to the rise of a neoliberalism which has both commoditized otherness and stripped away the buffers of the welfare state. The introduction establishes that, although Brennan critiques the formation of \"theory,\" he is not dismissive of theory in principle; he actually is deeply invested in a trajectory of theory, embodied in the Hegel-Marx line: Bakhtin, Lukács, Benjamin, Adorno, Marcuse, Gramsci, Bourdieu, and Said. Lamenting the predominance of the Nietszchèan line—in which he includes Heidegger, Deleuze and Guattari, Baudrillard, Lyotard, Derrida, Vattimo, Negri, and Virilio—Brennan blasts the celebratory and uncritical use of \"poststructuralism's infinitely interchangeable metaphors of dispersal: decentered subjects, nomadism, ambivalence, the supplement, rhizomatic identity, and the constructed self—terms whose sheer quantity nervously intimates a lack of variation.\" At such polemical textual moments, we feel the full force of Brennan's bile at a discipline that has abnegated its responsibilities; at the same time, the polemic (as all polemics do) tends to create the fantasy of an other whose totality is self-evident and whose heterogeneity is merely superficial. What, indeed, about the politically engaged work of Cary Nelson, BarrettWatten, Michael Bibby, andMichael True, among others, not to mention the intellectuals left of Noam Chomsky, whose dissident work may share the anarchism of the academic left, but whose consequences have been real and whose relationship to dissenting movements in the US and throughout the world is undeniable? (Chomsky gets three short mentions in this book.) Brennan's relative exclusion of contemporary examples of Gramscian intellectuals actively engaged with social movements makes Wars ofPosition a difficult book, because it offers few models for emerging from the malaise that the academy seems to Detailfrom cover suffer from. Yet Brennan is clearly at his best when he is arguing against the received versions of theorists, engaging his Hegelian impulses to reverse the unexamined consensus. His critical reassessment of Orientalism (1978), for example, suggests that Said's foundational text on the Western fantasies of the Middle East has been misread as a Foucauldian project; rather, for Brennan, while Orientalism clearly borrows heavily from Foucault, Said ultimately is arguing against the poststructuralist doxa that underwrite much of contemporary postcolonial theory. Said, in Brennan's reading, is a crucial figure not only for his resistance to the sacred cows of poststructuralism, but also for his embrace of the public responsibilities of the intellectual.

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Spaces of Global Capitalism: Towards a Theory of Uneven Geographical Development

As breaking news unfolds people increasingly rely on social media to stay abreast of the latest updates. The use of social media in such situations comes with the caveat that new information being released piecemeal may encourage rumours, many of which remain unverified long after their point of release. Little is known, however, about the dynamics of the life cycle of a social media rumour. In this paper we present a methodology that has enabled us to collect, identify and annotate a dataset of 330 rumour threads (4,842 tweets) associated with 9 newsworthy events. We analyse this dataset to understand how users spread, support, or deny rumours that are later proven true or false, by distinguishing two levels of status in a rumour life cycle i.e., before and after its veracity status is resolved. The identification of rumours associated with each event, as well as the tweet that resolved each rumour as true or false, was performed by journalist members of the research team who tracked the events in real time. Our study shows that rumours that are ultimately proven true tend to be resolved faster than those that turn out to be false. Whilst one can readily see users denying rumours once they have been debunked, users appear to be less capable of distinguishing true from false rumours when their veracity remains in question. In fact, we show that the prevalent tendency for users is to support every unverified rumour. We also analyse the role of different types of users, finding that highly reputable users such as news organisations endeavour to post well-grounded statements, which appear to be certain and accompanied by evidence. Nevertheless, these often prove to be unverified pieces of information that give rise to false rumours. Our study reinforces the need for developing robust machine learning techniques that can provide assistance in real time for assessing the veracity of rumours. The findings of our study provide useful insights for achieving this aim.

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Analysing how people orient to and spread rumours in social media by looking at conversational threads

In this article, I inquire into Facebook’s development as a platform by situating it within the transformation of social network sites into social media platforms. I explore this shift with a histo...

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The Platformization of the Web: Making Web Data Platform Ready

Avoiding Politics: How Americans Produce Apathy in Everyday Life

Despite the increasing use of social media platforms for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i.e., items of information that are unverified at the time of posting. At the same time, the openness of social media platforms provides opportunities to study how users share and discuss rumours, and to explore how to automatically assess their veracity, using natural language processing and data mining techniques. In this article, we introduce and discuss two types of rumours that circulate on social media: long-standing rumours that circulate for long periods of time, and newly emerging rumours spawned during fast-paced events such as breaking news, where reports are released piecemeal and often with an unverified status in their early stages. We provide an overview of research into social media rumours with the ultimate goal of developing a rumour classification system that consists of four components: rumour detection, rumour tracking, rumour stance classification, and rumour veracity classification. We delve into the approaches presented in the scientific literature for the development of each of these four components. We summarise the efforts and achievements so far toward the development of rumour classification systems and conclude with suggestions for avenues for future research in social media mining for the detection and resolution of rumours.

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Detection and Resolution of Rumours in Social Media: A Survey

This study focuses on journalists Paul Lewis (The Guardian) and Ravi Somaiya (The New York Times), the most frequently mentioned national and international journalists on Twitter during the 2011 UK summer riots. Both actively tweeted throughout the four-day riot period and this article highlights how they used Twitter as a reporting tool. It discusses a series of Twitter conventions in detail, including the use of links, the taking and sharing of images, the sharing of mainstream media content and the use of hashtags. The article offers an in-depth overview of methods for studying Twitter, reflecting critically on commonly used data collection strategies, offering possible alternatives as well as highlighting the possibilities for combining different methodological approaches. Finally, the article makes a series of suggestions for further research into the use of Twitter by professional journalists.

Twitter as a reporting tool for breaking news

For social scientists, the widespread adoption of social media presents both an opportunity and a challenge. Data that can shed light on people’s habits, opinions and behaviour is available now on a scale never seen before, but this also means that it is impossible to analyse using conventional methodologies and tools. This article represents an experiment in applying a computationally assisted methodology to the analysis of a large corpus of tweets sent during the August 2011 riots in England.

Reading the riots on Twitter: methodological innovation for the analysis of big data

YouTube is one of the world's most popular websites and hosts numerous amateur and professional videos. Comments on these videos might be researched to give insights into audience reactions to important issues or particular videos. Yet, little is known about YouTube discussions in general: how frequent they are, who typically participates, and the role of sentiment. This article fills this gap through an analysis of large samples of text comments on YouTube videos. The results identify patterns and give some benchmarks against which future YouTube research into individual videos can be compared. For instance, the typical YouTube comment was mildly positive, was posted by a 29-year-old male, and contained 58 characters. About 23% of comments in the complete comment sets were replies to previous comments. There was no typical density of discussion on YouTube videos in the sense of the proportion of replies to other comments: videos with both few and many replies were common. The YouTube audience engaged with each other disproportionately when making negative comments, however; positive comments elicited few replies. The biggest trigger of discussion seemed to be religion, whereas the videos attracting the least discussion were predominantly from the Music, Comedy, and How to & Style categories. This suggests different audience uses for YouTube, from passive entertainment to active debating. © 2012 Wiley Periodicals, Inc.

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Commenting on YouTube videos: From guatemalan rock to El Big Bang

In this article we examine the hundreds of videos that were posted to YouTube in response to the fierce anti-Islam video Fitna. We use this case to analyse whether and how the participatory opportu...

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Performing citizenship on YouTube: Activism, satire and online debate around the anti-Islam video Fitna

This paper looks at how data is ‘made’, by whom and how. Rather than assuming data already exists ‘out there’, waiting to simply be recovered and turned into findings, the paper examines how data is co–produced through dynamic research intersections. A particular focus is the intersections between the application programming interface (API), the researcher collecting the data as well as the tools used to process it. In light of this, this paper offers three new ways to define and think about Big Data and proposes a series of practical suggestions for making data.

Farida Vis

Papers

Twitter as a reporting tool for breaking news

Reading the riots on Twitter: methodological innovation for the analysis of big data

Commenting on YouTube videos: From guatemalan rock to El Big Bang

Performing citizenship on YouTube: Activism, satire and online debate around the anti-Islam video Fitna

A critical reflection on Big Data: Considering APIs, researchers and tools as data makers