Tweeting is believing?: understanding microblog credibility perceptions

By Meredith Ringel Morris, Scott Counts, Asta Roseway, Aaron Hoff, Julia Schwarz. Conference on Computer Supported Cooperative Work, 2012

510 citations recorded by the source corpus.

Subfields: Communication, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/cfda14e3b9bd0494a2c5f031e945c3ddab7047c1

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Citations in the corpus

TweetCred: Real-Time Credibility Assessment of Content on Twitter (2014, background): “Morris et al. [14] conducted a survey to understand users’ perceptions regarding credibility of content on Twitter.”
A review of volunteered geographic information quality assessment methods (2017, background): “Morris et al. (2012) designed a pilot study with participants (with no technical background) to extract a list of features that are useful to make their credibility judgments.”
Predicting information credibility in time-sensitive social media (2013, background): “[Morris et al., 2012] showed that there are discrepancies between features people rate as relevant to determine credibility and those used by major search engines as Google and Bing.”
Novel Visual and Statistical Image Features for Microblogs News Verification (2017, background): “[20] release a survey result that user profile image has important impact on information credibility published by this user.”
False rumors detection on Sina Weibo by propagation structures (2015, background): “These include the time and location of the account registration, gender and age of the user, username and avatar [33], whether this is a verified account, number of friends, number of followers, the description and the personal home page of the user, number of messages post in the past, etc.”
Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts (2015, background): “Although rumors have long been a hot subject in multiple disciplines (e.g., [9, 31, 22]), research on identifying rumors from online social media through computational methods has only begun in recent years.”
Distilling the Outcomes of Personal Experiences: A Propensity-scored Analysis of Social Media (2017, background): “, [20]); assigning different weights to data coming from different users according to their overall trustworthiness or their topical authority [48]; or augmenting our results with additional information about the users that mention certain outcomes [69].”
Disinformation on the Web: Impact, Characteristics, and Detection of Wikipedia Hoaxes (2016, background): “Such survey-based studies have been carried out both on the Web in general [12, 13, 26] as well as on Twitter in particular [28].”
How Twitter is Changing the Nature of Financial News Discovery (2016, methodology): “This decision can be aided by methods that contextualize tweets [35], such as displaying inferred location [16, 20, 47, 9, 7, 28, 39, 14, 13] or topic context [29, 33].”
Automatic detection of rumor on Sina Weibo (2012, background): “Some of the features have been studied in previous works [3] [11] [9].”
SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums (2019, background): “Morris et al. (2012) studied how Twitter users judge truthfulness.”
Fake images: The effects of source, intermediary, and digital media literacy on contextual assessment of image credibility online (2018, background): “Previous research in this area has either predominantly focused on fake image detection using machine learning approaches (Gupta et al., 2013), or on the credibility of textual information, such as websites and blogs (Allcott and Gentzkow, 2017; Morris et al., 2012; Wineburg and McGrew, 2016).”
Comparing Person- and Process-centric Strategies for Obtaining Quality Data on Amazon Mechanical Turk (2015, background): “This task aligns with scholarly work done on credibility annotations in social media [4,25,31].”