The Anatomy of a Scientific Rumor

By Manlio De Domenico, Antonio Lima, Paul Mougel, Mirco Musolesi. Scientific Reports, 2013

352 citations recorded by the source corpus.

The announcement of the discovery of a Higgs boson-like particle at CERN will be remembered as one of the milestones of the scientific endeavor of the 21st century. In this paper we present a study of information spreading processes on Twitter before, during and after the announcement of the discovery of a new particle with the features of the elusive Higgs boson on 4th July 2012. We report evidence for non-trivial spatio-temporal patterns in user activities at individual and global level, such as tweeting, re-tweeting and replying to existing tweets. We provide a possible explanation for the observed time-varying dynamics of user activities during the spreading of this scientific “rumor”. We model the information spreading in the corresponding network of individuals who posted a tweet related to the Higgs boson discovery. Finally, we show that we are able to reproduce the global behavior of about 500,000 individuals with remarkable accuracy.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/72f6fa34d979945ad0b586513e135227c6543939

How researchers see this paper

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

In Sociology and Political Science

1. Alex Arenas
2. Yamir Moreno
3. Hao Ma
4. Jian Tang
5. Yuxiao Dong
6. Jie Tang
7. M. A. Porter
8. J. P. Gleeson

In Statistical and Nonlinear Physics

1. M. A. Porter
2. Alex Arenas
3. Yamir Moreno
4. Marc Barthelemy
5. Jie Tang
6. J. P. Gleeson
7. Yuxiao Dong
8. Kuansan Wang

Citations in the corpus

• Representation Learning for Attributed Multiplex Heterogeneous Network (2019, background): “consists of various types of interactions; Twitter dataset4 [6] also contains various types of links.”
• Finding and Characterizing Information Warfare Campaigns (2020, methodology): “There have since been many different evolutions from the original models, the most prominent including the SI model [79], SIS (susceptible, infected, susceptible) model [125], SIHR (Susceptible, Infected, Hibernators, Recovered) model [267] and the SEIR (Susceptible, Exposed, Infected, Recovered) mo”
• DeepInf: Social Influence Prediction with Deep Learning (2018, methodology): “Twitter [12] The Twitter dataset was built after monitoring the spreading processes on Twitter before, during and after the announcement of the discovery of a new particle with the features of the elusive Higgs boson on Jul. 4th, 2012.”
• Multilayer networks (2013, background): “For example, a multilayer interbank network can include different types of credit relations in different layers [18], a social network can include different types of social relations in different layers, a Twitter network can include different hashtags in different layers [1, 101], a communication n”