Decomposing responses to mobile notifications

Notifications from mobile devices frequently prompt us with information, either to merely inform us or to elicit a reaction. This has led to increasing research interest in considering an individual’s interruptibility prior to issuing notifications, in order for them to be positively received. To achieve this, predictive models need to be built from previous response behaviour where the individual’s interruptibility is known. However, there are several degrees of freedom in achieving this, from different definitions in what it means to be interruptible and a notification to be successful, to various methods for collecting data, and building predictive models. The primary focus of this thesis is to improve upon the typical convention used for labelling interruptibility, an area which has had limited direct attention. This includes the proposal of a flexible framework, called the decision-on-information-gain model, which passively observes response behaviour in order to support various interruptibility definitions. In contrast, previous studies have largely surrounded the investigation of influential contextual factors on predicting interruptibility, using a broad labelling convention that relies on notifications being responded to fully and potentially a survey needing to be completed. The approach is supported through two in-the-wild studies of Android notifications, one with 11,000 notifications across 90 users, and another with 32,000,000 across 3000 users. Analysis of these datasets shows that: a) responses to notifications is a decisionmaking process, whereby individuals can be reachable but not receptive to their content, supporting the premise of the approach; b) the approach is implementable on typical Android devices and capable of adapting to different notification designs and user preferences; and c) the different labels produced by the model are predictable using data sources that do not require invasive permissions or persistent background monitoring; however there are notable performance differences between different machine learning strategies for training and evaluation

Diary mining: predicting emotion from activities, people and places

Diary methods are concerned with collecting qualitative information from people about their everyday lives and are commonly used in many fields such as psychology, sociology and medicine to understand human behaviour and improve mental health. By its nature, the data is difficult to analyse and time-consuming to process manually, creating a gap between collection, analysis and intervention. Technologies such as machine learning have the potential to shrink this gap, save time and effort, and hence give deeper insight into the diary data. Computer science technologies have been heavily used by many disciplines to understand humans. One such application is emotion detection from text, which is the process of automatically identifying the emotion that is either directly expressed by the author or the underlying emotion that prompted the author to write a text. Studies have shown promising results using different features extracted, whether linguistic or others (e.g., number of followers). However, very few have used activities for emotion prediction from text, and none of these have combined activities with other associated situational features from the relevant event. The research in this thesis proposes an approach to predict emotion from self-recorded personal textual diaries using a small set of domain-specific features. Daily activities, in association with people and places, are used as the main indicators of an individual's current situation. The association of these factors with emotion has been well-studied independently in psychology, which has motivated this investigation to validate the combination of all three features and test their ability to predict emotion from a computer science perspective. This research begins by proposing a framework to classify short diary entries into a small number of high-level personal activities (work/study, social/family, food/drink, leisure, essentials) and represents them as low dimensional probability vectors using unsupervised (clustering) and supervised (classification) machine learning techniques. In view of the fact that these entries are characterised by sparseness, and that there is lack of training data as they are highly personal, this framework applies a transfer learning approach by exploiting previously acquired knowledge as a foundation step, using a pre-trained word embedding model on similar, but not identical, and easily obtained publicly available data (tweets). Furthermore, references to people and places are also recognised from the text using information extraction techniques. These automatically extracted features are then used for predicting emotion, utilising different emotion schemes, including Ekman's basic emotion model, the Circumplex model, together with simpler classification into pleasantness/unpleasantness, and emotional/ neutral states. In addition, different learning strategies for predicting emotion are compared, including the use of personalised and global training data. This research has shown that activities, people, and places can successfully predict some emotions from the text, especially `happiness' and `neutral'