When Attention is not Scarce – Detecting Boredom from Mobile Phone Usage (UbiComp ’15)

[Looking for the app to recommend a URL of your choice when it thinks you are bored? Click here]

In times of information overload, attention has become a limiting factor in the way we consume information. Hence, researchers suggested to treat attention as a scarce resource coined the phrase attention economy. Given that attention is also what pays the bills of many free internet services through ads, some even speak of the Attention War. Soon, this war may start extending to our mobile devices, where already today, apps try to engage you through proactive push notifications.

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Yet, attention is not always scarce. When being bored, attention is abundant, and people often turn to their phones to kill time. So, wouldn’t it be great if more services sought your attention when you are bored and left you alone when you were busy?

Since mobile phones are often used to kill time, we — that’s Tilman Dinger from the hciLab of the University of Stuttgart, and Jose San Pedro Wandelmer, Nuria Oliver, and me from Telefonica’s scientific group — saw an opportunity in detecting those moments automatically. If phones knew when their users are killing time, maybe they could suggest them to make better use of the moment.

To identify, which usage patterns are indicative for boredom, we logged phone usage patterns of 54 volunteers for 2 weeks. At the same time, we asked them to frequently report how bored they felt. We found that patterns around the recency of communication activity, context, demographics, and phone usage intensity were related to boredom.

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These patterns allow us to create a model that predicts when a person is more bored than usual with an AUCROC of 74.5%. It achieves a precision of over 62%, when its sensitivity is tuned detecting 50% of the boredom episodes.

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While this is far from perfect, we proved its effectiveness in a follow-up study: we created an app (available on Google Play, more info here) that, at random times, created notifications, which suggest to read news articles.

buzzfeednotification

When predicted bored, the participants opened those articles in over 20% of the cases and kept reading the article for more than 30 seconds in 15% of the cases. In contrast, when they were not bored, they opened the article in only 8% of the cases and kept reading it for more than 30 seconds in only 4% of the cases.
buzzfeedresults
Statistical analysis shows that the predicting accounts for significant share of the observed increase.

While we certainly don’t feel that recommending Buzzfeed articles will be the cure peoples’ boredom, at least not for the majority of them, the study provides evidence that the prediction works.

Now how can mobile phones better serve users, when they can detect phases of boredom? We see four application scenarios:

  • Engage users with relevant contents to mitigate boredom,
  • Shield users from non-important interruptions when not bored,
  • Propose useful but not necessarily boredom-curing activities, such as clearing a backlog of To Do’s or revisiting vocabulary lists, and
  • Suggest to stop killing time with the phone and embrace boredom, as it is essential to creative processes and self-reflection.

Relatedly to this work, in a follow-up study, we also showed that mobile phones can predict the boredom proneness, the predisposition of experiencing boredom.

The work was presented in September 2015 at the ACM International Joint Conference on Pervasive and Ubiquitous Computing, taking place in Osaka, Japan, where it received best-paper award.

When Attention is not Scarce – Detecting Boredom from Mobile Phone Usage.
Martin Pielot, Tilman Dingler, Jose San Pedro, and Nuria Oliver
UbiComp’ 15: ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2015.

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Quantifying Attentiveness towards Mobile Messaging (MobileHCI ’15)

Social norm has it that people are expected to respond to mobile phone messages quickly. However, notifications may arrive to our mobile phones at any place and any time, which, depending on the concurrent activity, can be disruptive for the user. Hence, research has explored ways to reduce the chance of disrupting users by deferring the delivery of notifications until opportune moments.

nummsgs For most people, the majority of notifications come from messengers, such as WhatsApp or SMS. This type of communication goes along with high this type of communication social expectations. The majority of the people expect people with whom they frequently communicate to respond within a few minutes. Thus, deferral cannot be indefinite: it requires a bound, that is, a maximum delay before the notification is delivered, not matter how disrupting it might be.

But, what is the right bound?
Social expectations suggest a few minutes maximum. However, how likely is it that an opportune moment occurs within 5 minutes?

We collected evidence regarding these questions in our work I’ll be there for you: Quantifying Attentiveness towards Mobile Messaging.

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This diagram visualizes how attentive people where predicted to be on average for the different hours of the day.
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This diagram visualizes how attentive people where predicted to be on average for the different days of the week.

Over the course of two weeks, we collected more than 55,000 message notifications from 42 mobile phone users. On the basis of this data, we trained our previously described machine-learning model to predict attentiveness. This model uses sensor data from the phone to predict with close to 80% accuracy, whether a mobile phone user will attend to a message within 2 minutes or not.

We used this model to compute each participant’s predicted attentiveness for each minute of the study. In summary, our data shows that people are attentive to messages 12.1 hours of the day, attentiveness is higher during the week than on the weekend, and people are more attentive during the evening. When being inattentive, people return to attentive states within 1-5 minutes in the majority (75% quantile) of the cases.

Consequently, a bound of 5 minutes or less will ensure that bounded deferral strategies are likely to deliver messages in opportune moments, while reducing the likelihood to violate social expectations.

The results are presented at MobileHCI ’15: ACM International Conference on Human-Computer Interaction with Mobile Devices and Services.

Tilman Dingler and Martin Pielot
I’ll be there for you: Quantifying Attentiveness towards Mobile Messaging
MobileHCI ’15: ACM International Conference on Human-Computer Interaction with Mobile Devices and Services. 2015.

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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:

  1. On average, how fast do they typically respond to one of your messages?
  2. 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.

How fast do THEY respond

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.

How fast do YOU respond

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.

 

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The Do Not Disturb Challenge (CHI ’15)

Do Not Disturb Mode

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.

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