Pub Date : 2023-07-01DOI: 10.1177/20539517231202983
Aurora Zhang
A reparative approach to algorithmic justice provides a compelling alternative to existing fairness-based frameworks, which are often inadequate for challenging the technological perpetuation of unjust social hierarchies. The definition of “reparations,” however, is philosophically contested. I discuss two interrelated but distinct notions of reparations: reparations as accountability and redress for past injustice, and reparations as a constructive worldmaking project focused on present and future justice. Each of these perspectives offers different recommendations and provocations for how to implement algorithmic reparations. I apply this to a case study of housing injustice in the US and offer three interpretations of “algorithmic reparations” in context: first, we can litigate instances of algorithmic discrimination in housing. Second, we can use computational methods to compute damages and demand redress for structural housing injustice in the past. Finally, we can repurpose algorithmic methods to imagine more radical resistance efforts that connect incremental reform to large-scale structural change for the future.
{"title":"Redress and worldmaking: Differing approaches to algorithmic reparations for housing justice","authors":"Aurora Zhang","doi":"10.1177/20539517231202983","DOIUrl":"https://doi.org/10.1177/20539517231202983","url":null,"abstract":"A reparative approach to algorithmic justice provides a compelling alternative to existing fairness-based frameworks, which are often inadequate for challenging the technological perpetuation of unjust social hierarchies. The definition of “reparations,” however, is philosophically contested. I discuss two interrelated but distinct notions of reparations: reparations as accountability and redress for past injustice, and reparations as a constructive worldmaking project focused on present and future justice. Each of these perspectives offers different recommendations and provocations for how to implement algorithmic reparations. I apply this to a case study of housing injustice in the US and offer three interpretations of “algorithmic reparations” in context: first, we can litigate instances of algorithmic discrimination in housing. Second, we can use computational methods to compute damages and demand redress for structural housing injustice in the past. Finally, we can repurpose algorithmic methods to imagine more radical resistance efforts that connect incremental reform to large-scale structural change for the future.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135851624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/20539517231210278
Chris Till
Spotify dominates the audio streaming industry and offers an almost limitless library of music and other ‘sounds’. They have recently made various interventions into health, exercise and wellness with the development of curated and personalised playlists focused on activities such as running, weightlifting and meditation and guided workouts interspersed with algorithmically generated playlists. This article suggests that the company are developing new means of datafying health, exercise and wellness practices such as monitoring activities, heart rate, mood and broadly the rhythms and tempos of their lives. While this is presented as beneficial to users to provide a more personalised experience, analysis of patent applications, financial statements and promotional materials targeting advertisers and investors suggest other objectives. Audio consumption is combined with the newly datafied activities to ‘bundle’ users into ‘audience commodities’ to be sold to advertisers. Furthermore, such innovations, and the potential to attract advertisers, form the materials through which Spotify construct stories to potential investors about the future profitability, or at least growth in market value, of the company essential for firms integrated into ‘financialised capitalism’. This represents a further opening up of aspects of everyday lives to commercial exploitation through datafication and contributes to an attempt to reposition health-related practices as assets which can be packaged for investment portfolios. The publications analysed in this article demonstrate some of the ways in which Spotify seek to both monitor and shape practices of users to make them more amenable to financialisation.
{"title":"Spotify as a technology for integrating health, exercise and wellness practices into financialised capitalism","authors":"Chris Till","doi":"10.1177/20539517231210278","DOIUrl":"https://doi.org/10.1177/20539517231210278","url":null,"abstract":"Spotify dominates the audio streaming industry and offers an almost limitless library of music and other ‘sounds’. They have recently made various interventions into health, exercise and wellness with the development of curated and personalised playlists focused on activities such as running, weightlifting and meditation and guided workouts interspersed with algorithmically generated playlists. This article suggests that the company are developing new means of datafying health, exercise and wellness practices such as monitoring activities, heart rate, mood and broadly the rhythms and tempos of their lives. While this is presented as beneficial to users to provide a more personalised experience, analysis of patent applications, financial statements and promotional materials targeting advertisers and investors suggest other objectives. Audio consumption is combined with the newly datafied activities to ‘bundle’ users into ‘audience commodities’ to be sold to advertisers. Furthermore, such innovations, and the potential to attract advertisers, form the materials through which Spotify construct stories to potential investors about the future profitability, or at least growth in market value, of the company essential for firms integrated into ‘financialised capitalism’. This represents a further opening up of aspects of everyday lives to commercial exploitation through datafication and contributes to an attempt to reposition health-related practices as assets which can be packaged for investment portfolios. The publications analysed in this article demonstrate some of the ways in which Spotify seek to both monitor and shape practices of users to make them more amenable to financialisation.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135857027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/20539517231210272
Wonyoung So, Catherine D’Ignazio
The racial wealth gap in the United States remains a persistent issue; white individuals possess six times more wealth than Black individuals. Leading scholars and public figures have pointed to slavery and post-slavery discrimination as root cause factors and called for reparations. Yet the institutionalization of race-neutral ideologies in policies and practices hinders a reparative approach to closing the racial wealth gap. This study models the use of algorithmic methods in the service of reparations to Black Americans in the domain of housing, where most American wealth is built. We examine a hypothetical scenario for measuring the effectiveness of race-conscious Special Purpose Credit Programs (SPCPs) in reducing the housing racial wealth gap compared to race-neutral SPCPs. We use a predictive model to show that race-conscious, people-based lending programs, if they were nationally available, would be two to three times more effective in closing the racial housing wealth gap than other, existing forms of SPCPs. In doing so, we also demonstrate the potential for using algorithms and computational methods to support outcomes aligned with movements for reparations, another possible meaning for the emerging discourse on “algorithmic reparations.”
{"title":"Race-neutral vs race-conscious: Using algorithmic methods to evaluate the reparative potential of housing programs","authors":"Wonyoung So, Catherine D’Ignazio","doi":"10.1177/20539517231210272","DOIUrl":"https://doi.org/10.1177/20539517231210272","url":null,"abstract":"The racial wealth gap in the United States remains a persistent issue; white individuals possess six times more wealth than Black individuals. Leading scholars and public figures have pointed to slavery and post-slavery discrimination as root cause factors and called for reparations. Yet the institutionalization of race-neutral ideologies in policies and practices hinders a reparative approach to closing the racial wealth gap. This study models the use of algorithmic methods in the service of reparations to Black Americans in the domain of housing, where most American wealth is built. We examine a hypothetical scenario for measuring the effectiveness of race-conscious Special Purpose Credit Programs (SPCPs) in reducing the housing racial wealth gap compared to race-neutral SPCPs. We use a predictive model to show that race-conscious, people-based lending programs, if they were nationally available, would be two to three times more effective in closing the racial housing wealth gap than other, existing forms of SPCPs. In doing so, we also demonstrate the potential for using algorithms and computational methods to support outcomes aligned with movements for reparations, another possible meaning for the emerging discourse on “algorithmic reparations.”","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135857751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/20539517231203669
Shiloh Deitz
Technologies in the smart city, such as autonomous vehicles and delivery robots, promise to increase the mobility and freedom of people with disabilities. These technologies have also failed to “see” or comprehend wheelchair riders, people walking with service animals, and people walking with bicycles—all outliers to machine learning models. Big data and algorithms have been amply critiqued for their biases—harmful and systematic errors—but the harms that arise from AI's inherent inability to handle nuance, context, and exception have been largely overlooked. In this paper, I run two machine learning models across nine cities in the United States to attempt to fill a gap in data about the location of curb ramps. I find that while curb ramp prediction models may achieve up to 88% accuracy, the rate of accuracy varied in context in ways both predictable and unpredictable. I look closely at cases of unpredictable error (outlier bias), by triangulating with aerial and street view imagery. The sampling of cases shows that while it may be possible to conjecture about patterns in these errors, there is nothing clearly systematic. While more data and bigger models might improve the accuracy somewhat, I propose that a bias toward outliers is something fundamental to machine learning models which gravitate to the mean and require unbiased and not missing data. I conclude by arguing that universal design or design for the outliers is imperative for justice in the smart city where algorithms and data are increasingly embedded as infrastructure.
{"title":"Outlier bias: AI classification of curb ramps, outliers, and context","authors":"Shiloh Deitz","doi":"10.1177/20539517231203669","DOIUrl":"https://doi.org/10.1177/20539517231203669","url":null,"abstract":"Technologies in the smart city, such as autonomous vehicles and delivery robots, promise to increase the mobility and freedom of people with disabilities. These technologies have also failed to “see” or comprehend wheelchair riders, people walking with service animals, and people walking with bicycles—all outliers to machine learning models. Big data and algorithms have been amply critiqued for their biases—harmful and systematic errors—but the harms that arise from AI's inherent inability to handle nuance, context, and exception have been largely overlooked. In this paper, I run two machine learning models across nine cities in the United States to attempt to fill a gap in data about the location of curb ramps. I find that while curb ramp prediction models may achieve up to 88% accuracy, the rate of accuracy varied in context in ways both predictable and unpredictable. I look closely at cases of unpredictable error (outlier bias), by triangulating with aerial and street view imagery. The sampling of cases shows that while it may be possible to conjecture about patterns in these errors, there is nothing clearly systematic. While more data and bigger models might improve the accuracy somewhat, I propose that a bias toward outliers is something fundamental to machine learning models which gravitate to the mean and require unbiased and not missing data. I conclude by arguing that universal design or design for the outliers is imperative for justice in the smart city where algorithms and data are increasingly embedded as infrastructure.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135813043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/20539517231206809
Xiaojuan Zhang, Farhan Khan, Xiaoguang Wang, Changle Tang
The development of open government data has attracted interest from academics and practitioners. However, only a few studies have examined a culture-based account of open government data development. This study empirically investigates the impact of national culture on open government data. Through the data investigation and analysis of 55 countries, this research finds that the development of open government data is positively linked with national culture with respect to individualism, indulgence and long-term orientation and is negatively related to power distance. Furthermore, this study shows that economic development moderates the relationship between national culture and open government data development, especially with respect to individualism and long-term orientation. Practically, the findings of this research can help policymakers better understand the multifaceted impacts of national culture on the development of open government data, including the promotion of cultural values (i.e. high individualism, high indulgence, and high long-term orientation) and the change in the passive and conservative attitude of citizens toward the openness of government data in countries where power distance culture is high.
{"title":"Exploring the impact of national culture on the development of open government data: A cross-cultural analysis","authors":"Xiaojuan Zhang, Farhan Khan, Xiaoguang Wang, Changle Tang","doi":"10.1177/20539517231206809","DOIUrl":"https://doi.org/10.1177/20539517231206809","url":null,"abstract":"The development of open government data has attracted interest from academics and practitioners. However, only a few studies have examined a culture-based account of open government data development. This study empirically investigates the impact of national culture on open government data. Through the data investigation and analysis of 55 countries, this research finds that the development of open government data is positively linked with national culture with respect to individualism, indulgence and long-term orientation and is negatively related to power distance. Furthermore, this study shows that economic development moderates the relationship between national culture and open government data development, especially with respect to individualism and long-term orientation. Practically, the findings of this research can help policymakers better understand the multifaceted impacts of national culture on the development of open government data, including the promotion of cultural values (i.e. high individualism, high indulgence, and high long-term orientation) and the change in the passive and conservative attitude of citizens toward the openness of government data in countries where power distance culture is high.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135856436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/20539517231208455
Jillian Crandall
Recently blockchain has become a tool for spatial coordination and appropriation. Globally, the tokenization of land and housing has led to new forms of datafication and increased financialization. In the case of land non-fungible tokens), security token offerings, and blockchain-based real estate investment trusts, blockchains act as exclusionary digital platforms, with new socio-technical assemblages emerging as complex predatory formations of speculation that are intentionally obfuscatory and difficult to regulate. With the security token offering, crowdfunding and venture capital are combined with cryptocurrency to create a “tokenized venture capital fund” tied to tangible assets, such as ownership rights in housing, real estate, or land. Distributed ledgers are proposed to be used as the digital technology underlying new forms of land/property documentation, ownership, and inhabitation – from conducting and recording land surveys and title creation to transference of land/property rights. This paper addresses the question: how equitable is tokenized equity – does it prioritize the right to the city for all or to all but a very few? This paper looks toward the means of contestation against extractive crypto-settlements, speculation, and housing financialization, critically comparing a range of proposed distributed ledger technology projects that claim to inject equity in the system, pose alternative housing economies, or leverage distributed ledgers for land rights and data sovereignty. I question the utility and limits of datafication and explore how engaging with digital technology – with or without distributed ledgers – can raise awareness and enact alternative forms of housing and land stewardship, from cooperativism to Community Land Trusts and to counter-hegemonic commoning practices.
{"title":"Living on the block: How equitable is tokenized equity?","authors":"Jillian Crandall","doi":"10.1177/20539517231208455","DOIUrl":"https://doi.org/10.1177/20539517231208455","url":null,"abstract":"Recently blockchain has become a tool for spatial coordination and appropriation. Globally, the tokenization of land and housing has led to new forms of datafication and increased financialization. In the case of land non-fungible tokens), security token offerings, and blockchain-based real estate investment trusts, blockchains act as exclusionary digital platforms, with new socio-technical assemblages emerging as complex predatory formations of speculation that are intentionally obfuscatory and difficult to regulate. With the security token offering, crowdfunding and venture capital are combined with cryptocurrency to create a “tokenized venture capital fund” tied to tangible assets, such as ownership rights in housing, real estate, or land. Distributed ledgers are proposed to be used as the digital technology underlying new forms of land/property documentation, ownership, and inhabitation – from conducting and recording land surveys and title creation to transference of land/property rights. This paper addresses the question: how equitable is tokenized equity – does it prioritize the right to the city for all or to all but a very few? This paper looks toward the means of contestation against extractive crypto-settlements, speculation, and housing financialization, critically comparing a range of proposed distributed ledger technology projects that claim to inject equity in the system, pose alternative housing economies, or leverage distributed ledgers for land rights and data sovereignty. I question the utility and limits of datafication and explore how engaging with digital technology – with or without distributed ledgers – can raise awareness and enact alternative forms of housing and land stewardship, from cooperativism to Community Land Trusts and to counter-hegemonic commoning practices.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135857031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/20539517231206804
Anoush Margaryan
The development and diffusion of artificial intelligence (AI) technologies in workplaces are transforming the nature of work practices and their constituent skill requirements. This dual transformation is challenging for workers, organisations and societies, who are faced with the need to develop and enhance extant and new skills required to succeed in increasingly AI-mediated work settings. Although literature has recognised skills as a key factor in the development and uptake of AI technologies, there has been paucity of empirical research on the precise nature of skill requirements in AI-mediated workplaces. This commentary argues that to advance our understanding of skill requirements in AI-mediated workplaces, an integrative, multidisciplinary, multimethod and multistakeholder approach is required. The commentary proposes an agenda for future research in this societally important but poorly understood area.
{"title":"Artificial intelligence and skills in the workplace: An integrative research agenda","authors":"Anoush Margaryan","doi":"10.1177/20539517231206804","DOIUrl":"https://doi.org/10.1177/20539517231206804","url":null,"abstract":"The development and diffusion of artificial intelligence (AI) technologies in workplaces are transforming the nature of work practices and their constituent skill requirements. This dual transformation is challenging for workers, organisations and societies, who are faced with the need to develop and enhance extant and new skills required to succeed in increasingly AI-mediated work settings. Although literature has recognised skills as a key factor in the development and uptake of AI technologies, there has been paucity of empirical research on the precise nature of skill requirements in AI-mediated workplaces. This commentary argues that to advance our understanding of skill requirements in AI-mediated workplaces, an integrative, multidisciplinary, multimethod and multistakeholder approach is required. The commentary proposes an agenda for future research in this societally important but poorly understood area.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135851622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/20539517231205476
Onur Bakiner
This paper examines the potential promises and limitations of the human rights framework in the age of AI. It addresses the question: what, if anything, makes human rights well suited to face the challenges arising from new and emerging technologies like AI? It argues that the historical evolution of human rights as a series of legal norms and concrete practices has made it well placed to address AI-related challenges. The human rights framework should be understood comprehensively as a combination of legal remedies, moral justification, and political analysis that inform one another. Over time, the framework has evolved in ways that accommodate the balancing of contending rights claims, using multiple ex ante and ex post facto mechanisms, involving government and/or business actors, and in situations of diffuse responsibility that may or may not result from malicious intent. However, the widespread adoption of AI technologies pushes the moral, sociological, and political boundaries of the human rights framework in other ways. AI reproduces long-term, structural problems going beyond issue-by-issue regulation, is embedded within economic structures that produce cumulative negative effects, and introduces additional challenges that require a discussion about the relationship between human rights and science & technology. Some of the reasons for why AI produces problematic outcomes are deep rooted in technical intricacies that human rights practitioners should be more willing than before to get involved in.
{"title":"The promises and challenges of addressing artificial intelligence with human rights","authors":"Onur Bakiner","doi":"10.1177/20539517231205476","DOIUrl":"https://doi.org/10.1177/20539517231205476","url":null,"abstract":"This paper examines the potential promises and limitations of the human rights framework in the age of AI. It addresses the question: what, if anything, makes human rights well suited to face the challenges arising from new and emerging technologies like AI? It argues that the historical evolution of human rights as a series of legal norms and concrete practices has made it well placed to address AI-related challenges. The human rights framework should be understood comprehensively as a combination of legal remedies, moral justification, and political analysis that inform one another. Over time, the framework has evolved in ways that accommodate the balancing of contending rights claims, using multiple ex ante and ex post facto mechanisms, involving government and/or business actors, and in situations of diffuse responsibility that may or may not result from malicious intent. However, the widespread adoption of AI technologies pushes the moral, sociological, and political boundaries of the human rights framework in other ways. AI reproduces long-term, structural problems going beyond issue-by-issue regulation, is embedded within economic structures that produce cumulative negative effects, and introduces additional challenges that require a discussion about the relationship between human rights and science & technology. Some of the reasons for why AI produces problematic outcomes are deep rooted in technical intricacies that human rights practitioners should be more willing than before to get involved in.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135851814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/20539517231206810
Emillie de Keulenaar
New media studies invested in online political conflict, radical and antagonistic subcultures have taken an interest in the affordances that shape memes, vernaculars and online political communication. One often overlooked affordance is the ensemble of social, communication, platform and legal frameworks stipulating what users can and cannot say, which I call “speech affordances.” To explore this concept, I look at the strategic communication of 4chan, Twitter and YouTube subcultures tied to a historical meme, “Kekistan,” often perceived as a key example of the ideological cacophony of the 2015–2017 online “culture wars.” I focus on how 4chan's policy of user anonymity, YouTube's unmoderated comment sections and Twitter's more proactive moderation practices brought some influencers to alter the original connotations of the meme into “overt” messages tolerable to Twitter and YouTube out-groups and platform moderation policies. Speech affordances bear methodological implications for historical studies of speech moderation and the overall mechanisms in which problematic language adapts to spaces with distinct speech norms.
{"title":"The affordances of extreme speech","authors":"Emillie de Keulenaar","doi":"10.1177/20539517231206810","DOIUrl":"https://doi.org/10.1177/20539517231206810","url":null,"abstract":"New media studies invested in online political conflict, radical and antagonistic subcultures have taken an interest in the affordances that shape memes, vernaculars and online political communication. One often overlooked affordance is the ensemble of social, communication, platform and legal frameworks stipulating what users can and cannot say, which I call “speech affordances.” To explore this concept, I look at the strategic communication of 4chan, Twitter and YouTube subcultures tied to a historical meme, “Kekistan,” often perceived as a key example of the ideological cacophony of the 2015–2017 online “culture wars.” I focus on how 4chan's policy of user anonymity, YouTube's unmoderated comment sections and Twitter's more proactive moderation practices brought some influencers to alter the original connotations of the meme into “overt” messages tolerable to Twitter and YouTube out-groups and platform moderation policies. Speech affordances bear methodological implications for historical studies of speech moderation and the overall mechanisms in which problematic language adapts to spaces with distinct speech norms.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135857030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-01DOI: 10.1177/20539517231190078
Fernanda Odilla, Alice Mattoni
Developed by tech-savvy citizens, Rosie is a bot that autonomously checks the public spending of elected representatives of the Brazilian Lower Chamber and uses Twitter to engage peoplein discussing suspicious findings. Rosie is the most visible face of Operação Serenata de Amor (Operation Love Serenade), a data-enabled activism initiative that revolves around the creation, use and dissemination of open data to hold politicians accountable and empower citizens to react against the misuse of public funds. The article draws on an original data set – including interviews, participant online observation notes and secondary qualitative materials – to examine Operação Serenata de Amor, focusing on how material and symbolic elements related to both human and non-human actors shape the organisational patterns of this type of initiative. The findings suggest that there are three organisational patterns, each with further specific challenges, based on the presence of three modes of participation that depend on different types of engagement with digital technologies and data. Findings indicate that data-enabled activism can emerge with typical characteristics and values of tech startups, such as the goal of creating a sustainable budget and providing strategic content by validating it with user feedback, while also retaining some traits of online activism, such as ad hoc and temporal networks of highly autonomous actors concerned with specific contentious issues. In this respect, the eventual demobilisation of Operação Serenata de Amor's initiators due to commercial values and struggles to maintain it active and engaging can be seen as a cautionary tale for data-enabled activism, particularly for initiatives closely associated with civic innovation and social tech startups.
罗西是一个机器人，由精通技术的公民开发，它可以自动检查巴西下议院当选代表的公共支出，并使用Twitter与人们讨论可疑的发现。罗西是“爱的小夜曲行动”(opera o Serenata de Amor)最引人关注的面孔，这是一个以数据为基础的行动主义倡议，围绕开放数据的创造、使用和传播，让政治家承担责任，并赋予公民权力，反对滥用公共资金。本文利用原始数据集——包括访谈、参与者在线观察笔记和二手定性材料——来研究歌剧《爱的Serenata》，重点关注与人类和非人类行动者相关的物质和符号元素如何塑造这类倡议的组织模式。研究结果表明，有三种组织模式，每一种都有进一步的具体挑战，基于三种参与模式的存在，这些模式依赖于与数字技术和数据的不同类型的接触。研究结果表明，数据支持的行动主义可能具有科技初创公司的典型特征和价值观，例如创建可持续预算的目标，并通过用户反馈验证提供战略性内容，同时也保留了在线行动主义的一些特征，例如关注特定争议问题的高度自主行为者的临时和临时网络。在这方面，opera o Serenata de Amor的发起人由于商业价值而最终解散，并努力保持其活跃和参与，这可以被视为数据支持行动主义的警示故事，特别是与公民创新和社会科技初创公司密切相关的倡议。
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