Information credibility on twitter

By Carlos Castillo, Marcelo Mendoza, Bárbara Poblete. The Web Conference, 2011

2,604 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/53295bc919e02289b78918b07d1252b3e4c5e3ca

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Citations in the corpus

A review of volunteered geographic information quality assessment methods (2017, methodology): “…as an information foraging source (MacEachren et al. 2011), in journalism to disseminate data to the public in near real-time basis (O’Connor 2009, Castillo et al. 2011), detect disease spreading (Chunara et al. 2012), event detection (Bosch et al. 2013), and for gaining insights on social…”
Correlating financial time series with micro-blogging activity (2012, background): “[4] go farther as they study the veracity of tweets for particular events.”
Computational Fact Checking from Knowledge Networks (2015, methodology): “We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted fromWikipedia.”
Detection and Resolution of Rumours in Social Media (2017, methodology): “Two methods reported by earlier studies [Castillo et al. 2011] and [Yang et al. 2012] are adopted for the evaluation.”
Detect Rumors Using Time Series of Social Context Information on Microblogging Websites (2015, methodology): “For example, on Twitter, it takes our model around 15 hours to get the highest accuracy of RF, the second best baseline, while RF needs more than 25 hours; on Weibo, our model needs about 20 hours but RF needs nearly 70 hours to achieve the similar performance.”
Assessing the Bias in Communication Networks Sampled from Twitter (2012, background): “…and credibility, user mobility patterns, spikes of collective attention, or trends in public sentiment (Bakshy et al., 2011; Bollen et al., 2011; Castillo et al., 2011; Cha et al., 2012; Dodds et al., 2011; Lehmann et al., 2012; Paltoglou and Thelwall, 2012; Quercia et al., 2012; Romero et al.,…”
A Framework and Tool for Collaborative Extraction of Reliable Information (2013, background): “Work to automatically assess the reliability of information may focus on classifiers assessing the text, user, topic, and dispersal patterns of the said information (Castillo et al., 2011) or comments on so-”
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].”
FANG: Leveraging Social Context for Fake News Detection Using Graph Representation (2020, methodology): “me,graphmodellingofsocialcontext,deeplearning,inductiveness,andrepresentationlearning. Approach Social Entities & Interactions Temporal Graphical Deep Inductive Representative Feature engineering [6, 26, 32, 44] 1, 2 ✓ Popat [33] 2, 3, 6 ✓ CSI [35] 1, 2, 4, 5 ✓ ✓ ✓ TriFN [39] 1, 2, 3, 4, 5, 6 ✓ ”
Real-time Rumor Debunking on Twitter (2015, methodology): “We compared our proposed method with two state-of-the-art algorithms [1, 11].”
Predicting and Characterising User Impact on Twitter (2014, background): “Naturally, issues arise, like user credibility (Castillo et al., 2011) and content attractiveness (Suh et al., 2010), and quite often trustful or appealing information transmitters are identiﬁed by an impact assessment.”
A Convolutional Approach for Misinformation Identification (2017, methodology): “…misinformation identification models leverage handcrafted features from user credibility and microblog post2 content at post level [Castillo et al., 2011; Qazvinian et al., 2011; Gupta et al., 2013], at event3 level [Kwon et al., 2013; Ma et al., 2015; Zhao et al., 2015] or…”
Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks (2020, methodology): “Figure 4 shows the performances of our Bi-GCN method versus RvNN, SVM-TS, SVM-RBF and DTC at various deadlines for the Weibo and Twitter datasets.”
Do all birds tweet the same?: characterizing twitter around the world (2011, methodology): “These features have also been used in prior work, such as [2]:”
Combating Fake News (2019, methodology): “[15] along with other textual and propagation features for fake”
Evaluating Event Credibility on Twitter (2012, methodology): “Unlike [4], where the features can be assigned only for events, our approach is entity-type-aware and exploits inter-entity relationships.”
What to Expect When the Unexpected Happens: Social Media Communications Across Crises (2015, methodology): “This practice follows customary procedures used for using crowdsourced annotation of text messages for both information type [4, 10, 24, 34] and information source [14, 34].”