The social in the platform trap: Why a microscopic system focus limits the prospect of social machines

“Filter bubble”, “echo chambers”, “information diet” – the metaphors to describe today’s information dynamics on social media platforms are fairly diverse. People use them to describe the impact of the viral spread of fake, biased or purposeless content online, as witnessed during the recent race for the US presidency or the latest outbreak of the Ebola virus (in the latter case a tasteless racist meme was drowning out any meaningful content). This unravels the potential envisioned to arise from emergent activities of human collectives on the World Wide Web, as exemplified by the Arab Spring mass movements or digital disaster response supported by the Ushahidi tool suite

Understanding social machines

The framework introduced in this paper aims to reflect the characteristics that social machines have been described to have. The framework uses a mixed methods approach underpinned by social theory to provide a detailed and rich understanding of the socio-technical nature of a social machine. The strength of this lies in the diversity of the data being used; whilst the quantitative approach can provide mathematical rigor to the structure and properties of the networks and appreciate its scale, the qualitative approach seeks to examine the 'social relations', and the context to how the social machine is enabling humans and technologies to interact and shape each other. Like many studies using empirical-based research, this framework takes advantage of the complementary nature that mixed methods offers, and pushes it further by using an analytical socio-technical lens.<br/

Identifying communicator roles in Twitter

Twitter has redefined the way social activities can be coordinated; used for mobilizing people during natural disasters, studying health epidemics, and recently, as a communication platform during social and political change. As a large scale system, the volume of data transmitted per day presents Twitter users with a problem: how can valuable content be distilled from the back chatter, how can the providers of valuable information be promoted, and ultimately how can influential individuals be identified?To tackle this, we have developed a model based upon the Twitter message exchange which enables us to analyze conversations around specific topics and identify key players in a conversation. A working implementation of the model helps categorize Twitter users by specific roles based on their dynamic communication behavior rather than an analysis of their static friendship network. This provides a method of identifying users who are potentially producers or distributers of valuable knowledge

Using mixed methods to track the growth of the Web: tracing open government data initiatives

In recent years, there have been a rising number of Open Government Data (OGD) initiatives; a political, social and technical movement armed with a common goal of publishing government data in open, re-usable formats in order to improve citizen-to-government transparency, efficiency, and democracy. As a sign of commitment, the Open Government Partnership was formed, comprising of a collection of countries striving to achieve OGD. Since its initial launch, the number of countries committed to adopting an Open Government Data agenda has grown to more than 50; including countries from South America to the Far East.Current approaches to understanding Web initiatives such as OGD are still being developed. Methodologies grounded in multidisciplinarity are still yet to be achieved; typically research follows a social or technological approach underpinned by quantitative or qualitative methods, and rarely combining the two into a single analytical framework. In this paper, a mixed methods approach will be introduced, which uses qualitative data underpinned by sociological theory to complement a quantitative analysis using computer science techniques. This method aims to provide an alternative approach to understanding the socio-technical activities of the Web. To demonstrate this, the activities of the UK Open Government Data initiative will be explored using a range of quantitative and qualitative data, examining the activities of the community, to provide a rich analysis of the formation and development of the UK OGD community

From coincidence to purposeful flow? properties of transcendental information cascades

In this paper, we investigate a method for constructing cascades of information co-occurrence, which is suitable to trace emergent structures in information in scenarios where rich contextual features are unavailable. Our method relies only on the temporal order of content-sharing activities, and intrinsic properties of the shared content itself. We apply this method to analyse information dissemination patterns across the active online citizen science project Planet Hunters, a part of the Zooniverse platform. Our results lend insight into both structural and informational properties of different types of identifiers that can be used and combined to construct cascades. In particular, significant differences are found in the structural properties of information cascades when hashtags as used as cascade identifiers, compared with other content features. We also explain apparent local information losses in cascades in terms of information obsolescence and cascade divergence; e.g., when a cascade branches into multiple, divergent cascades with combined capacity equal to the original

Exploring the global adoption of citizen science

In recent years there has been a growing interest toward the application of Web-based citizen science platforms. Such platforms use crowdsourcing techniques to support scientific advancements, and in several cases, have lead to new scientific discoveries which were not originally considered. Our work explores the highly successful Web-based citizen science platform, Zooniverse, a crowdsourcing platform with a userbase of over 1 million participants who volunteer their free time to support scientific enquiries. We focus on the growth of the Zooniverse platform, which has evolved from a rudimentary crowdsourcing platform where users were presented with tasks, into a platform which has become a rich community of citizen scientists, discussion, and interaction. Building upon existing research into the motivations and design considerations of developing and sustaining citizen science projects, this paper explores the space of citizen science engagement within the Zooniverse, and ask the question of whether citizen science has become a worldwide activity

The role of data science in web science

Web science relies on an interdisciplinary approach that seeks to go beyond what any one subject can say about the World Wide Web. By incorporating numerous disciplinary perspectives and relying heavily on domain knowledge and expertise, data science has emerged as an important new area that integrates statistics with computational knowledge, data collection, cleaning and processing, analysis methods, and visualization to produce actionable insights from big data. As a discipline to use within Web science research, data science offers significant opportunities for uncovering trends in large Web-based datasets. A Web science observatory exemplifies this relationship by offering an online platform of tools for carrying out Web science research, allowing users to carry out data science techniques to produce insights into Web science issues such as community development, online behavior, and information propagation. The authors outline the similarities and differences of these two growing subject areas to demonstrate the important relationship developing between them.<br/