Extreme speech

Extreme speech is a critical conceptual framework that aims to uncover vitriolic online cultures through comparative and ethnographic excavations of digital practices. It is not one more new definition or a term replaceable with extremist speech. Rather, it is a conceptual framework developed to foreground historical awareness, critical deconstruction of existing categories, and a grounded understanding of evolving practices in online communities, in ways to holistically analyze the contours and consequences of contemporary digital hate cultures. This framework suggests that the close contextualization of proximate contexts - of media affordances in use or situated speech cultures - should accompany deep contextualization, which accounts for grave historical continuities and technopolitical formations unfolding on a planetary scale. Through such elaborate forays into everyday practices and deeper histories, extreme speech theory proposes to nuance normative and regulatory efforts to classify and isolate hate speech and disinformation

Shadow politics: Front stage and the veneer of volunteerism

This article proposes the metaphor of “shadow” to examine two interrelated aspects of digital politics in India: online surveillance of politically inclined actors and datafied shadow texts aimed at managing front stage politics. The specificity of “shadow politics” emerges from ongoing transformations that are deeply interwoven with the digital, first with the data driven confidence around the “total certainty” of tracking and calibrating voter sentiments, and second, with the ideology of digital participation and related claims that data machines are merely tapping into people’s sovereign expressions online

Social media, religious politics, and national (un)belonging in India and the diaspora

In India and its diaspora in the UK, online activities of various sorts—tweeting, blogging, messaging, trolling, and tagging—have become central to tensions surrounding religion's presence in public life and the stakes of belonging to the nation. Three clusters of social media practices undergird these digital mediations: piety, surveillance, and fun. Such practices reveal how internet-enabled mediations reenergize religion as a political category of difference under majoritarian right-wing regimes and the transnational context of Islamophobia, while also offering distinct possibilities for imagining politics through the pleasures, visibilities, and reflections induced by digital circulations. Rather than approaching the internet as an abstract technological context or discrete channels for communication, this analysis theoretically positions it as an arena of “multiple interfaces.” It signals contiguities and collisions that digital practice has opened up among the very real communities and structures of authority, under conditions shaped by longer colonial histories

Nationalism in the Digital Age: Fun as a Metapractice of Extreme Speech

Critical assessments of the recent resurgence of right-wing nationalism have rightly highlighted the role of social media in these troubling times, yet they are constrained by an overemphasis on celebrity leaders defined as populists. This article departs from a leader-centric analysis and the liberal frame that still largely informs assessment of political action, to foreground “fun” as a salient aspect of right-wing mobilization. Building on ethnographic fieldwork among the Hindu nationalists in India, I argue that fun is a metapractice that shapes the interlinked practices of fact-checking, abuse, assembly, and aggression among online volunteers for the right-wing movement. Furthermore, fun remains crucial for an experience of absolute autonomy among online users in ideological battles. Providing the daily drip feed for exclusion, fun as a metapractice bears a formal similarity to objectivity in its performative effects of distance and deniability

Mediatised Terror: Terror in the Age of Media Explosion

The media coverage of the Mumbai terror incidents (between 26 and 28 November 2008) and their aftermath has been widely criticised for various reasons. Yet it is necessary not to frame these criticisms without understanding the structural models that drive news production – which is subsumed largely to capital and is not free of political content

Beyond Acquiescence and Surveillance: New Directions for Media Regulation

The increasingly complex and elusive media landscape has thrown fresh challenges to an unsettled ecosystem of media policy in India. This paper traces some of the challenges posed by the new communications technologies and the variegated field of media practices to argue that the fragmented media policy framework requires a complete makeover in terms of its regulatory objectives, strategies and public media obligations. Emphasising the need for incorporating anthropological and technologically informed perspectives on the nature and implications of current media expansion, the paper proposes that the policy framework should include a modular and unbundled approach to media regulation. Policymakers should also invest research energy into the exercise of mapping the diversity of media practices and multiple logics driving rapid proliferation of media across the country. In the context of growing state practices of surveillance and staggered acquiescence to corporate interests, policy interventions should move beyond the contradictory impulses of “policin

Ethical scaling for content moderation: Extreme speech and the (in)significance of artificial intelligence

In this article, we present new empirical evidence to demonstrate the severe limitations of existing machine learning content moderation methods to keep pace with, let alone stay ahead of, hateful language online. Building on the collaborative coding project “AI4Dignity” we outline the ambiguities and complexities of annotating problematic text in AI-assisted moderation systems. We diagnose the shortcomings of the content moderation and natural language processing approach as emerging from a broader epistemological trapping wrapped in the liberal-modern idea of “the human”. Presenting a decolonial critique of the “human vs machine” conundrum and drawing attention to the structuring effects of coloniality on extreme speech, we propose “ethical scaling” to highlight moderation process as political praxis. As a normative framework for platform governance, ethical scaling calls for a transparent, reflexive, and replicable process of iteration for content moderation with community participation and global parity, which should evolve in conjunction with addressing algorithmic amplification of divisive content and resource allocation for content moderation

Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP

When trained on large, unfiltered crawls from the internet, language models pick up and reproduce all kinds of undesirable biases that can be found in the data: they often generate racist, sexist, violent or otherwise toxic language. As large models often require millions of training examples to achieve good performance, it is difficult to completely prevent them from being exposed to such content. In this paper, we investigate whether pretrained language models at least know when they exhibit some undesirable bias or produce toxic content. Based on our findings, we propose a decoding algorithm that reduces the probability of a model producing problematic text given only a textual description of the undesired behavior. This algorithm does not rely on manually curated word lists, nor does it require any training data or changes to the model's parameters. While our approach does by no means eliminate the issue of language models generating biased text, we believe it to be an important step in this direction