Invited talk: Manuel Gómez Rodríguez (Max Planck Institute for Software Systems)

Join us for at the Seminar “Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation ” with Manuel Gómez Rodríguez of Max Planck Institute for Software Systems.

  • Title: Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation.
  • Seminar Date: 2018, May 11; 12:00 – 13:30.
  • Location: 4.1.E02 Room; Torres Quevedo Building; Leganés Campus; Universidad Carlos III de Madrid.


Online social networking sites are experimenting with the following crowd-powered procedure to reduce the spread of fake news and misinformation: whenever a user is exposed to a story through her feed, she can flag the story as misinformation and, if the story receives enough flags, it is sent to a trusted third party for fact checking. If this party identifies the story as misinformation, it is marked as disputed. However, given the uncertain number of exposures, the high cost of fact checking, and the trade-off between flags and exposures, the above mentioned procedure requires careful reasoning and smart algorithms which, to the best of our knowledge, do not exist to date. In this work, we first introduce a flexible representation of the above procedure using the framework of marked temporal point processes. Then, we develop a scalable online algorithm, Curb, to select which stories to send for fact checking and when to do so to efficiently reduce the spread of misinformation with provable guarantees. In doing so, we need to solve a novel stochastic optimal control problem for stochastic differential equations with jumps, which is of independent interest. Experiments on two real-world datasets gathered from Twitter and Weibo show that our algorithm may be able to effectively reduce the spread of misinformation.

An open source implementation of the algorithm is available at


Manuel Gómez Rodríguez is a tenure-track research group leader at Max Planck Institute for Software Systems. Manuel develops machine learning and large-scale data mining methods for the analysis and modeling of large real-world networks and processes that take place over them. He is particularly interested in problems arising in the Web and social media and has received several recognitions for his research, including an Outstanding Paper Award at NIPS’13 and a Best Research Paper Honorable Mention at KDD’10. Manuel holds a PhD in Electrical Engineering from Stanford University and a BS in Electrical Engineering from Carlos III University in Madrid (Spain). You can find more about him at