of different context types and the usage
of different abstraction levels

8. ContextCapture (1/6)
• Architecture
– a mobile application and a server-side application,
integrated with Facebook (and Twitter)

9. ContextCapture (2/6)
• Mobile application
– Symbian 5th Edition and
onwards (Qt), Android
2.2 and onwards
– Presents context
abstractions to the user
on a selectable list,
sorted by relevance

10. ContextCapture (3/6)
• Context recognition is based on different
sensors of activity, such as…
– accelerometer, ambient light detector, GPS data, open
applications on the device, the device system information
and nearby Wifi access points and Bluetooth devices
– for example:
• based on the accelerometer data, a decision is made
whether the user is moving or still by using movement
detection algorithms
• nearby Facebook friends can be detected using
Bluetooth scanning

11. ContextCapture (4/6)
• Context items used in ContextCapture
– Activity – physical activity of the user
– Applications – currently open applications
– Device – device information, such as the device type
– Friends – nearby Facebook friends using ContextCapture
– Location – abstrations using GPS, network and Wifi scan
data, current street address, cell ID
– Surroundings – abstractions of physical surroundings using
ambient light detector, weather etc.

12. (Example)
• Creating a message:
“*User-defined message]
Sent from [Location] while [Activity] [Description] [Topic] and
[Applications Activity] with [Friends+.”
• As an example, a status update message generated
with the previous rule could be:
“I think this is the killer app for ubicomp!
Sent from Conference Room 1 at UbiComp 2011, Beijing, China
while listening to an interesting presentation by Dr. Firstname
Lastname and using Notepad with 12 Facebook friends nearby.”

13. ContextCapture (5/6)
• “Collective” context is gathered from nearby
devices (running ContextCapture)
– If lacking, the mobile client can ask nearby devices for
additional context information, such as GPS coordinates,
address, weather etc.
– Bluetooth communication is used with a simple protocol
over RFCOMM
• Request:
CCRAControlProtocol:Client:ClientBluetoothNam
e:WTHR:Request
• Response:
CCRAControlProtocol:Server:ServerBluetoothNam
e:WTHR:-3 degrees Celsius,Sunny

14. ContextCapture (6/6)
• Server-side application (Facebook and
Twitter integrated)
– Context data is stored on the server in a semantic model
(RDF)
– Formatted status updates are aggregated to social media
(Facebook and Twitter)

15. User study
• 12 participants used ContextCapture for two
weeks using their own mobile phones in their
everyday lives
Research questions
Do users perceive an application supporting manual status
RQ1 updates through automatic context recognition and collective
context as useful or valuable?
What kind of abstraction levels (regarding the semantics) are
RQ2
understandable for the user?

16. The participants…
• …were between 30-46 years, 37.25 years on average, six males
and six females
• …used their own mobile devices and personal Facebook
accounts during the trial
• …were experienced Facebook users as 25% of them had used
the service 1-2 years and the rest for over two years

17. Trial setup
The participants…
1. …were emailed a short description of the study
– Purpose, a short manual, a link with installation instructions and a
link to the initial Web questionnaire
2. …used the application for two (2) weeks
– During that time, they could tell their experiences through a Web
diary (we asked them to fill in the diary at least five times)
3. …were interviewed at the end of the trial
– The interviews were semi-structured, including questions about the
users’ expectations, attitudes, privacy and the most pleasing and
unpleasing experiences related to the usage
– The participants also filled a Web questionnaire about their
experiences

18. Findings (1/3)
• Location was rated as the most useful context
field (average: 4.1/5.0)
– Status updates with location information were seen most
informative as people can use them to also reference their
current activities or point out features from the
environment
5 = Very useful
5.0
4.1
4.0
3.2
2.8 2.9
3.0
2.4 2.3
2.0
1.0
1 = Not useful at all
Locat ion Device Friends Applicat ions Act ivit y Surroundings

19. Findings (2/3)
• Weather information, which was related to
Surroundings field, was also seen highly
interesting
– The study was done only in Finland, so this might be a
“cultural characteristic”
• Application and Device were considered as the
least useful fields (average: 2.3/5.0 and 2.4/5.0)
– It seemed that many participants did not want to
“advertise” the device they were using;
– Open applications were often unrelated or uninteresting

20. Findings (3/3)
• The participants were clearly aware of their privacy
and had thought about it while using the application
– E.g. the participants did not use the addresses of their
homes or the kindergarten their children were, even
though the audience consisted of Facebook friends
– The accurate location of places was too sensitive to be
shared, many of the participants stated that the semantic
meaning of the place is enough
• E.g. stating “I’m at home” is adequate enough for the people the
message is meant for
– In many participants’ opinion sharing friends’ location
without permission is not acceptable, participants
preferred to use more abstract words, like “group of
friends”, instead of giving the exact names

21. Discussion
• Context information was seen as interesting and
useful addition, but the participants hoped that they
could have had even more control of the level of
abstraction (or more relevant suggestions)
• Also the abstract labels for context information were
preferred and used more often, such as “home”,
“work”, “kindergarten”
• The participants also preferred labels referring to
the type activity, place or event (e.g. “at the movie”
or “at the botanical garden”)

22. Conclusions
• The current location, activity and surroundings were
the most relevant context types (in this study)
• Disclosing the nearby friends or colleagues in the
status updates was seen as relevant but problematic
due to privacy issues
• The context types were seen as most meaningful
when the used abstraction level was high
– Participants felt that exact information, such as street
address or coordinates, conveyed a too matter-of-fact type
description
– Whereas more abstract descriptions, such as “at the movie
theatre” or “at the botanical garden” were seen as more
illustrative, interesting and meaningful

23. Lessons learned…
1. With applications dealing with privacy sensitive
information, the information disclosure and
privacy should be fully controlled by the user
2. By giving freedom for users to control the
disclosure and abstraction level of contextual
information, it creates:
– meaningfulness and motivation for the users
– and in the same time allows the system to gather a
set of user-defined context labels with different
abstraction levels (which can be associated with the
gathered low-level sensor data)

24. Demo @ UbiComp 2011
• ContextCapture was demonstrated
at UbiComp 2011 in Beijing, China
(demo + poster)
• The demo version was deployed
Social Media
including additional information:
– Indoor location using Bluetooth
ContextCapture
Server
Internet beacons (coupled with the
conference rooms)
User with
– Conference program (for suggesting
WLAN connection
Mobile
Phone
Bluetooth
Bluetooth
Beacon
ongoing talks)
connection

25. Demo @ UbiComp 2011

26. Demo @ UbiComp 2011
• Lessons learned from the demo
• Indoor location using Bluetooth beacons worked well
• Done using three Nokia N95 devices placed in the rooms with simple
software for configuring and providing the indoor location information
(e.g. ”Conference Room 1 at UbiComp 2011, Beijing, China”)
• Including specific
context information
about the event
enhanced the
meaningfulness of the
application (and was
actually useful!)
• We included items in the conference program (e.g. ”Talk by Patel et al.”)

27. Thank you!
Questions?
Ville Antila, ville.antila@vtt.fi
Jussi Polet, jussi.polet@vtt.fi