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