Category: Research

Notifications on mobile phones alert users about new messages, emails, social network updates, and other events. However, little is understood about the nature and effect of such notifications on the daily lives of mobile users. Hence, we conducted a one-week, in-situ study involving 15 mobile phones users, where we collected real-world notifications through a smartphone logging application alongside subjective perceptions of those notifications through an online diary.

In summary, we found that mobile phone users have to deal with a large volume of notifications, mostly from messengers and email, each day (63.5 on average per day), which was perceived as the usual. Notifications were largely checked within a few minutes of arrival, regardless of whether the phone was in silent mode or not. Notifications from messengers and social networks were checked fastest.

In particular in the case of personal communication, explanations for these fast reaction times related to high social expectations and the exchange of time-critical information.
Increasing numbers of notifications, in particular from email and social networks, correlated with negative emotions, such as stress and feeling overwhelmed. Personal communication, on the other hand, also related to increased feelings of being connected with others.

These findings highlight that strategies are needed to lower negative emotions. Reviewing previously explored approaches, our findings imply that reducing interruptions and deferring notifications may work in a professional context. For a personal context, strategies around communicating (un)availability and managing expectations appear more suited.

This research is described in detailed in the paper An In-Situ Study of Mobile Phone Notifications, which will be presented at the ACM SIGCHI Conference on Human-Computer Interaction with Mobile Devices and Services, held in Toronto, Canada in September 2014.

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Roughly 1/3 of all phones calls are not picked up. With this work, we explored whether the called phone can know in advance, whether its user is likely to pick up a call. This would allow to, amongst other things, communicate (non)availability in advance to the call or trigger intelligent muting.

This work shows that mobile phones can predict with an accuracy of 83.2% whether its user will accept an incoming phone call or not. When personalizing those models, the accuracy can be increased to 87%.Therefore, the phone needs to keep track of 15 features, such as the time since the last call, the day of the week, or the ringer mode. The 5 strongest predictors are:

(1) time since the last ringer mode change,
(2) time since the screen was last turned on or off,
(3) screen status (on/off),
(4) time since the phone was last (un)plugged, and
(5) time since the last call.

These findings show that it is possible to create an automated availability status for phone calls. Integrated into any phone call application, it could help to manage expectations by sharing the availability prediction with potential callers, and through that greatly impact the overall user experience. Further, knowing whether is a user is likely to take a call might be useful to intelligently allocate resources in a multi-device messenger environment.

To obtain the necessary data, we instrumented a previously-developed application called Silencer with anonymous data-collection facilities. During a two-month period, the app logged how 418 users reacted to 31311 phone calls. Alongside each call, the above mentioned 15 features were collected. Using a Random Forest, we computed the accuracy of a generic model, of personalized models with different numbers of calls (in average, 50 or more calls are needed to outperform the generic model – so it should be very quick to generate accurate personalized models), and to determine the prediction strength of each feature.

This research is described in detail in the paper Large-Scale Study of Call-Availability Prediction, which will be presented at the ACM International Joint Conference on Pervasive and Ubiquitous Computing, held in September 2014 in Seattle, USA.

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Last Seen = Fast Response?

When sending a message with WhatsApp, senders often check the receivers “last seen” status to judge whether the message will be read soon. It shows when the receiver had last openend the application.

“it gives me a timeframe and allows me to estimate when my message will be read”

Intuitively, if the receiver was online only recently, s/he is likely to be near the phone and see the message soon.

However, results from our recent study on predicting how fast people attend to message notifications indicates that “last seen” is almost as weak as a random guess.

How fast people view WhatsApp messages

We installed an app on the phones of 24 volunteers, which logged, amongst other things, each time that

• WhatsApp is opened or closed,
• a WhatsApp message is received, and
• the user sees the WhatsApp message, either in the notification drawer or in the app

For these volunteers, the median delay between receiving and seeing a WhatsApp was 7.81 minutes, i.e. half of the messages were viewed within 7.81 minutes and the other half later.

We used this time to split the data set into two parts: fast = seen within 7.81 min, slow = seen after 7.81 min. This means, a random guess whether a users sees then message fast or slow has a chance of 50% to be correct.

Not much better than random guess

Next, we used the log data to train a state-of-the-art machine-learning model. We checked how well “last seen” allows it to predict whether the message is seen fast or slow.

It turned out that the prediction was correct in 58.8% of the cases — only 8.8% better than the random guess.

Do not overly rely on “last seen”

Of course, this study has its limitations. The 24 volunteers were in their late twenties and early thirties. Other demographics might exhibit different behavior.

However, the results indicate that we should not overly rely on “last seen” when we want to estimate the availability of our friends.

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“Didn’t you see my message?!”

For the younger generations, not receiving a timely response to a SMS or message is a major source of irritation and frustration.

However, people cannot or do not want to always attend to their phones all the time.

What if your phone would infer these situations and communicate them to your friends?

Only how would the phone know?

Our research at Telefonica Research shows that these predictions can be done by simply monitoring a phone’s screen status (on/off), ringer mode, proximity sensor, the hour of the day, and when the user last visited the notification center.

In a user study, where we tried the system with 24 participants over 2 weeks, we learned that half of the messages are viewed within 6.15 minutes, and the other half after that.

A machine-learning model created on the basis of this data can predict with an accuracy of 70.6% whether a message will be viewed within 6 minutes or later. If the prediction is that the message is going to be viewed within those 6.15 minutes, it is even more conservative: the precision of the model is 81.2% in this case.

This research will be presented at the ACM CHI Conference on Human Factors in Computing Systems, held in Toronto, Canada in May 2014.

Martin Pielot, Rodrigo de Oliveira, Haewoon Kwak, Nuria Oliver
Didn’t You See My Message? Predicting Reactiveness in Mobile Instant Messaging
Proc. CHI ’14 Conference on Human Factors in Computing Systems, ACM, 2014.

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Telefonica Research will be represented with 2 full papers and 2 ToCHI articles at ACM CHI ’14, the premier international conference of Human-Computer Interaction.

Didn’t You See My Message?

Martin Pielot, Rodrigo de Oliveira, Haewoon Kwak, Nuria Oliver

We found that monitoring the phone (screen activity, notification center access, proximity sensor, ringer mode) allows to predict whether a person will attend to a received message fast or not (pdf).

A brief but more detailed description can be found in in more recent blog post.

Large-scale assessment of mobile notifications

Alireza Sahami Shirazi, Niels Henze, Martin Pielot, Dominik Weber, Albrecht Schmidt

As part of the study, we published an Android app on Google Play that forwards all phone notifications to the browser (via plugin). More than 40,000 people thought this was a brilliant idea and downloaded the app. We used the app as a vehicle to log and analyze all notifications that users receive (pdf).

A Large-scale Study of Daily Information Needs

Karen Church, Mauro Cherubini, Nuria Oliver

My colleagues have conducted one of the most comprehensive studies of information needs to date. For three months, they probed information needs via experience sampling and daily diaries, to understand “the types of needs that occur from day to day, how those needs are addressed and how contextual and demographic factors impact on those needs” (details on Karen’s website.)

Influence of Personality on Satisfaction with Mobile Phone Services

Rodrigo de Oliveira, Mauro Cherubini, Nuria Oliver

My colleagues connected the phone use habits of 603 volunteers with personality traits and customer satisfaction, and found that “(1) extroversion, conscientiousness, and intellect have a significant impact on customer satisfaction—positively for the first two traits and negatively for the latter; (2) extroversion positively influences mobile phone usage; and (3) extroversion and conscientiousness positively influence the users’ perceived usability of mobile services” (ACM Digital Library).

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