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There is a well-established critique of current forms of electronic information systems (IS) in social work organisations and attention is now turning to their redesign for the future. In this article we go beyond critiques that have established how this occurred to explore one of the reasons why current forms of IS have been observed to undermine frontline practice. In the same way that technological artefacts are observed to mediate human action by 'configuring the user', IS have also been developed, or configured, according to ideas about how things should be done, known as 'embodied structures'. In this article, examples of IS functionality are drawn upon to demonstrate how the logics of New Public Management (NPM) have been embodied in current forms of IS. It is argued that the logics of NPM must be challenged if new forms of IS are to be developed that amplify the ability of practitioners.

AbstractThis paper introduces and evaluates the Coordination Network Toolkit, an open-source software package and methodological framework designed to detect and analyse coordinated behaviour on social media platforms. As the dynamics of online communication continue to evolve, coordination analysis has emerged as an important field of study with significant implications for understanding online influence, digital astroturfing, and online activism. Recognising the absence of a comprehensive, open-source tool for constructing coordination networks, our approach fills this gap, catering to multiple behaviors across diverse social media platforms. Our approach synthesises and significantly enhances various methods to provide a methodological framework for 'multi-behaviour' coordination detection, utilising weighted, directed multigraphs to capture intricate coordination dynamics. We evaluate our approach by revisiting a case study of the 2020 #ReopenAmerica Covid protest movement on Twitter. The paper concludes with a set of recommendations for future work, emphasising the need for a tailored statistical framework for coordination analysis and a deeper exploration into the motives behind online coordination.

Der Kurznachrichtendienst Twitter hat sich zu einer bedeutsamen Plattform der politischen Online-Kommunikation entwickelt. Doch nicht nur Politiker/innen und Parteien sowie das Publikum des politischen Systems scheinen auf Twitter aktiv zu sein. Zunehmend spielen auch automatisierte Accounts - sogenannte Socialbots - eine wichtige Rolle in der Online-Kommunikation und werden systematisch dazu eingesetzt, laufende Kommunikationsprozesse zu beeinflussen und zu manipulieren. Doch auch wenn entsprechende automatisierte Accounts gemeinhin als Problem und Gefahr für Prozesse der Meinungsbildung im Internet betrachtet werden, existiert bisher nur wenig gesichertes Wissen darüber, inwieweit es ihnen tatsächlich gelingt, öffentliche Debatten auf Twitter zu beeinflussen. Vor diesem Hintergrund zielt der Beitrag am Beispiel von Online-Konversationen zur US-Präsidentschaftswahl 2016 darauf ab, erste Einsichten über Popularität und Einfluss automatisierter Accounts in politischer Online-Kommunikation zu vermitteln. Diese Einsichten wurden mithilfe eines Methodenmixes aus formalen Methoden der sozialen Netzwerkanalyse und qualitativen Methoden zur Kategorisierung der Eigenschaften und Aktivitäten von Twitter-Accounts gewonnen und verweisen darauf, dass automatisierte Accounts es zwar nicht schaffen Spitzenwerte hinsichtlich Popularität und Einfluss auf laufende Konversationen zu erlangen, aber dennoch im Vergleich zu »normalen« User/innen überdurchschnittlich erfolgreich in ihren Bemühungen darum sind.

In this article, we examine two interrelated hashtag campaigns that formed in response to the Victorian State Government's handling of Australia's most significant COVID-19 second wave of mid-to-late 2020. Through a mixed-methods approach that includes descriptive statistical analysis, qualitative content analysis, network analysis, computational sentiment analysis and social bot detection, we reveal how a small number of hyper-partisan pro- and anti-government campaigners were able to mobilise ad hoc communities on Twitter, and – in the case of the anti-government hashtag campaign – co-opt journalists and politicians through a multi-step flow process to amplify their message. Our comprehensive analysis of Twitter data from these campaigns offers insights into the evolution of political hashtag campaigns, how actors involved in these specific campaigns were able to exploit specific dynamics of Twitter and the broader media and political establishment to progress their hyper-partisan agendas, and the utility of mixed-method approaches in helping render the dynamics of such campaigns visible.

Gaining the attention of legislators in a crowded advocacy landscape is a key dilemma for organized interests. Yet, there has not been a great deal of direct analysis of whether groups are indeed recognized as important by politicians in the context of them advancing political arguments. In this article, we examine under what conditions interest groups achieve prominence among political elites. Drawing on a supervised machine learning approach to code prominence from legislative speech, we exploit variations in levels of prominence for the entire Australian interest groups system. We find that prominence is highly concentrated and that it covaries with the need to align with prevailing policy agendas and the logic of conflict expansion. Conversely, we do not find evidence of a strong partisan or ideological dimension of prominence. This contributes to our understanding of the responsiveness and representativeness of democratic political systems in which the interest group sector is expected to funnel public preferences into policymaking.