Miracle-dz/UniFAR-FTUs huggingface dataset:
Secular trends and social inequalities in child behavioural problems across three Brazilian cohort studies (1993, 2004 and 2015)

Abstract Aims Previous epidemiological evidence identified a concerning increase in behavioural problems among young children from 1997 to 2008 in Brazil. However, it is unclear whether behavioural problems have continued to increase, if secular changes vary between sociodemographic groups and what might explain changes over time. We aimed to monitor changes in child behavioural problems over a 22-year period from 1997 to 2019, examine changing social inequalities and explore potential explanations for recent changes in behavioural problems between 2008 and 2019. Methods The Child Behaviour Checklist was used to compare parent-reported behavioural problems in 4-year-old children across three Brazilian birth cohorts assessed in 1997 (1993 cohort, n = 633), 2008 (2004 cohort, n = 3750) and 2019 (2015 cohort, n = 577). Response rates across all three population-based cohorts were over 90%. Moderation analyses tested if cross-cohort changes differed by social inequalities (demographic and socioeconomic position), while explanatory models explored whether changes in hypothesized risk and protective factors in prenatal development (e.g., smoking during pregnancy) and family life (e.g., maternal depression and harsh parenting) accounted for changes in child behavioural problems from 2008 to 2019. Results Initial increases in child behavioural problems from 1997 to 2008 were followed by declines in conduct problems (mean change = −2.75; 95% confidence interval [CI]: −3.56, −1.94; P < 0.001), aggression (mean change = −1.84; 95% CI: −2.51, −1.17; P < 0.001) and rule-breaking behaviour (mean change = −0.91; 95% CI: −1.13, −0.69 P < 0.001) from 2008 to 2019. Sex differences in rule-breaking behaviour diminished during this 22-year period, whereas socioeconomic inequalities in behavioural problems emerged in 2008 and then remained relatively stable. Consequently, children from poorer and less educated families had higher behavioural problems, compared to more socially advantaged children, in the two more recent cohorts. Changes in measured risk and protective factors partly explained the reduction in behavioural problems from 2008 to 2019. Conclusions Following a rise in child behavioural problems, there was a subsequent reduction in behavioural problems from 2008 to 2019. However, social inequalities increased and remained high. Continued monitoring of behavioural problems by subgroups is critical for closing the gap between socially advantaged and disadvantaged children and achieving health equity for the next generation.
Mobilizing Manufactured Reality: How Participatory Disinformation Shaped Deep Stories to Catalyze Action during the 2020 U.S. Presidential Election

Claims of election fraud throughout the 2020 U.S. Presidential Election and during the lead up to the January 6, 2021 insurrection attempt have drawn attention to the urgent need to better understand how people interpret and act on disinformation. In this work, we present three primary contributions: (1) a framework for understanding the interaction between participatory disinformation and informal and tactical mobilization; (2) three case studies from the 2020 U.S. election analyzed using detailed temporal, content, and thematic analysis; and (3) a qualitative coding scheme for understanding how digital disinformation functions to mobilize online audiences. We combine resource mobilization theory with previous work examining participatory disinformation campaigns and "deep stories" to show how false or misleading information functioned to mobilize online audiences before, during, and after election day. Our analysis highlights how users on Twitter collaboratively construct and amplify alleged evidence of fraud that is used to facilitate action, both online and off. We find that mobilization is dependent on the selective amplification of false or misleading tweets by influencers, the framing around those claims, as well as the perceived credibility of their source. These processes are a self-reinforcing cycle where audiences collaborate in the construction of a misleading version of reality, which in turn leads to offline actions that are used to further reinforce a manufactured reality. Through this work, we hope to better inform future interventions.
Infrastructures for Virtual Volunteering at Online Music Festivals

Volunteering benefits recipients, volunteers, communities, and society, while digital technologies establish new opportunities for virtual volunteering. We describe how volunteers transitioned the UK's long-established Oxjam grassroots music festival online in response to the COVID pandemic, delivering a local pilot before scaling up nationwide. We adopt an infrastructural perspective to reveal how two teams of volunteers defined a flexible festival format, knitted together diverse technologies into a technical platform, and operated this to deliver the festival. We highlight the need for teams of volunteers to orchestrate both audience and performer trajectories through festivals. We argue for deliberately designing in volunteer labour rather than automating it out by translating traditional roles online while defining new digital ones. We propose to make these roles rewarding through a more social volunteer experience, including privileged backstage access. We highlight the challenges of using social media for such events, including complying with algorithmic policing of rights.
I See What You're Hearing: Facilitating The Effect of Environment on Perceived Emotion While Teleconferencing

Our perception of emotion is highly contextual. Changes in the environment can affect our narrative framing, and thus augment our emotional perception of interlocutors. User environments are typically heavily suppressed due to the technical limitations of commercial videoconferencing platforms. As a result, there is often a lack of contextual awareness while participating in a video call, and this affects how we perceive the emotions of conversants. We present a videoconferencing module that visualizes the user's aural environment to enhance awareness between interlocutors. The system visualizes environmental sound based on its semantic and acoustic properties. We found that our visualization system was about 50% effective at eliciting emotional perceptions in users that was similar to the response elicited by environmental sound it replaced.The contributed system provides a unique approach to facilitate ambient awareness on an implicit emotional level in situations where multimodal environmental context is suppressed.
"Strike at the Root": Co-designing Real-Time Social Media Interventions for Adolescent Online Risk Prevention

Adolescent online safety researchers have emphasized the importance of moving beyond restrictive and privacy invasive approaches to online safety, towards resilience-based approaches for empowering teens to deal with online risks independently. Unfortunately, many of the existing online safety interventions are focused on parental mediation and not contextualized to teens' personal experiences online; thus, they do not effectively cater to the unique needs of teens. To better understand how we might design online safety interventions that help teens deal with online risks, as well as when and how to intervene, we must include teens as partners in the design process and equip them with the skills needed to contribute equally to the design process. As such, we conducted User Experience (UX) bootcamps with 21 teens (ages 13-17) to first teach them important UX design skills using industry standard tools, so they could create storyboards for unsafe online interactions commonly experienced by teens and high-fidelity, interactive prototypes for dealing with these situations. Based on their storyboards, teens often encountered information breaches and sexual risks with strangers, as well as cyberbullying from acquaintances or friends. While teens often blocked or reported strangers, they struggled with responding to risks from friends or acquaintances, seeking advice from others on the best action to take. Importantly, teens did not find any of the existing ways for responding to these risks to be effective in keeping them safe. When asked to create their own design-based interventions, teens frequently envisioned "nudges" that occurred in real-time. Interestingly, teens more often designed for risk prevention (rather than risk coping) by focusing on nudging the risk perpetrator (rather than the victim) to rethink their actions, block harmful actions from occurring, or penalizing perpetrators for inappropriate behavior to prevent it from happening again in the future. Teens also designed personalized sensitivity filters to provide teens the ability to manage content they wanted to see online. Some teens also designed personalized nudges, so that teens could receive intelligent, guided advice from the platform that would help them know how to handle online risks themselves without intervention from their parents. Our findings highlight how teens want to address online risks "at the root" by putting the onus of risk prevention on those who perpetrate them - rather than on the victim. Our work is the first to leverage co-design with teens to develop novel online safety interventions that advocate for a paradigm shift from youth risk protection to promoting good digital citizenship.
The Work to Make Facial Recognition Work

Facial recognition technology (FRT) has become a significant topic in CSCW owing to widespread adoption and related criticisms: the use of FRT is often considered an assault on privacy or a kind of neo-phrenology. This discussion has revolved around uses of FRT for identification, which are often non-voluntary, in particular for surveillance wherein people are (by and large) unwittingly recognized by FRT systems. At the same time, we have also seen a rise of forms of FRT for verification (e.g., passport control or Apple's Face ID), which typically are overt and interactive. In this paper we study an interactive FRT system used for guest check-in at a hotel in China. We show how guests and bystanders engage in 'self-disciplining work' by controlling their facial (and bodily) comportment both to get recognized and at times to avoid recognition. From our analysis we discuss the role of preparatory and remedial work, as well as dehumanization, and the importance of CSCW paying closer attention to the significance of interactional compliance for people using and bystanding facial recognition technologies.
PATHWiSE: An Authoring Tool to Support Teachers to Create Robot-Supported Social Learning Experiences During Homework

Educational technologies can provide students with adaptive feedback and guidance, but these systems lack personal interactions that make social and cultural connections to the student's own classroom and prior experiences. Social or companion robots have a high capacity for these types of interactions, but typically require advanced levels of expertise to program. In this study, we examined teachers use of an authoring tool to enable them to leverage their classroom-based expertise to design robot-assisted homework assignments, and explore how seeing a robot enact their designs influences their work. We found that the tool enabled the teachers to create novel social interactions for homework activities that were similar to their classroom interaction patterns. These interaction designs evolved over time and were shaped by the teacher's emerging mental model of the social robot, their concept of the students' perspective of these interactions, and a shift towards informal classroom-like interaction paradigms, thus transforming their view of what they can achieve with homework. We discuss how these findings demonstrate how the context of the activity can influence initial mental models of social activities and suggest practical guidance on designing authoring tools to best facilitate the creation of computer or robot supported social activities, such as homework.
Multiple Flow Regimes in the Northern Hemisphere Winter. Part II: Sectorial Regimes and Preferred Transitions

Abstract This paper presents an observational analysis of recurrent flow patterns in the Northern Hemisphere (NH) winter, based on a 37-year series of daily 700-mb height anomalies. Large-scale anomaly patterns that appear repeatedly and persist beyond synoptic time scales are identified by searching for local maxima of probability density in a phase subspace, which is spanned by the leading empirical orthogenal functions (EOFs). By using an angular probability density function (PDF), we focus on the shape, not magnitude, of the anomaly patterns. The PDF estimate is nonparametric; that is, our algorithm makes no a priori assumption on symmetry with respect to the climatological mean as in one-point correlation and rotated EOF analyses. The local density maxima are searched by iterative bump hunting. Based on observed partial decoupling between the Pacific (PAC) and the Atlantic-Eurasian (ATL) sectors, the classification algorithm is applied separately to each of the two. Seven PAC and six ATL patterns are.
On the Potential of Mediation Chatbots for Mitigating Multiparty Privacy Conflicts - A Wizard-of-Oz Study

Sharing multimedia content, without obtaining consent from the people involved causes multiparty privacy conflicts (MPCs). However, social-media platforms do not proactively protect users from the occurrence of MPCs. Hence, users resort to out-of-band, informal communication channels, attempting to mitigate such conflicts. So far, previous works have focused on hard interventions that do not adequately consider the contextual factors (e.g., social norms, cognitive priming) or are employed too late (i.e., the content has already been seen). In this work, we investigate the potential of conversational agents as a medium for negotiating and mitigating MPCs. We designed MediationBot, a mediator chatbot that encourages consent collection, enables users to explain their points of view, and proposes solutions to finding a middle ground. We evaluated our design using a Wizard-of-Oz experiment with N = 32 participants, where we found that MediationBot can effectively help participants to reach an agreement and to prevent MPCs. It produced a structured conversation where participants had well-clarified speaking turns. Overall, our participants found MediationBot to be supportive as it proposes useful middle-ground solutions. Our work informs the future design of mediator agents to support social-media users against MPCs.
Understanding the Use of Images to Spread COVID-19 Misinformation on Twitter

While COVID-19 text misinformation has already been investigated by various scholars, fewer research efforts have been devoted to characterizing and understanding COVID-19 misinformation that is carried out through visuals like photographs and memes. In this paper, we present a mixed-method analysis of image-based COVID-19 misinformation in 2020 on Twitter. We deploy a computational pipeline to identify COVID-19 related tweets, download the images contained in them, and group together visually similar images. We then develop a codebook to characterize COVID-19 misinformation and manually label images as misinformation or not. Finally, we perform a quantitative analysis of tweets containing COVID-19 misinformation images. We identify five types of COVID-19 misinformation, from a wrong understanding of the threat severity of COVID-19 to the promotion of fake cures and conspiracy theories. We also find that tweets containing COVID-19 misinformation images do not receive more interactions than baseline tweets with random images posted by the same set of users. As for temporal properties, COVID-19 misinformation images are shared for longer periods of time than non-misinformation ones, as well as have longer burst times. %\ywi added "have'' %\ywFor RQ2, we compare non-misinformation images instead of random images, and so it is not a direct comparison. When looking at the users sharing COVID-19 misinformation images on Twitter from the perspective of their political leanings, we find that pro-Democrat and pro-Republican users share a similar amount of tweets containing misleading or false COVID-19 images. However, the types of images that they share are different: while pro-Democrat users focus on misleading claims about the Trump administration's response to the pandemic, as well as often sharing manipulated images intended as satire, pro-Republican users often promote hydroxychloroquine, an ineffective medicine against COVID-19, as well as conspiracy theories about the origin of the virus. Our analysis sets a basis for better understanding COVID-19 misinformation images on social media and the nuances in effectively moderate them.
Fishing for Validation: Understanding Promises and Challenges of a Private Social Media Group for COVID-19 Long-Hauler Patients

The COVID-19 pandemic has affected more than 301 million people worldwide so far. Many communities (such as minority communities) suffered disproportionately more difficulties throughout the pandemic. In this paper, we would like to focus on one such community: COVID-19 long-haulers community. Long-hauler community consists of people affected by Coronavirus, but their symptoms do not cure in a couple of weeks; instead, they experience lingering symptoms for months. The concerns of this community were initially ignored by health care providers primarily because of limited information. In this paper, we have analyzed the social media discussion of a private Facebook group dedicated to the long-hauler community. In addition, we interviewed the community members to investigate their motivations for joining the group and how the group has impacted their lives as long-hauler patients. Our analyses revealed the primary discussion topics of this community. It also showed how a minority community could stand by each other using social media groups during a crisis. We concluded the paper with long-term implications of our findings for health care systems, policies, and existing literature on cooperative AI.
On the Verwey transition in magnetite: the soft modes of the metal-insulator transition

Fe$_3$O$_4$ is the earliest discovered magnetic material. It is widely used in recording devices or as a catalyst [1, 2], and some of its electronic and magnetic properties hold promise for future applications [3]. Magnetite undergoes a structural phase transition (PT) accompanied by a Metal-Insulator Transition (MIT) below 120 K, which was originally proposed by Verwey to be driven by the onset of a periodic modulation of the charge on the iron sites [4]. While the pattern of the charge ordering has been shown to be far more complex than the original Verwey hypothesis [5, 6], the mechanism behind its formation is still a subject of debate. As an alternative to a purely electronic model, the interplay between structural and electronic degrees of freedom has been proposed to drive the MIT [7, 8]. However, direct evidence for the involvement of the structure has never been obtained, since no clear anomalies of any phonon mode have been observed across the critical region. Here, we reveal by means of ultrafast broadband optical spectroscopy and spontaneous Raman scattering that the primary order parameters of the structural PT are strongly coupled to the charge density wave, and undergo a sizable softening in the proximity of the critical temperature. Furthermore, we show that signatures of the low-temperature phase can be induced by impulsive charge transfer excitation even at room temperature, where fluctuations of the charge order have recently been shown to persist [9]. These results clarify the mechanism for the Verwey transition and open novel scenarios in the coherent control of MITs.
"My Culture, My People, My Hometown": Chinese Ethnic Minorities Seeking Cultural Sustainability by Video Blogging

Ethnic minorities face challenges in sustaining their culture in regions dominated by ethnic majorities. With the growing popularity of video blogging (vlogging) in China, many ethnic minority vloggers are using vlogs to present and promote their ethnic culture online. In this study, we interviewed 16 vloggers onDouyin to understand why and how vlogs can be used to sustain ethnic culture. We found that both ethnic cultural experts and non-experts were involved in ethnic vlog making and sharing activities onDouyin, and cultural experts took more initiative in preserving and promoting ethnic culture while non-experts were more motivated by getting more traffic and income. Vloggers' imagined audiences included both intra-ethnic and mainstream viewers, impacting their vlog-making strategies, the utilized platform features, and the created vlog content. For example, vloggers taught ethnic language and built an identity for intra-ethnic viewers. Both ethnic minority vloggers and viewers protected their culture from misinterpretation by mainstream viewers. Our findings suggest the potential of using video blogging to address the challenges of cultural sustainability, providing design implications for future ICTs to support the cultural sustainability of ethnic minorities.
Working With AI to Persuade: Examining a Large Language Model's Ability to Generate Pro-Vaccination Messages

Artificial Intelligence (AI) is a transformative force in communication and messaging strategy, with potential to disrupt traditional approaches. Large language models (LLMs), a form of AI, are capable of generating high-quality, humanlike text. We investigate the persuasive quality of AI-generated messages to understand how AI could impact public health messaging. Specifically, through a series of studies designed to characterize and evaluate generative AI in developing public health messages, we analyze COVID-19 pro-vaccination messages generated by GPT-3, a state-of-the-art instantiation of a large language model. Study 1 is a systematic evaluation of GPT-3's ability to generate pro-vaccination messages. Study 2 then observed peoples' perceptions of curated GPT-3-generated messages compared to human-authored messages released by the CDC (Centers for Disease Control and Prevention), finding that GPT-3 messages were perceived as more effective, stronger arguments, and evoked more positive attitudes than CDC messages. Finally, Study 3 assessed the role of source labels on perceived quality, finding that while participants preferred AI-generated messages, they expressed dispreference for messages that were labeled as AI-generated. The results suggest that, with human supervision, AI can be used to create effective public health messages, but that individuals prefer their public health messages to come from human institutions rather than AI sources. We propose best practices for assessing generative outputs of large language models in future social science research and ways health professionals can use AI systems to augment public health messaging.