Competing or aiming to be average?: Normification as a means of engaging digital volunteers

Engagement, motivation and active contribution by digital volunteers are key requirements for crowdsourcing and citizen science projects. Many systems use competitive elements, for example point scoring and leaderboards, to achieve these ends. However, while competition may motivate some people, it can have a neutral or demotivating effect on others. In this paper we explore theories of personal and social norms and investigate normification as an alternative approach to engagement, to be used alongside or instead of competitive strategies. We provide a systematic review of existing crowdsourcing and citizen science literature and categorise the ways that theories of norms have been incorporated to date. We then present qualitative interview data from a pro-environmental crowdsourcing study, Close the Door, which reveals normalising attitudes in certain participants. We assess how this links with competitive behaviour and participant performance. Based on our findings and analysis of norm theories, we consider the implications for designers wishing to use normification as an engagement strategy in crowdsourcing and citizen science systems

Introduction

This book brings together for the first time the collected wisdom of international leaders in the theory and practice in the emerging field of cultural heritage crowdsourcing. It features eight accessible case studies of groundbreaking projects from leading cultural heritage and academic institutions, and four thought-­‐provoking essays that reflect on the wider implications of this engagement for participants and on the institutions themselves

Beautiful and damned. Combined effect of content quality and social ties on user engagement

User participation in online communities is driven by the intertwinement of the social network structure with the crowd-generated content that flows along its links. These aspects are rarely explored jointly and at scale. By looking at how users generate and access pictures of varying beauty on Flickr, we investigate how the production of quality impacts the dynamics of online social systems. We develop a deep learning computer vision model to score images according to their aesthetic value and we validate its output through crowdsourcing. By applying it to over 15B Flickr photos, we study for the first time how image beauty is distributed over a large-scale social system. Beautiful images are evenly distributed in the network, although only a small core of people get social recognition for them. To study the impact of exposure to quality on user engagement, we set up matching experiments aimed at detecting causality from observational data. Exposure to beauty is double-edged: following people who produce high-quality content increases one's probability of uploading better photos; however, an excessive imbalance between the quality generated by a user and the user's neighbors leads to a decline in engagement. Our analysis has practical implications for improving link recommender systems.Comment: 13 pages, 12 figures, final version published in IEEE Transactions on Knowledge and Data Engineering (Volume: PP, Issue: 99

A theoretical model for the application of Web 2.0 in e-Government

Government organisations in many countries have started embracing modern technologies such as second generation web (Web 2.0) in an attempt to maximize on the benefits of these technologies as well as keeping up with the current trend. Nevertheless, the advancement and the adoption of these of technologies is in its initial stages in the public sector. Therefore, the research problem is that the literature surrounding the application of Web 2.0 is still highly tentative and exploratory. In particular, there is a lack of research exploring the application of Web 2.0 technologies in the context of local e-Government. This study aims to address this research problem by presenting a comprehensive decision-making tool to aid the effective application of Web 2.0 technologies amongst local government authorities (LGAs). In doing so, resulting in the development of a theoretical model that is underpinned by information systems evaluation criteria and impact factors of Web 2.0 from an internal organizational perspective. By addressing the research problem, this study will make a significant contribution to the normative literature by providing new insights of Web 2.0 technologies within the public sector. This will be of specific relevance to scholars, policy makers, LGAs and practitioners who are interested in the adoption of Web 2.0 technologies in an e-Government context. This paper presents the proposed theoretical model and is largely devoted to an explanation on the development of the model

Build an app and they will come? Lessons learnt from trialling the GetThereBus app in rural communities

Acknowledgements The research described here was supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.Peer reviewedPostprin

Fake News Detection in Social Networks via Crowd Signals

Our work considers leveraging crowd signals for detecting fake news and is motivated by tools recently introduced by Facebook that enable users to flag fake news. By aggregating users' flags, our goal is to select a small subset of news every day, send them to an expert (e.g., via a third-party fact-checking organization), and stop the spread of news identified as fake by an expert. The main objective of our work is to minimize the spread of misinformation by stopping the propagation of fake news in the network. It is especially challenging to achieve this objective as it requires detecting fake news with high-confidence as quickly as possible. We show that in order to leverage users' flags efficiently, it is crucial to learn about users' flagging accuracy. We develop a novel algorithm, DETECTIVE, that performs Bayesian inference for detecting fake news and jointly learns about users' flagging accuracy over time. Our algorithm employs posterior sampling to actively trade off exploitation (selecting news that maximize the objective value at a given epoch) and exploration (selecting news that maximize the value of information towards learning about users' flagging accuracy). We demonstrate the effectiveness of our approach via extensive experiments and show the power of leveraging community signals for fake news detection

The social web and archaeology's restructuring: impact, exploitation, disciplinary change

From blogs to crowdfunding, YouTube to LinkedIn, online photo-sharing sites to open-source community-based software projects, the social web has been a meaningful player in the development of archaeological practice for two decades now. Yet despite its myriad applications, it is still often appreciated as little more than a tool for communication, rather than a paradigm-shifting system that also shapes the questions we ask in our research, the nature and spread of our data, and the state of skill and expertise in the profession. We see this failure to critically engage with its dimensions as one of the most profound challenges confronting archaeology today. The social web is bound up in relations of power, control, freedom, labour and exploitation, with consequences that portend real instability for the cultural sector and for social welfare overall. Only a handful of archaeologists, however, are seriously debating these matters, which suggests the discipline is setting itself up to be swept away by our unreflective investment in the cognitive capitalist enterprise that marks much current web-based work. Here we review the state of play of the archaeological social web, and reflect on various conscientious activities aimed both at challenging practitioners’ current online interactions, and at otherwise situating the discipline as a more informed innovator with the social web’s possibilities