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Verified account prediction on Sina Weibo with deep learning


The focus of the paper is to use a single weibo from a user to predict whether the user account is verified, referred to as verified account prediction, on Sina Weibo. To the best of our knowledge, verified account prediction on Sina Weibo has not been studied. For better understanding of the prediction problem, a comprehensive data analysis of weibos related to verified accounts is conducted first. Then, verified account prediction is formulated as a sequence learning problem. Specifically, a weibo from a user is represented as a sequence of feature values by feature hashing and whether the user account is verified is the corresponding label to predict. A deep learning approach is proposed for solving verified account prediction in this formulation. The proposed approach significantly outperforms the shallow learning methods in the comparisons in terms of accuracy and F1 by large margins in the experiments.

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Enquiries about data availability should be directed to the authors.


  1. Twitter’s and Sina Weibo’s verified account programs let people know that an account is authentic. People and companies can request to verify an account by submitting a request with supporting evidences such as a verified phone number or a confirmed email address. For example, common verified accounts include news agents, organizations and public figures.


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We would like to thank the reviewers for their valuable comments and suggestions.


No funding was received for this research.

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The authors contributed to the study conception and design, material preparation, data collection and analysis, and manuscript preparation. The authors read and approved the final manuscript.

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Correspondence to Shuhua Monica Liu.

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A sample implementation of the proposed methods in Keras is shown below.

figure a

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Monica Liu, S., Chen, JH. Verified account prediction on Sina Weibo with deep learning. Soft Comput 27, 3941–3954 (2023).

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  • Verified account prediction
  • Sina Weibo
  • Deep learning