English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation

Kim, J., Tabibian, B., Oh, A., Schoelkopf, B., & Gomez Rodriguez, M. (2017). Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation. Retrieved from http://arxiv.org/abs/1711.09918.

Item is

Files

show Files
hide Files
:
arXiv:1711.09918.pdf (Preprint), 894KB
Name:
arXiv:1711.09918.pdf
Description:
File downloaded from arXiv at 2018-03-16 12:22 To appear at the 11th ACM International Conference on Web Search and Data Mining (WSDM 2018)
OA-Status:
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-

Locators

show

Creators

show
hide
 Creators:
Kim, Jooyeon1, Author
Tabibian, Behzad1, Author
Oh, Alice1, Author
Schoelkopf, Bernhard1, Author
Gomez Rodriguez, Manuel2, Author           
Affiliations:
1External Organizations, ou_persistent22              
2Group M. Gomez Rodriguez, Max Planck Institute for Software Systems, Max Planck Society, ou_2105290              

Content

show
hide
Free keywords: cs.SI,Computer Science, Human-Computer Interaction, cs.HC,Statistics, Machine Learning, stat.ML
 Abstract: 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 paper, 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 fake news and misinformation.

Details

show
hide
Language(s): eng - English
 Dates: 2017-11-272017
 Publication Status: Published online
 Pages: 15 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 1711.09918
URI: http://arxiv.org/abs/1711.09918
BibTex Citekey: Kim2017a
 Degree: -

Event

show

Legal Case

show

Project information

show

Source

show