Twitter under crisis: can we trust what we RT?

By Marcelo Mendoza, Bárbara Poblete, Carlos Castillo. SOMA '10, 2010

1,106 citations recorded by the source corpus.

Subfields: Communication, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/2141a4655f65c09dce412bc9125d960b453ed64d

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Citations in the corpus

Dynamical classes of collective attention in twitter (2011, background): “…including its network topology [18, 19, 20], the relations and types of messages between users [21, 22], the internal information propagation [23, 24, 25], the credibility of information [26, 27], and even its potential as an indicator of the state of mind of a population [28, 29, 30, 31, 32, 25].”
The COVID-19 social media infodemic (2020, background): “he virus that causes it. 1 arXiv:2003.05004v1 [cs.SI] 10 Mar 2020 The term infodemic [2, 3] has been coined to outline the perils of misinformation phenomena during the management of virus outbreaks2 [4, 5], since it could even speed up the epidemic process by in uencing and fragmenting social respo”
Automatic detection of rumor on Sina Weibo (2012, background): “Mendoza et al. [8] use tweets to analyze the behavior of Twitter users under bombshell events such as the Chile earthquake in 2010.”
Big data and disaster management: a systematic review and agenda for future research (2017, background): “In addition, researchers in this cluster explored the behaviour of Twitter users under an emergency situation (Mendoza et al., 2010; Mandel et al., 2012) and developed systems that capture the information received from Twitter that helps in avoiding and monitoring emergency situations, like…”
Real-time Rumor Debunking on Twitter (2015, background): “People often express mixed beliefs such as support, denial and neutrality in response to events[6, 7].”
Information credibility on twitter (2011, background): “Indeed, in [19] we observed that immediately after the 2010 earthquake in Chile, when information from official sources was scarce, several rumors”
Detection and Resolution of Rumours in Social Media (2017, background): “In an earlier study, [Mendoza et al. 2010] had found strong correlations between rumour support and veracity, showing that a majority of users support true rumours, while a higher number of users denies false rumours.”
Twitter for crisis communication: lessons learned from Japan's tsunami disaster (2011, background): “In another study, Mendoza et al.（2010）examined the responses to true and false information that emerged on Twitter after the 2010 Chile earthquake.”
Epidemiological modeling of news and rumors on Twitter (2013, neutral): “[11].”
Natural Language Processing to the Rescue? Extracting "Situational Awareness" Tweets During Mass Emergency (2011, background): “A recent surge in research on computer-mediated communication during mass emergency situations looks at content in blogs, social media sites and microblogging services (Mendoza et al., 2010; Palen et al., 2009; Qu et al., 2009; Starbird et al., 2010; Vieweg et al., 2010).”
Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts (2015, background): “Mendoza et al. found on a small set of cases that false tweets were questioned much more than conﬁrmed truths [21].”
Computational Fact Checking from Knowledge Networks (2015, background): “Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowl- edge graphs.”
Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter (2017, background): “In addition to contrasting linguistic realizations behind different types of suspicious news on Twitter, we are interested in qualitatively evaluating differences in retweet patterns across suspicious news types (Lumezanu and Klein, 2012; Mendoza et al., 2010; Kumar et al., 2016).”