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Now, Later, Never: A Study of Urgency in Mobile Push-Notifications

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Advances in Mobile Computing and Multimedia Intelligence (MoMM 2022)


Push-notifications, by design, attempt to grab the attention of subscribers and impart new or valuable information in a particular context. These nudges are commonly initiated by marketing teams and subsequent delivery interruptions tend to conflict with subscriber priorities and activities. In this work, we present a definition of urgency applied to notifications. We describe its value in an ontology for push-notification annotation and also evaluate a variety of classification models tasked with distinguishing urgency levels in notification text. The best model achieved an F1-score of 0.89. The proposed models have the potential to benefit subscribers by helping them better prioritize incoming notifications and also aid marketers in creating time-relevant campaigns.

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    “people will be more likely to perform an unimportant task over an important task (...) when the unimportant task is merely characterized by spurious urgency”.

  2. 2.

    For a complete definition see

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    Time ontology:

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    Online documentation for APN is available at

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    Crowdsource annotation platform:

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    App push statistics available at

  8. 8.

    More information available at

  9. 9.

    References available at

  10. 10.

    See a complete list of used works at


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This research has been supported by the EU’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 813497 as well as with the financial support of Enterprise Ireland, the ERDF under Ireland’s ESIF Programme 2014–2020 and SFI under Grant Agreement No 13/RC/2106_P2 at the ADAPT Centre at Trinity College.

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Correspondence to Beatriz Esteves .

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Esteves, B., Fraser, K., Kulkarni, S., Conlan, O., Rodríguez-Doncel, V. (2022). Now, Later, Never: A Study of Urgency in Mobile Push-Notifications. In: Delir Haghighi, P., Khalil, I., Kotsis, G. (eds) Advances in Mobile Computing and Multimedia Intelligence. MoMM 2022. Lecture Notes in Computer Science, vol 13634. Springer, Cham.

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  • Print ISBN: 978-3-031-20435-7

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