107 research outputs found
Online Networks of Support in Distressed Environments: Solidarity and Mobilization during the Russian Invasion of Ukraine
Despite their drawbacks and unintended consequences, social media networks
have recently emerged as a crucial resource for individuals in distress,
particularly during times of crisis. These platforms serve as a means to seek
assistance and support, share reliable information, and appeal for action and
solidarity. In this paper, we examine the online networks of support during the
Russia-Ukraine conflict by analyzing four major social media networks- Twitter,
Facebook, Instagram, and YouTube. Using a large dataset of 68 million posts, we
explore the temporal patterns and interconnectedness between these platforms
and online support websites. Our analysis highlights the prevalence of
crowdsourcing and crowdfunding websites as the two main support platforms to
mobilize resources and solicit donations, revealing their purpose and contents,
and investigating different support-seeking and -receiving practices. Overall,
our study underscores the potential of social media in facilitating online
support in distressed environments through grassroots mobilization,
contributing to the growing body of research on the positive impact of online
platforms in promoting social good and protecting vulnerable populations during
times of crisis and conflict
How Does Twitter Account Moderation Work? Dynamics of Account Creation and Suspension During Major Geopolitical Events
Social media moderation policies are often at the center of public debate,
and their implementation and enactment are sometimes surrounded by a veil of
mystery. Unsurprisingly, due to limited platform transparency and data access,
relatively little research has been devoted to characterizing moderation
dynamics, especially in the context of controversial events and the platform
activity associated with them. Here, we study the dynamics of account creation
and suspension on Twitter during two global political events: Russia's invasion
of Ukraine and the 2022 French Presidential election. Leveraging a large-scale
dataset of 270M tweets shared by 16M users in multiple languages over several
months, we identify peaks of suspicious account creation and suspension, and we
characterize behaviours that more frequently lead to account suspension. We
show how large numbers of accounts get suspended within days from their
creation. Suspended accounts tend to mostly interact with legitimate users, as
opposed to other suspicious accounts, often making unwarranted and excessive
use of reply and mention features, and predominantly sharing spam and harmful
content. While we are only able to speculate about the specific causes leading
to a given account suspension, our findings shed light on patterns of platform
abuse and subsequent moderation during major events
Identifying and Characterizing Behavioral Classes of Radicalization within the QAnon Conspiracy on Twitter
Social media provide a fertile ground where conspiracy theories and radical ideas can flourish, reach broad audiences, and sometimes lead to hate or violence beyond the online world itself.
QAnon represents a notable example of a political conspiracy that started out on social media but turned mainstream, in part due to public endorsement by influential political figures. Nowadays, QAnon conspiracies often appear in the news, are part of political rhetoric, and are espoused by significant swaths of people in the United States. It is therefore crucial to understand how such a conspiracy took root online, and what led so many social media users to adopt its ideas. In this work, we propose a framework that exploits both social interaction and content signals to uncover evidence of user radicalization or support for QAnon. Leveraging a large dataset of 240M tweets collected in the run-up to the 2020 US Presidential election, we define and validate a multivariate metric of radicalization. We use that to separate users in distinct, naturally-emerging, classes of behaviors associated with radicalization processes, from self-declared QAnon supporters to hyper-active conspiracy promoters. We also analyze the impact of Twitter's moderation policies on the interactions among different classes: we discover aspects of moderation that succeed, yielding a substantial reduction in the endorsement received by hyperactive QAnon accounts. But we also uncover where moderation fails, showing how QAnon content amplifiers are not deterred or affected by the Twitter intervention. Our findings refine our understanding of online radicalization processes, reveal effective and ineffective aspects of moderation, and call for the need to further investigate the role social media play in the spread of conspiracies
What are Your Pronouns? Examining Gender Pronoun Usage on Twitter
Stating your gender pronouns, along with your name, is becoming the new norm
of self-introductions at school, at the workplace, and online. The increasing
prevalence and awareness of nonconforming gender identities put discussions of
developing gender-inclusive language at the forefront. This work presents the
first empirical research on gender pronoun usage on large-scale social media.
Leveraging a Twitter dataset of over 2 billion tweets collected continuously
over two years, we find that the public declaration of gender pronouns is on
the rise, with most people declaring as using she series pronouns, followed by
he series pronouns, and a smaller but considerable amount of non-binary
pronouns. From analyzing Twitter posts and sharing activities, we can discern
users who use gender pronouns from those who do not and also distinguish users
of various gender identities. We further illustrate the relationship between
explicit forms of social network exposure to gender pronouns and their eventual
gender pronoun adoption. This work carries crucial implications for
gender-identity studies and initiates new research directions in gender-related
fairness and inclusion, as well as support against online harassment and
discrimination on social media.Comment: 23 pages, 11 figures, 2 table
Epidemiology, Clinical Features and Prognostic Factors of Pediatric SARS-CoV-2 Infection: Results From an Italian Multicenter Study
Background: Many aspects of SARS-CoV-2 infection in children and adolescents
remain unclear and optimal treatment is debated. The objective of our study was
to investigate epidemiological, clinical and therapeutic characteristics of pediatric
SARS-CoV-2 infection, focusing on risk factors for complicated and critical disease.
Methods: The present multicenter Italian study was promoted by the Italian Society
of Pediatric Infectious Diseases, involving both pediatric hospitals and general
pediatricians/family doctors. All subjects under 18 years of age with documented
SARS-CoV-2 infection and referred to the coordinating center were enrolled from
March 2020.
Results: As of 15 September 2020, 759 children were enrolled (median age 7.2 years,
IQR 1.4; 12.4). Among the 688 symptomatic children, fever was the most common
symptom (81.9%). Barely 47% of children were hospitalized for COVID-19. Age was
inversely related to hospital admission (p < 0.01) and linearly to length of stay (p = 0.014).
One hundred forty-nine children (19.6%) developed complications. Comorbidities were
risk factors for complications (p < 0.001). Viral coinfections, underlying clinical conditions,
age 5\u20139 years and lymphopenia were statistically related to ICU admission (p < 0.05).
Garazzino et al. SARS-CoV-2 in Children and Adolescents
Conclusions: Complications of COVID-19 in children are related to comorbidities and
increase with age. Viral co-infections are additional risk factors for disease progression
and multisystem inflammatory syndrome temporarily related to COVID-19 (MIS-C) for
ICU admission
COVID-19 in Infants Less than 3 Months: Severe or Not Severe Disease?
Compared to adults, severe or fatal COVID-19 disease is much less common in children. However, a higher risk for progression has been reported in infants. Different pediatric COVID-19 severity scores are reported in the literature
Brachytelephalangic chondrodysplasia punctata caused by new small hemizygous deletion in a boy presenting with hearing loss
Association Testing Of Copy Number Variants in Schizophrenia and Autism Spectrum Disorders
Background: Autism spectrum disorders and schizophrenia have been associated with an overlapping set of copynumber variant loci, but the nature and degree of overlap in copy number variants (deletions compared toduplications) between these two disorders remains unclear.Methods: We systematically evaluated three lines of evidence: (1) the statistical bases for associations of autismspectrum disorders and schizophrenia with a set of the primary CNVs thus far investigated, from previous studies;(2) data from case series studies on the occurrence of these CNVs in autism spectrum disorders, especially amongchildren, and (3) data on the extent to which the CNVs were associated with intellectual disability anddevelopmental, speech, or language delays. We also conducted new analyses of existing data on these CNVs inautism by pooling data from seven case control studies.Results: Four of the CNVs considered, dup 1q21.1, dup 15q11-q13, del 16p11.2, and dup 22q11.21, showed clearstatistical evidence as autism risk factors, whereas eight CNVs, del 1q21.1, del 3q29, del 15q11.2, del 15q13.3, dup16p11.2, dup 16p13.1, del 17p12, and del 22q11.21, were strongly statistically supported as risk factors forschizophrenia. Three of the CNVs, dup 1q21.1, dup 16p11.2, and dup 16p13.1, exhibited statistical support as riskfactors for both autism and schizophrenia, although for each of these CNVs statistical significance was nominal fortests involving one of the two disorders. For the CNVs that were statistically associated with schizophrenia but werenot statistically associated with autism, a notable number of children with the CNVs have been diagnosed withautism or ASD; children with these CNVs also demonstrate a high incidence of intellectual disability anddevelopmental, speech, or language delays.Conclusions: These findings suggest that although CNV loci notably overlap between autism and schizophrenia,the degree of strongly statistically supported overlap in specific CNVs at these loci remains limited. These analysesalso suggest that relatively severe premorbidity to CNV-associated schizophrenia in children may sometimes bediagnosed as autism spectrum disorder
The nuclear receptor ERβ engages AGO2 in regulation of gene transcription, RNA splicing and RISC loading
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