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Since transition to higher education emerged as a research topic in the early 1970s, scholarly inquiry has focused on students without impairments and, what is more, little attention has been paid to the role of digital technologies. This article seeks to address this knowledge gap by looking at the university experiences of a group of first-year students with vision impairments from New Zealand, and the way they use digital tools, such as social media and mobile devices, to manage their transition-related challenges. The article summarises the findings from a longitudinal qualitative project which was methodologically informed by action research (AR). The article explores and discusses scholarly inquiry of transition to university and introduces a conceptual framework which includes five overlapping stages, the transition issues faced by the students and the roles played by digital technologies. The article updates and expands the theoretical understanding of transition to higher education and provides empirical evidence for practitioners to support the needs, inclusion, and participation of young people with disabilities in the tertiary setting., 25 pages

Forthcoming in American Behavioral Scientist. The structured inequalities built into our sociotechnical institutions shape access to knowledge. During the COVID-19 pandemic, knowledge acquisition was shaped by news sources, class, and race. Through analysis of nationally representative data using logistic models, this study reveals how the use of different news sources differentially shapes access to accurate knowledge about COVID-19 topics for different demographics. Those who rely on informal and local news sources have the largest knowledge gaps about these topics, while those who seek information from national or international news outlets and politicians have the most accurate knowledge. Race and class influence knowledge of government operations, public health, and science of COVID-19. In particular, Black people, people with less education, and those with lower incomes are significantly less likely to have accurate knowledge about COVID-19, all else equal. These findings have implications for knowledge dissemination that impacts public health, as well as for how news media target different audiences in an increasingly fractured landscape.

In the twenty-first century, Americans have put more of their lives online while the US government has expanded its mass surveillance apparatus. Interest in anonymity-granting technologies like The Onion Router (Tor) has grown substantially as citizens seek to protect their privacy. However, this same technology can be used to engage in illegal activity on the dark web. This study examines how interest in the dark web, public attention to the 2013 Snowden revelations, and metro-area political ideology are associated with public interest in Tor. We link data from multiple sources including Google Trends, the American Community Survey, the Bureau of Labor Statistics, and the Cooperative Congressional Election Study for the forty-nine largest US Metropolitan Statistical Areas (MSAs) from 2006–2015 (n=490). Broadly, we find that metro areas with liberal citizen ideology and greater interest in the dark web were more likely to search for Tor. When controlling for the level of interest in the dark web, the Snowden revelations of 2013 had no significant impact on interest in Tor. These findings suggest that the lure of the dark web and left-leaning ideological contexts offer stronger explanations for interest in anonymity-granting technology than the public attention brought to mass surveillance by the Snowden revelations.

In the recent decades, privacy scholarship has made significant progress. Most of it was achieved in monodisciplinary works. However, privacy has a deeply interdisciplinary nature. Most importantly, societies as well as individuals experience privacy as being influenced by legal, technical, and social norms and structures. In this article, we hence attempt to connect insights of different academic disciplines into a joint model, an Interdisciplinary Privacy and Communication Model. The model differentiates four different elements: communication context, protection needs, threat and risk analysis, as well as protection enforcement. On the one hand, with this model, we aim to describe how privacy unfolds. On the other hand, the model also prescribes how privacy can be furnished and regulated. As such, the model contributes to a general understanding of privacy as a theoretical guide and offers a practical basis to address new challenges of the digital age.

Unpaid or ‘free’ labour is an important element of how precarity has been theorized. It is also an issue that is often seen as endemic to cultural and creative work, rightly attracting a range of criticism. Questions as to the role of unpaid work, for example internships, have become central to understanding the social exclusiveness of many cultural and creative jobs. This paper develops this existing analysis by comparing and contrasting the meaning of 'free' work over the life course of a range of creative occupations, historicising the impact of unpaid labour on the creative sector and showing how it has been stratified by social class, age and career stage. The paper uses two datasets drawn from the Panic! What happened to social mobility in the arts? project, to outline the differing experiences of unpaid labour in cultural and creative occupations. By demonstrating the stratification of unpaid work as a form of precariousness in cultural jobs, along with the social distribution of benign narratives of unpaid work, the paper aims to offer new empirical evidence for those seeking to resist precarious forms of labour.

Has disease stigma declined? Our ability to answer this question has been hampered by the lack of comparable data across diseases and over time. Using word embeddings, we analyze 4.5 million news articles to create new measures of stigma for 107 health conditions. We find that in the 1980s, most diseases were marked by strong connotations of disgust, danger, impurity, and negative personality traits. Since then, stigma has declined dramatically for most physical illnesses; cancers, neurological conditions, genetic diseases, and many other conditions have shed most of their negative connotations. But this decline was uneven: mental illnesses, eating disorders, and addictions saw no stigma declines, and stigma declined more slowly for infectious diseases than for chronic conditions. Using multivariate regression, we find that patients’ activism explains some but not all of the variation in stigma. Stigma has transformed from a sea of negative connotations surrounding most diseases to a narrower set of judgments targeting conditions where the primary symptoms are aberrant behaviors.

Over the past decade, smartphones have permeated all domains of adolescents’ everyday lives, with research dominated by “smartphone addiction.” This study compares one of the most used measures of smartphone addiction with a new alternative measure, the Smartphone Pervasiveness Scale for Adolescents (SPS-A), which focuses on the frequency of smartphone use at key social and physiological moments of daily life. A sample of 3,289 Italian high school students was used to validate the two constructs and compare their suitability for research on academic performance. SPS-A was moderately correlated with smartphone addiction, showed measurement invariance (across ethnic origins, parental education, and gender), and negatively predicted language and math test scores. SPS-A is a nonpathologizing instrument suitable for analyzing the role of smartphone use in academic achievement in combination with students’ social backgrounds.

The COVID-19 pandemic brought Zoom explosive growth and a major privacy and security crisis in March 2020. This research advances a producer’s perspective that directs attentions to institutional and organizational actors and draws on theories of privacy management and organizational crisis communication to examine Zoom’s response to its privacy and security crisis. We primarily use data from 14 weekly Ask Eric Anything webinars from April 8 to July 15, 2020 to illustrate the strategies of Zoom’s crisis response, especially organizational representation, the contours of its analytic account acknowledging and reducing responsibility, and patterns of corrective and preventive action for user education and product improvement. Results demonstrate the usefulness of the producer’s perspective and shed light on how Zoom navigated the privacy and security crisis through mobilizing networks of executives, advisors, consultants, and clients for expertise, endorsement, and collaboration. Moreover, its response strategies have built on and contributed to Zoom’s organizational mission and culture, reframing the crisis as a growth opportunity for prioritizing privacy and security rather than mere growing pains. Zoom’s nimble, reasonable, collaborative, interactive and curated organizational response to its privacy and security crisis as an unintended consequence of its sudden rise to prominence amid a global pandemic offers a useful model for tech firms’ crisis response at a crucial moment for the tech industry around the world. Implications are relevant to understanding the socio-technical and economic consequences of this ongoing global pandemic.

Computational methods are increasingly popular in criminal justice research. As more criminal justice data becomes available in big data and other digital formats, new means of embracing the computational turn are needed. In this article, we propose a framework for data collection and case sampling using computational methods, allowing researchers to conduct thick qualitative research – analyses concerned with the particularities of a social context or phenomenon – starting from big data, which is typically associated with thinner quantitative methods and the pursuit of generalizable findings. The approach begins by using open-source web scraping algorithms to collect content from a target website, online database, or comparable online source. Next, researchers use computational techniques from the field of natural language processing to explore themes and patterns in the larger data set. Based on these initial explorations, researchers algorithmically generate a subset of data for in-depth qualitative analysis. In this computationally driven process of data collection and case sampling, the larger corpus and subset are never entirely divorced, a feature we argue has implications for traditional qualitative research techniques and tenets. To illustrate this approach, we collect, subset, and analyze three years of news releases from the Royal Canadian Mounted Police website (N = 13,637) using a mix of web scraping, natural language processing, and visual discourse analysis. To enhance the pedagogical value of our intervention and facilitate replication and secondary analysis, we make all data and code available online in the form of a detailed, step-by-step tutorial.

This paper introduces the concept of algorithmic opportunity structures to explore how the efficacy of online activism is contingent on the interaction between algorithms, activists, and audiences. In particular, I examine how far-right actors have gamed ranking and recommendation algorithms by producing content designed to generate high engagement rates. This tactic attracts algorithmic amplification, increasing their visibility and reach on social media. I consider the case of Britain First, a far-right, anti-Muslim movement that used Facebook to rapidly build the largest audience of any political organization in the United Kingdom. I use digital trace data, time series analysis, and topic modeling to study Britain First’s activity, recruitment, and support on Facebook. I identify dynamic equilibria indicative of algorithmically-mediated feedback loops, highlighting how variation in these processes is largely a function of user engagement. The content of the group’s posts and exogenous events, including elections and terrorist attacks, are also associated with short-term fluctuations in online mobilization. The results suggest that Britain First’s success is attributable to its exploitation of Facebook’s algorithms, demonstrating how technological assemblages designed and controlled by corporations can structure political competition and moderate opportunities for activism.

Labor platform scams are an opportunity to integrate scholarship about governance across social media and labor platforms. Labor platforms have borrowed governance mechanisms from social media to cultivate trust among users and remove problematic content. However, while these platforms may share governance strategies, labor platforms mediate employment relationships between workers and clients with different amounts of power. Based on a multi-stakeholder ethnography of carework labor platforms, online careworker forums, and interviews, this study describes scams on carework labor platforms. Labor platforms narrate workers into the role of technology consumers, constricting their own obligations to workers. Workers’ explanations of scams vary, with some contesting and others aligning with platform narratives. Some workers seek support in online forums, which remediate the harm of scams for some, but also enroll workers in unpaid labor. These scams challenge the assumption of antagonism between the interests of workers and platform companies and highlights the consumerization of work.

Wikipedia, a collaborative crowd-sourced platform, is the largest, most visited online encyclopedia. Since it spreads information freely in more than 300 languages, many users, tools, and dashboards rely on its content. Hence, there is a need to maintain its fairness and completeness. However, previous research has indicated the existence of a significant gender gap in Wikipedia biographical articles. We already know that a minimal proportion of those articles portray women and there are gender asymmetries in the textual content of these articles, but little has been reported about the visual aspects (e.g., image volume or image quality) of the gender gap. Here, we analyze all biographies available on Wikipedia across 300 occupations in the ten most widely spoken languages, and undertake quantitative and qualitative analysis of gender differences in the written and visual content. The cross-lingual results indicate that (1) much of the male bias in content arises when editors select which personalities should have a Wikipedia page, (2) the trends in written and visual content are quite dissimilar, (3) men biographies tend to have more images across languages, and (4) female biographies average better visual quality. A more granular analysis is performed on English Wikipedia, distinguishing trends in occupations and qualitatively analyzing science and technology biographies. The overall results shed light on the kinds of visual biases that emerge in the collaborative creation of Wikipedia and yield guidelines for future management of contributions on the platform.

One of todays most controversial and consequential questions is whether the rapid, worldwide uptake of digital media is causally related to a decline in democracy. We conducted a systematic review of causal and correlational evidence (N=498 articles) on the link between digital media and different political variables, such as trust, polarization or news consumption. We further focused on the subset of articles that employed causal inference methods. Across methods, the articles report associations between digital media use and most political variables. Some associations, such as increases in political participation and information consumption, are likely to be beneficial for democracy and were often observed in the Global South and emerging democracies. Other consistently reported associations, such as declining political trust, advantages for populists, and growing polarization, are likely to be detrimental to democracy and were more pronounced in established democracies. We conclude that while the impact of digital media on democracy depends on the specific political variable and the political system in question, several variables show clear directions of associations. We believe that the evidence calls for further research efforts and vigilance by governments and civil societies to better understand and actively design the intimate interplay of digital media and democracy.

Social integration is known to be beneficial for mental health. However, it is not clear whether this applies to online as well as offline relationships. In this paper, we explore the association between online friendship and symptoms of depression among adolescents. We combine data from the popular social networking site with survey data on high school students (N = 144) and find that integration into the online network is a protective factor against depression. We also find that not all online connections are equally important: friendship ties with students from the same schools are stronger associated with depression than outside ties. In addition to friendship ties, we explore the effect of online interaction (“likes”). Overall, our results suggest that online relationships are associated with depression as well as offline friendship. However, the effect of more distant online connections is limited, while immediate social environment and peer relationships at school are more important.

This research shows how face masks became politicized during the COVID-19 pandemic in the United States. While differences in mask wearing behaviors between liberals and conservatives declined over the course of the pandemic, masks remained controversial in the American public sphere. We argue that political divisions over masks cannot be understood by looking to partisan differences in mask wearing behaviors alone. Instead, we show how the mask became a political symbol enrolled into larger patterns of affective polarization, defined by animosity toward the opposing party. This study relies primarily on a combination of qualitative coding and computational text analysis of a large corpus of opinion articles published during the first 10 months of 2020 (n = 7,970). It also relies on supplemental analyses of social media data (from Twitter), the transcripts of major news networks, and longitudinal survey data. We show that backlash against mask refusal—rather than mask refusal itself—was the primary way that masks took on political significance in the American public sphere. Anti-mask discourse consistently occupied a marginal role in the public sphere, while backlash against mask refusal came to prominence and did not decline even as mask wearing behaviors normalized and partly depolarized. We argue that the mask refusal backlash discourse appealed primarily to liberals and show that it was particularly resonant with national political discourses. Beyond the case, this research demonstrates how to use media data to understand how a new set of issues and objects becomes integrated into broader patterns of political polarization.

This paper explores how a global health crisis affects the causes and consequences of social movements. Drawing on media coverage, press releases, emails, and other available primary data sources, we examine how the pandemic changed the opportunities and conditions for activists on the right and left and those they challenge. We begin by considering the nature of the COVID-19 pandemic and the concomitant government response, which alters the structure of political opportunities activists face. We then look at the development of a range of protest campaigns that have emerged in response, assessing changes in opportunities for activists to reach and mobilize target constituencies, the construction of grievances, nature of alliances, as well as innovation in tactics and organization. Finally, we consider the potential outcomes of these protests during the pandemic and extending afterward.

On January 6, 2021, the world watched as Donald Trump’s supporters stormed the US Capitol. However, in this paper, I argue that social scientists should not simply focus on Trump or the Republicans who have supported his false claims that the presidency was stolen from him. Instead, researchers need to leverage the insights provided by sociology, political science and information studies and communication to unpack the increasingly dysfunctional movement-party dynamics in the U.S., which not only made the January 6th riots possible but continue to erode democratic processes. Here, I outline four developments over the last thirty years that help account for the contemporary political moment and underscore the role of digital technologies in these developments.

In an era often characterised by the notions of “post-truth” and “identity politics”, the nexus between communication, knowledge, and identity forms a crucial realm for social-scientific analysis. However, existing approaches, inspired by content, frame, and discourse analysis often rest on a limited conceptualisation of the agency exercised by those appearing in mediated discourse as speakers or subjects. To remedy this, we argue for an intervention inspired by Actor-Network Theory (ANT) and propose a framework combining ANT and poststructuralist Discourse Theory (PDT). Building on a discussion of the theoretical similarities between the two, our core proposal is to study the associations between identity markers and knowledge claims at the sentence level to make transparent how and by whom they are articulated. The nuance gained deepens our understanding of the discursive construction of identity and knowledge, shedding light on the links between identity polarisation and the politicisation of facts.

In the past decade, the public discourse on immigration in Germany has been strongly affected by right-wing populist, racist, and Islamophobic positions. This becomes evident especially in the comment sections of news websites and social media platforms, where user discussions often escalate and trigger hate comments against refugees and immigrants and also against journalists, politicians, and other groups. In view of the threatening consequences such sentiments can have for groups who are targeted by right-wing extremist violence, we take a closer look into such user discussions to gain detailed insights into the various forms of hate speech and offensive language against these groups. Using a modularized framework that goes beyond the common “hate/no-hate” dichotomy in the field, we conducted a structured text annotation of 5,031 user comments posted on German news websites and social media in March 2019. Most of the hate speech we found was directed against refugees and immigrants, while other groups were mostly exposed to various forms of offensive language. In comments containing hate speech, refugees and Muslims were frequently stereotyped as criminals, whereas extreme forms of hate speech, such as calls for violence, were rare in our data. These findings are discussed with a focus on their potential consequences for public discourse on immigration in Germany.

Since the 1980s, Mexico has had the National System of Researchers (SNI), a program that recognizes the prestige and quality of the scientific production of the academics that comprise it. Through this, an economic stimulus is granted according to the level obtained, which can be: candidate, level I, II, III, and emeritus. The main of this work is to analyze how the 125 researchers of level III of the area of Sociology have incorporated the use of science 2.0 tools for the visibility and dissemination of their scientific production, from the creation and updating of academic profiles in Google Scholar and on two academic social networks: ResearchGate, and Academia.edu. For this, regardless of the particular indicators of each of these platforms, we review: level of update of the profile; type of documents deposited; number of downloads, cites and visits to their profile.

How do individual information-seeking preferences affect collective problem-solving in diverse settings? We often choose whom we collaborate with or seek information from. Self-selection can be driven by proclivities towards perceived merit or a preference for those who offer a different perspective. Yet our preferences can have profound systemic outcomes, even if opportunities abound to interact with diversity. We build upon the extensive tradition of collective problem-solving using agent-based modeling (ABM) to test this. We populate communicative networks of diverse problem-solving agents tasked with solving a complex problem too difficult to do alone. Agents can either exploit their neighbors’ solutions or explore for a new solution using their unique and diverse problem-solving ability. However, agents are also allowed to seek out new ties from the network. We test three conditions, where diverse agents in the network harbor proclivities towards (1) diversity (different types of neighbors), (2) homophily (same type of neighbors), or (3) merit (the current performance of their neighbors irrespective of type). We find that diversity-seeking not only leads to higher quality solutions, but also allows for these solutions to better disseminate to the rest of the network.

COVID-19 exposure-notification apps have struggled to gain adoption. Existing literature posits as potential causes of this low adoption: privacy concerns, insufficient data transparency, and the type of appeal used to pitch the pro-social behavior of installing the app. In a field experiment,we advertised CovidDefense, Louisiana's COVID-19 exposure-notification app, at the time it was released. We find that all three hypothesized factors -- privacy, data transparency, and appeals framing -- relate to app adoption, even when controlling for age, gender, and community density. Specifically, we find that collective-good appeals are effective in fostering pro-social COVID-19 app behavior in the field. Our results empirically support existing policy guidance on the use of collective-good appeals and offer real-world evidence in the on-going debate on the efficacy of such appeals. Further, we offer nuanced findings regarding the efficacy of transparency -- about both privacy and data collection -- in encouraging health technology adoption and pro-social COVID-19 behavior. Our results may aid in fostering pro-social public-health-related behavior and for the broader debate regarding privacy and data transparency in digital healthcare.

Codebuch der annotationsbasierten Inhaltsanalyse von Nutzerkommentaren zum Thema Flucht und Migration aus März 2019 im Rahmen des BMBF-Verbundprojekts „NOHATE – Bewältigung von Krisen öffentlicher Kommunikation im Themenfeld Flüchtlinge, Migration, Ausländer“ (Förderkennzeichen: 01UG1735AX).

In this essay—a review of the reviews of Shoshana Zuboff’s The Age of Surveillance Capitalism (2019)—we ask whether the published reactions to Zuboff’s tome say something about the legibility problem. After reading them all, we came to wonder if the reviews themselves construct an additional hurdle to knowing Google, Facebook, and the rest: the Babelist din of competing knowledge cultures. With so many distinct dialects, the project of general understanding might be hobbled from the start.

Climate change concern varies widely across countries. In 2019, 80% of Greeks were at least somewhat worried about climate change, compared to 20% of Egyptians. We argue that variation in climate change concern is influenced by regime type as providing civil liberties limit government’s ability to control the public discourse on anthropogenic climate change. We provide empirical evidence for the effect of democracy on climate change concern using data from 611,909 individuals from 118 countries collected between 2007 and 2019. Exploit-ing variation across country-cohorts and time, we find one unit change in the 7-point civil liberty index to influence climate change concern by 2.3 [95% CI: ±1] percentage points. The effect is much stronger in wealthy countries and less educated cohorts. We also present evidence for our causal pathway using qualitative interviews and by modeling the association between democracy, climate protest, climate media coverage, and climate concern with simultaneous equations.

In this paper, I propose the concept of “algorithmic dissonance”, which characterizes the inconsistencies that emerge through the fissures that lie between algorithmic systems that utilize system identities, and sociocultural systems of knowledge that interact with them. A product of human-algorithm interaction, algorithmic dissonance builds upon the concepts of algorithmic discrimination and algorithmic awareness, offering greater clarity towards the comprehension of these sociotechnical entanglements. By employing Du Bois’ concept of “double consciousness” and black feminist theory, I argue that all algorithmic dissonance is racialized. Next, I advocate for the use of speculative methodologies and art for the creation of critically informative sociotechnical imaginaries that might serve a basis for the sociological critique and resolution of algorithmic dissonance. Algorithmic dissonance can be an effective check against structural inequities, and of interest to scholars and practitioners concerned with running “algorithm audits”.

When Covid-19 rushed into our lives, it sent shockwaves across the globe – suddenly we faced “lockdown” – we said goodbye to the way it was but did not understand what this brave new world of isolation and separation would mean and how it would the impact life as we knew it – our identity, relationships and freedoms we enjoyed, and wondered what daily routine post-Covid-19 would look like, while the experiences defining life itself were up for grabs. With social distancing, masks and work from home mandates, the arts and performing arts from theatres, museums, galleries and the public square were shuttered – their very existence challenged and spiralling out of control as staff were laid-off, exhibitions cancelled while concurrently creating an urgency to go online to dwell in cyberspace, the new daily destination.

COVID-19 has posed unprecedented challenges to health systems around the world, including blood collection agencies (BCAs). Many countries, such as Canada and Australia, that rely on non-remunerated voluntary donors, saw an initial drop in donors in the early days of the pandemic followed by a return to sufficient levels of the blood supply. BCA messaging plays a key role in communicating the needs of the blood operator, promoting and encouraging donation, educating, and connecting with the public and donors. This paper reports on discourse analysis (Bloor and Bloor, 2013) of BCA messaging in Canada and Australia from March 1-July 31, 2020 to understand how BCAs constructed donation to encourage donation during this period and what this can tell us about public trust and blood operators. Drawing on multiple sources of online content and print media, our analysis identified four dominant messages during the study period: 1) blood donation is safe; 2) blood donation is designated an essential activity; 3) blood is needed; and 4) blood donation is a response to the pandemic. In Canada and Australia, our analysis suggests that: 1) implicit within constructions of blood donation as safe is the message that BCAs can be trusted; 2) messages that construct blood donation as essential and needed implicitly ask donors to trust BCAs in order to share in the commitment of meeting patient needs; and 3) the pandemic has made possible the construction of blood donation as both an exceptional and commonplace activity. For BCAs, our analysis supports donor communications that are transparent and responsive to public concerns, and the local context, to support public trust. Beyond BCAs, health organizations and leaders cannot underestimate the importance of building and maintaining public trust as countries continue to struggle with containment of the virus and encourage vaccine uptake.

The main objective of this report is to test the hypothesis that the adoption of an active information-seeking practice related to the health crisis on social networks can be understood as a risk practice in the Covid-19 infodemic. A second objective is to identify the existence of different vulnerability profiles in the infodemic and to understand the information practices associated with these different profiles at risk of misinformation. The approach adopted is therefore firstly a comparative approach between different types of profile. It is not a question of carrying out a longitudinal study representative of the evolution of the French-speaking Belgian population's experience of the crisis. The CoviCom survey is a four-wave questionnaire survey that was conducted in French-speaking Belgium between 30 March 2020 (i.e. 12 days after the entry into force of the first containment in Belgium) and 29 March 2021. In total, the survey collected 10,148 responses to the four waves of the survey (April 2020 containment, May 2020 decontainment, November 2020 second wave epidemic and March 2021 third wave epidemic).

As states increasingly use algorithms to improve the legibility of society, particularly during the COVID-19 pandemic, it is common for concerns about the expanding power of the algorithm or the state to be raised in a deterministic manner. However, how are the algorithms for states’ legibility projects enacted, contested, and reconfigured? Drawing on interviews and media data, this study fills this gap by examining Health Code ( jiankangma), the Chinese contact tracing and risk assessment algorithmic system that serves as the COVID-19 health passport. I first explore the intensive and invisible work and infrastructures that enact and stabilize Health Code’s sociotechnical assemblage. I then show how this assemblage is frequently challenged and destabilized by errors, breakdowns, and exclusions. Facing unintended engagements from heterogeneous social actors, local interests, and power hierarchies, Health Code reassembles into multiple and contradictory assemblages at different periods and social localities. Finally, I examine how people game and bypass the algorithm’s surveillance with their agencies. Recognizing this messiness and heterogeneity contributes to a more nuanced and realistic understanding of states’ use of algorithms, including the risks. Doing so also urges us to rethink the politics of citizenship and inequality in the digital age beyond inclusion.

What is the nature of the relationship between online and offline social life? Specifically, how does participation in the traditional forms of social life of a community shape social media adoption? Using a unique, two-wave panel dataset with saturated network data from over 20,000 students in 56 New Jersey middle schools, we test how measures of integration into a community are associated with adoption and discontinuation of social media platforms over the course of a school year. Social media adoption and discontinuation are related to the extent of students’ bonding integration (social connections and social support) and competitive integration (related to status and dominance hierarchies) into the social life of the school, where more integrated students are more likely to adopt more social media platforms. Social media provides a space for community members to be in the know and to be known, both of which are more valuable with greater social integration.

A review essay discussing three advice books for social scientists. Sociologists, in responding to the imperative to make their work more influential, must go beyond doing “public sociology” to embrace doing sociology “in public” (Healy 2017). Rather than using public engagement primarily for publicity – to make our research matter – we should use engagement to help us do research that matters in the first place. Next, I caution that the drive to be professionally rewarded for public intellectualism is fraught with conflicts that may be irreconcilable. To be a public intellectual today requires being both public in one’s intellectual life and intellectual in one’s public life, and for academics in the era of the “market university” (Berman 2011), trying to get paid for that leads to a neoliberal trap. Finally, I argue for a move beyond personal strategies toward the development of the open scholarship as an institutional response that ultimately may be responsible for sociology’s survival.

BACKGROUND. Experimental vignette research methods have been used to study a diverse range of theoretical and practical issues. Vignettes are designed to create hypothetical cultural/normative contexts for the study of variation in self-reported attitudes. A key problem in such research, however, is potential social desirability response bias. METHOD. A vignette experimental test of an hypothesis derived from a dual-process theory (the MODE framework initially developed by Fazio) linking explicit vs. implicit self-reported attitude measurement and social desirability response bias is reported here. RESULTS. The data show that measuring the social approval of a central vignette character explicitly results in greater social desirability responding than measuring such approval implicitly, supporting MODE theory. CONCLUSIONS. Vignette research methodologies provide a rich, flexible toolkit for studying many important social psychological topics, including issues of inequality and equity. However, researchers can and should design a measurement strategy that carefully manages inferences drawn in light of conditions likely to produce social desirability response bias.

In this study I analyse through machine learning the content of all Friday khutbas (sermons) read to millions of citizens in thousands of Mosques of Turkey since 2015. I focus on six non-religious and recurrent topics that feature in the sermons, namely business, family, nationalism, health, trust, and patience. I demonstrate that the content of the sermons respond strongly to events of national importance. I then link the Friday sermons with ~4.8 million tweets on these topics to study whether and how the content of sermons affects social media behaviour. I find generally large effects of the sermons on tweets, but there is also heterogeneity by topic. It is strongest for nationalism, patience, and health and weakest for business. Overall, these results show that religious institutions in Turkey are influential in shaping the public’s social media content and that this influence is mainly prevalent on salient issues. More generally, these results show that mass offline religious activity can have strong effects on social media behavior.

Humans are impressive social learners. Researchers of cultural evolution have studied the many biases that enable solutions and behaviours to spread socially from one human to the next, selecting from whom we copy and what we copy. In a digital society, algorithmic and human agents both contribute to transmission of knowledge. One hypothesis is that machines may influence the patterns of social transmission not only by providing a means for spreading human behavior but also by providing novel behaviors themselves. We propose that certain algorithms might show (either by learning or by design) different behaviors, biases and problem-solving abilities than their human counterparts. This may in turn foster better decisions in environments where diversity in problem-solving strategies is beneficial. In this study, we ask whether machines with complementary biases to humans could boost cultural evolution in a lab-based planning task, where humans show suboptimal biases. We conducted a large behavioral study and an agent-based simulation to test the performance of transmission chains with human and machine players. In half of the chains, an algorithmic bot replaced a human participant. We show that the bot boosts the performance of immediately following participants in the chain, but this gain is lost for participants further down the transmission chain. Our findings suggest that machines can potentially improve performance, but human bias can hinder machine solutions from being preserved, especially under conditions of uncertainty or high cognitive load. Our results suggest that the conditions for hybrid social learning and cultural evolution may be limited by task environment and human biases.

The stereotype of the “ideal victim” often determines who is considered deserving of victim status, especially in sexual violence cases. In this Chapter, we explore how is the so-called “ideal victim” stereotype constructed and what are the elements necessary for the perception of “ideal victimhood.” We use empirical data from an unmoderated anonymous Estonian online forum that hosts various topic threads from children and young people, including posts about personally experienced sexual violence (N=28) and replies to these posts (N=361). The data was analyzed by combining a discursive psychological approach with qualitative thematic analysis. Results reveal and illustrate how the stereotype is constructed from various elements and characteristics of social scripts, perceived gender roles, and misconceptions about sexuality. We unveil how these social constructions affect responses and attitudes towards sexual abuse victims to provide input for designing prevention efforts that support disclosure and help-seeking.This Chapter will soon appear in the book Child Sexual Abuse and the Media (publisher: NOMOS).

Conceptions of privacy differ by culture. In the Internet age, digital tools continuously challenge the way users, technologists, and governments define, value, and protect privacy. National and supranational entities attempt to regulate privacy and protect data managed online. The European Union passed the General Data Protection Regulation (GDPR), which took effect on 25 May 2018. The research presented here draws on two years of media reporting on GDPR from French, German, UK, and US sources. We use the unsupervised machine learning method of topic modelling to compare the thematic structure of the news articles across time and geographic regions. Our work emphasises the relevance of regional differences regarding valuations of privacy and potential obstacles to the implementation of unilateral data protection regulation such as GDPR. We find that the topics and trends over time in GDPR media coverage of the four countries reflect the differences found across their traditional privacy cultures., Comment: Under Review

In Metrics at Work: Journalism and the Contested Meaning of Algorithms, an ethnographic study of newsrooms, Angèle Christin walks the fine line between utopian and dystopian analyses of technology with a kind of rigorous empiricism. In these newsrooms, Christin finds web journalists using algorithmically mediated metrics (or “analytics”) that document “the preferences of their readers, the popularity of their articles, and the reactions of their followers” (pxiii). The book demonstrates conclusively that these analytics have had far-reaching impacts on how newsrooms operate but these may not be what we commonly assume: analytics software did not lead journalists to maximize their clicks by producing more standardized material for the lowest common denominator. Instead, journalists responded in complex ways, drawing on their traditions and craft to balance their conflicting goals, such as seeking approval from their audiences and peers, and producing “good” journalism while also making a living wage. Christin labels these conflicts as arising from journalists’ “click-based” versus “editorial” modes of evaluation. The metrics were thus symbols through which journalists debated and constructed their “algorithmic publics”; they inspired debate on what journalism was, who it was for, and what its relationship to other institutions of society might be.

Interorganizational trust plays an important role in facilitating business relationships, especially for the organizational adoption of new services. Prior research suggests that interorganizational trust develops when the trustor has adequate confidence in the reliability of the trustee’s services. Nevertheless, reliability breakdowns are also an inevitable part of service provisioning. Such breakdowns are especially prominent and visible in the context of platform-based services. Yet platform-based services continue to be adopted and used by organizational customers. This increased adoption and use of such services despite their inconsistent reliability pose the following question. How is trust produced in platform-mediated interorganizational relationships? To examine this question, I conducted a 20-month field study of a cloud computing platform provider and its customers, focusing on the practices of trust production in the wake of reliability breakdowns. Here, I describe customer concerns about the platform’s inconsistent reliability that hampered the development of interorganizational trust. I then identify four practices of trust work enacted by the platform provider to address some of these concerns and to co-opt the occupational gatekeepers in customer organizations who are responsible for technology adoption decisions. Following this, I describe how and why these occupational gatekeepers performed justification work to rationalize the continued use of the platform despite its inconsistent reliability. Together, trust work and justification work facilitate the coproduction of interorganizational trust through normalizing reliability breakdowns as “business-as-usual” events. Synthesizing these findings, I developed a normalization model of trust production, and discuss the implications of normalized trust for platform-mediated interorganizational relationships in the digital economy.

The history of globalization is intertwined with the history of communication technologies. From a sociological perspective, innovations and developments of both mass media and symbolically generalized media shape the structure of social contacts. Concerning the nexus between globalization and communication, I distinguish three analytically distinct yet partially overlapping phases of globalization: In a first phase, communication became universal by incorporating the globe into networks of exchange, power and belief. In a second phase, communication became instantaneous by means of telecommunication. In a third phase, increasing differentiation within subsystems of society created incentives to communicate across the globe. In their combination, these processes have resulted in the fusion of horizons of meaningful action into a singular global frame of reference. However, that does not imply uniformity since that global frame of frame has also institutionalized a preference for continuous change and variation.

During the first two months after the first COVID-19 case was identified in Wuhan, Chinese authorities suppressed information, causing delayed disease control. When the outbreak became too severe to be kept from the public in January 2020, Chinese authorities radically changed their stance and started to battle COVID-19 with intensive efforts, facing the double challenge of a legitimacy crisis and the uncertain virus. Expanded surveillance protocols were established while intensive surveillance practices were introduced and mobilized. Many locations reported that people who had been wanted by the police for years were turning themselves in, as expanded and routinized surveillance made it more difficult for them to hide. Here, Liu discusses the Chinese residents' perceptions of surveillance during the pandemic.

The tremendous growth and impact of fake news as a hot research field gained the public’s attention and threatened their safety in recent years. However, there is a wide range of developed fashions to detect fake contents, either those human-based approaches or machine-based approaches; both have shown inadequacy and limitations, especially those fully automatic approaches. The purpose of this analytic study of media news language is to investigate and identify the linguistic features and their contribution in analyzing data to detect, filter, and differentiate between fake and authentic news texts. This study outlines promising uses of linguistic indicators and adds a rather unconventional outlook to prior literature. It utilizes qualitative and quantitative data analysis as an analytic method to identify systematic nuances between fake and factual news in terms of detecting and comparing 16 attributes under three main linguistic features categories (lexical, grammatical, and syntactic features) assigned manually to news texts. The obtained datasets consist of publicly available right documents on the Politi-fact website and the raw (test) data set collected randomly from news posts on Facebook pages. The results show that linguistic features, especially grammatical features, help determine untrustworthy texts and demonstrate that most of the test news tends to be unreliable articles.

This review uses host-parasite interactions in nonhuman species to frame the poor US response to the SARS-CoV-2 pandemic. The co-evolutionary interaction between host and parasite results in changes to hosts that are achieved through genetic evolution but involve collective information sharing and behavioral adaptation at the colony level. Common examples are described among ants, bees, and other insects. The US defenses against SARS-CoV-2 were weakened by malformations in the information ecosystem that disrupted the dissemination of information while spreading misinformation and disinformation. Distortions arising from political corruption, and magnified by social media platforms, were especially consequential. I conclude that this failure may ultimately result in a social evolution that weakens US global dominance. On the other hand, if the crisis contributes to innovation and reform in the information ecosystem, that may contribute to a more egalitarian and democratic system for the production and dissemination of knowledge.

Our social lives are segmented into various circles including family, friends, and colleagues. Differences in social norms and expectations between these circles can create tension, especially on large online social networks (OSNs), where their boundaries are blurred. It is unclear whether such phenomenon, called context collapse, outweighs the convenience of having diverse communities in one place for users of OSNs. To better understand this trade-off, we analyze whether ego-network characteristics suggestive of context collapse can explain exit choices from iWiW, a defunct Hungarian OSN with over 3.5 million active users at its peak. We measured context collapse with the presence of two conditions: the first is that communities of the user are non-overlapping, while the second is that these communities are different from each other. We find that users having merely overlapping communities were more likely to stay on the site. This result suggests that the benefits of being connected to diverse communities outweighs the tension from context collapse. Differences in gender composition of alter communities were associated with leaving, while having geographically distant connections were associated with staying longer on iWiW. Our results suggest that the tradeoff between access to diverse contacts and the stress of context collapse is a salient dimension in predicting user churn.

Since 2016, Russia has engaged in a dedicated influence operation against the United States to exacerbate existing cleavages in American society and to undermine US national security. Although distinctly modern in its use of social media platforms, the current methods align with old Soviet doctrine using information warfare to gain a strategic edge over competitors. We examine Russia’s use of military-related content and profiles in their influence operation on Twitter and, in particular, the strategic deployment of military narratives. Using data from Twitter’s comprehensive data archive of state-backed information operations, we find that 12.14% of the 1,408,712 tweets in English from 2009 through February 2021 contain military-related content. In addition, of the 2,370 fake accounts on Twitter tied to the IRA and GRU, 148 were from accounts posing as military or military-adjacent, and these accounts posted 12.7% of the influence operation’s tweets. Together, tweets containing military-related content or coming from fake military and military-adjacent profiles account for 22.6% of the tweets identified as part of the Russian influence operation on Twitter. The Russians used narratives related to veterans, particularly sacrifice narratives and post-Vietnam government betrayal of sacrifice narratives. Patriotic sacrifice narratives were used to gather and engage an audience and to legitimize and amplify the content and accounts. In contrast, betrayal of sacrifice narratives were weaponized to amplify and escalate divisive social issues by tying them to veterans’ sacrifices. We conclude the Russians amplified existing military narratives in American culture and used fake American military profiles to wrap anti-government sentiment in patriotic trappings to exacerbate existing social divisions. Turning Americans against their government achieves Russian strategic goals of removing American influence abroad and allowing Russia to have greater impact on the levers of international power that serve Russian interests.

Since 2016, Russia has engaged in a dedicated influence operation against the United States. Although distinctly modern in its use of social media platforms, the current methods align with old Soviet doctrine using information warfare to gain a strategic edge over competitors. Using the WARP framework, a heuristic tool for understanding narrative weaponization for mobilization and radicalization, we focus on Russia’s use of narratives to exacerbate existing cleavages in American society and to undermine US national security. Using data from Twitter’s comprehensive data archive of state-backed information operations, we find that military and patriotic narratives constitute one of the most frequently deployed narrative sets (second only to Trump), comprising 12.9% of the 1,357 million English-language tweets. The Russians weaponized these narratives and profiles to support or smear a variety of political actors and to escalate the urgency of various social causes. Moreover, these narratives actively recruited audiences to embrace New War cultural identity, an anti-government ideology focused on defending their families from an increasingly hostile state and world. Using a variety of persuasive strategies, the Russians leveraged these narratives to deliver emotionally resonant signals about allies and perceived enemies, both foreign (e.g. Muslims and immigrants) and domestic (e.g. government officials and the media), and to set the groundwork for potentially violent mobilization. Impersonating military profiles increased message resonance, and posturing as credible sources may have aided normalization and legitimization of New War cultural identity and divisive messaging. We conclude the Russians used military and patriotic narratives and profiles to wrap anti-government sentiment in patriotic trappings and to set the stage for Americans to engage in armed domestic conflict.

This paper examines how the sheer volume of personal information recorded and searchable online (online artifacts) has transformed the situated activity system central to Goffman’s dramaturgical theories. In-depth interviews reveal that individuals believe disembodied information based on online artifacts is a more accurate representation of others than embodied information from spatially and temporally bounded face-to-face processes because they represent how others have behaved over time and are attested by their online contacts. However, the n-adic structure of online interaction leads to mismatched expectations about whether disembodied information is taken into account during face-to-face encounters, and consequently can result in embarrassment.

In order to identify factors that may help explain when individual claimsmaking converges around a common set of messages on different forums across a complex media system, we examine individual discourse during the two-week period in which the "Marjory Stoneman Douglas High School Public Safety Act" was debated, passed, and signed into law by Governor Rick Scott. Specifically, we assess the extent to which two factors identified in the literature as relevant to individual expression – elite frames and movement mobilization – seem to influence claimsmaking in three mediated forums - Twitter, Letters to the Editor and op-eds that appear in mainstream news outlets, and emails sent to Rick Scott about the legislation. We find that movement mobilization, in particular, seems to have influenced individual claimsmaking across the forums. Additionally, we find that there is more coalescence in claimsmaking around the gun control movement than the gun rights movement, and that movement coordination may help explain these differences.

How is social etiquette performed when offline interpersonal interactions are mediated online? Sixty semi-structured interviews with WeChat users, China’s most popular social media platform, show that the app’s technical designs facilitated a set of online etiquette rules which reproduced those of acquaintance communities. Three dimensions of social etiquette rules were considered and observed: respect, elegance, and tidiness. Users honored the face of others and avoided causing others to lose face, often presented a positive but restrained self-image, and strived to preserve the tidiness of the online public space by avoiding the sharing of and exposing others to “negative energy”.A video abstract is available at https://youtu.be/2tG40y8J11E.

The gender gap in Wikipedia content is a complex phenomenon that comprises several asymmetries, discursive dimensions, and social concerns. However, there is no theoretical framework to organise this complexity consistently. Based on writings by Foucault, Deleuze and Tkacz, we interpret Wikipedia as a field of visibility and provide a framework to analyse its content gaps. Then we use that model to organise the complexity of the content gender gap, performing a systematic overview of the asymmetries tested in empirical research. We suggest that this analysis is relevant for the effective planning of governance processes that seek to avoid womens subordination in digital platforms discourses.