When Attention is not Scarce – Detecting Boredom from Mobile Phone Usage

When Attention is not ScarceDetecting Boredom from Mobile Phone Usage

Research

UbiComp ‘15, Osaka, Japan

Martin Pielot

TelefonicaResearch

Tilman Dingler

University of Stuttgart

JoseSan Pedro

TelefonicaResearch

Nuria Oliver

TelefonicaResearch

times square night 2013. chensiyuan. Apr 16, 2013 via Wikipedia. CC BY-SA 4.0

War on

Attention*

* http://www.forbes.com/sites/onmarketing/2012/10/19/the-attention-war/

Revenue per active user$45 in Q1 2014 = 50 cents per day

SocialMediaCube. Yoel Ben-Avraham. Apr 8, 2013 via Flickr. CC BY-ND 2.0

The trade we make:Our attention so they can pay their bills

Our engagement is now defined by push-driven notifications rather than the traditional pull-driven experience. We’re “hunting and pecking” through our app grid a lot less; the apps that notify us (without over-notifying to the point of uninstall) are rewarded with our engagement (and our dollars).

Example: Push-Driven Notifications

‚Attention is a limited resource—a person has only so much of it ‘ [Matthew B. Crawford]

Attention Economy: treating human attention as a scarce commodity[Davenport and Beck, 2001]

times square night 2013. chensiyuan. Apr 16, 2013 via Wikipedia. CC BY-SA 4.0

Wild-West Land-Grab Phase

“Wild West Hotel, Calamity Av., Perry, 0. T., Sept. 93”. National Archives and Records Administration. Public Domain

Overload

“Ahhhhhhh” by Kenny Louie, Jun 06, 2010, via Flickr, CC BY 2.0

From “Banner Blindness: New and Old Findings” by Jakob Nielsen on August 20, 2007

Banner Blindness

Overload

“Ahhhhhhh” by Kenny Louie, Jun 06, 2010, via Flickr, CC BY 2.0

Notification blindness

Wild-West Land-Grab Phase

“Wild West Hotel, Calamity Av., Perry, 0. T., Sept. 93”. National Archives and Records Administration. Public Domain

If the tradeattention for free servicesis to be sustainedwe need to better protect mobile phone users

Boredom as part of the solution

Attention is not always scarce

Show them this photo if someone said technology … . Adam Rifkin. May 21, 2014 via Flickr. CC BY 2.0



Boredom displeasure caused by “lack of stimulation” [Fenichel, 1951]

“a bored person is not just someone who does not have anything to do; it’s someone who is actively looking for stimulation” [Eastwood, 2002]





Mobile phones are a commonly used tool to kill time when bored [Brown et al. 2014]


Mobile phones are a commonly used tool to fill or kill time when bored [Brown et al. 2014]




If phones knew when their users are killing time

maybe they could suggest them to make better use of the moment

How well can we detect boredom from mobile phone usage patterns?

Borapp – Sensor-Data Collection

Always collected

Only collected if phone in use

Experience Sampling“Right now, I feel bored” [5-point Likert scale]

Min. 6 times per dayPreferably triggered when phone in use

Borapp – Experience Sampling

Data Collection

54 Participantsaged 21 – 46 (M = 30.6) years11 female, 23male, 19 not disclosed

For two weeks in July 2014Over 40M sensor log entries4398 valid self-reports of boredom

Absolute ground truthBored: ratings 3, 4446 (10.1%) instances

Absolute ground truthBored: ratings 3, 4446 (10.1%) instances

Normalized ground truthZ-score per personBored: z > 0.251518 (34.5%) instances-2 -1 0 1 2

0

400

800

1200

1600

2000

Normalized Subjective Boredom, (higher number = more bored than

usual)

Freq

uenc

y

Category Example Feature Explanation

Context Semantic Location Home, work, other, unknown

Demographics Age, gender 38, female

Last Communication Activity

Time last incoming call Time passed since somebody called the participants

Usage (intensity) Bytes received Number of bytes downloaded in the last 5 minutes

Usage (externally triggered) Number of notifications Number of notifications received in the last 5 minutes

Usage (idling) Number of apps Number of apps launched in the last 5 minutes

Usage (type) Most used app App used for the most time in the last 5 minutes.

35 Features, 7 Categories

RQ1: how well can phones detect killing-time boredom events from these usage patterns?

RQ2: which usage patterns are related to killing time with the phone?

RQ3 is the model good enough to be useful?

normalized

absolute

0.0% 20.0% 40.0% 60.0% 80.0% 100.0%

74.6%

82.9%

Model Performance | Random Forest (AUCROC)

normalized

absolute

0.0% 20.0% 40.0% 60.0% 80.0% 100.0%

76.5%

82.5%

74.6%

82.9%

Model Performance (AUCROC) Including Boredom Proneness scores of 22 participants

normalized

absolute

0.0% 20.0% 40.0% 60.0% 80.0% 100.0%

76.5%

82.5%

74.6%

82.9%

Model Performance (AUCROC) Including Boredom Proneness scores of 22 participants

Primary data set

0% 20% 40% 60% 80% 100%0%

20%

40%

60%

80%

100%

34.7%42.8%

48.3%52.1%

56.6%62.4%

66.2%70.1%

74.3%76.3%

Recall

Precision

Precision: 70.1% for 30% recall,

62.4% for

50% recall

Boredom can be detected from phone-usage patterns with an accuracy of ca. 75% to 83% AUCROC

Take Away #1




Recency of communication activity i.e., time since last incoming or outgoing communication;


Phase of the dayi.e., hour of the day, ambient light



Demographics, i.e., gender and age;




General usage intensity i.e, phone out of pocket, or time since last phone use …;




General usage intensity i.e, phone out of pocket, or time since last phone use …;

Intensity of recent usage i.e. # of unlocks, or # of apps launched in last 5 minutes, …

Apps

Co-occur with being bored Co-occur with NOT bored

… and uncategorized apps

Boredom was related to Regency of communication Phase of the day Demographics Intensity and type of phone usage Type of used apps

Take Away #2




Borapp2

Model running on Mobile PhoneUsing primary data set with

Constantly predicts when user is bored on the fly

Suggest Reading Buzzfeed Articles

Data Collection

16 Participants (different from 1st study)aged 18 – 51(M = 39) years13 male, 2 female, rest did not disclose

For two weeks in Feb 2015941 Buzzfeed recommendations48% when predicted bored

Click-ratio

Fraction of times people clicked on notification (Mdn)

8% when not bored20.5% when bored(as inferred by the model)

Difference significantz = -2.102, p = .018

Large effectr = -.543

Engagement-ratio

Fraction of times people spent more than 30 sec reading (Mdn)

4% when not bored15% when bored(as inferred by the model)

Difference significantz = -2.102, p = .018

Large effectr = -.511

When predicted bored, participants were …

More likely to click More likely to read for > 30 seconds

The generic model was powerful enough to create significant, large effects on click- and engagement-ratios

Take Away #3

Application Scenarios


Recommend content to alleviate boredom

Shield user from non-important interruptions during non-bored times

Suggest useful but not necessarily boredom-curing activities

Encourage embracing boredom




Encourage embracing boredomX









Being bored is good for you

Why don’t you turn me off?





When Attention is not ScarceDetection Boredom from Mobile Phone Usage

Research

Contact: [email protected] | @martinpielot | UbiComp ‘15, Osaka, JapanNuria Oliver

Jose San Pedro

Tilman Dingler

Martin Pielot

MotivationIn general, attention is scarce, hence valuableThread of overload / notification blindnessHowever, boredom is defined is state of seeking stimuli

ContributionsA machine learning model to predict boredom from mobile phone usage patternsAn analysis of usage patterns related to boredomEvidence that people are more likely to engage with suggested content when bored

ApplicationEngage user with proactive recommendations – possibly to alleviate boredomShield from interruptions when not boredSuggest useful, but not necessarily boredom-curing activitiesEncourage to embrace boredom to foster creativity

mailto:[email protected]

Technology

When Attention is not Scarce – Detecting Boredom from Mobile Phone Usage