Can environmental citizenship be enhanced through social media? A case study of engagement in a UK University

The research presented in this thesis focuses around the question: “can social media tools be used effectively to foster a participatory process that increases environmental citizenship and promote pro-environmental behaviour-change?”. The research aims to understand the role of staff and students in the socio-technical system that influences an institution’s environmental impact. Users need not to be educated, but empowered in order to be able to take decisions that would reduce the environmental impact of their institutions. Therefore a participatory process is suggested as the right tool to nurture environmental citizens, who will be able to take ‘right’ and ‘good’ decisions about their pro-environmental actions. In the last years, social media have emerged as a worldwide phenomenon. But alongside the grand claims of a social media inspired ‘revolution’ lie more nuanced questions around the role of digital tools in ‘every day’ contexts, and whether or not they are facilitating a cultural change or merely adding to the noise of modern life. The thesis contributes to the debate through presenting findings from an action research study at an East Midlands University in which a case study approach was implemented to explore the potentialities offered by participating in decision-making regarding pro-environmental issues in the institutional context, as they are mediated by social media. To generate behaviour-change the two correlated theories of public engagement and environmental citizenship were tested. Findings indicate that behaviour change and enhanced environmental citizenship are achievable through participation using social media, as several interviewees reported a change or a reinforcement of already existing pro-environmental behaviours as a consequence of the campaign. However, the reported changes were minor and it is difficult to advocate that they could noticeably contribute to the requested reduction targets on carbon emission from behaviour-change of the HE sector

Visualizing and Quantifying Impact and Effect in Twitter Narrative using Geometric Data Analysis

We use geometric multivariate data analysis which has been termed a methodology for both the visualization and verbalization of data. The general objectives are data mining and knowledge discovery. In the first case study, we use the narrative surrounding very highly profiled tweets, and thus a Twitter event of significance and importance. In the second case study, we use eight carefully planned Twitter campaigns relating to environmental issues. The aim of these campaigns was to increase environmental awareness and behaviour. Unlike current marketing, political and other communication campaigns using Twitter, we develop an innovative approach to measuring bevavioural change. We show also how we can assess statistical significance of social media behaviour.Comment: 34 pages, 11 figure

Semantic mapping of discourse and activity, using Habermas’s theory of communicative action to analyze process

Our primary objective is evaluation of quality of process. This is addressed through semantic mapping of process. We note how this is complementary to the primacy of output results or products. We use goal-oriented discourse as a case study. We draw benefit from how social and political theorist, Jürgen Habermas, uses what was termed “communicative action”. An orientation in Habermas’s work, that we use, is analysis of communication or discourse. For this, we take Twitter social media. In our case study, we map the discourse semantically, using the Correspondence Analysis platform for such latent semantic analysis. This permits qualitative and quantitative analytics. Our case study is a set of eight carefully planned Twitter campaigns relating to environmental issues. The aim of these campaigns was to increase environmental awareness and behaviour. Each campaign was launched by an initiating tweet. Using the data gathered in these Twitter campaigns, we sought to map them, and hence to track the flow of the Twitter discourse. This mapping was achieved through semantic embedding. The semantic distance between an initiating act and the aggregate semantic outcome is used as a measure of process effectiveness

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe