Are You Prone to be Bored? Your Phone Can Tell

This work is a follow-up project on our research on When Attention is not Scarce – Detecting Boredom from Mobile Phone Usage

People using their mobile phone in the metro to kill time.
People using their mobile phone in the metro to kill time.

We might think that technology has solved the problem of boredom. More and more devices provide us with an ample source of entertainment at our fingertips.

Paradoxically, today we appear to be more prone to boredom than ever before. The explanation might be that over time people habituate to an increasing exposure to stimuli such that, when the level of stimulation drops, they become bored.

In an extension on our study of detecting phases of boredom from mobile phone usage, in this study, we (Aleksandar Matic, Nuria Oliver, and me of the Scientific Group of Telefonica) explored to what extent technology use is intertwined with boredom proneness, and whether the level of boredom proneness can be inferred from it. We collected data on the accumulated daily mobile phone usage patterns of 22 volunteers, such as, the average number of apps started in a day or the variance of the amount of notifications received per day. Then, those participants filled our the standardized Boredom Proneness Scale.

We found that daily usage patterns can estimate whether the person is above-average prone to boredom with an accuracy of over 80%. Individuals with high boredom proneness were having more unstable daily phone usage patterns: they launched a higher number of apps per day, had strong peaks of social network activity, and turned on the phone a lot. However, surprisingly, the overall time of using the phone was not higher than for individuals with lower boredom proneness.

Boredom proneness is related to a number of negative outcomes, such as depression, drug & alcohol consumption, or anxiety. Obtaining boredom proneness in an unobtrusive, automatic way can, amongst other things, help in the adjustment of the treatments of such health issues.

The work was presented in September 2015 at the ACM International Joint Conference on Pervasive and Ubiquitous Computing, which took place in Osaka, Japan.

The details of the work are described in
Boredom-Computer Interaction: Boredom Proneness and The Use of Smartphone
Aleksandar Matic, Martin Pielot, Nuria Oliver
UbiComp’ 15: ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2015.

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

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.

bored

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.

precrecallfull

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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Boredom-Triggered Proactive Recommendations

The business model of many internet-service companies is primarily build around your attention: they offer best-in-class services for free in exchange for the users’ eyeballs, i.e. them paying attention to the contents of the services they offer. They pay for their expenses and generate revenue by selling the attracted attention to companies and individuals who’d like to promote their content.

One of the upcoming frontiers in this battle for the user’s attention are mobile devices. Engagement is now defined by push-driven notifications rather than the traditional pull-driven experience. Recommendations will become proactive and notifications will be one essential path to deliver them.

In this battle, we may be facing the tragedy of the commons: when individual companies behave rationally according to their self-interest by increasing their attempts to seek people’s attention, they behave contrary to the best interests of the whole group by depleting the attentional resources of the user and risk that people develop notification blindness (as an analogy to banner blindness).

Attention is not always scarce

However, attention is not always scarce. For example, when people are bored, attention is abundant, and people often turn to their phones to kill time.

In our recent research on When Attention is not Scarce – Detecting Boredom from Mobile Phone Usage, presented in September 2015 at the ACM International Joint Conference on Pervasive and Ubiquitous Computing, we showed that it is possible to detect phases of boredom from how people use their mobile phones. As part of the same research project, we showed that people are more likely to engage with suggested content when they are bored, as inferred by the detection algorithm.

Boredom-Triggered Proactive Recommendations

This finding opens the door to using boredom as a content-independent trigger for proactive recommendations. Assuming that proactive recommendations delivered via mobile phone notifications will become more common in the future, using boredom as trigger will benefit service providers as well as the end users:
End users will receive fewer recommendations that are triggered during times when they are busy. Service providers can use it to reduce the fraction of unsuccessful recommendations, which, for example, decreases the likelihood that users develop notification blindness towards proactive recommendations.

The results will be presented at the Workshop: Smarttention, Please! Intelligent Attention Management on Mobile Devices — Workshop @ MobileHCI ’15: ACM International Conference on Human-Computer Interaction with Mobile Devices and Services, 2015 to be held from Aug 24 – 27 in Copenhagen, Denmark.

Boredom-Triggered Proactive Recommendations.
Martin Pielot, Linas Baltrunas, and Nuria Oliver.
Smarttention, Please! Intelligent Attention Management on Mobile Devices — Workshop @ MobileHCI, 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.

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