Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2020

Abstract

We address rumor detection by learning to differentiate between the community’s response to real and fake claims in microblogs. Existing state-of-the-art models are based on tree models that model conversational trees. However, in social media, a user posting a reply might be replying to the entire thread rather than to a specific user. We propose a post-level attention model (PLAN) to model long distance interactions between tweets with the multi-head attention mechanism in a transformer network. We investigated variants of this model: (1) a structure aware self-attention model (StA-PLAN) that incorporates tree structure information in the transformer network, and (2) a hierarchical token and post-level attention model (StA-HiTPLAN) that learns a sentence representation with token-level self-attention. To the best of our knowledge, we are the first to evaluate our models on two rumor detection data sets: the PHEME data set as well as the Twitter15 and Twitter16 data sets. We show that our best models outperform current state-of-the-art models for both data sets. Moreover, the attention mechanism allows us to explain rumor detection predictions at both token-level and post-level.

Keywords

Attention mechanisms, Attention model, Long distance interactions, Social media, State of the art, Structure-aware, Tree structures, User interaction

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Proceedings of the 34th AAAI Conference on Artificial Intelligence, New York, USA, 2020 February 7-12

First Page

8783

Last Page

8790

ISBN

9781577358350

Identifier

10.1609/aaai.v34i05.6405

Publisher

AAAI

City or Country

New York

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1609/aaai.v34i05.6405

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