94 research outputs found
The right to audit and power asymmetries in algorithm auditing
In this paper, we engage with and expand on the keynote talk about the Right
to Audit given by Prof. Christian Sandvig at the IC2S2 2021 through a critical
reflection on power asymmetries in the algorithm auditing field. We elaborate
on the challenges and asymmetries mentioned by Sandvig - such as those related
to legal issues and the disparity between early-career and senior researchers.
We also contribute a discussion of the asymmetries that were not covered by
Sandvig but that we find critically important: those related to other
disparities between researchers, incentive structures related to the access to
data from companies, targets of auditing and users and their rights. We also
discuss the implications these asymmetries have for algorithm auditing research
such as the Western-centrism and the lack of the diversity of perspectives.
While we focus on the field of algorithm auditing specifically, we suggest some
of the discussed asymmetries affect Computational Social Science more generally
and need to be reflected on and addressed
Dashboard of sentiment in Austrian social media during COVID-19
To track online emotional expressions of the Austrian population close to
real-time during the COVID-19 pandemic, we build a self-updating monitor of
emotion dynamics using digital traces from three different data sources. This
enables decision makers and the interested public to assess issues such as the
attitude towards counter-measures taken during the pandemic and the possible
emergence of a (mental) health crisis early on. We use web scraping and API
access to retrieve data from the news platform derstandard.at, Twitter and a
chat platform for students. We document the technical details of our workflow
in order to provide materials for other researchers interested in building a
similar tool for different contexts. Automated text analysis allows us to
highlight changes of language use during COVID-19 in comparison to a neutral
baseline. We use special word clouds to visualize that overall difference.
Longitudinally, our time series show spikes in anxiety that can be linked to
several events and media reporting. Additionally, we find a marked decrease in
anger. The changes last for remarkably long periods of time (up to 12 weeks).
We discuss these and more patterns and connect them to the emergence of
collective emotions. The interactive dashboard showcasing our data is available
online under http://www.mpellert.at/covid19_monitor_austria/. Our work has
attracted media attention and is part of an web archive of resources on
COVID-19 collected by the Austrian National Library.Comment: 23 pages, 3 figures, 1 tabl
This Sample seems to be good enough! Assessing Coverage and Temporal Reliability of Twitter's Academic API
Because of its willingness to share data with academia and industry, Twitter
has been the primary social media platform for scientific research as well as
for the consulting of businesses and governments in the last decade. In recent
years, a series of publications have studied and criticized Twitter's APIs and
Twitter has partially adapted its existing data streams. The newest Twitter API
for Academic Research allows to "access Twitter's real-time and historical
public data with additional features and functionality that support collecting
more precise, complete, and unbiased datasets." The main new feature of this
API is the possibility of accessing the full archive of all historic Tweets. In
this article, we will take a closer look at the Academic API and will try to
answer two questions. First, are the datasets collected with the Academic API
complete? Secondly, since Twitter's Academic API delivers historic Tweets as
represented on Twitter at the time of data collection, we need to understand
how much data is lost over time due to Tweet and account removal from the
platform. Our work shows evidence that Twitter's Academic API can indeed create
(almost) complete samples of Twitter data based on a wide variety of search
terms. We also provide evidence that Twitter's data endpoint v2 delivers better
samples than the previously used endpoint v1.1. Furthermore, collecting Tweets
with the Academic API at the time of studying a phenomenon rather than creating
local archives of stored Tweets, allows for a straightforward way of following
Twitter's developer agreement. Finally, we will also discuss technical
artifacts and implications of the Academic API. We hope that our work can add
another layer of understanding of Twitter data collections leading to more
reliable studies of human behavior via social media data
Collective moderation of hate, toxicity, and extremity in online discussions
How can citizens moderate hate, toxicity, and extremism in online discourse?
We analyze a large corpus of more than 130,000 discussions on German Twitter
over the turbulent four years marked by the migrant crisis and political
upheavals. With a help of human annotators, language models, machine learning
classifiers, and longitudinal statistical analyses, we discern the dynamics of
different dimensions of discourse. We find that expressing simple opinions, not
necessarily supported by facts but also without insults, relates to the least
hate, toxicity, and extremity of speech and speakers in subsequent discussions.
Sarcasm also helps in achieving those outcomes, in particular in the presence
of organized extreme groups. More constructive comments such as providing facts
or exposing contradictions can backfire and attract more extremity. Mentioning
either outgroups or ingroups is typically related to a deterioration of
discourse in the long run. A pronounced emotional tone, either negative such as
anger or fear, or positive such as enthusiasm and pride, also leads to worse
outcomes. Going beyond one-shot analyses on smaller samples of discourse, our
findings have implications for the successful management of online commons
through collective civic moderation
Agent-based simulations for protecting nursing homes with prevention and vaccination strategies
Due to its high lethality amongst the elderly, the safety of nursing homes
has been of central importance during the COVID-19 pandemic. With test
procedures becoming available at scale, such as antigen or RT-LAMP tests, and
increasing availability of vaccinations, nursing homes might be able to safely
relax prohibitory measures while controlling the spread of infections (meaning
an average of one or less secondary infections per index case). Here, we
develop a detailed agent-based epidemiological model for the spread of
SARS-CoV-2 in nursing homes to identify optimal prevention strategies. The
model is microscopically calibrated to high-resolution data from nursing homes
in Austria, including detailed social contact networks and information on past
outbreaks. We find that the effectiveness of mitigation testing depends
critically on the timespan between test and test result, the detection
threshold of the viral load for the test to give a positive result, and the
screening frequencies of residents and employees. Under realistic conditions
and in absence of an effective vaccine, we find that preventive screening of
employees only might be sufficient to control outbreaks in nursing homes,
provided that turnover times and detection thresholds of the tests are low
enough. If vaccines that are moderately effective against infection and
transmission are available, control is achieved if 80% or more of the
inhabitants are vaccinated, even if no preventive testing is in place and
residents are allowed to have visitors. Since these results strongly depend on
vaccine efficacy against infection, retention of testing infrastructures,
regular voluntary screening and sequencing of virus genomes is advised to
enable early identification of new variants of concern.Comment: Supplementary material is included in the manuscript PD
Just Another Day on Twitter: A Complete 24 Hours of Twitter Data
At the end of October 2022, Elon Musk concluded his acquisition of Twitter.
In the weeks and months before that, several questions were publicly discussed
that were not only of interest to the platform's future buyers, but also of
high relevance to the Computational Social Science research community. For
example, how many active users does the platform have? What percentage of
accounts on the site are bots? And, what are the dominating topics and
sub-topical spheres on the platform? In a globally coordinated effort of 80
scholars to shed light on these questions, and to offer a dataset that will
equip other researchers to do the same, we have collected all 375 million
tweets published within a 24-hour time period starting on September 21, 2022.
To the best of our knowledge, this is the first complete 24-hour Twitter
dataset that is available for the research community. With it, the present work
aims to accomplish two goals. First, we seek to answer the aforementioned
questions and provide descriptive metrics about Twitter that can serve as
references for other researchers. Second, we create a baseline dataset for
future research that can be used to study the potential impact of the
platform's ownership change
Impact of safety-related dose reductions or discontinuations on sustained virologic response in HCV-infected patients: Results from the GUARD-C Cohort
BACKGROUND:
Despite the introduction of direct-acting antiviral agents for chronic hepatitis C virus (HCV) infection, peginterferon alfa/ribavirin remains relevant in many resource-constrained settings. The non-randomized GUARD-C cohort investigated baseline predictors of safety-related dose reductions or discontinuations (sr-RD) and their impact on sustained virologic response (SVR) in patients receiving peginterferon alfa/ribavirin in routine practice.
METHODS:
A total of 3181 HCV-mono-infected treatment-naive patients were assigned to 24 or 48 weeks of peginterferon alfa/ribavirin by their physician. Patients were categorized by time-to-first sr-RD (Week 4/12). Detailed analyses of the impact of sr-RD on SVR24 (HCV RNA <50 IU/mL) were conducted in 951 Caucasian, noncirrhotic genotype (G)1 patients assigned to peginterferon alfa-2a/ribavirin for 48 weeks. The probability of SVR24 was identified by a baseline scoring system (range: 0-9 points) on which scores of 5 to 9 and <5 represent high and low probability of SVR24, respectively.
RESULTS:
SVR24 rates were 46.1% (754/1634), 77.1% (279/362), 68.0% (514/756), and 51.3% (203/396), respectively, in G1, 2, 3, and 4 patients. Overall, 16.9% and 21.8% patients experienced 651 sr-RD for peginterferon alfa and ribavirin, respectively. Among Caucasian noncirrhotic G1 patients: female sex, lower body mass index, pre-existing cardiovascular/pulmonary disease, and low hematological indices were prognostic factors of sr-RD; SVR24 was lower in patients with 651 vs. no sr-RD by Week 4 (37.9% vs. 54.4%; P = 0.0046) and Week 12 (41.7% vs. 55.3%; P = 0.0016); sr-RD by Week 4/12 significantly reduced SVR24 in patients with scores <5 but not 655.
CONCLUSIONS:
In conclusion, sr-RD to peginterferon alfa-2a/ribavirin significantly impacts on SVR24 rates in treatment-naive G1 noncirrhotic Caucasian patients. Baseline characteristics can help select patients with a high probability of SVR24 and a low probability of sr-RD with peginterferon alfa-2a/ribavirin
Two-Year Progress of Pilot Research Activities in Teaching Digital Thinking Project (TDT)
This article presents a progress report from the last two years of the Teaching Digital Thinking (TDT) project. This project aims to implement new concepts, didactic methods, and teaching formats for sustainable digital transformation in Austrian Universities’ curricula by introducing new digital competencies. By equipping students and teachers with 21st-century digital competencies, partner universities can contribute to solving global challenges and organizing pilot projects. In line with the overall project aims, this article presents the ongoing digital transformation activities, courses, and research in the project, which have been carried out by the five partner universities since 2020, and briefly discusses the results. This article presents a summary of the research and educational activities carried out within two parts: complementary research and pilot projects
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