Portraying matters as other than they truly are is an important part of everyday human communication. In this paper, we use a survey to examine ways in which people fabricate, omit or alter the truth online. Many reasons are found, including creative expression, hiding sensitive information, role-playing, and avoiding harassment or discrimination. The results suggest lying is often used for benign purposes, and we conclude that its use may be essential to maintaining a humane online society.

Personal Data Stores are among the many efforts that are currently underway to try to re-decentralise the Web, and to bring more control and data management and storage capability under the control of the user. Few of these architectures, however, have considered the needs of supporting decentralised social software from the user's perspective. In this short paper, we present the results of our design exercise, focusing on two key design needs for building decentralised social machines: that of supporting heterogeneous social apps and multiple, separable user identities. We then present the technical design of a prototype social machine platform, INDX, which realises both of these requirements, and a prototype heterogeneous microblogging application which demonstrates its capabilities.

The many and varied personal activity trackers on the market have the potential to provide unprecedented detail and insight on our everyday activities. However, effective use and interpretation of data from them can be challenging due to common issues. Such issues include false readings due to sensing approaches taken, or missing data arising from a number of different causes. In order to understand user perceptions on this topic, we performed a preliminary survey, which found that users desired the ability to annotate, retroactively repair, and compare their data. Based on insights from this survey, we designed a direct-manipulation interface permitting the consolidated annotation and revision of activity data from multiple devices. A pilot study of this interface found that users understood readily how to use the features offered, and valued the ability to edit, yet preserve the provenance of their data.

Web Observatories aim to develop techniques and methods to allow researchers to interrogate and answer questions about society through the multitudes of digital traces people now create. In this paper, we propose that a possible path towards surmounting the inevitable obstacle of personal privacy towards such a goal, is to keep data with individuals, under their own control, while enabling them to participate in Web Observatory-style analyses in situ. We discuss the kinds of applications such a global, distributed, linked network of Personal Web Observatories might have, a few of the many challenges that must be resolved towards realising such an architecture in practice, and finally, our work towards a fundamental reference building block of such a network.

The formal structure of the information on the Semantic Web lends itself to faceted browsing, an information retrieval method where users can filter results based on the values of properties ("facets"). Numerous faceted browsers have been created to browse RDF and Linked Data, but these systems use their own ontologies for defining how data is queried to populate their facets. Since the source data is the same format across these systems (specifically, RDF), we can unify the different methods of describing how to query the underlying data, to enable compatibility across systems, and provide an extensible base ontology for future systems. To this end, we present FacetOntology, an ontology that defines how to query data to form a faceted browser, and a number of transformations and filters that can be applied to data before it is shown to users. FacetOntology overcomes limitations in the expressivity of existing work, by enabling the full expressivity of SPARQL when selecting data for facets. By applying a FacetOntology definition to data, a set of facets are specified, each with queries and filters to source RDF data, which enables faceted browsing systems to be created using that RDF data.

Can the Web help people live healthier lives? This paper seeks to answer this question through an examination of sites, apps and online communities designed to help people improve their fitness, better manage their disease(s) and conditions, and to solve the often elusive connections between the symptoms they experience, diseases and treatments. These health social machines employ a combination of both simple and complex social and computational processes to provide such support. We first provide a descriptive classification of the kinds of machines currently available, and the support each class offers. We then describe the limitations exhibited by these systems and potential ways around them, towards the design of more effective machines in the future.

The state of the art in human interaction with computational systems blurs the line between computations performed by machine logic and algorithms, and those that result from input by humans, arising from their own psychological processes and life experience. Current socio-technical systems, known as "social machines" exploit the large-scale interaction of humans with machines. Interactions that are motivated by numerous goals and purposes including financial gain, charitable aid, and simply for fun. In this paper we explore the landscape of social machines, both past and present, with the aim of defining an initial classificatory framework. Through a number of knowledge elicitation and refinement exercises we have identified the polyarchical relationship between infrastructure, social machines, and large-scale social initiatives. Our initial framework describes classification constructs in the areas of contributions, participants, and motivation. We present an initial characterisation of some of the most popular social machines, as demonstration of the use of the identified constructs. We believe that it is important to undertake an analysis of the behaviour and phenomenology of social machines, and of their growth and evolution over time. Our future work will seek to elicit additional opinions, classifications and validation from a wider audience, to produce a comprehensive framework for the description, analysis and comparison of social machines.

Can the Web help people live healthier lives? This paper seeks to answer this question through an examination of sites, apps and online communities designed to help people improve their fitness, better manage their disease(s) and conditions, and to solve the often elusive connections between the symptoms they experience, diseases and treatments. These health social machines employ a combination of both simple and complex social and computational processes to provide such support. We first provide a descriptive classification of the kinds of machines currently available, and the support each class offers. We then describe the limitations exhibited by these systems and potential ways around them, towards the design of more effective machines in the future.

Millions of recommendations, opinions and experiences are shared across popular microblogging platforms and services each day. Yet much of this content becomes quickly lost in the stream shortly after being posted. This paper looks at the feasibility of identifying useful content in microblog streams so that it might be archived to facilitate wider access and reference. Towards this goal, we present an experiment with a game-with-a-purpose called Twiage that we designed to determine how well the deluge of content in "raw" microblog streams could be turned into filtered and ranked collections using ratings from players. Experiments with Twiage validate the feasibility of applying human-computation to this problem, finding strong agreement about what constitutes the "most useful" content in our test dataset. Second, we compare the effectiveness of various methods of eliciting such ratings, finding that a "choose-best" interface and Elo rating ranking scheme yield the greatest agreement in the fewest rounds. External validation of resulting top-rated twitter content with a domain expert found that while the top Twiage-ranked "tweets" were among the best of the set, there was a tendency for players to also select what we term "weak spam" - e.g., promotional content disguised as articles or reviews, indicating a need for more stringent content filtering.

We give away our data to multiple data services without, for the most part, being able to get that data back to reuse in any other way, leaving us, at best, to re-find, re-cover, retype, remember and re-manage this material. In this work in progress, we hypothesize that if we facilitate easy interaction to store, access and reuse our personal, social and public data, we will not only decrease time spent to recreate it for multiple walled data contexts, but in particular, we will develop novel interactions for new kinds of knowledge building. To facilitate exploration of this hypothesis, we propose Page Blossom an exemplar of such dynamic data interaction that is based on data reuse via our open data platform Webbox + Active (active knowledge technology) lenses

Most existing PIM tools either suffer from having data isolated by being siloed in an application or only interact with specific tools offered through specific services. The consequences are that what people can do with their own data becomes constrained by what services application developers afford. We propose a web-standards based architecture called WebBox to support easy maintenance and repurposing of one's own data for private, social or public publishing, collaboration and reuse.

Faceted browsers offer an effective way to explore relationships and build new knowledge across data sets. So far, web-based faceted browsers have been hampered by limited feature performance and scale. QWIC, Quick Web Interface Control, describes a set of design heuristics to address performance speed both at the interface and the backend to operate on large-scale sources.

While we witness an explosion of exploration tools for simple datasets on Web 2.0 designed for use by ordinary citizens, the goal of a usable interface for supporting navigation and sense-making over arbitrary linked data has remained elusive. The purpose of this paper is to analyse why - what makes exploring linked data so hard? Through a user-centered use case scenario, we work through requirements for sense making with data to extract functional requirements and to compare these against our tools to see what challenges emerge to deliver a useful, usable knowledge building experience with linked data. We present presentation layer and heterogeneous data integration challenges and offer practical considerations for moving forward to effective linked data sensemaking tools.