Detecting Fake News with Weak Social Supervision

Kai Shu, Ahmed Hassan Awadallah, Susan Dumais, Huan Liu

Research output: Contribution to journalArticlepeer-review

Abstract

Limited labeled data is becoming one of the largest bottlenecks for supervised learning systems. This is especially the case for many real-world tasks where large scale labeled examples are either too expensive to acquire or unavailable due to privacy or data access constraints. Weak supervision has shown to be effective in mitigating the scarcity of labeled data by leveraging weak labels or injecting constraints from heuristic rules and/or extrinsic knowledge sources. Social media has little labeled data but possesses unique characteristics that make it suitable for generating weak supervision, resulting in a new type of weak supervision, i.e., weak social supervision. In this article, we illustrate how various aspects of social media can be used as weak social supervision. Specifically, we use the recent research on fake news detection as the use case, where social engagements are abundant but annotated examples are scarce, to show that weak social supervision is effective when facing the labeled data scarcity problem. This article opens the door to learning with weak social supervision for similar emerging tasks when labeled data is limited.

Original languageEnglish (US)
JournalIEEE Intelligent Systems
DOIs
StateAccepted/In press - 2020

Keywords

  • Data privacy
  • Intelligent systems
  • Labeling
  • Noise measurement
  • Social network services
  • Supervised learning
  • Task analysis
  • social media
  • social networking
  • weak supervision

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Artificial Intelligence

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