STEM: Unsupervised STructural EMbedding for Stance Detection

Ron Korenblum Pick, Vladyslav Kozhukhov, Dan Vilenchik, Oren Tsur

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Stance detection is an important task, supporting many downstream tasks such as discourse parsing and modeling the propagation of fake news, rumors, and science denial. In this paper, we propose a novel framework for stance detection. Our framework is unsupervised and domain-independent. Given a claim and a multi-participant discussion - we construct the interaction network from which we derive topological embedding for each speaker. These speaker embedding enjoy the following property: speakers with the same stance tend to be represented by similar vectors, while antipodal vectors represent speakers with opposing stances. These embedding are then used to divide the speakers into stance-partitions. We evaluate our method on three different datasets from different platforms. Our method outperforms or is comparable with supervised models while providing confidence levels for its output. Furthermore, we demonstrate how the structural embedding relate to the valence expressed by the speakers. Finally, we discuss some limitations inherent to the framework.

Original languageAmerican English
Title of host publicationAAAI-22 Technical Tracks 10
Number of pages9
ISBN (Electronic)1577358767, 9781577358763
StatePublished - 28 Jun 2022
Event36th AAAI Conference on Artificial Intelligence, AAAI 2022 - Virtual, Online
Duration: 22 Feb 20221 Mar 2022

Publication series

NameProceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022


Conference36th AAAI Conference on Artificial Intelligence, AAAI 2022
CityVirtual, Online


  • Data Mining & Knowledge Management (DMKM)
  • Speech & Natural Language Processing (SNLP)

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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