Geospatial data is essential for the development of the blue economy: for sustainable coastal management of coastal areas and to unlock economic potential from marine and ocean resources. In developing countries, such as South Africa, there are often gaps in the data with significant implications for the blue economy. We conducted a project aimed at addressing these data gaps by experimenting with a circular process where geospatial data for selected areas on the South African coastline were collected through mapathons and used in applications that were developed during hackathons. We validated this circular approach with two iterations of mapathons and hackathons, and found that 1) the size and location of the map area need to be carefully chosen; 2) those creating the apps needed a huge amount of help in dealing with the geospatial data; and 3) any geospatial data is useful for the blue economy, not only data with a very specific purpose in the blue economy context, such as coastal access points. Overall, the geospatial data usability improved from one iteration to another and would certainly improve if more iterations were added. Similar to the deployment of mapathons for disaster relief, future research could focus on hosting hackathons for the rapid development of apps to assist with disaster relief operations. Generally, the hosting of mapathons and hackathons in lockstep is a novel way of exposing students to interdisciplinary collaboration in international teams with a common goal.

Disengagement and disenchantment with the Parliamentary process is an important concern in today’s Western democracies. Members of Parliament (MPs) in the UK are therefore seeking new ways to engage with citizens, including being on digital platforms such as Twitter. In recent years, nearly all (579 out of 650) MPs have created Twitter accounts, and have amassed huge followings comparable to a sizable fraction of the country’s population. This paper seeks to shed light on this phenomenon by examining the volume and nature of the interaction between MPs and citizens. We find that although there is an information overload on MPs, attention on individual MPs is focused during small time windows when something topical may be happening relating to them. MPs manage their interaction strategically, replying selectively to UKbased citizens and thereby serving in their role as elected representatives, and using retweets to spread their party’s message. Most promisingly, we find that Twitter opens up new avenues with substantial volumes of cross-party interaction, between MPs of one party and citizens who support (follow) MPs of other parties.

This paper aims to shed light on alternative news media ecosystems that are believed to have influenced opinions and beliefs by false and/or biased news reporting during the 2016 US Presidential Elections. We examine a large, professionally curated list of 668 hyper-partisan websites and their corresponding Facebook pages, and identify key characteristics that mediate the traffic flow within this ecosystem. We uncover a pattern of new websites being established in the run up to the elections, and abandoned after. Such websites form an ecosystem, creating links from one website to another, and by `liking' each others' Facebook pages. These practices are highly effective in directing user traffic internally within the ecosystem in a highly partisan manner, with right-leaning sites linking to and liking other right-leaning sites and similarly left-leaning sites linking to other sites on the left, thus forming a filter bubble amongst news producers similar to the filter bubble which has been widely observed among consumers of partisan news. Whereas there is activity along both left- and right-leaning sites, right-leaning sites are more evolved, accounting for a disproportionate number of abandoned websites and partisan internal links. We also examine demographic characteristics of consumers of hyper-partisan news and find that some of the more populous demographic groups in the US tend to be consumers of more right-leaning sites.

Everyday, millions of users save content items for future use on sites like Pinterest, by "pinning" them onto carefully categorised personal pinboards, thereby creating personal taxonomies of the Web. This paper seeks to understand Pinterest as a distributed human computation that categorises images from around the Web. We show that despite being categorised onto personal pinboards by individual actions, there is a generally a global agreement in implicitly assigning images into a coarse-grained global taxonomy of 32 categories, and furthermore, users tend to specialise in a handful of categories. By exploiting these characteristics, and augmenting with image-related features drawn from a state-of-the-art deep convolutional neural network, we develop a cascade of predictors that together automate a large fraction of Pinterest actions. Our end-to-end model is able to both predict whether a user will repin an image onto her own pinboard, and also which pinboard she might choose, with an accuracy of 0.69 (Accuracy@5 of 0.75).