14,856 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
Emergence of influential spreaders in modified rumor models
The burst in the use of online social networks over the last decade has
provided evidence that current rumor spreading models miss some fundamental
ingredients in order to reproduce how information is disseminated. In
particular, recent literature has revealed that these models fail to reproduce
the fact that some nodes in a network have an influential role when it comes to
spread a piece of information. In this work, we introduce two mechanisms with
the aim of filling the gap between theoretical and experimental results. The
first model introduces the assumption that spreaders are not always active
whereas the second model considers the possibility that an ignorant is not
interested in spreading the rumor. In both cases, results from numerical
simulations show a higher adhesion to real data than classical rumor spreading
models. Our results shed some light on the mechanisms underlying the spreading
of information and ideas in large social systems and pave the way for more
realistic diffusion models.Comment: 14 Pages, 6 figures, accepted for publication in Journal of
Statistical Physic
A Computational Model and Convergence Theorem for Rumor Dissemination in Social Networks
The spread of rumors, which are known as unverified statements of uncertain
origin, may cause tremendous number of social problems. If it would be possible
to identify factors affecting spreading a rumor (such as agents' desires, trust
network, etc.), then this could be used to slowdown or stop its spreading. A
computational model that includes rumor features and the way a rumor is spread
among society's members, based on their desires, is therefore needed. Our
research is centering on the relation between the homogeneity of the society
and rumor convergence in it and result shows that the homogeneity of the
society is a necessary condition for convergence of the spreading rumor.Comment: 29 pages, 7 figure
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Twitter in local government: A study of Greater London authorities
Copyright @ 2011 SIG eGOVMicroblogging services are considered an emerging opportunity for authorities seeking to establish new communication channels with their public. Potential benefits evolve around enhancing transparency and interactivity, as well as sharing information regularly or during emergency events. The purpose of this exploratory study is to advance our empirical understanding of microblogging in local government. In particular, we reflect on online data collected to profile the use of Twitter by 29 Greater London local authorities (LAs). The study shows that London LAs have been accumulating significant experience with Twitter mainly over the past two years. In fact, many of them appear to incorporate conversational characteristics in their Tweets other than simply disseminating information. Furthermore, an analysis of Tweets during the August 2011 riots in England indicates the usefulness of the medium for responsibly informing the public and preventing rumours. Nevertheless, the study also identifies several points of improvement in the way public authorities are building their online networks; for example, in terms of connecting with each other and exploiting even more the conversational characteristics of Twitter
Learning and Designing Stochastic Processes from Logical Constraints
Stochastic processes offer a flexible mathematical formalism to model and
reason about systems. Most analysis tools, however, start from the premises
that models are fully specified, so that any parameters controlling the
system's dynamics must be known exactly. As this is seldom the case, many
methods have been devised over the last decade to infer (learn) such parameters
from observations of the state of the system. In this paper, we depart from
this approach by assuming that our observations are {\it qualitative}
properties encoded as satisfaction of linear temporal logic formulae, as
opposed to quantitative observations of the state of the system. An important
feature of this approach is that it unifies naturally the system identification
and the system design problems, where the properties, instead of observations,
represent requirements to be satisfied. We develop a principled statistical
estimation procedure based on maximising the likelihood of the system's
parameters, using recent ideas from statistical machine learning. We
demonstrate the efficacy and broad applicability of our method on a range of
simple but non-trivial examples, including rumour spreading in social networks
and hybrid models of gene regulation
Online Misinformation: Challenges and Future Directions
Misinformation has become a common part of our digital media environments and it is compromising the ability of our societies to form informed opinions. It generates misperceptions, which have affected the decision making processes in many domains, including economy, health, environment, and elections, among others. Misinformation and its generation, propagation, impact, and management is being studied through a variety of lenses (computer science, social science, journalism, psychology, etc.) since it widely affects multiple aspects of society. In this paper we analyse the phenomenon of misinformation from a technological point of view.We study the current socio-technical advancements towards addressing the problem, identify some of the key limitations of current technologies, and propose some ideas to target such limitations. The goal of this position paper is to reflect on the current state of the art and to stimulate discussions on the future design and development of algorithms, methodologies, and applications
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