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Climate change is one of the key societal challenges of our times, and its debate takes place across scientific disciplines and into the public realm, traversing platforms, sources, and fields of study. The analysis of such mediated debates has a strong tradition, which started in communication science and has since then been applied across a wide range of academic disciplines. So-called 'content analysis' provides a means to study (mass) media content in many media shapes and formats to retrieve signs of the zeitgeist, such as cultural phenomena, representation of certain groups, and the resonance of political viewpoints. In the era of big data and digital culture, in which websites and social media platforms produce massive amounts of content and network this through hyperlinks and social media buttons, content analysis needs to become adaptive to the many ways in which digital platforms and engines handle content. This book introduces Networked Content Analysis as a digital research approach, which offers ways forward for students and researchers who want to work with digital methods and tools to study online content. Besides providing a thorough theoretical framework, the book demonstrates new tools and methods for research through case studies that study the climate change debate with search engines, Twitter, and the encyclopedia project of Wikipedia.

Disinformation and so-called fake news are contemporary phenomena with rich histories. Disinformation, or the willful introduction of false information for the purposes of causing harm, recalls infamous foreign interference operations in national media systems. Outcries over fake news, or dubious stories with the trappings of news, have coincided with the introduction of new media technologies that disrupt the publication, distribution and consumption of news -- from the so-called rumour-mongering broadsheets centuries ago to the blogosphere recently. Designating a news organization as fake, or der Lügenpresse, has a darker history, associated with authoritarian regimes or populist bombast diminishing the reputation of 'elite media' and the value of inconvenient truths. In a series of empirical studies, using digital methods and data journalism, the authors inquire into the extent to which social media have enabled the penetration of foreign disinformation operations, the widespread publication and spread of dubious content as well as extreme commentators with considerable followings attacking mainstream media as fake.

This chapter enquires into the resonance of junk news on Twitter during the campaign periods prior to the 2019 Dutch Provincial elections and European Parliamentary elections. Querying Twitter for political topics related to the two elections, and various divisive social issues such as Zwarte Piet and MH17, we analyse the spread and prominence of problematic sources. We also examined the claim that Twitter is susceptible to abuse by bot and troll-like users, and found that troll-like users were active across all political and issue spaces during the Dutch Provincial elections of 2019. Divisive issues remain steadily (even if marginally) active in junk news and tendentious news throughout the tested time frames, suggesting these issues are year-round rather than event-based or seasonal, as they are in the mainstream media.

Wikipedia is often considered as an example of 'collaborative knowledge'. Researchers have contested the value of Wikipedia content on various accounts. Some have disputed the ability of anonymous amateurs to produce quality information, while others have contested Wikipedia's claim to accuracy and neutrality. Even if these concerns about Wikipedia as an encyclopaedic genre are relevant, they misguidedly focus on human agents only. Wikipedia's advance is not only enabled by its human resources, but is equally defined by the technological tools and managerial dynamics that structure and maintain its content. This article analyses the sociotechnical system — the intricate collaboration between human users and automated content agents — that defines Wikipedia as a knowledge instrument.

This work offers an approach to conceptualizing, demarcating and analyzing a national web. Instead of defining a priori the types of websites to be included in a national web, the approach put forward here makes use of web devices (platforms and engines) that purport to provide (ranked) lists of URLs relevant to a particular country. Once gathered in such a manner, the websites are studied for their properties, following certain of the common measures (such as responsiveness and page age), and repurposing them to speak in terms of the health of a national web: Are sites lively, or neglected? The case study in question is Iran, which is special for the degree of Internet censorship undertaken by the state. Despite the widespread censorship, we have found a highly responsive Iranian web. We also report on the relationship between blockage, responsiveness and freshness, i.e., whether blocked sites are still up, and also whether they have been recently updated. Blocked yet blogging portions of the Iranian web show strong indications of an active Internet censorship circumvention culture. In seeking to answer, additionally, whether censorship has killed content, a textual analysis shows continued use of language considered critical by the regime, thereby indicating a dearth of self-censorship, at least for websites that are recommended by the leading Iranian platform, Balatarin. The study concludes with the implications of the approach put forward for national web studies, including a description of the benefits of a national web health index.

Many of the papers and more-than-textual proposals submitted for this special issue included machine vision technologies and other data- and AI- mediated practices. To provide a critical per­spective on data-driven (design) research, we decided to explore the emerging field of data fem­inism through online interviews with three scholars and practitioners who apply intersectional feminist theory and practice to the realm of data-driven work: Catherine D'Ignazio, Lauren Klein, and Maya Livio. With Catherine D'Ignazio and Lauren Klein, authors of the book Data Feminism (2020), we touch upon the idea of data feminism as a way of thinking about (and acting upon) data and data science, informed by intersectional feminist thinking. From examining and challenging power structures in the data collection process to embracing pluralism beyond binaries and hierarchies, they outline a research program that clarifies why and how data science needs intersectional feminism. With them, we discuss how art and (speculative) design practices can make power imbalances visible. We also discuss the limitations and advantages of participatory data practices and the responsibil­ity that lies upon data collectors when making visible an issue through data can cause more harm than good to those affected by it. We discuss how sometimes one needs to reject ground rules of data visualization to pursue higher political goals beyond simple analytical needs. We conclude this conversation with an invitation to embrace complexity when applying feminist principles to data work, while being aware of our personal standpoints and limitations. With Maya Livio, researcher and curator at the University of Colorado Boulder, we discuss how an intersectional feminist approach to data science can also consider more-than-human beings. We talk about her work on animal interfaces, in which she explores how the contact points between the human and more-than-human worlds are permeated with technology. Maya Livio then takes us through her experiences in feminist labs, explaining how the first step of incorporating a fem­inist practice is to take stock of and codify the work being done, cultivating attention towards (of­ten unspoken or unwritten) methods and practices. We also discuss how she and her colleagues developed a framework for operationalizing the art of noticing as a methodological contribution. Finally, we touch upon her personal research approach, characterized by a mix of experimental multidisciplinary practices, moving from writing to curating to design and art-making. ; Muchos de los artículos y las propuestas más-que-textuales que se presentaron para este número especial incluían tecnologías de visión artificial y otras prácticas mediadas por datos e inteligencia artificial (IA). Con el propósito de ofrecer una perspectiva crítica sobre la investigación (de diseño) basada en datos, decidimos explorar el campo emergente del feminismo de datos a través de en­trevistas en línea con tres académicas y profesionales que aplican la teoría y la práctica feminista interseccional al trabajo basado en datos: Catherine D'Ignazio, Lauren Klein y Maya Livio. Con Catherine D'Ignazio y Lauren Klein, autoras del libro Data Feminism (2020), abordamos la idea del feminismo de datos como una manera de pensar (y actuar) sobre los datos y la ciencia de datos, la que se caracteriza por estar informada por el pensamiento feminista interseccional. Desde la necesidad de examinar y desafiar las estructuras de poder en el proceso de recopilación de da­tos hasta la necesidad de abrazar el pluralismo más allá del pensamiento binario y las jerarquías, D'Ignazio y Klein esbozan un programa de investigación que aclara por qué y cómo la ciencia de datos necesita el feminismo interseccional. Con ellas discutimos cómo el arte y las prácticas de diseño (especulativo) pueden hacer visibles los desequilibrios de poder. También discutimos las limitaciones y ventajas de las prácticas participativas de datos y la responsabilidad que recae sobre quienes recolectan datos cuando usar datos para hacer visible un tema puede causar más daño que beneficios a los afectados. Discutimos cómo, a veces, es necesario rechazar las reglas básicas de la visualización de datos para alcanzar objetivos políticos más elevados que las simples necesidades analíticas. Concluimos esta conversación con una invitación a abrazar la complejidad al momento de aplicar los principios feministas al trabajo con datos, siendo conscientes de nuestros puntos de vista y limitaciones personales. Con Maya Livio, investigadora y curadora de la Universidad de Colorado Boulder, hablamos de la manera en que un enfoque feminista interseccional de la ciencia de datos puede tener en cuenta también a los seres más-que-humanos. Conversamos sobre su trabajo con interfaces animales, en el que explora cómo los puntos de contacto entre los mundos humano y más-que-humano están im­pregnados de tecnología. A continuación, Maya Livio nos lleva a sus experiencias en los labora­torios feministas, para explicarnos que el primer paso para incorporar una práctica feminista es hacer un balance o inventario y codificar el trabajo que se está realizando, cultivando asimismo la atención hacia los métodos y las prácticas (a menudo tácitos o no escritos). También discutimos cómo ella y sus colegas desarrollaron un marco para operacionalizar el "arte de notar" como una contribución metodológica. Por último, nos referimos a su enfoque personal de investigación, ca­racterizado por una mezcla de prácticas multidisciplinares experimentales, que van desde la es­critura hasta la curatoría, pasando por el diseño y la creación artística.

There is growing awareness about how social media circulate extreme viewpoints and turn up the temperature of public debate. Posts that exhibit agitation garner disproportionate engagement. Within this clamour, fringe sources and viewpoints are mainstreaming, and mainstream media are marginalized. This book takes up the mainstreaming of the fringe and the marginalization of the mainstream. In a cross-platform analysis of Google Web Search, Facebook, YouTube, Reddit, Twitter, Instagram, 4chan and TikTok, we found that hyperpartisan web operators, alternative influencers and ambivalent commentators are in ascendency. The book can be read as a form of platform criticism. It puts on display the current state of information online, noting how social media platforms have taken on the mantle of accidental authorities, privileging their own on-platform performers and at the same time adjudicating between claims of what is considered acceptable discourse