Credibility ranking of tweets during high impact events

By Aditi Gupta, Ponnurangam Kumaraguru. PSOSM '12, 2012

347 citations recorded by the source corpus.

Subfields: Information Systems, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/b73d7c8b021372746440360adf009cee5d61c43e

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Citations in the corpus

How Twitter is Changing the Nature of Financial News Discovery (2016, background): “In contrast, news propagated on Twitter suffers from a lack of verification, making some tweet content inaccurate or outright false [50, 19].”
Misinformation in Social Media: Definition, Manipulation, and Detection (2019, background): “In addition, other contextual information like network structures has also been incorporated [42; 12; 34].”
Detection and Resolution of Rumours in Social Media (2017, background): “— TweetCred 29 [Gupta and Kumaraguru 2012; Gupta et al. 2014] is a real-time, web-based system to assess credibility of content on Twitter.”
Faking Sandy: characterizing and identifying fake images on Twitter during Hurricane Sandy (2013, background): “The authors in a follow up study applied machine learning algorithms (SVM Rank) and information retrieval techniques (relevance feedback) to assess credibility of content on Twitter [13].”
Overview of the Special Issue on Trust and Veracity of Information in Social Media (2016, background): “…content that is circulated in such contexts [Castillo et al. 2013; Gupta et al. 2013], and to compare the features of trustworthy content with those of content carrying malicious or erroneous information [Canini et al. 2011; Castillo et al. 2011; Gupta and Kumaraguru 2012; O’Donovan et al. 2012].”
A review of volunteered geographic information quality assessment methods (2017, methodology): “The work of Gupta and Kumaraguru (2012) is similar to Castillo et al. (2011), and follows a supervised feature classiﬁcation PageRank like method to propagate the credibility on a network of Twitter events.”
Processing Social Media Messages in Mass Emergency (2014, methodology): “Credible information newsworthy topics, credibility [Castillo et al. 2013]; credible topics [Canini et al. 2011]; content credibility [Gupta and Kumaraguru 2012]; users and content credibility [Gupta et al. 2014] Rumors rumor [Hughes et al. 2014b; Castillo et al. 2013]”
Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts (2015, methodology): “This method echoes the common approaches to detecting trending topics and then identifying rumors among them [4, 14].”
TweetCred: Real-Time Credibility Assessment of Content on Twitter (2014, methodology): “We use a collection of features from previous work [1, 4, 8, 22], restricting ourselves to those that can be derived from single tweets in real-time.”