TweetVista: An AI-Powered Interactive Tool for Exploring Conversations on Twitter

We present TweetVista, an interactive web-based tool for mapping the conversation landscapes on Twitter. TweetVista is an intelligent and interactive desktop web application for exploring the conversation landscapes on Twitter. Given a dataset of tweets, the tool uses advanced NLP techniques using deep neural networks and a scalable clustering algorithm to map out coherent conversation clusters. The interactive visualization engine then enables the users to explore these clusters. We ran three case studies using datasets about the 2016 US presidential election and the summer 2016 Orlando shooting. Despite the enormous size of these datasets, using TweetVista users were able to quickly and clearly make sense of the various conversation topics around these datasets

Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters

Conversations on Twitter create networks with identifiable contours as people reply to and mention one another in their tweets. These conversational structures differ, depending on the subject and the people driving the conversation. Six structures are regularly observed: divided, unified, fragmented, clustered, and inward and outward hub and spoke structures. These are created as individuals choose whom to reply to or mention in their Twitter messages and the structures tell a story about the nature of the conversatio

Becoming the Olympics: The Sound Proof series of exhibition (2008-2012)

Presentation at the Heritage Architecture LanDesign conference. Organised by Le Vie dei Mercanti and sponsored by Forum UNESCO. http://www.leviedeimercanti.it/2013eng

Social information landscapes: automated mapping of large multimodal, longitudinal social networks

Purpose – This article presents a Big Data solution as a methodological approach to the automated collection, cleaning, collation and mapping of multimodal, longitudinal datasets from social media. The article constructs Social Information Landscapes. Design/methodology/approach – The research presented here adopts a Big Data methodological approach for mapping user-generated contents in social media. The methodology and algorithms presented are generic, and can be applied to diverse types of social media or user-generated contents involving user interactions, such as within blogs, comments in product pages and other forms of media, so long as a formal data structure proposed here can be constructed. Findings – The limited presentation of the sequential nature of content listings within social media and Web 2.0 pages, as viewed on Web browsers or on mobile devices, do not necessarily reveal nor make obvious an unknown nature of the medium; that every participant, from content producers, to consumers, to followers and subscribers, including the contents they produce or subscribed to, are intrinsically connected in a hidden but massive network. Such networks when mapped, could be quantitatively analysed using social network analysis (e.g., centralities), and the semantics and sentiments could equally reveal valuable information with appropriate analytics. Yet that which is difficult is the traditional approach of collecting, cleaning, collating and mapping such datasets into a sufficiently large sample of data that could yield important insights into the community structure and the directional, and polarity of interaction on diverse topics. This research solves this particular strand of problem. Research limitations/implications – The automated mapping of extremely large networks involving hundreds of thousands to millions of nodes, over a long period of time could possibly assist in the proving or even disproving of theories. The goal of this article is to demonstrate the feasibility of using automated approaches for acquiring massive, connected datasets for academic inquiry in the social sciences. Practical implications – The methods presented in this article, and the Big Data architecture presented here have great practical values to individuals and institutions which have low budgets. The software-hardward integrated architecture uses open source software, and the social information landscapes mapping algorithms are not difficult to implement. Originality/value – The majority of research in the literatures uses traditional approach for collecting social networks data. The traditional approach is slow, tedious and does not yield a large enough sample for the data to be significant for analysis. Whilst traditional approach collects only a small percentage of data, the original methods presented could possibility collect entire datasets in social media due to its scalability and automated mapping techniques

Tweets on the road

The pervasiveness of mobile devices, which is increasing daily, is generating a vast amount of geo-located data allowing us to gain further insights into human behaviors. In particular, this new technology enables users to communicate through mobile social media applications, such as Twitter, anytime and anywhere. Thus, geo-located tweets offer the possibility to carry out in-depth studies on human mobility. In this paper, we study the use of Twitter in transportation by identifying tweets posted from roads and rails in Europe between September 2012 and November 2013. We compute the percentage of highway and railway segments covered by tweets in 39 countries. The coverages are very different from country to country and their variability can be partially explained by differences in Twitter penetration rates. Still, some of these differences might be related to cultural factors regarding mobility habits and interacting socially online. Analyzing particular road sectors, our results show a positive correlation between the number of tweets on the road and the Average Annual Daily Traffic on highways in France and in the UK. Transport modality can be studied with these data as well, for which we discover very heterogeneous usage patterns across the continent.Comment: 15 pages, 17 figure

Vulnerability in Social Epistemic Networks

Social epistemologists should be well-equipped to explain and evaluate the growing vulnerabilities associated with filter bubbles, echo chambers, and group polarization in social media. However, almost all social epistemology has been built for social contexts that involve merely a speaker-hearer dyad. Filter bubbles, echo chambers, and group polarization all presuppose much larger and more complex network structures. In this paper, we lay the groundwork for a properly social epistemology that gives the role and structure of networks their due. In particular, we formally define epistemic constructs that quantify the structural epistemic position of each node within an interconnected network. We argue for the epistemic value of a structure that we call the (m,k)-observer. We then present empirical evidence that (m,k)-observers are rare in social media discussions of controversial topics, which suggests that people suffer from serious problems of epistemic vulnerability. We conclude by arguing that social epistemologists and computer scientists should work together to develop minimal interventions that improve the structure of epistemic networks

“Carbon literacy practices”: textual footprints between school and home in children’s construction of knowledge about climate change

This paper examines the notion of “carbon literacy practices” through reporting on a small research project aimed at understanding how children make sense of climate change, and their subsequent related practices at school, at home, and in the community. Drawing on a background in New Literacy Studies (e.g. Barton et al 2000; Satchwell & Ivanic 2009 and 2010), the paper explores the relationships among children’s understanding of climate change, their literacy practices in relation to climate change, and their environmental social practices. Data is included from a project involving children and their families from three primary schools – with and without “eco-school” status, which asked: What and how do children learn about climate change at school? What and how do they learn at home and outside of school? How do these kinds of learning relate to each other, and how is what they learn put into practice? Put simply, how might children become “carbon literate” citizens? This article will report on the methodological challenges of the project and the use of some innovative methods to address these using mobile technologies. In addition, the paper interrogates the notion of children as agents of change. The concept of children influencing the behaviour of others sounds convincing, but is based on a straightforward model, described by Shove (2010) as the ABC model – which is considered an effective strategy in health care (stopping parents smoking) and in marketing (persuading parents to buy certain products), but is not necessarily transferable to other contexts. Further, it is clear from work in literacy studies and education (Ivanic et al 2009; Gee 2003; Reinking et al 1998; Tuomi-Grohn and Engestrom 2003) that the transfer of linguistic and semiotic signs is by no means equivalent to the transfer of knowledge, values or functions. In other words, a school lesson or a computer game about climate change and its effects does not automatically mean that a child will turn the lights off at home. The paper considers these issues with reference to qualitative data collected from observations, conversations on “Twitter”, focus groups, and individual interviews