The spread of true and false news online

By Soroush Vosoughi, Deb K. Roy, Sinan Aral. 2018

7,469 citations recorded by the source corpus.

Subfields: Information Systems, Sociology and Political Science, Statistical and Nonlinear Physics, Natural Language Processing, AI Ethics, Fairness & Society

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

In Sociology and Political Science

In Statistical and Nonlinear Physics

In Natural Language Processing

In AI Ethics, Fairness & Society

Citations in the corpus

Automatic Detection of Generated Text is Easiest when Humans are Fooled (2019, background): “It propogates quickly (Vosoughi et al., 2018), sets political agendas”
Reasoning Over Semantic-Level Graph for Fact Checking (2019, background): “Vosoughi et al. (2018) show that false news reaches more people than the truth.”
Predicting Factuality of Reporting and Bias of News Media Sources (2018, background): “…that is typically created either ( a ) to attract network trafﬁc and gain ﬁnan-cially from showing online advertisements, e.g., as is the case of clickbait , or ( b ) to affect individual people’s beliefs, and ultimately to inﬂuence major events such as political elections ( Vosoughi et al., 2018).”
The MAD Model of Moral Contagion: The Role of Motivation, Attention, and Design in the Spread of Moralized Content Online (2019, background): “election that was more likely to be emotional and novel (Vosoughi et al., 2018).”
Countering Misinformation and Fake News Through Inoculation and Prebunking (2021, background): “particular concern that false news on Twitter spreads faster, deeper, and broader than does truth (Vosoughi et al., 2018).”
“Fake News” Is Not Simply False Information: A Concept Explication and Taxonomy of Online Content (2019, background): “As Vosoughi, Roy, and Aral (2018) point out, the term “fake news” has been “irredeemably polarized” in that it has been co-opted by politicians to refer to any information put out by sources that do not support their partisan positions.”
Citizens Versus the Internet: Confronting Digital Challenges With Cognitive Tools (2019, background): “Recent research has found that false news on Twitter spreads faster, deeper, and broader than does truth (Vosoughi et al., 2018).”
GPT is an effective tool for multilingual psychological text analysis (2024, background): “…datasets are typically analyzed for the presence of various psychological constructs, such as sentiment (i.e., positivity versus negativity) (18–22), discrete emotions such as anger or sadness (23, 24), offensiveness (25), moral emotions (26, 27), out - party animosity (5, 9), or toxicity (28, 29).”
Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text (2016, background): “…analyzed sentiment in tweets pertaining to the 2010 Toyota crisis; Kim, Jeong, Kim, Kang, and Song (2016) tracked sentiment in Ebola-related tweets; Vosoughi, Roy, and Aral (2018) published influential work that showed that false stories elicited responses of fear, disgust, and surprise, whereas…”
Preserving integrity in online social networks (2020, result): “In contrast, Vosoughi et al. [126] came to a different conclusion.”
Moderating with the Mob: Evaluating the Efficacy of Real-Time Crowdsourced Fact-Checking (2021, background): “It is unclear to what extent their findings generalize to the evaluation of articles immediately after publication—the period during which news articles are most likely to be seen (Vosoughi, Roy, and Aral 2018).”
Fake News Detection on Social Media using Geometric Deep Learning (2019, methodology): “One of the characteristics of our dataset (as well as the dataset in the study of [42]) is the abundance of small cascades containing just a few users (see Figure 2).”