martin – Page 2 – Martin's Blog

How fast people expect responses to texts and messages

In February 2013, we did a survey across 44 mobile phone users asking two questions:

Think about the people you exchange the most messages with via your mobile phone:

On average, how fast do they typically respond to one of your messages?
On average, how fast do you typically respond to one of their messages?

The results are stunning:

64% of the respondents believe that people with whom they message the most typically respond to their messages immediately or within a few minutes. Only 9% expect responses after more than an hour.

68% of the respondents believe that they typically respond to people with whom they exchange a lot of messages immediately or within a few minutes. Only 6% typically respond after more than an hour.

These numbers are notable, because they reflect people’s expectations. If a friend typically responds immediately, it might feel strange when one day s/he doesn’t. Also, if oneself typically responds within minutes, one might start feeling anxious if circumstances prevent to respond to a message for hours.

In another study, the Do Not Disturb Challenge, where people disabled notifications across all devices for a day, we actually had instances where participants did not respond fast enough and friends got angry as a consequence.

Think about how drastic these expectations are: many activities, such as meetings, driving to work, attending classes, last a lot longer than a few minutes – and they require people’s full attention. Hence, people are faced with a choice: text during meetings or from behind the wheel, or violate expectations.

Share this:

The Do Not Disturb Challenge (CHI ’15)

Notifications are alerts intended to draw attention to new online content. Traditionally used in text messaging, email clients and desktop instant messengers, notifications are becoming used by all types of applications across all types of computing devices.

Today in 2015, we are still living in the ‘wild-west land-grab phase’ of notifications: more and more OSes introduce notification centers and more and more apps generate notifications. However, little is known about how the increasing number of notifications affect us.

Hence, in a collaboration between the Scientific Group of Telefonica R&D and Human-Computer Interaction Institute at Carnegie Mellon University, Luz Rello and I envisioned the Do Not Disturb Challenge. As part of challenge, participants disable notifications on their phones, tablets, and computers for a full day.

In December 2014, we rolled out a pilot of the Do Not Disturb Challenge with 12 participants. While participants reacted wildly different to the lack of notifications, for many, it was a strong experience.

The hugest impact was social. People have come to expect timely responses to their messages. Without notifications, many participants felt no longer able to meet these expectations. Some were informing others before the study that they would be less responsive, some kept constantly checking the phone.

At the same time, many participants noted that without the constant interruptions by notifications, they felt more focus, relaxed, and productive. Others realised that not all notifications are the same and deserve the same treatment. For example, many participants felt relieved by the absence of group-chat notifications.

Probably the main take-away so far is that people have very strong and polarized opinions towards (missing) notification alerts. The only consistent findings across the participants was that none of them would keep notifications disabled altogether. Notifications may affect people negatively, but they are essential: can’t live with them, can’t live without them.

The results will be presented at CHI ’15: the ACM Conference on Human Factors in Computing Systems (CHI) to be held from April 18 – 23 in Seoul, South Korea.

Martin Pielot and Luz Rello
The Do Not Disturb Challenge – A Day Without Notifications
CHI EA ’15: Extended abstracts on Human factors in Computing Systems, 2015.

Share this:

Correlations and Causation

Why Using Your Phone Less Won’t Necessarily Make You Healthier

“There is evidence that resisting the pull of your device can lead to healthier living.”

This is the conclusion of the article Trying to Live in the Moment (and Not on the Phone) from citing “a recent study by researchers at Kent State University found that students who were heavy cellphone users tended to report higher anxiety levels and dissatisfaction with life than their peers who used their phones less often.“

Does this mean you should throw your mobile phone out of the window right now to live a healthier life??

The answer is no.

What we are reading in this except from the article is a classic misinterpretation of causation and correlation.

Let’s assume the findings are universally true and students who use their cellphone a lot report higher anxiety levels and dissatisfaction with life, then there are three possible explanations:

As the article concludes, the use of cellphones indeed increases anxiety and dissatisfaction. In this case, use of cellphone is the cause and anxiety and dissatisfaction the effects.
However, it could as well be true that cause and effect are reversed: anxiety and dissatisfaction turn people into heavy cellphone users.
Finally, there is the possibility of a tertium quid, an unknown third factor that causes both. For example, people who find it more difficult to interact with others directly may prefer to use the phone, and at the same time be more anxious and dissatisfied with life.

Thus, using the phone less may not make anxiety and dissatisfaction disappear.

Share this:

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.

An In-Situ Study of Mobile Phone Notifications from Martin Pielot

Share this:

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.

Large-Scale Evaluation of Call-Availability Prediction from Martin Pielot

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.

Share this:

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.

Share this:

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.

Share this:

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).

Share this:

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.

Background

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.

Share this:

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.

Results

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.

Conclusions

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.

Publication

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.

Share this: