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This chapter introduces the phenomenon of political bots on social media, with a particular focus on Twitter. First, an explanation is given of what political bots are. Second, a brief orientation is provided on how they can be approached theoretically. Third, an example dataset—covering #antifa tweets 2016–2020—is used to illustrate how attempts can be made to detect bots. Finally, fourth, concise examples are given of how one might proceed with computational analyses (SNA and NLP) informed by the bot detection information. The aim of this chapter is to give initial conceptual and methodological guidance for scholars in the area of computational social science who venture into the field of studying political bots.

This chapter delves into the significant yet underexplored role of bots—automated social media accounts designed to mimic human behaviour—in the realm of progressive politics, with a focus on their impact within the #blacklivesmatter and #metoo movements on Twitter, two emblematic movements in progressive politics. The chapter first introduces political bots as a sociotechnical phenomenon, followed by a review of existing literature on social bots and their role in politics and social movements. It presents an explorative analysis of the prevalence and influence of bots within the #blacklivesmatter and #metoo movements, highlighting how bots can occupy significant positions within social network structures, thereby affecting the dynamics of hashtag activism. Despite the common association of bots with conservative and populist movements, this chapter reveals their substantial presence within progressive political movements, challenging traditional notions of political engagement and suggesting a complex interplay between humans and machines in contemporary activism.

This article explores how “the left” meme and the character and emotional reception of taboo-breaking therein via the case of r/DankLeft—a USA-centric Marxist, Anarchist, and Democratic Socialist Internet meme community. It asks: what themes do popular r/DankLeft Internet memes relate to, how does taboo feature within popular r/DankLeft Internet memes, and can any differences in the ways in which taboo-related r/DankLeft Internet memes are received be discerned. In turn, it carries out a thematic analysis of 366 popular memes, a multimodal critical discourse analysis of 41 taboo-related popular memes, and a comparative sentiment analysis of the comments these and other memes have received in r/DankLeft. The article finds that popular memes in r/DankLeft primarily relate to perceived threats to its community of users. It also shows that taboo-breaking does feature in r/DankLeft memes and that when it does correlative patterns emerge in terms of popularity and emotional reception.

In contemporary society, we live and act within institutions. Institutions are understood here broadly as behavioural regularities in social interaction, which may result from people following formal or informal rules, explicit or tacit norms, or even taken-for-granted ways of acting in society. Understood broadly in this way, as many sociologists, anthropologists, political scientists, and economists do, social institutions are ubiquitous. When we buy and sell, learn and teach, or even simply socialize, our behaviours take place within social institutional frameworks of one kind or another. Despite the obvious implications of institutions for how one should act and live, and the importance accorded them in other human sciences, social psychology's engagement with institutions has been fragmented. In an attempt to orient a coherent approach, this chapter develops a social psychological perspective on institutions, discusses existing relevant psychological research in this perspective, and explores the implications of this perspective for future research.

Automated social agents-bots-are increasingly central to digital environments, yet definitions of what constitutes a bot vary across expert communities. This article analyses how bots are conceptualised in academic and technological discourse by examining scholarly publications (Scopus) and developer discussions (Stack Overflow). Using computational methods, including keyword-in-context analysis and topic modelling, we trace epistemic differences in bot definitions across disciplines. Findings reveal structural discursive silos, with technical fields emphasising functional properties and social sciences focusing on sociotechnical entanglements. These definitional divergences have implications for research, regulation, and governance in an era of AI-driven automation.

Conflicts over the management and governance of forests seem to be increasing. Previous media studies in this area have largely focused on analysing the portrayal of specific conflicts. This study aims to review how a broad range of forest conflicts are portrayed in the Swedish media, analysing their temporal, spatial, and relational dimensions. We applied topic modelling, a machine learning approach, to analyse 53,600 articles published in the Swedish daily press between 2012 and 2022. We identified 916 topics, of which 94 were of interest for this study. Our results showed ten areas of forest conflicts: hunting and fishing (35 % of total coverage), energy (24 %), recreation and tourism (11 %), nature conservation (8 %), forest damages (6 %), international issues (5 %), forestry (5 %), reindeer husbandry (4 %), media and politics (2 %), and mining (1 %). The overall coverage of forest conflicts increased significantly over the study period, potentially reflecting an actual increase in forest conflicts. Some of the conflicts were continuously reported upon over time, while the coverage of others exhibited seasonal or event-related patterns. Four conflicts received most of their coverage in specific regions, while others were covered across the whole of Sweden. A relational analysis of the conflicts revealed three clusters of forest conflicts focused respectively on industrial, cultural, and conservation conflicts. Our results emphasise the value of using topic modelling to understand the overall patterns and trends of the media coverage of current land use conflicts, while also highlighting potential areas of emerging conflicts that may be of special interest for planners and policy-makers to monitor and manage.

The use of hashtags has become an effective tool for activists to mobilize public support. This study explores whether, and in what ways, such hashtags have been adopted by politicians in power. Conducting a systematic, cross-national analysis, we examine how politicians use, what we call, activism-related hashtags. Using data from the Twitter Parliamentarian Database, we analyze the hashtagging practices of politicians in 10 countries: Australia, Denmark, France, Germany, Italy, Norway, Spain, Sweden, the United Kingdom, and the United States. The analysis explores what types of hashtags politicians use, and to what extent these tags are activism-related. We also analyze what activist causes hashtags used by politicians are related to, to better understand what causes are the most palatable to politicians. We further analyze qualitatively how the activism-related hashtags are used by the politicians. Through a combination of thematic analysis and frame analysis, we find that, in relation to the wide range of hashtags that politicians use, activism-related hashtags constitute a limited share. Our analysis also indicates that although politicians do indeed use activism-related hashtags, this can be for many different reasons and purposes, beyond merely supporting the cause or position of the original activist initiative. We find that politicians may join in with the key contention behind the hashtag, renegotiate the meaning of the hashtag to be able to align party-political ideologies with it, or engage with it by questioning or subverting it.

Growing awareness of the societal consequences of datafication in recent years has given rise to a new form of civil society engagement called data activism. This article examines the discourse surrounding data activism on the social media platform Twitter. Through a mixed-methods approach combining computational analysis of Twitter content and close readings of Twitter profiles, we explore how new forms of civil society action related to data justice are articulated and linked to other forms of activism, conflicts and problems, and the actors involved in these articulations. Our analysis reveals a distinction between two articulatory patterns in the data activism discourse. The first involves grassroots actors, such as community organisations and individual citizens, who challenge existing power structures and advocate for social change. The second, on the other hand, is associated with academics, capitalists and policymakers who already hold positions of power and influence. This asymmetry is consistent with previous findings in data activism research. We encourage future research to extend these patterns, using additional methods and case studies, to further refine and contextualise the understanding of data activism within the civil society realm.

This Crosscurrent contribution presents programmable politics as an emerging keyword for understanding the complex interplay between technology, society and politics in the 21st century. Programmable politics has gained heightened importance in the aftermath of the pandemic that has sped up digitalisation processes that are the preconditions for programmable politics to emerge. Turning increasingly to engagement online, the pandemic constitutes a catalyst for programmable politics. The concept highlights both the potential for enhancing democratic engagement, and the risks of undermining it through the centralisation of control and manipulation of information flows. We discuss the transition from digital politics, characterised by the integration of the internet and social media into political discourse and action, to programmable politics, a concept that highlights the impact of platform architectures, algorithms, artificial intelligence (AI) and non-human agency on the political landscape. In doing so, we call for a critical examination of how current digital technology reshapes the dynamics of power, control and resistance within the political domain.

We live in an increasingly digitised society. In an age of digital identities, rapid developments in Artificial Intelligence and ever more sophisticated software available, our methods for researching digital media must be flexible and adaptable. This book will help you to understand why researchers in this field choose and use particular research methods, equipping you to put these methods into practice across the whole range of undergraduate media courses. This book shows you how research methods can help us to make sense of the myriad of information we encounter online every day, from Tiktok influencers to viral Twitter posts. Complete with case studies in each chapter, the book covers both well-established methods, such as network analysis, and cutting-edge ones, such as interface analysis. It provides a crucial foundation for research in digital media, demonstrating the scope and potential of these tools. The book adopts an easy-to-navigate structure, taking you through specific methods in a systematic way. It shows you examples of classic uses of each method, and directs you towards further resources after each chapter.