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JargonFM: A Framework with Multiple Interpretation Modes for Jargon Understanding in Online Communities
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  • Zhengqing Guan ,
  • Peng Zhang ,
  • Baoxin Liu ,
  • Hansu Gu ,
  • Tun lu ,
  • Ning Gu
Zhengqing Guan
Fudan University

Corresponding Author:[email protected]

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Peng Zhang
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Baoxin Liu
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Jargon words are commonly used in the communication of online communities. These words are characterized by special and implicit meanings that can only be comprehended by a small group of users, which brings challenges to community regulation and user engagement. For this problem, we present JargonFM, a framework with multiple interpretation modes for jargon understanding in online communities. JargonFM is designed based on the scientific explanation framework and supports three interpretation modes: jargon category prediction based on a Jargon Classifier, similar word identification based on a Jargon Synonym Selector, and representative text selection based on an Example Sentence Selector. We also implemented a jargon interpreter to demonstrate the usage and usefulness of our interpretation framework. Automatic and human evaluations suggest that JargonFM can explain jargon words more accurately and more efficiently than the existing interpretation methods, leading to its wide acceptance among the evaluation participants.
2023Published in IEEE Transactions on Computational Social Systems on pages 1-12. 10.1109/TCSS.2023.3281674