=Paper=
{{Paper
|id=None
|storemode=property
|title=Designing Social Mobile Interfaces: Experiences with MobiMood, a Mobile Mood Sharing Application
|pdfUrl=https://ceur-ws.org/Vol-565/paper8.pdf
|volume=Vol-565
}}
==Designing Social Mobile Interfaces: Experiences with MobiMood, a Mobile Mood Sharing Application==
https://ceur-ws.org/Vol-565/paper8.pdf
Designing social mobile interfaces: experiences with
MobiMood, a mobile mood sharing application
Karen Church Eve Hoggan Nuria Oliver
Telefonica Research Dept. of Computer Science Telefonica Research
Via Augusta 177, 08021 University of Glasgow Via Augusta 177, 08021
Barcelona, Spain Glasgow, G12 8QQ Barcelona, Spain
karen@tid.es eve@dcs.gla.ac.uk nuriao@tid.es
ABSTRACT A rich body of research has been generated to explore the
The exploration of mood and emotions in HCI is emerg- challenges involved in establishing, monitoring and com-
ing as an important field of research. Our moods are af- municating individuals moods with physiological sensing,
fected by many different factors and can change multiple mood blogs and diaries, all offering a range of trade offs
times throughout each day. Furthermore, our moods can in terms of validity, accuracy and privacy [4], [2] [12], [13].
have significant implications on our social interactions and More recently, a variety of mobile services and applications
our willingness to interact with others. We have developed have emerged that help users communicate status updates in
MobiMood, a novel proof-of-concept social mobile applica- the form of a short textual messages [7]. These status mes-
tion that enables groups of friends to share their moods with sages often convey mood information, e.g. “I feel fine”.
each other. In this paper, we present some high level results
of an exploratory field study of MobiMood and highlight key Current mobile devices include a range of sensors that allow
implications in the design of future social mobile interfaces. monitoring of contextual factors such as location, time and
activity of users. Given that mobile phones are personal de-
Author Keywords vices, always on and always with us, they provide a great
Moods, mobile interfaces, social interfaces, context, visual- opportunity to capture mood information and gather feed-
ization, mobile interaction, field study back from users anytime, anywhere, in addition to serving
as an almost constant communication channel to friends
ACM Classification Keywords
and family. Mobile users may also be interested in sharing
moods with each other and may actually be able to positively
H.5.2 Information interfaces and presentation (e.g., HCI):
impact each others moods.
User Interface, H.3.3 Information Storage and Retrieval: In-
formation Search and Retrieval.
However, mobile environments present a number of key chal-
lenges. Mobile devices are inherently limited; their small
General Terms screens and restricted input and interaction capabilities present
Design, Experimentation, Human Factors a number of usability and interface challenges. Mobile users
are on-the-move and as such their needs are affected by chang-
INTRODUCTION ing contexts, e.g. location, temporal, social. Designing so-
The exploration of mood and emotions in HCI is emerging cial interfaces is also difficult. Social applications aim to in-
as an important field of research. Our moods are affected crease communication, collaboration and awareness among
by many different factors and can change multiple times groups. As such social media tends to be awkward because
throughout each day. Sharing our moods and learning about social interfaces are designed to be used by multiple users
the moods and emotions of others is a common theme in and are dynamic in nature.
conversations among friends, thus highlighting the extent to
which mood plays an important role in our daily life [1]. MobiMood1 is a social mobile application, combining both
Furthermore, our mood can have significant implications on the social and mobile spaces. MobiMood allows users to
our social interactions. For example, it has been shown that submit and share moods and their associated mobile con-
when we are happy we are more inclined to interact with texts with friends and others while on-the-move. It supports
others, while when we are sad we tend to distance ourselves sharing moods in a similar way to microblogging services
from friends and family [8]. such as twitter2 , which allows status updates to be submitted
and shared easily and quickly in the form of short snippets
of text, however, mood input via MobiMood is much more
visual in nature. In this paper we describe the MobiMood
prototype and provide a high level overview of the results
1
Workshop on Visual Interfaces to the Social
Note that the MobiMood prototype was developed while Eve
and Semantic Web (VISSW2010), IUI2010,
Hoggan was an intern in Telefonica Research, Barcelona, Spain.
2
Feb 7, 2010, Hong Kong, China. www.twitter.com
Copyright is held by the author/owner(s).
1
obtained from a 2-week user study involving 5 different so- There has also been some previous research with a focus
cial groups. Using the lessons learned during the design and on using mood detection in health applications. The Mo-
evaluation of MobiMood in the wild, we highlight key im- bile Heart Health project by Intel [9] also uses physiological
plications in the design of future social mobile interfaces. sensing to detect moods through the Mood Phone applica-
tion and then combines this with mobile feedback for pre-
ventative cardiology. The goal of the project was to enable
RELATED WORK users to become more self-aware of their emotions and feel-
In this section we highlight two areas of related work to the ings so they may develop better coping mechanisms when
MobiMood project. The first related to mood tracking and they encounter stressful situations.
mood detection, while the second relates area focuses on vi-
sualization of mood data. These applications attempt to provide accurate measurements
of moods and some have made use of this information to
Mood tracking and detection promote awareness of the emotional state of user. However,
The earliest research projects focusing on moods originated they have not examined the impact of contextual factors such
in psychology and investigated methods of mood detection as location and social situations on the moods of users nor
[10]. For example, there have been several studies investi- have they investigated the effects of sharing moods on users
gating the discovery of moods in blog posts, analysing large and their friends or the best way to present this information.
amounts of text to capture national responses to news or
sporting events, e.g. [3]. This topic has generated a lot of in- Visualizing moods
terest online recently3 with websites such as We Feel Fine4 . Also related to this work is the visualization of moods. This
The We Feel Fine project scans blog posts for the phrases “I is an interesting research area, and a particularly challeng-
feel” and “I am feeling” periodically. The project has been ing one for mobile environments where screen real-estate
running since 2005 and over 12 million feelings have been and interaction capabilities are limited. The We Feel Fine
collected to date. project mentioned in the previous section includes dynamic
visualizations that attempt to convey the collective mood of
Wanner et al. [14] explores the use of sentiment analysis a variety of users around the globe. These playful and inter-
with visualization to extract and convey the emotional con- active visualizations include a montage of pictures submit-
tent of RSS news feeds from the 2008 US presidential elec- ted, detailed metrics by feeling, gender, age, location and
tion. Others have applied machine learning to interpret the weather, as well as a visualization called mobs in which
emotions of bloggers [6]. Such approaches which attempt to colours, shapes and sizes are used to convey mood and inten-
extract mood data from short pieces of text may eventually sity. The We Feel Fine project also provides an API designed
enable the development of innovative applications that auto- to allow artists to access and explore these human emotions.
matically detect emotion and adapt to changes in users emo-
tions. However, these approaches are limited in that they can Similarly the MoodJam project6 from the Human Computer
only detect moods in text if bloggers use certain key phrases Interaction Institute at Carnegie Mellon University provides
or terms to convey their moods. a visualization of moods and other peoples moods based on
color strips. The website allows users to keep a record of
Several mobile applications are already commercially avail- their moods, to learn about mood trends and to share moods
able that use mood information obtained through manual with others. The goal of the project is to increase mood
user input. In particular, Nokia developed the ContextWatcher awareness among users and groups.
application [5] that associates mood information to context.
The My-Mood5 iPhone/iPod Touch application allows users Using a more abstract approach, Boehner et al. [1] devel-
to share their mood on the web with one of its unusual char- oped Miro, a prototype system that projected a representa-
acters. Users can set their mood and publish it online to blog tion of the overall emotion of workers based on responses
or other webpage. to an online emotional survey through an animated abstract
painting. Changes to the background colour, the number and
Other methods of mood detection include physiological mea- cluster of dots in the painting, and the speed of animation re-
surements such as heart rate, temperature, blood pressure flected averages of the survey answers (e.g. happy or sad).
and even posture. Gluhak et al. [4] used physiological sig-
nals to determine the moods of users and then proposed us- Although not incorporated in the current MobiMood proto-
ing mood as an additional element of context much like lo- type, the goal would be to provide interesting visual feed-
cation or time for instant messaging style applications. In back to the end user regarding their past mood histories, the
their application, physiological measures are displayed be- moods of their friends and the moods of their group. We will
side messages so friends know how the user was feeling at return to this topic at a later stage in this paper.
the time of the message.
3
Mining the web for feelings: MOBIMOOD
http://www.nytimes.com/2009/08/24/technology/internet/24 MobiMood is a proof of concept mobile prototype that en-
emotion.html? r=1&th=&emc=th&pagewanted=all ables groups of friends to record and share their moods with
4
http://www.wefeelfine.org
5 6
http://video.yahoo.com/watch/3981791/10793640 http://moodjam.org/
2
Figure 1. Screenshots of the MobiMood application.
each other while on-the-move, thus increasing awareness bination of these two dimensions, i.e. varying degrees of
within social groups. MobiMood is a visually rich and highly both valence and arousal. We chose a set of standard moods
interactive iPhone application. The software architecture of from each of these dimensions. To increase the visual aspect
MobiMood consists of two components: (1) an iPhone appli- of the user interface, each mood category is associated with a
cation that allows users to record and share moods as well as different colour based on those established by Wexner: blue-
comment on the moods of others; (2) a server that synchro- sad, green-energetic, purple-tense, red-angry, yellow-happy
nizes and stores all mood details in the MobiMood database7 . [15]. We chose orange to represent the custom mood (see
Figure 1b).
The server feeds the mobile application with an up-to-date
list of all moods. The server also comprises an email and Given that the application is iPhone based, we decided to
SMS notification facility that informs members of the ap- focus heavily on visually rich touch-based input and inter-
propriate social network about new moods and new mood action to allow users convey the intensity of their mood at a
comments from friends. In addition, the server logs all the given point in time. To input a mood, the user presses one
interactions between the user and the GUI of the iPhone ap- of the buttons at the bottom of the screen and a bubble will
plication for off-line analysis of user behaviour. begin to grow in the rounded box in middle of the screen
(Figure 1a). The longer the user holds their finger on the
Mood Entry button, the bigger the bubble will grow. The size of the re-
When a user launches the MobiMood application, they are sulting bubble is mapped to the intensity of the mood (1 to
presented with a mood input screen. The mood input in- 10, where 1 is represents the lowest intensity and 10 repre-
terface shows six different coloured buttons at the bottom sents the highest intensity). The intensity is also displayed
of the screen, each representing a different mood. Users on a progress bar so that more absolute feedback is provided.
can choose from one of five standard moods (sad, energetic, For example, if the yellow button is pressed and held until
tense, happy and angry) or they can input their own custom the bubble grows to its largest size, the participant’s mood is
mood, e.g. ‘bored’, ‘very excited’, etc. The standard moods considered to be ‘happy’ with an intensity of 10.
are all derived from a subset of moods found in Russell’s
Circumplex of Affect [10] and XMPP [11]. Russell’s Cir- Contexts
cumplex is widely used in psychology and XMPP is a comon Once the user has selected his/her mood and intensity level,
standard used in Web based social applications. Both con- (s)he clicks ‘next’ and is taken to the context-input screen.
tain a similar set of moods. The Circumplex model of affect On the context-input screen users record their situational and
proposes that all affective states arise from two fundamental social contexts. The situational context allows us to deter-
neurophysiological systems: one relates to valence (a plea- mine more about the location of the user and is given by
sure - displeasure continuum) and the other relates to arousal selecting one of four options: at home, at work, commut-
or alertness. Each mood can be understood as a linear com- ing, other. The social context allows us to determine more
7
We use Apache for the server requirements of the application and about who the user is in the presence of when submitting
all data is stored in a MySQL database. a new mood. The social context is introduced by selecting
3
one of five pre-defined options: alone, partner, colleague, Group 1 2 3 4 5
family, other. Both contexts are inputting using the standard # Users 3 2 3 2 5
iPhone picker input. In addition, the device ID, the date and # Male 2 2 2 1 4
time and the physical location (in latitude/longitude form) # Female 1 0 1 1 1
of each user, are logged with each mood. Storing the phys- Country Scotland USA Italy Scotland Wales
ical location of each user allows us to map meanings to the
situational contexts. Table 1. MobiMood participants: showing number of users per group,
ratio of males to females and country of origin.
Mood Lists
After submitting a mood, a final screen is presented that lists demographic information, details about their use of online
the last thirty moods of their friends. The mood lists shows social network sites as well as information about their moods
the name of the user who submitted the mood, the mood in and the factors that contribute to their moods. The live field
both textual and colour format, the date and time and the study took place over two weeks in August 2009. During the
situational context. By using the tabs at the bottom of the study, we collected a series of log data for post-task analysis
screen, the user can also view their own previous moods (My which included: listings of the calls and SMS sent between
Moods tab) or the moods of everyone else (Everyone tab) participants8 , the time, location, type and intensity of each
(see Figure 1c). Users can view the details of any of the submitted mood (participants could choose to omit their lo-
listed moods by double-tapping on the mood in question. cation data), what moods were viewed, comments on moods
as well as whom the participants were with at the time of
Supported Interactions mood entry. Finally, participants were asked to complete a
This mood detail screen (Figure 1d) provides more com- post-study survey to gather subjective information on their
plete details about the users mood. The detail screen lists experiences with the application.
the user’s name, the mood, the date and time submitted, the
intensity of that mood as well as any comments submitted In an effort to maintain the motivation amongst participants,
by the users friends about the mood. This screen also in- the ability to view moods was restricted. MobiMood only
cludes 3 buttons that allow a user’s friend to interact with allows users to view other moods and associated comments
the mood entry. Specifically the screen includes a button to once they have submitted a mood. There is no other way
add a comment, a button which initiates a phone call to the to access this information. Furthermore, participants had to
user in question and a button which initiates an SMS to the submit at least 15 moods before being able to see the sub-
user in question. mitted moods and comments of everyone in the study. Prior
to this, participants could only view and comment on their
USER STUDY own moods and those of their friends. At the end of study,
In this paper we are interested in investigating the lessons we we provided two visualizations of the submitted moods to all
learned in terms of designing social mobile interfaces based participants: one visualization at the group level and a sec-
on user experiences with the MobiMood prototype. As such ond visualization showing the moods of all 15 participants
we provide an high-level overview of the user evaluation we over the 2-week period.
carried out and describe some basic usage results. Later we
discuss more details of the user study in relation to implica- We used email and SMS notifications to keep users informed
tions for the design of social mobile interfaces. of the interactions of others within the study. Whenever
a participant submitted a new mood, all of the participants
Participants and Procedure friends received an email notification. When a comment was
In order to take part in the user study participants were re- added to a particular mood, the participant who originally
quired to own an iPhone and be part of a group willing to par- entered that mood received an SMS to inform her of the new
ticipate in a 2-week study where they input and share their comment.
moods with friends. We chose to study the use of MobiMood
in five close-knit groups who lived, worked and studied in RESULTS
different countries around the world. In total 15 participants In this section we present some high level usage results from
took part in the study (11 male and 4 female), ranging in age the field study.
between 23 and 43 years (avg=28.6). The participants had
a diverse set of occupations, including a journalist, solici- Basic Results
tors, teacher, IT professionals and students. The participants In total, participants submitted 311 moods over the 2-week
were given a small incentive of £20 for taking part and a period. 112 were standard moods (36%), while 176 were
£200 raffle was held at the end of the study and given to one custom mood entries (56.6%)9 . Figure 2 shows the distribu-
participant. Table 1 shows more details about each of the tion of mood entries per type.
five groups: number of users per group, the ratio of males to 8
females and the country of origin for each group. Note that we could only count calls and SMS message initiated
from within the MobiMood application
9
The type of the remaining 23 mood entries (i.e. standard or cus-
Before the field test began, users were asked to complete a tom) is unknown. The type is unknown for mood entries where the
pre-study questionnaire and to install the MobiMood appli- user did not want the application to track their location. We will
cation. The pre-study questionnaire was used to gather basic discuss this later in the paper.
4
Figure 2. Distribution of moods across custom and standard mood en-
tries. Figure 3. Number of moods submitted per user per group.
The most common standard mood was “happy” and the least moods submitted via the MobiMood application, this is diffi-
common was “sad”, suggesting that users tend to share their cult to evaluate fully. For example, when the majority of par-
positive emotions more easily than their negative emotions. ticipants recorded their location as “at work”, energetic, sad
In future work, we plan to investigate why users are ready or and angry levels were considerably lower (1.6% of moods at
not to communicate their moods with a view to understand- work were energetic, sad and angry), while “tense” (9.8%)
ing how users might be persuaded to share their mood even was chosen more often. Whereas when location was set to
when they are sad. “home”, energetic levels were higher.
Of the custom moods, 103 (58.5%) were unique custom mood According to the answers from the post study questionnaire,
entries, indicating that diverse sets of custom moods were users accessed the MobiMood application at home because
submitted by participants. Interestingly, we found 3 types there were often “at a loose end” and had more time to inter-
of custom mood entries: (1) basic moods, e.g. “tired”, “ex- act with their device.
cited” and “rushed”. 77% of custom moods fell into this
category; (2) status update moods (16% of the custom moods), Location Context # Mood %
e.g. “hating busy hot slow bus in the rain” and “looking At home 130 41.6
forward to yoga”; and (3) combination moods, made up of At work 56 18.3
a combination of a basic mood followed by a description of Commuting 45 14.4
why the participant was in a particular mood: e.g., “grumpy Other Location 57 18.3
(about going back to work)”, “glad, Monday done”, and
“bored (of big brother!)”. 7% of custom moods fell into Table 2. Number and percentage of moods per location context.
this category.
Table 3 shows the distribution of social contexts associated
We found an average of 20.1 moods per participant and an with mood entries. We found that users submitted most of
average of 62.2 moods per group. Figure 3 shows the num- their mood entries when they were alone (35.5%). Interest-
ber of moods submitted per user per group. We can see that ingly, there were absolutely no recorded sad moods when
even groups with only 2 participants (e.g. group 4) generate participants were with friends and no angry moods submit-
a high number of mood entries (> 70 moods submitted). ted with family. Furthermore, a considerably higher num-
ber of custom moods were submitted when participants were
Context and Mood alone (49% of custom moods).
We explored two different types of context in the MobiMood
study: (1) location and (2) social context. Note that the ma- When we asked participants if the social context helped them
jority of previous research into moods has been static, i.e. understand more about their friends’ moods, only 7 (46.7%)
it was not conducted using mobile devices as the primary answered yes. Of the users who said no, some of these users
recording mechanism. By conducting this research in a mo- reported not noticing the social context labels as one reason.
bile setting, it has been possible to log users moods at differ- This is perhaps due to poor interface design choices in repre-
ent locations and in different social settings. senting and capturing context information. Overall, based on
user responses, it appears that social context has less of an
Table 2 shows the distribution of location contexts associ- effect than location context in terms of understanding why
ated with mood entries. The most popular location context someone is in a particular mood.
chosen by participants was “home”, with 41.6% of moods
submitted at this location. Our preliminary results suggest Self-awareness of Moods
that location can affect the types of mood experienced by Another interesting issue in this research is that of self aware-
users, however, given the volume and diversity of “custom” ness. That is, what is the effect on the users’ moods if we
5
(a) Visualization
of the average
moods of
everyone in the
study after 2
weeks
(b) Visualization
of the average
moods of
participants of
Group 2 in the
study after 2
weeksk
(c) Visualization
of the average
moods of
participants of
Group 3 in the
study after 2
weeks
Figure 4. Sample visualizations provided to participants at the end of the two week study. (a) represents the global visualization, that is across all
users while (b) and (c) are group level visualizations for groups 2 and 3 respectively.
6
Social Context # Mood % Give users control
Alone 111 35.5 As mentioned earlier, a large proportion of mood entries
With friends 38 12.8 were “custom” moods (almost 60%). The participants ten-
With family 34 10.9 dency to express their moods through the “custom” option
With partner 30 9.6 suggests a need to express more information and to pro-
With colleagues 54 17.6 vide a more in-depth insight into the participants submitted
Other social context 21 6.7 mood. In terms of expressing moods, the post study ques-
tionnaire revealed that participants would have liked to have
Table 3. Number and percentage of moods submitted at various social more control over mood categories. In the words of one par-
contexts. ticipant: “I found this a bit limiting”. Therefore when de-
signing social mobile interfaces it’s important to provide end
provide them with tools to visualize their moods and enable users with enough control so that they can interact with and
users to become more self-aware of their moods? One par- use the application in the way they wish without them caus-
ticipant said: “I think that we often dismiss our state of mind ing disruption to other users of the application.
or our state of being because we’re so used to it. Like not
being able to see the wood for the trees. Having an objective Before deploying the MobiMood user study we made the
device track moods might make for some interesting and un- conscious design decision to not allow users to view the
expected revelation and patterns that can ultimately be used moods of others’ before submitting their own moods. One
as a foundation for improved health and happiness.” of the issues with evaluating social applications is that often
they need to reach some mass level of usage before the sys-
Ideally the MobiMood application would have included sup- tem can be useful to users. Our study was relatively short
port for providing end-users with real-time, interactive vi- and we wanted to ensure that enough mood entries were col-
sualizations of their moods and the moods of their group. lected and shared. Given the natural imbalance that exists
However, this feature is not implemented in the current pro- between producers and consumers of content within social
totype. However, at the end of the study we did provide each systems its unlikely that these behaviours could emerge so
participant with two mood visualizations. One visualization quickly/naturally in a short time-frame. However, users did
for their group and a second visualization of all users across feel this was too restrictive and in the deployment of a real
the 2-week period. Figure 4 (a) shows this global visual- prototype, we would remove this constraint so that users
ization, while Figure 4 (b) and (c) shows the group-based have more control over the interaction and flow within the
visualizations for group 2 and group 3 respectively. When application.
asked how they felt about the visualization, 13 participants
agreed that it was useful. Users also wanted to control the visual elements of the ap-
plication. For example, one user commented on the colours
Some of the comments regarding the visualization included: associated with the custom mood category: “you could be
“It was cool to compare it to the public one. Also interest- able to pick the colour of the other mood rather than it being
ing to see that we all used the custom mood the most”, “I the same colour all the time as most of the time you want
think that this showed me that the notion of mood becomes to personalise your own mood and when you look at the re-
complex when you try to think about it. Its hard to distin- sults of friends/everyone it’s mainly in orange”, while an-
guish between mood and ’current status’.”, “I liked the look other user suggested being able to submit combinations of
of it, and it’d be fun to see how you fit in.”, “It was a really moods, “Allow users to select combinations of more generic
nice report and quite interesting to read” and “I seen that moods. Remember often used custom moods and make these
the moods most used was custom and that suggested me that easier to enter, with their own random colour.”.
everyone likes to tell precisely how feels himself”.
One approach in future versions of MobiMood is to increase
Users did express some issues with the provided visualiza- the visual elements of the interface. We need to support more
tions that may have prevented them in getting as much value than 5 pre-defined moods, to allow users to submit a combi-
from the visualization as possible. We will return to this is- nation of moods and to enable users to include a description
sue in the next section on design implications. with each mood to enable users to explain their choice of
mood. From a user interface and interaction design perspec-
tive, this presents a number of challenges, especially given
DESIGN IMPLICATIONS the limited screen real-estate and interaction capabilities pro-
Designing social interfaces is a difficult task and when social vided by mobile handsets.
interfaces are deployed in a mobile setting the environment
becomes even more challenging. Our 2 week evaluation of
the MobiMood prototype has taught us a number of impor- Increasing self-awareness through better visualization
tant lessons when it comes to designing mobile social inter- We mentioned previously that at the end of the two week
faces. In this section we outline a number of key implica- study users were presented with two visualizations to convey
tions and discussion points in the design of such interfaces both the average moods of all participants and the average
based on the set of results presented in this paper. We try moods of people within their group over the 2-week study
to focus in particular on the lessons that relate to interfaces period. Most users enjoyed this visualization, however, it is
and/or visualizations. also clear that participants would have liked additional in-
7
formation within the visualization. MobiMood is a novel of the lessons we have learned. We plan to carry out a lon-
mobile application and as such provides support for captur- gitudinal field study involving more participants and more
ing contextual data such as physical latitude/longitude, sit- groups of friends. We would like to explore the social con-
uational context and social context. This information was text of moods in more detail and investigate how to facili-
not conveyed in the provided visualizations but it appears tate more fruitful communications and conversations among
that some users would have liked to see their mood his- users. We also believe there is interesting work to be done in
tories based on these dimensions. For example, one user the area of visualising moods over time and providing such
commented: “I thought it was interesting, but I was look- visualizations to mobile users to make them more aware of
ing forward to seeing how location and company affected their mood.
the moods, so I thought that ultimately, the visualisations
were quite superficial and contextless.”, while another user REFERENCES
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