13,357 research outputs found
An Exploratory Study of COVID-19 Misinformation on Twitter
During the COVID-19 pandemic, social media has become a home ground for
misinformation. To tackle this infodemic, scientific oversight, as well as a
better understanding by practitioners in crisis management, is needed. We have
conducted an exploratory study into the propagation, authors and content of
misinformation on Twitter around the topic of COVID-19 in order to gain early
insights. We have collected all tweets mentioned in the verdicts of
fact-checked claims related to COVID-19 by over 92 professional fact-checking
organisations between January and mid-July 2020 and share this corpus with the
community. This resulted in 1 500 tweets relating to 1 274 false and 276
partially false claims, respectively. Exploratory analysis of author accounts
revealed that the verified twitter handle(including Organisation/celebrity) are
also involved in either creating (new tweets) or spreading (retweet) the
misinformation. Additionally, we found that false claims propagate faster than
partially false claims. Compare to a background corpus of COVID-19 tweets,
tweets with misinformation are more often concerned with discrediting other
information on social media. Authors use less tentative language and appear to
be more driven by concerns of potential harm to others. Our results enable us
to suggest gaps in the current scientific coverage of the topic as well as
propose actions for authorities and social media users to counter
misinformation.Comment: 20 pages, nine figures, four tables. Submitted for peer review,
revision
Detecting and Explaining Crisis
Individuals on social media may reveal themselves to be in various states of
crisis (e.g. suicide, self-harm, abuse, or eating disorders). Detecting crisis
from social media text automatically and accurately can have profound
consequences. However, detecting a general state of crisis without explaining
why has limited applications. An explanation in this context is a coherent,
concise subset of the text that rationalizes the crisis detection. We explore
several methods to detect and explain crisis using a combination of neural and
non-neural techniques. We evaluate these techniques on a unique data set
obtained from Koko, an anonymous emotional support network available through
various messaging applications. We annotate a small subset of the samples
labeled with crisis with corresponding explanations. Our best technique
significantly outperforms the baseline for detection and explanation.Comment: Accepted at CLPsych, ACL workshop. 8 pages, 5 figure
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Temporal variability of emotions in social media posts
Employing the metadata from 627,632 Instagram posts for the Austrian capital city of Vienna over the period of October 30th, 2011 to February 7th, 2018, the present study extracts sentiment, as well as single basic emotions according to Plutchik\u27s Wheel of Emotions, from the photo captions including hashtag terms. In doing so, an algorithm falling into the category of dictionary-based approaches to study emotions contained in written text was developed and applied. Not only are the overall polarity and the single emotions contained in Instagram posts within Vienna investigated, but also the top 54 Viennese Instagram locations. A particular novelty of this study is the measurement of longitudinal developments from emotive text and the fine-grained analysis of single emotions in addition to the overall polarity. One crucial empirical result of the study is that more experience and self-confidence in Instagram posting, as well as increasing expectations, seem to result in becoming a more critical poster over time. Companies interested in the use of influencer marketing to promote their products and services via Instagram should take this finding into consideration in order to be successful
Everyday Automation
This Open Access book brings the experiences of automation as part of quotidian life into focus. It asks how, where and when automated technologies and systems are emerging in everyday life across different global regions? What are their likely impacts in the present and future? How do engineers, policy makers, industry stakeholders and designers envisage artificial intelligence (AI) and automated decision-making (ADM) as solutions to individual and societal problems? How do these future visions compare with the everyday realities, power relations and social inequalities in which AI and ADM are experienced? What do people know about automation and what are their experiences of engaging with âactually existingâ AI and ADM technologies? An international team of leading scholars bring together research developed across anthropology, sociology, media and communication studies and ethnology, which shows how by rehumanising automation, we can gain deeper understandings of its societal impacts
THE QUANTIFIED PANDEMIC: Digitised surveillance, containment and care in response to the COVID-19 crisis
In this chapter, I present a sociocultural analysis of how automated decision-making (ADM) tools and related software were deployed or anticipated in response to the COVID-19 crisis during the first year of the pandemic. These technologies included apps used to monitor people in quarantine and self-isolation, contact tracing apps, surveillance drones, digitised temperature checking devices, apps for delivering COVID test results, software for identifying âat riskâ patients and for selecting recipients of vaccines, and digital vaccine âpassportâ apps, as well as automated symptom checker apps, platforms and chatbots designed to help people determine whether they were infected with the novel coronavirus or needed to seek medical attention. Building on scholarship in critical public health, technocultures and critical data studies, I identify and discuss the social and political contexts and effects of these technologies. I demonstrate that despite techno-utopian promissory narratives routinely promoting their advantages, while some of these technologies have assisted with COVID-19 surveillance, control and medical care, many have failed. Furthermore, the deployment of these technologies has in many cases exacerbated existing socioeconomic disadvantage and stigmatisation, excluded some social groups and populations from economic support or healthcare and flouted human rights relating to privacy and freedom of movement
The future of Cybersecurity in Italy: Strategic focus area
This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management
Detecting Offensive Statements towards Foreigners in Social Media
Recently, politicians and media companies identified an increasing number of offensive statements directed against foreigners and refugees in Europe. In Germany, for example, the political group âPegidaâ drew international attention by frequently publishing offensive content concerning the religion of Islam. As a consequence, the German government and the social network Facebook cooperate to address this problem by creating a task force to manually detect offensive statements towards refugees and foreigners. In this work, we propose an approach to automatically detect such statements aiding personnel in this labor-intensive task. In contrast to existing work, we assess severity values to offensive statements and identify the referenced targets. This way, we are able to selectively detect hostility towards foreigners. To evaluate our approach, we develop a dataset containing offensive statements including their target. As a result, a substantial amount of offensive statements and a moderate amount of the referenced victims was detected correctly
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