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AMAE: Adversarial multimodal auto-encoder for crisis-related tweet analysis


Social media platforms have grown in importance as sources of information and as a complement to traditional media. If informative and relevant tweets on social media platforms can be effectively detected and analyzed during crisis events, it will help humanitarian organizations with situational awareness and planning relief activities. In this work, we propose an adversarial multimodal auto-encoder model for detecting and analyzing crisis-related tweets, which analyzes the complex multimodal content of tweets and integrates adversarial strategies to generate a joint representation containing information from multiple sources. Through extensive experiments on real datasets, we demonstrate the superior performance of the proposed model.

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The authors would like to thank the anonymous referees for their valuable comments and helpful suggestions. This work was supported by National Key R &D Program of China(No. 2019YFB1404700).


This work was supported by National Key R &D Program of China(No. 2019YFB1404700).

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Authors and Affiliations



Jiandong Lv, Xingang Wang contributed to the conception of the study; Jiandong Lv performed the experiment; Jiandong Lv, Xingang Wang contributed significantly to analysis and manuscript preparation; Jiandong Lv, Xingang Wang,Cuiling Shao performed the data analyses and wrote the manuscript; Jiandong Lv, Xingang Wang,Cuiling Shao helped perform the analysis with constructive discussions.

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Correspondence to Xingang Wang.

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Lv, J., Wang, X. & Shao, C. AMAE: Adversarial multimodal auto-encoder for crisis-related tweet analysis. Computing (2022).

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  • Adversarial
  • Social media
  • Multi-modal fusion
  • Situational awareness