Title: NotiMind: Utilizing Responses to Smart Phone Notifications as Affective sensors,
Abstract: Today's mobile phone users are faced with large numbers of notifications on
social media, ranging from new followers on Twitter and emails to messages
received from WhatsApp and Facebook. These digital alerts continuously disrupt
activities through instant calls for attention. This paper examines closely the
way everyday users interact with notifications and their impact on users'
emotion. Fifty users were recruited to download our application NotiMind and
use it over a five-week period. Users' phones collected thousands of social and
system notifications along with affect data collected via self-reported PANAS
tests three times a day. Results showed a noticeable correlation between
positive affective measures and keyboard activities. When large numbers of Post
and Remove notifications occur, a corresponding increase in negative affective
measures is detected. Our predictive model has achieved a good accuracy level
using three different classifiers "in the wild" (F-measure 74-78%
within-subject model, 72-76% global model). Our findings show that it is
possible to automatically predict when people are experiencing positive,
neutral or negative affective states based on interactions with notifications.
We also show how our findings open the door to a wide range of applications in
relation to emotion awareness on social and mobile communication.