The research works reviewed in this Chapter show the high potential benefit of adaptive user interfaces to the end user’s experience and satisfaction. Adaptive user interfaces have been touted as potential solutions for problems such as learning to use complex systems, assisting disabled and elderly people providing help and sup- port in commercial software application, enhancing users’ experience in domains such as music and news, and many others. On the downside, they are faced with possible problems and pitfalls such as privacy issues, confusion, user control and freedom. Prior research works have shown mixed results in users’ willingness to adopt adaptive user interfaces but clearly their potential is huge if the right presen- tation is given to the user at the right time and in the right context. An important element, of course, that may influence users’ willingness to adopt such systems
2.5. Summary 41 is the underlying intelligent mechanism in which an effective user model needs to be acquired for the users of a system. The recent technological advancements in terms of data availability, power processing and computational methods, open the door for effective user modelling acquisition and exploitation, which in turn can be transformed into a successful adaptive user interface.
In the domain of news particularly, while there is an abundance of literature in relation to news content recommendation, personalisation of the user interface of news services has received less attention. Due to the individual nature of consuming digital news, the need of more personalised news access is evident; personalisation that can be achieved by adaptive user interfaces. Today’s news apps’ user inter- faces have limited personalisation, and if they have, it is achieved by manual user interface customisation (i.e. adaptable). Adaptive news interfaces that adapt ‘auto- matically’ to the way the individual user reads the news in a particular context are not found other than by re-ordering menus of headlines to take account of previous reading choices. Simply, this adaptation is restricted to what content users access. Nevertheless, it could be far more extensive and multi-dimensional, for example, to take account of users’ idiosyncratic patterns of browsing news headlines or the different ways in which different users read news articles (Grzeschik et al., 2011; Westlund, 2008). Adaptive user interfaces for news apps have, therefore, the poten- tial to transform the way people consume news including not only, ‘what’ content they access, but also ‘how’ they access it and interact with.
Chapter 3
Understanding Mobile News Reading
Behaviour
The previous Chapter discussed related work and relevant literature from the areas of Adaptation and Personalisation, Adaptive User Interfaces and User Modelling. It reviewed literature from the broader area of Adaptive Systems, summarised chal- lenges and difficulties in building such systems and established the gap that exist in mobile news apps personalisation. In summary, personalisation in news apps interfaces has received less attention as opposed to news content recommendation. Evidently, as discussed in Chapter 2, there is a lack of research around news apps that systematically monitor users’ behaviour and adapt themselves in response to users’ news reading behaviour. To reach the point where news apps could auto- matically respond to the specific needs of mobile news readers, requires a clear understanding of people’s differences in mobile news reading.
In this Chapter we will present work conducted in the early stages of this research, which aims to explore people’s differences and stereotypical behaviour whilst reading news on mobile devices. This Chapter addresses questions such as how people differ whilst reading the news on mobile devices, what factors discrim- inate mobile news reading behaviour and what mobile news reader stereotypes can be formed. Although many studies and reports (Institute, 2014; Reuters Institute, 2015; American Press Institute, 2014; Pew Research Centre, 2017a; Reuters Insti- tute, 2014; Ofcom, 2014; Pew Research Centre, 2012) have been published over
the years about mobile news consumption, the Chapter presents a study that differ- entiates from previous attempts as it places emphasis on categorising news readers and generating a News Reader Typology. Clearly all prior work have categorised news readers but it was a different categorisation for different purposes. The cate- gorisation that this Chapter proposes is about the patterns of consumption of digital news on mobile apps rather than the content itself that is being consumed. Forming a mobile news reader typology, therefore, is essential in the scope of this research as it will drive the user model acquisition and construction that will be discussed in subsequent Chapters.
The Chapter begins with a discussion of related studies and conclusions that helped the design of this study. It then presents the design of an online questionnaire used in the study and then reports the analysis conducted as well as the findings of the study. The Chapter ends with a discussion of the findings.
The News Reader Typology presented in this Chapter has appeared in the Pro- ceedings of MobileHCI ’15. (Constantinides, M., Dowell, J., Johnson, D., Malacria, S. Exploring mobile news reading interactions for news app personalisation. In Proc. MobileHCI 2015.)
The exploration of domain-independent factors presented in this Chapter has appeared in Adjunct Publication of the 26th Conference on User Modeling, Adap- tation and Personalization 2018. (Constantinides, M., Germanakos, P., Samaras, G., Dowell, J. Your Digital News Reading Habits Reflect Your Personality. In Adjunct Publication of UMAP.
3.1
Motivation
As discussed in Chapter 2, a significant amount of work has been conducted in the field of news focusing on different factors that affect news reading behaviour in the digital era and understanding the emerging challenges in consuming news on mo- bile devices. Previous studies (Pew Research Centre, 2012; Reuters Institute, 2014) have examined a diversity of factors ranging from contextual to sociodemographic. Even more, other studies have examined the relationship between print and digital
3.2. Online Questionnaire 45