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This paper examines tax justice advocacy (e.g., around multinational corporate tax avoidance) in the UK and Australia between 2010 and 2018 to understand how digital advocacy organizations relate to one another as well as different groups in a shared discursive space. Data from public Facebook pages is analyzed using topic modeling and network analysis, supplemented by interviews with organizational actors. The transnational organizational links between two high-profile digital advocacy organizations – 38 Degrees and GetUp – are not reflected by proximity in the discourse network, which instead displays dominant nationally specific differences around the concepts and corporate targets associated with tax justice advocacy. [ABSTRACT FROM AUTHOR]

This paper explores analysis of feedback data collected from student consultations at two mathematics support centres at universities in Australia and Ireland. Unstructured text data was collected over six years and includes qualitative data on student queries collected during the consultations from mathematics and statistics related subjects. Topic modelling and clustering algorithms are used to uncover key themes in the data across stages. Common areas of difficulty experienced by undergraduate students at both universities are investigated and a comparison between them is shown. The results suggest that, despite institutional differences, there is considerable overlap in the types of mathematical and statistical difficulties experienced by students in their first and second year of university at these institutions. We discuss how the ability to uncover such common mathematical and statistical themes with the aid of text mining techniques can be used to improve the support provided by mathematics support centres in terms of providing an efficient and effective service. The code for analyses at both institutions is provided in a GitHub repository so other academic support centres may use it. Outcomes of this analysis have implications for mainstream mathematics and statistics instructors who wish to gain further insights into their students' learning. [ABSTRACT FROM AUTHOR]

As online mental health support groups become increasingly popular, they require more support from volunteers and trained moderators who help their users through "interventions" (i.e., responding to questions and providing support). We present a system that supports such human interventions using Natural Language Generation (NLG) techniques. The system generates draft responses aimed at reducing moderators' workload, and improving their efficacy. NLG and human interventions were compared through the ratings of 35 psychology interns. The NLG-based system was capable of generating messages that are grammatically correct with clear language. The system needs improvement, however, moderators can already use it as draft responses. [ABSTRACT FROM AUTHOR]