Understanding and Designing Attention for Dual-Screen Media

Recently there has been a transformative shift towards engaging with mobile devices while watching television. Content creators, therefore, wish to create applications to support these behaviours to provide more engaging multi-device TV. Currently, their designs do not reflect the subtle variations in viewer attention, our physiological capabilities, or the additional mental effort such scenarios imply. We investigate this in two primary ways: by further understanding the current issues faced by users when dual-screening, and by designing a series of technological interventions for managing cross-device attention. First, we conduct two studies to better understand the user experience of second screening. Through a large-scale online questionnaire and a series of interviews we document the problems faced by users when second screening and how they compensate and mitigate for missing content when engaging with mobile devices. We then conduct an investigation to explore the effect of dual-screen visual complexity in terms of objective and subjective experience of participants when exposed to content of varying complexity across two screens.For our technological interventions, we first investigate how visual complexity on a mobile device may be varied to account for the perceived complexity of TV material by loading textual material at varying levels of complexity. We explore the tradeoff of user autonomy and content creator control by contrasting the effects of users adjusting the complexity themselves, and automatic adjustment around heuristics. Then, we consider how different audio-visual stimuli may be used to direct a user’s attention between screens at key moments. Finally, we explore how we can support users to reduce the switching costs and cognitive effort associated with engaging with cross-device media mirroring unattended visual information in the experience on an attended screen. Throughout the thesis we show that many of our interventions are a beneficial state of the art and form a series of guidelines for each. The thesis concludes by offering an outline of our contributions and a framework for others to extend our work

TV program detection in tweets

Posting comments on social networks using second screen devices (e.g., tablets) while watching TV is becoming very common. The simplicity of microblogs makes Twitter among the preferred social services used by the TV audience to share messages about TV shows and movies. Thus, users’ comments about TV shows are considered a valuable indicator of the TV audience preferences. However, eliciting preferences from a tweet requires to understand if the tweet refers to a specific TV program, a task particularly challenging due to the nature of tweets - e.g., the limited length and the massive use of slangs and abbreviations. In this paper, we present a solution to identify whether a tweet posted by a user refers to one among a set of known TV programs. In such process, referred to as item detection, we assume the system is given a set of items (e.g., the TV shows or movies) together with some features (e.g., the title of the TV show). We tested the solution on a dataset composed by approximately 32000 tweets, where the optimal configuration reached a precision of about 92% with a recall equals to about 65%