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The paper aims at creating a bridge between media and migration studies and critical algorithm studies. By adopting a media ecological approach and a mutual shaping of technology and society perspective, in this paper, we explore the factors that lead, especially in Italy, to discriminant and stigmatizing image search results, related to specific groups of immigrants living in the country. We performed a content analysis of Google-Images search results with regard to the largest immigrant communities hosted in France, Germany, Italy, and the United Kingdom. Results show that the depiction of Romanian, Albanian, Moroccan, and Algerian immigrant communities on Google.it is flattened on a univocal stigmatized representation that shows them as criminals, which is not the case in other countries. Most of these stigmatizing images derive from local online newspapers, which questions the interplay between newsmaking choices and routines, and algorithms logics. [ABSTRACT FROM AUTHOR]

In recent years, deepfakes have become part and parcel of contemporary algorithmic culture. It is regularly claimed that they have the potential to introduce novel modes of societal disruption, violence, and harm. Yet, over-emphasising the power of deepfakes risks occluding frictions, struggles, and logics that already persist in the digital landscape. Arguing for a conceptualisation of deepfakes as an assemblage of differential tensions in society, we explore how they represent both a rupture and a continuation of the variegated politics of the image in the social world. The paper analyses the tensions of deepfakes through three distinct case studies: bodies, politics, and ideas of objectivity. Ultimately, we argue that the tensions and ethicopolitical implications of deepfakes are not reducible to a problem that can be solved through a logic of algorithmic detection and verification. [ABSTRACT FROM AUTHOR]

This article examines the datafication of ageing by drawing on a practice approach toward care. We describe the datafication of ageing as a matter of care, achieved through the local tinkering of actors – technology designers, care staff, older adults, and highlighting the practices necessary to develop, maintain and implement data infrastructures. This paper draws on research conducted in a qualitative interview study in a LTC facility that uses AI-supported sensors to detect, predict and alarm care staff about falls of older residents. 18 interviews with developers, staff, residents and interest groups were conducted, as well as 24 h of participant observation in the care facility. The results reveal how AI-development for older target groups is characterized by absent data on these populations. Designers turn to practices that decontextualize data from the realities of older adults, relying on domain experts or synthetic data. This decontextualization of data requires recontextualization, with staff and older residents ensuring that the system functions smoothly, adapting their behavior, protecting the system from making false decisions and making existing care arrangements 'fit' the databases used to monitor activities in these arrangements. The ambivalent position of older adults in this data assemblage is further highlighted, as their caring practices are made invisible by different actors through ageist stereotypes, positioning them as being too frail to understand and engage with the system. While their bodily behavior is core for the databases, their perspective on and engagements with the operating system are marginalized, rendering some aspects of ageing hyper-visible, and others invisible. [ABSTRACT FROM AUTHOR]

This paper argues that the social power of algorithms comes to the fore through the narratives they generate about individuals. Proposing the notion of 'algorithmic emplotment', the article showcases the ways in which algorithms construct and tell narratives about us, participating in shaping people's encounters with the world and their perceptions of it. The concept denotes the processes through which data, people, experiences, and complex temporalities are ordered, woven together, and presented as coherent, frictionless narratives in the present. Through an analysis of the smartphone feature called Apple Memories, the paper seeks to highlight the narratives algorithms tell, how they are constructed, and the potential impacts they may have on everyday life. The concept of algorithmic emplotment is used to scrutinise the ways in which people's lives are rendered sequential, ordered, and ultimately meaningful and actionable by algorithmic processes. [ABSTRACT FROM AUTHOR]

Digital Platforms consist of algorithms and rules that shape consumer behaviour. When faced with these embodiments of the platform's interests, how do consumers protect their own interests? Through multi-method, qualitative fieldwork focused on commuters using ride-hailing platforms in Metro Manila, this paper shows that consumers develop strategies to achieve better terms for themselves. This paper contributes to the literature on algorithmic control and user agency in two ways. First, it proposes a fine-grained typology of consumer strategies used in algorithmic digital platforms, consisting of 5 major types and 18 sub-types. Second, the typology sheds light on the distinct characteristics of consumer strategies and their implications. Future studies into user strategies, algorithmic systems, and digital platforms will benefit from the typology and implications laid out here. [ABSTRACT FROM AUTHOR]

The recent dispersion of algorithms throughout a large part of social life makes them valid analytical objects for sociology in the twenty-first century. The ubiquity of algorithms has led to increased public attention, scrutiny and, consequently, regulation. That is the focus of this paper. I will show that such regulatory processes are not just aimed at preventing certain algorithmic activities, but that they are also co-producing algorithms. They determine, in specific settings, what an algorithm is and what it ought to do. I will illustrate this by comparing two different European regulations aimed at algorithmic practices: the regulation of trading algorithms in the German High Frequency Trading Act and in the Markets in Financial Instruments Directive (MiFID II), and the regulation of personal data processing in the General Data Protection Regulation (GDPR). [ABSTRACT FROM AUTHOR]

Algorithms make highly consequential decisions and, thereby, exercise considerable power. In this study, I investigate how social power through algorithms is exercised in media consumption, particularly through curation algorithms. This conceptual paper then contributes to the understanding of social power through algorithms by suggesting the concept of algorithmic meta-capital. The concept derives from Bourdieu's theory on meta-capital which has also been applied to legacy media. I then argue that this algorithmic meta-capital is an extension of the power traditionally held by the state and legacy media. The study also contributes to the understanding of meta-capital as it proposes how the meta-capital possessed by digital intermediaries functions. It does so by legitimating representations of the world and by creating a necessity for algorithmic visibility across different fields, thereby shaping habitus. This Bourdieusian approach enables researchers to take a balanced view on the power of algorithms on the structure/agency continuum. [ABSTRACT FROM AUTHOR]

In the algorithmic era, both users and the platform battle for visibility. Chinese fans are savvy users who explore the hidden algorithms behind platform functions. With the collectively developed algorithmic imaginary, digital fandom communities negotiate with the platform over algorithms to optimize the visibility of celebrities they endorse. Drawing from participatory observation and semi-structured interviews in Chinese online fandoms of an idol group, INTO1, we detailed how fans as digital users collectively explore, interpret, and creatively utilize algorithms to increase their idol's visibility. We conclude that visibility, as a representation of algorithm power, is co-defined through the constant push-and-pull between digital users and the platform. This paper contributes to both algorithm and fandom studies by describing large-scale non-professional users' daily construction of the algorithmic imaginary in the unique context of Chinese fandom and beyond. It also discusses broader civic implications of fans' algorithmic practices to wider digital users in China. [ABSTRACT FROM AUTHOR]

Algorithms are an integral part of our everyday lives and shape the selection and presentation of information and communication on the internet. At the same time, media users are faced with a lack of control and transparency when interacting with these systems because algorithms largely remain black boxes to end users. Relying on the notion that algorithms are socio-technical systems that comprise both technical and human components, this paper examines internet users' awareness of algorithms in different areas of internet use and inquires into users' perceptions of the impact of algorithms on their autonomy when interacting online. Empirically, we rely on qualitative interviews with 30 German internet users. Findings indicate that users in general are aware of algorithms operating in a wide range of applications and demonstrate a basic understanding of how these systems work. In line with the third-person effect, users perceive algorithms to have a stronger impact on others' internet use than on their own. Further, users' awareness of algorithms was found to be closely related to their perceived autonomy. When users feel in control of their interactions online, they are less aware of the impact of algorithms governing their interactions. Based on these results, we discuss the implications for transparency measures in algorithm regulation. [ABSTRACT FROM AUTHOR]

The rapid development and dissemination of data analysis techniques permits the creation of ever more intricate algorithmic models. Such models are simultaneously the vehicle and outcome of quantification practices and embody a worldview with associated norms and values. A set of specialist skills is required to create, use, or interpret algorithmic models. The mechanics of an algorithmic model may be hard to comprehend for experts and can be virtually incomprehensible to non-experts. This is of consequence because such black boxing can introduce power asymmetries and may obscure bias. This paper explores the practices through which experts and non-experts determine the credibility of algorithmic models. It concludes that (1) transparency to (non-)experts is at best problematic and at worst unattainable; (2) authoritative models may come to dictate what types of policies are considered feasible; (3) several of the advantages attributed to the use of quantifications do not hold in policy making contexts. [ABSTRACT FROM AUTHOR]

Music streaming services all provide affordable and easy access to massive databases of music and instead attempt to increase customer loyalty by optimizing personalized recommendations and offering opportunities for listeners to build their 'own' music libraries. In this paper, these features are operationalized as the convenience, price, archive and algorithmic value of streaming services. Existing studies detail the multitude of ways of making sense of these services, but do not allow for specifying what features matter more. Drawing on an online survey and interviews, this study indicates that price, convenience and archive value predict continued intention to use music streaming services. Survey results negate the importance of algorithmic value, but the interviews suggest that for some, algorithmic individuation is too evasive to be noticed. Those who 'see' and value recommendations 'cater for algorithms' and consider their own listening and archiving practices as input for optimizing output. Combined, the different features set the stage for both overt and covert tethering of people and streaming services. [ABSTRACT FROM AUTHOR]

Following concerns about social media's role in politics (fostering polarization and spreading disinformation), many activists and civic hackers have developed alternative digital democracy platforms for both deliberation and the representation of public opinion. But how are we to study the role of these platforms, and in particular, their algorithms in the development of issues and the publics that gather around them? This article employs a simple quali-quantitative data visualization to study how a particular digital democracy platform, vTaiwan (an implementation of Pol.is – a tool for generating opinions and consensus about public issues) – formats political participation. We investigate how one particular issue (Uber legalization) was formed and reformed by users, moderators, and algorithms on the vTaiwan platform over time. while the algorithm sorted opinions into a binary of pro and anti-Uber positions, we find that the comments themselves and their sequence suggest more nuanced positions and the potential for dialogue. We argue that vTaiwan may be limited by its focus on simple quantitative data points (positive or negative votes as opposed to the texts themselves) and a forced separation of participants into in-or-out opinion groups. This study contributes to critical algorithm studies and digital democracy studies by offering an effective way to analyse the role of algorithms in democratic politics. [ABSTRACT FROM AUTHOR]

This article discusses how Brazilian ubertubers – Uber drivers that manage YouTube channels focused on their riding experience – systematise and make public different ways of knowing surge pricing (SP), an algorithmic-oriented system that uses price adjustments to redistribute drivers across urban space. Taken by the Uber as an instrument to measure and regulate market conditions, SP mediates drivers' pragmatic and affective daily practices, materialising a asymmetrical power relation embedded into a neoliberal governmentality. The study explores 25 videos produced and shared by five consolidated Brazilian ubertubers, focusing on how this specific kind of digital influencer systematises and perform collective knowledge on how to increase earnings with surge pricing. The metaphors, hypotheses, and theories, the ubertuber's tactics to deal with SP's instability and with the risks of working on peripheral areas, and the efforts to investigate the logics of a 'new surge' are the main issues approached in the case study. In the conclusions, we discuss how ubertubers' ambivalent relations with surge pricing reveal their wider attempts to navigate neoliberal governmentality and precarious conditions. [ABSTRACT FROM AUTHOR]

This article advances extant research that has audited search algorithms for misinformation in four respects. Firstly, this is the first misinformation audit not to implement a national but a cross-national research design. Secondly, it retrieves results not in response to the most popular query terms. Instead, it theorizes two semantic dimensions of search terms and illustrates how they impact the number of misinformative results returned. Furthermore, the analysis not only captures the mere presence of misinformative content but in addition whether the source websites are affiliated with a key misinformation actor (Russia's ruling elites) and whom the conspiracy narratives cast as the malicious plotters. Empirically, the audit compares Covid-19 conspiracy theories in Google search results across 5 key target countries of Russia's foreign communication (Belarus, Estonia, Germany, Ukraine, and the US) and Russia as of November 2020 (N = 5280 search results). It finds that, across all countries, primarily content published by mass media organizations rendered conspiracy theories visible in search results. Conspiratorial content published on websites affiliated with Russia's ruling elites was retrieved in the Belarusian, German and Russian contexts. Across all countries, the majority of conspiracy narratives suspected plotters from China. Malicious actors from the US were insinuated exclusively by sources affiliated with Russia's elites. Overall, conspiracy narratives did not primarily deepen divides within but between national communities, since – across all countries – only plotters from beyond the national borders were blamed. To conclude, the article discusses methodological advice and promising paths of research for future cross-national search engine audits. [ABSTRACT FROM AUTHOR]

Filters and apps purporting to analyze the face and provide insight into a person's personality, heritage, or future have become a popular part of social media interactivity. This article examines the marketing and design of such products in relation to historical systems of racial, ethnic, moral, and psychological differentiation based on the face, such as physiognomy and eugenics. In particular, it explores user-generated 'Which Are You?' filters and apps such as FaceApp, Fantastic Face, and Gradient, which provide visual forecasting, horoscopes, and beauty and ethnic analysis based primarily on face scans. It argues that affinities of these automated optical tools to racist and discriminatory historical systems of face analysis – especially within the context of the representation or performance of identity on social media – can have important implications for the adoption of more extensive systems of facial detection and recognition. Packaging diagnostic and predictive face analysis in these ways can generate acceptance of, and support for, government and corporate applications of these technologies to identify or categorize individuals and predict their behavior, despite widespread social justice concerns over the design, accuracy, administration, and ethics of such systems. [ABSTRACT FROM AUTHOR]

Algorithmic profiling has become increasingly prevalent in many social fields and practices, including finance, marketing, law, cultural consumption and production, and social engagement. Although researchers have begun to investigate algorithmic profiling from various perspectives, socio-technical studies of algorithmic profiling that consider users' everyday perceptions are still scarce. In this article, we expand upon existing user-centered research and focus on people's awareness and imaginaries of algorithmic profiling, specifically in the context of social media and targeted advertising. We conducted an online survey geared toward understanding how Facebook users react to and make sense of algorithmic profiling when it is made visible. The methodology relied on qualitative accounts as well as quantitative data from 292 Facebook users in the United States and their reactions to their algorithmically inferred 'Your Interests' and 'Your Categories' sections on Facebook. The results illustrate a broad set of reactions and rationales to Facebook's (public-facing) algorithmic profiling, ranging from shock and surprise, to accounts of how superficial – and in some cases, inaccurate – the profiles were. Taken together with the increasing reliance on Facebook as critical social infrastructure, our study highlights a sense of algorithmic disillusionment requiring further research. [ABSTRACT FROM AUTHOR]

Despite the increasing prevalence of curation algorithms on everyday social network sites (SNSs), they often are imperceptible and difficult to become knowledgeable about since little insight in the actual working of these algorithms is given. To address this, we developed an online interface as a visual feedback tool to decrease the ignorance about the Instagram curation algorithm, named 'Instawareness' (). As such, the goal of this article is to validate the actual effectiveness of Instawareness and to demonstrate how people can be made aware of the Instagram curation algorithm using a quasi-experiment. Validating the actual effectiveness of Instawareness allowed us to connect additional findings about the influence of awareness and understanding of Instagram's curation algorithm to our primary validated findings about achieving such awareness. These show that it is not cognitive understanding about Instagram's algorithms but solely awareness about them that appears to be sufficient for people to indicate increased critical concerns towards SNSs. Furthermore, Instawareness proved to be efficient in increasing cognitive media literacy (CML) and in indirectly stimulating critical concerns towards SNSs. [ABSTRACT FROM AUTHOR]

The ability of citizens to establish the credibility of information and information sources through critical assessment is often emphasized as essential for the upholding of a democratic society and for people's health and safety. Drawing on material-discursive conceptualizations, the article asks, how does critical assessment of information and information sources play out as it is folded into a networked information infrastructure in which different types of information are mediated and shaped by the same algorithms and flattened into the same interfaces? The empirical material comprises dyadic interviews with 61 adolescents. The interviews were analysed using an interpretative approach focusing on the construction of action and meaning. The analysis foregrounds trust and agency as two dimensions. This way normative assumptions become visible as stereotypes, sometimes positioned as ideals towards which to strive, other times as deterrent examples: the non-evaluator, the naïve evaluator, the skeptical evaluator and the confident evaluator. The created stereotypes help to comprehend different understandings of critical assessment of information and how these can bring about different actions. The article argues that critical assessment of information as an element in media and information literacy must be understood not just in relation to how it is used to assess the credibility of information, but also regarding how it is performatively enrolled in the shaping of knowledge and in the creation of ignorance and doubt. [ABSTRACT FROM AUTHOR]