1,082 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
Rumor Detection on Social Media: Datasets, Methods and Opportunities
Social media platforms have been used for information and news gathering, and
they are very valuable in many applications. However, they also lead to the
spreading of rumors and fake news. Many efforts have been taken to detect and
debunk rumors on social media by analyzing their content and social context
using machine learning techniques. This paper gives an overview of the recent
studies in the rumor detection field. It provides a comprehensive list of
datasets used for rumor detection, and reviews the important studies based on
what types of information they exploit and the approaches they take. And more
importantly, we also present several new directions for future research.Comment: 10 page
How Misinformation and Mistrust Compound the Threat of Epidemics
This thesis was conducted to study the effects of misinformation and medical mistrust on the public health field. I use the events of the Chapare Virus outbreak in Bolivia in the summer of 2019 and the public dialogue during that time period to discuss these themes. I used data from market survey\u27s in La Paz, newspaper articles from Página Siete, and Tweets from the time period of the outbreak. My findings suggest that misinformation and medical mistrust affected public health measures, which has major implications for the way the public health field should address future public health events
Quantifying echo chamber effects in information spreading over political communication networks
Echo chambers in online social networks, in which users prefer to interact
only with ideologically-aligned peers, are believed to facilitate
misinformation spreading and contribute to radicalize political discourse. In
this paper, we gauge the effects of echo chambers in information spreading
phenomena over political communication networks. Mining 12 million Twitter
messages, we reconstruct a network in which users interchange opinions related
to the impeachment of the former Brazilian President Dilma Rousseff. We define
a continuous {political position} parameter, independent of the network's
structure, that allows to quantify the presence of echo chambers in the
strongly connected component of the network, reflected in two well-separated
communities of similar sizes with opposite views of the impeachment process. By
means of simple spreading models, we show that the capability of users in
propagating the content they produce, measured by the associated spreadability,
strongly depends on their attitude. Users expressing pro-impeachment sentiments
are capable to transmit information, on average, to a larger audience than
users expressing anti-impeachment sentiments. Furthermore, the users'
spreadability is correlated to the diversity, in terms of political position,
of the audience reached. Our method can be exploited to identify the presence
of echo chambers and their effects across different contexts and shed light
upon the mechanisms allowing to break echo chambers.Comment: 9 pages, 4 figures. Supplementary Information available as ancillary
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Event Detection from Social Media Stream: Methods, Datasets and Opportunities
Social media streams contain large and diverse amount of information, ranging
from daily-life stories to the latest global and local events and news.
Twitter, especially, allows a fast spread of events happening real time, and
enables individuals and organizations to stay informed of the events happening
now. Event detection from social media data poses different challenges from
traditional text and is a research area that has attracted much attention in
recent years. In this paper, we survey a wide range of event detection methods
for Twitter data stream, helping readers understand the recent development in
this area. We present the datasets available to the public. Furthermore, a few
research opportunitiesComment: 8 page
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