The study of terrorism informatics utilizing the Twitter microblogging service has not been given apt attention in the past few years. Twitter has been identified as both a potential facilitator and also a powerful deterrent to terrorism. Based on observations of Twitter's role in civilian response during the recent 2009 Jakarta and Mumbai terrorist attacks, we propose a structured framework to harvest civilian sentiment and response on Twitter during terrorism scenarios. Coupled with intelligent data mining, visualization, and filtering methods, this data can be collated into a knowledge base that would be of great utility to decision-makers and the authorities for rapid response and monitoring during such scenarios. Using synthetic experimental data, we demonstrated that the proposed framework has yielded meaningful graphical visualizations of information, to reveal potential response to terrorist threats. The novelty of this study is that microblogging has never been studied in the domain of terrorism informatics. This paper also contributes to the understanding of the capability of conjoint structured data and unstructured content mining in extracting deep knowledge from noisy twitter messages, through our proposed structured framework.

No abstract

Many scholars agree that the Internet plays a pivotal role in self-radicalization, which can lead to behaviours ranging from lone-wolf terrorism to participation in white nationalist rallies to mundane bigotry and voting for extremist candidates. However, the mechanisms by which the Internet facilitates self-radicalization are disputed; some fault the individuals who end up self-radicalized, while others lay the blame on the technology itself. In this paper, we explore the role played by technological design decisions in online self-radicalization in its myriad guises, encompassing extreme as well as more mundane forms. We begin by characterizing the phenomenon of technological seduction. Next, we distinguish between top-down seduction and bottomup seduction. We then situate both forms of technological seduction within the theoretical model of dynamical systems theory. We conclude by articulating strategies for combatting online self-radicalization.

No abstract

Trust in vaccination is eroding, and attitudes about vaccination have become more polarized. This is an observational study of Twitter analyzing the impact that COVID-19 had on vaccine discourse. We identify the actors, the language they use, how their language changed, and what can explain this change. First, we find that authors cluster into several large, interpretable groups, and that the discourse was greatly affected by American partisan politics. Over the course of our study, both Republicans and Democrats entered the vaccine conversation in large numbers, forming coalitions with Antivaxxers and public health organizations, respectively. After the pandemic was officially declared, the interactions between these groups increased. Second, we show that the moral and non-moral language used by the various communities converged in interesting and informative ways. Finally, vector autoregression analysis indicates that differential responses to public health measures are likely part of what drove this convergence. Taken together, our results suggest that polarization around vaccination discourse in the context of COVID-19 was ultimately driven by a trust-first dynamic of political engagement.

No abstract

In this paper we develop and test hypotheses around organisations' behaviour on social media and its effect on consumers' responses. We draw on the notion of the market maven to underpin the research and suggest that organisations on social media need to focus on acting in a maven-like manner in order to influence audiences in Twitter. We collected data from the Twitter accounts of the entire brewing industry in Australia, analysing organisational postings and their impact on influence (follower numbers, retweets) of their respective Twitter accounts. In particular, we look at message formulation and language, native platform behaviour, reciprocity and persistency variables. Findings suggest that establishing a larger follower base requires an interactive, one-to-one and reciprocal approach. In order to influence audiences to retweet organisations need to speak the 'native platform language' and employ a soft-sell strategy. Maven-like behaviour tends to reside in the small independent craft breweries. We offer the conclusion that these craft breweries have realised that, on social media, a different approach to marketing is required: the organisations must act in a mavenlike manner.

No abstract