Scrutable and Persuasive Push-Notifications

Fraser, Kieran; Yousuf, Bilal; Conlan, Owen

doi:10.1007/978-3-030-17287-9_6

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…For example, Phrasee 1 is a tool which creates textual content, such as email subject lines, primed for maximising open rates. Other websites also advertise generative text capabilities for creating clickbait-like headlines 2 . Indeed, OpenAI expressed reservations toward releasing their generative model for fear of misuse.…”

Section: Related Workmentioning

confidence: 99%

“…Of the remaining 11 participants, on average, each received ≈ 40 notifications per day, ≈ 52% of which were opened, as illustrated by the median Click-Through-Rate (CTR) in Figure 1. There are many features which influence the action of a user toward opening or dismissing a push-notification -the app which posted the notification; the location of the user on receiving the message; the category of notification -previous work has explored these features in detail [2]. This paper focuses on the textual elements of notifications and, in particular, the potentially adversely enticing aspects of the text which manipulate participants to act positively (open) toward a notification, regardless of contextual value.…”

Section: Notification Enticementmentioning

confidence: 99%

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Enticing notification text & the impact on engagement

Fraser

Conlan

2020

Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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Push-notifications are a design tool used by mobile and web apps to alert subscribers to new information. In recent years, due to widespread adoption of the technology and the shrinking level of user attention available, marketing techniques have been deployed to persuade subscribers to engage positively with notifications. One such technique, known as the curiosity gap, exploits Lowenstein's Information-Gap theory. This paper explores the impact of enticing notification text, instilled by the curiosity gap, on subsequent engagement actions. A classifier was defined to identify enticing language in notifications. Features commonly paired with enticing text were identified. Intelligent notification delivery agents, trained using data captured in-the-wild, were evaluated using enticing and non-enticing notifications to demonstrate the influence of enticing text. Additionally, a solution was proposed and briefly evaluated for limiting subscriber susceptibility to enticing notifications. CCS CONCEPTS • Human-centered computing → Smartphones; • Computing methodologies → Natural language processing; Reinforcement learning.

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Section: Related Workmentioning

confidence: 99%

Section: Notification Enticementmentioning

confidence: 99%

Enticing notification text & the impact on engagement

Fraser

Conlan

2020

Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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“…The dataset associated with this proposed shared challenge is a simulated dataset that is based on mobile notifications gathered by the WeAreUs Android app. The dataset generation approach is described in [5]. The synthetic data provided in the challenge dataset is comprised of notification, engagement and contextual features.…”

Section: Challenge Datasetmentioning

confidence: 99%

A User Modeling Shared Challenge Proposal

Conlan

Fraser

Kelly

et al. 2019

Lecture Notes in Computer Science

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Comparative evaluation in the areas of User Modeling, Adaptation and Personalization (UMAP) is significantly challenging. It has always been difficult to rigorously compare different approaches to personalization, as the function of the resulting systems is, by their nature, heavily influenced by the behavior of the users involved in trialing the systems. Developing comparative evaluations in this space would be a huge advancement as it would enable shared comparison across research. Here we present a proposal for a shared challenge generation in UMAP, focusing on user model generation using logged mobile phone data, with an assumed purpose of supporting mobile phone notification suggestion. The dataset, evaluation metrics, and challenge operation are described.

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“…According to the analysis of the 20 persuasive applications, 75% of the apps have this feature. A study by Yousuf and Conlan indicate that the use of push notifications is followed by action, hence persuading the user to engage with the application [18]. As previously mentioned, positive and negative reinforcements incite action and engagement but have different levels and circumstances in which reinforcement will be best suited.…”

Section: Table 12 Solicitation/progress Persuasionmentioning

confidence: 99%

An Analysis of Guidelines for Persuasive Interfaces on Mobile Applications

Sasal¹

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Persuasive technologies in mobile applications have become more prevalent with the development of smartphones. The user being prompted to take on certain actions and behaviours as reminders that doctors, family members or friends would give is not often associated with mobile devices. The ability for this effort to succeed in some measure by applications depends on the effectiveness of the design elements on the interface of persuasive applications. What are a reliable set of design principles that make persuasive applications, persuasive? After realizing the gap in previous research on guidelines for persuasive interfaces, Alexandra Nemery and Eric Brangier propose a set of criteria that list the essential design elements for a persuasive interface: credibility, privacy, personalization, attractiveness, solicitation, priming, commitment, and ascendency, and divide them into two sets of criteria: static and dynamic. As a follow up research to their study, this research paper analyzes 20 persuasive applications on the guidelines of Nemery and Brangier by employing open coding. The results show that not only were Nemery and Brangier's guidelines are absent in most applications, but they also did not account for the following codes in their criteria: Personalization/ProfileDashboard, Solicitation/RemindersNotifications, Solicitation/ProgressPersuasion, Commitment/PreliminaryAssessment and Community. An autoethnographic study also finds that design elements are highly nuanced and subjective and require guidelines of their own. iii Acknowledgement This project would not have been possible without the support of many people. Many thanks to my supervisor, Victoria McArthur, who read my numerous revisions and helped put together the final version of this research. Many thanks to my friends and family who provided continuous moral support during a time when it was difficult to stay positive. Last but not least, I would like to pass on my sincere thanks to my faculty, especially Robert Biddle, who ignited my interest in persuasive technologies.

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Scrutable and Persuasive Push-Notifications

Cited by 5 publications

References 4 publications

Enticing notification text & the impact on engagement

Enticing notification text & the impact on engagement

A User Modeling Shared Challenge Proposal

An Analysis of Guidelines for Persuasive Interfaces on Mobile Applications

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