An In-Situ Study of Mobile Phone Notifications (MobileHCI ’14)

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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Large-Scale Evaluation of Call-Availability Prediction (UbiComp ’14)

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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The fallacy of WhatsApp’s “last seen” status

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?! (CHI ’14)

“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 at CHI ’14

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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Open CSV files by double click with Excel 2011 (OS X)

Excel can open and interpret CSV files on double click.

However, for some locales it may happen that all the content appears in the first row.

This is how I fixed it.

Clean up Preferred Languages

In my case, I am using Excel 2011 for OS X 10.9. The CSV was not interpreted correctly, because German appeared in the Preferred languages of the Language & Region settings.

The simple fix was to remove all preferred languages except from English (United States) so that it would become the primary language, and then re-add the other languages.

After that, Excel 2011 flawlessly opened a comma-separated CSV file on double click and distributed the values correctly over the rows.


German, and other languages, use the comma instead of the dot as decimal separator (e.g. 123,45 instead of 123.45). Thus, Excel on German machines expects semicolons to separate values in a CSV file. The fix above will help to work with CSV files in international environments.

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Ambient Timer – Unobtrusively Reminding Users of Upcoming Tasks with Ambient Light

Alice is working on a report for the head of her department. At the same time, there is a meeting scheduled in thirty minutes, which she has to attend. The Ambient Timer is already illuminating the wall behind her monitor in a low-attention state, so Alice feels confident that she will be reminded of the meeting. A few minutes before the meeting, the status of the ambient light display has changed to a more salient, intense output. While she is still working on her report, she slowly becomes aware of the nearing deadline and starts finishing the paragraph she is currently working on. One minute before the meeting the light has become so salient that it is hard to ignore. Alice stores the document on the server, puts her computer into sleep mode, and arrives at the meeting on time.


A timer that uses ambient light

The Ambient Timer is a research prototype developed in the Interactive Systems Group of the OFFIS Institute for Information Technology. Its goal is to gently remind information workers about upcoming events. such as illustrated in the scenario above. It uses LED glued to the back of the monitor to illuminate the wall in the peripheral field of vision of the worker.

User Study

With this prototype, we conducted a user study in collaboration with the HCI and Mobile Computing Group of Telefónica Research. We experimentally studied two instances of the Ambient Timer:

  • expo: a gradual change from green to red, becoming exponentially faster, and
  • sinus: a sinusoidal change between red and green which became increasingly faster.

We compared these reminders against two traditional techniques to keep track of appointments:

  • a clock, such as the one in the corner of your computer screen, and
  • a popup alarm, such as when you use Outlook, Lotus Notes, or the OS X Calendar for your appointments.

For the study, we asked participants to copy and correct texts. Meanwhile, a 10-minute timer was running in the background. The task was to finish as many texts as possible in 10 minutes, but without “overshooting”, i.e. having an unfinished text after 10 minutes. In the expo, sinus, and clock conditions, the remaining time was presented by the Ambient Timer or a clock, respectively. In the popup condition, no time was given, but a popup informed the participants 30 seconds before the end of the time limit.

The experiment used a repeated-measures design, i.e. each participant tested each of the four reminder systems in counter-balanced order.


Our results show that participants experienced significantly fewer interruptions when using Ambient Timer in the expo condition, i.e. with an exponential change from green to red, compared to all other reminder techniques in our experiment. Their average typing speed was significantly faster when in this condition, too. Participants ranked this design best, felt most confident using it and preferred it over all other techniques.


This experiment shows that using light in the periphery around the monitor is a great way to provide information workers with information in an ambient way. Used as Ambient Reminder, ambient light might help to structure typical office work, which is often a mix of concentrated desktop work and scheduled meetings and appointments. It allows office worker to avoid to constantly check the clock or be interrupted by alarming popups interrupt.


The details of the experiment have been published in the 14th IFIP TC13 Conference on Human-Computer Interaction, held in September 2013 in Cape Town, South Africa:

Heiko Müller, Anastasia Kazakova, Martin Pielot, Wilko Heuten and Susanne Boll.
Ambient Reminder: Unobtrusively Reminding Users of Upcoming Tasks with Ambient Light.
INTERACT ’13: 14th IFIP TC13 Conference on Human-Computer Interaction, 2013.

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Peripheral Vibro-Tactile Displays

If you are sitting, which parts of your body are currently touching the chair? Are you leaning on a backrest or an armrest?

Think about it! Now, you are aware! But have you been just before reading this post? Probably not!

This is the beauty of peripheral perception, i.e. perceiving sensory input in the periphery of our attention. Your brain perfectly knows how to process the touch input it gets from the different parts of your body so that you do not fall of the chair. At the same time, you can perfectly focus on reading this text.

Using our Sense of Touch for Periperphal Communication

But, could this property of our sense of touch also be used to communicate information in the periphery of attention via mobile and ubiquitous computing devices? For example, imagine a bracelet indicating the time remaining until your next appointment, or your mobile phone indicating that there are no unread emails, messages, or social network updates to attend to.

Peripheral Vibro-Tactile Displays

In our research on peripheral, vibro-tactile displays, we made first investigations to prove that such information presentation could be possible with vibration motors, or vibro-tactile displays, as they can be commonly found in our mobile phones.

Study: Exposing People to a Constant Heartbeat

15 participants wore a vibro-tactile display in their pocket for 3 days. The display was set to create a constant, soothing, heartbeat-like vibration pattern. Via mobile phone, the participants adjusted the intensity, so that the vibration was barely perceptible.

Death Events: Testing Awareness

To test whether the vibration was still perceived, it died after 15 to 60 minutes. As soon as the participants noticed, they had to acknowledge the death of the vibration by pressing a button on a mobile phone. In the study, the majority of the death events were noticed between 1 and 10 minutes after the vibration had died. This is an indicator that participants were still aware about the vibration, even though it was set to very low intensities.

Testing Ambientness

To check whether the vibration had left the participants conscious perception, i.e. the focus of attention, we opened a questionnaire on the phone once the participants had pressed the button. In 67.7% of the cases, the participants indicated that the subjectively did not think that they had noticed quickly that the vibration had died. Additionally, in 94.4% of the cases, the participants reported to not be annoyed by the vibration. These two results indicate that the heartbeat vibration was indeed not in the focus, but in the periphery of attention.


These results provide first evidence that vibration patterns can form non-annoying, lightweight information displays, which can be consumed at the periphery of a user’s attention.

However, these findings are only first steps. We need more evidence to back up the findings, and we need more insights into how to adjust the intensity of the vibration pattern to different situations, so that we always hit the sweet spot of being just barely perceptible.


The details of this study will be presented at ACM MobileHCI ’13, the 15th International Conference on Human-Computer Interaction with Mobile Devices and Services, held in August 2013 in Munich, Germany.

Martin Pielot and Rodrigo de Oliveira.
Peripheral Vibro-Tactile Displays.
MobileHCI ’13: 15th International Conference on Human-Computer Interaction with Mobile Devices and Services, 2013.

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iPad 4 or iPad Mini?

Planning to buy an iPad?

If you are thinking to join the Apple world of tablet computers, you probably have already thought about whether it should be a Mini or a ‘regular’ iPad.

iPad4 and iPad Mini

In this article, I give some potentially helpful information for your choice: I present my personal experience owing both devices.

My Experiences

The iPad is regularly use are a 16GB iPad 4 with WiFi only, and an iPad Mini with16GB + 3G/GPS module. My analysis is therefore based on these devices.

First of all, the iPad Mini is indeed “every inch an iPad“. If you are using the iPad Mini, you won’t notices that anything is missing. All iPad apps run perfectly. Even Siri is available. And, surprisingly, despite the smaller screen, it never feels too small.

Handling is where the iPad Mini shines. Its reduced size and weight will come in handy in many situations:

  • You can hold it in one hand for a long time without getting tired. The iPad 4, in contrast, quickly starts feeling heavy, thanks to the bigger battery required for the retina display.
  • When writing notes, an email, or a message, the size factor clearly favours the iPad Mini. It’s much easier to hold it with two hands and use the two thumbs for writing – they will much easier reach those keys that are in the middle of the screen.
  • Because it’s lighter and smaller, you will more often find you taking it on your trips. Therefore, you can use it in more situations, which will make it more useful – in particular if you invest into a 3G / GPS upgrade.

Graphics is where the iPad 4 shines. It’s retina display provides an amazing viewing experience. The screen is far more crisp. Once your eye got used to the 264 pixels per inch (ppi), other displays start to look disappointing. Also, when running apps for the iPhone in double-size, the iPad 4 displays them much smoother than the iPad Mini.

Regarding processing speed, the iPad 4 features a faster processor. If you put both devices next to each other and start an activity on both devices at the same moment, you will see that the iPad 4 is a tick faster than the iPad Mini. However, during normal use you will never think that the iPad Mini is slow. The iPad 4’s A6X processor with quad-core graphics might come in handy for hardcore games, but I haven’t yet felt a significant performance difference.

The amazing display of the iPad 4 come with a caveat: it requires a lot of energy, which in consequence creates a lot of heat. You will find the iPad 4 becoming hot much more often than the iPad Mini.


The decision strongly depends on what is important for you. I would slightly favour the iPad Mini, since all-in-all it’s more useful due to its form factor. However, if your main use for the iPad will be the couch, the iPad 4’s retina display makes it the better choice.

Of course, these are all my personal experiences and opinions. For your specific case, other factors might be more important. Yet, The conclusion matches with the reviews appeared on, The Telegraph, and ZDNet.

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I Never get the Good One’s! How many Paper to Review?

I never get the good papers for review.

If this thought has ever crossed your mind, you are probably not alone. Good conferences in the HCI field typically accept only 20-25% of the papers submitted for review.

So how many papers to accept for review

… to review at least one good one?

It may seem obvious: the number should depend on the acceptance rate of the conference: 4 papers for 25%, 5 papers for 20%, 6 papers for 16.6%.

So, you are doing this already, and you still seem to get only the to-be-rejected ones?

This is, because probability computation does not always follow the intuitive approach.

Compute by rejection rate

The key is to compute by rejection rates and multiply them per paper.

If you review one paper from a conference with 20% acceptance rate, it’s likelihood to be rejected is 80%.
For two papers from the same conference, the likelihood that both are rejected is 80% * 80% = 64%. (not 60%, as what our intuition might tell us)

The row continues:

  • three papers = 51.2%
  • four papers = 41.0%
  • five papers = 32.8%

So, even if you review five papers in this conference, the likelihood is 32.8% that all of them will be rejected.
See the diagram below for different acceptance rates (25%, 20%, 15%).

Likelihood of Reviewing Accepted Paper by Acceptance Rate of a Conference
Likelihood of Reviewing Accepted Paper by Acceptance Rate of a Conference


So, how many papers to accept for review?

If you want to have a 20% 80% chance of reviewing at least one accepted paper, you have to accept the following number of papers for review:

  • 6 papers for a venue with a 25% acceptance rate
  • 7-8 papers for a venue with a 20% acceptance rate
  • 10 papers for a venue with a 15% acceptance rate


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