This study analyses the amplification and diffusion of climate misinformation on Twitter during the COP26 and COP27 climate conferences. Drawing on a dataset of over 12 million English-language tweets, we combine machine learning classification, social network analysis, and qualitative content analysis to map how misinformation circulates across user communities. Climate misinformation is understood as content that denies or undermines the scientific consensus on anthropogenic climate change. Using a machine learning classifier trained on annotated climate datasets, tweets were labelled and assigned misinformation probabilities. Using community detection, we were able to distinguish between misinformation, non-misinformation, and mixed communities. Our findings show that misinformation does not remain isolated within echo chambers; instead, it often flows outward, particularly into mixed communities, which serve as key intermediaries between misinformation and non-misinformation communities. Through centrality measures, we identified a small set of influential user accounts that function as amplifiers and brokers of misinformation, both intentionally and inadvertently. These key users exhibit varying patterns of visibility, engagement, and connectivity, where we found two prominent user types to be those of the broadcaster and the mediator. The results show that the dynamics of misinformation dissemination are shaped by both content virality and underlying network structures.

Automated social 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.

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.

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.

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.

In the current mass media landscape with a few corporate owners and operating under the propaganda model of communication aimed at manufacturing system-supportive consent, and the algorithmic-rent seeking business models of most popular social media platforms, we set out to ask whether Peoples still have power to take collective real-world action that may be counter to prevailing media tendencies. We study interactions in social media and the reports in mass media during the Black Lives Matter (BLM) protests following the death of George Floyd. We implement open-source pipelines to process the data at scale and employ the self-exciting counting process known as Hawkes process to address our main question: is there a causal relation between interactions in social media and reports of street protests in mass media? Specifically, we use network models to identify such interactions in Twitter, that supported the BLM movement, and compared the timing of these interaction to those of news reports of street protests mentioning George Floyd, via the Global Database of Events, Language, and Tone (GDELT) Project. The comparison was made through a Bivariate Hawkes process model for a formal hypothesis test of Granger-causality. We show that interactions in social media that supported the BLM movement, at the beginning of nationwide protests, caused the global mass media reports of street protests in solidarity with the movement. We also use more general Hawkes process model to understand the diffusion of specific influential messages in social media. Our study suggests that BLM activists have harnessed social media to mobilise street protests across the planet despite the concentrated ownership of mass media and the algorithmic rent-seeking business models of social media platforms.

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.

This article explores the rapidly developing field of Critical AI Studies and its relation to issues of class and capitalism through a hybrid approach based on distant reading of a newly collected corpus of 300 full-text scientific articles, the creation of which is itself a first attempt at properly delineating the field. We find that words related to issues of class are predominantly but not exclusively confined to a set of studies that make up their own distinct subfield of Critical AI Studies, in contrast to, e.g., issues of race and gender, which are more broadly present in the corpus.

We focused in this study on how the private experience of pain is made public through online discourse by sufferers of endometriosis. Empirically, we analyse two highly active endometriosis communities on the online social platform Reddit. Drawing on a mixed-methods design, we leverage large-scale social data, and a combination of computational and interpretive approaches for text analysis to study the role and shape of interactions relating to 'pain' for the formation of epistemic community online around endometriosis. The dataset, consisting of 70,817 forum posts and comments, was collected in May of 2021. Our study shows how pain becomes meaningful for endometriosis sufferers in relation to a multidimensional discursive space of words and concepts that are used to express it. Pain was frequently disguised, underplayed or hidden altogether, from fears of misunderstanding, medical dismissal, and embarrassment. Clearly, peer validation can be found in the relative anonymity of Reddit discussions. While the experience of pain is individual and subjective, when communities share similar experiences this reinforces patient ownership of the pain, which in turn supports the epistemic authority of the patient collective. A detailed understanding of how and why pain is discussed in online spaces has much to contribute more broadly to discussions of experiential collective knowledge production among individuals with endometriosis and other chronic illnesses.

When #MeToo reached Sweden in the fall of 2017, it gave rise to nearly 80 industry-specific petitions that demanded a stop to sexual misconduct in the workplace, some with their own hashtags. This article examines the discourse of #MeToo on Twitter in Sweden in relation to these petition hashtags. Focusing on how #MeToo, petition hashtags, and other hashtags are co-articulated in Tweets, it maps the emergent network of hashtags using SNA and explores the resulting interpretative frames using discourse analysis. By co-articulating the MeToo and petition hashtags with hashtags related to Swedish politics and feminism, and by utilising the @-mention function to call out responsible politicians and industry executives, Twitter users extended the initial #MeToo frame beyond individualised problems and solutions common in connective action networks. We suggest that Twitter users utilise platform affordances to perform framing work in relation to political hashtags, not unlike framing work performed in traditional social movements.

: In this work we study interactions in social media and the reports in mass media during the Black Lives Matter (BLM) protests following the death of George Floyd. We implement open-source pipelines to process the data at scale and employ the self-exciting counting process known as Hawkes process to address our main question: is there a causal relation between interactions in social media and reports of street protests in mass media? Specifically, we use distributed label propagation to identify such interactions in Twitter, that supported the BLM movement, and compared the timing of these interaction to those of news reports of street protests mentioning George Floyd, via the Global Database of Events, Language, and Tone (GDELT) Project. The comparison was made through a Bivariate Hawkes process model for a formal hypothesis test of Granger-causality. We show that interactions in social media that supported the BLM movement, at the beginning of nationwide protests, caused the global mass media reports of street protests in solidarity with the movement. This suggests that BLM activists have harnessed social media to mobilise street protests across the planet.

By analysing 600 Instagram posts that use mental health related hashtags, this article investigates how mental health communication and support practices are enacted on Instagram, and how such practices relate to the perceptible affordances and hegemonic uses of the service. The article demonstrates how Instagram tends to privilege casual snapshots of individual recovery, in line with broader discourses of positive thinking and individual responsibility. Whereas this hegemonic way of using the service may be functional for many users, three examples of negotiated and oppositional use are also discussed in the article: motivational picture quotes, text-rich posts, and non-recovery oriented posts. It is suggested that different ways of imagining and approaching the affordances of the service engender different patterns of support practices.

In this article, we contrast policy understandings of digital care with older people's day-to-day digital care. In doing so, we discuss problems relating to deterministic approaches in government policy. Our policy analysis shows that digital care is articulated as an individual practice, and digital technologies as static actors. This bears clear marks of techno-deterministic reasoning. Our ethnographic study demonstrates the ongoing and collective character of older people's digital care. When policy is not aligned with everyday practice, there is a risk of excluding groups of users. We argue that a socio-technical approach in government policy could contribute to achieving important societal goals.

In recent years, several high-profile political protests and social movements have formed on and through social media. Whereas most large-scale datasets address singular social media movements political topical discussions, or events, Tweets Across the Political Spectrum 2016–2020 (TAPS) instead provides access to social media data on a broad range of digital political issues and social movements, spanning over several years. The TAPS dataset incorporates data based on a range of high-profile and widely used hashtags, ranging from those on the far left of the political spectrum to those on the far right. Concretely, we introduce a dataset consisting of 207 million tweets, posted between 2016-01-01 and 2020-12-31, using any of the 24 hashtags #alllivesmatter, #antifa, #black- livesmatter, #blackouttuesday, #blm, #bluelivesmatter, #deepstate, #extinctionrebellion, #fridaysforfuture, #greennewdeal, #maga, #marchforourlives, #metoo, #nobannowall, #nodapl, #pizzagate, #qanon, #qarmy, #takeaknee, #unitetheright, #whitegenocide, #whitelivesmatter, #womensmarch, and #wwg1wga. TAPS is relevant for research into political debates, social movements, polarisation and more, within a range of academic fields, including for instance computational social science, sociology, political science, data science, and communication studies. The data span over an extended period during which these hashtags were frequently used, and the dataset is comprehensive enough to identify large-scale patterns and to provide possibili- ties for comparisons over time, within or across political issues. To our knowledge this is the only large-scale dataset which incorporates, to this degree, tweets from different social movements, political discussions, and protests on different ends of the political spectrum.

the immediate aftermath of crisis events, there is a pressing demand among the public for information about what is unfolding. In such moments "information holes" occur, people and organizations collaborate to try to fill these in real time by sharing information. In this article, we approach such gaps not merely as the product of the actual lack of information, but as generated by the algorithmically underpinned social media platforms as such, and by the user behaviors that they proliferate. The lack of information is the result of the noisy and fragmented patchwork of information that social media platforms can generate. In this paper, we draw on a case study of one particular case of a false terrorism alarm and its unfolding on Twitter, that took place in London's Oxford Circus underground station in November of 2017. Using a combination of computational and interpretive methods - analyzing social network structure as well as textual expressions - we find that certain logics of platforms may affect emergency management and the work of emergency responders negatively.

The 22 May 2017 bombing of the Manchester Arena, which killed 22 and injured over 800 more, triggered a massive public response leading to, among other things, improvised memorials, spontaneous vigils, dedicated hashtags, and viral videos. Within this response, the memetic reinvigoration and, subsequently, brand adoption of one of Manchester's oldest civic symbols - the worker bee - was clearly discernible. In this article, we explore how the spread of the bee after the bombing contributed to a politics of post-terror togetherness. Conceptualising memes as 'more or less digital', we 'follow' the bee across bodies, streets and social media platforms via the analysis of approximately 53,000 Instagram images. We show how the initial memeification of the bee carried with it grassroots expressions of togetherness while the subsequent use of the bee in official city branding strategies created and obfuscated various political tensions.