Exploiting ensembled neural network model for social platform rumor detection

Research Article
Open access

Exploiting ensembled neural network model for social platform rumor detection

Bowen Huang 1 , Ruoheng Feng 2* , Jiahao Yuan 3
  • 1 Northeastern University    
  • 2 Beijing Technology and Business University    
  • 3 University of Shanghai for Science and Technology    
  • *corresponding author 7uanm@st.btbu.edu.cn
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/20/20231103
ACE Vol.20
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-031-8
ISBN (Online): 978-1-83558-032-5

Abstract

With the spread of the internet and social media, it has become difficult to detect rumors from the vast amount of event information. In order to improve the accuracy of rumor detection, deep learning neural network models are often used in rumor detection tasks. First, this paper reproduces the rumor detection experiments of four single neural network models: Long Short-term Memory Networks (LSTM), Text Convolutional Neural Networks (TextCNN), Text Recurrent Neural Network with Attention Mechanism (TextRNN_Att), and Transformer. On this basis, a model based on pre-trained feature extractor and ensemble learning is proposed, and a weighted average ensemble algorithm is adopted. The results show that the rumor-detecting ensemble learning model is better than the single model in all indicators. Then, aiming at the problem that the weighted average ensemble method cannot determine the optimal ensemble parameters, this paper proposes to improve the adaptive ensemble model. Multilayer Perceptron (MLP) is selected as the metamodel, and the weight parameters are automatically trained finetuning on the predicted output of the base model by weighted summation and MLP neural network is used, which improves the traditional integrated weighted average model and realizes the function of automatic weight adjustment. Finally, the Fast Gradient Sign Method (FGSM) algorithm is used to train the model adversarily. The results show that the ensemble model after adversarial training obtains stronger generalization, robustness and attack resistance under the premise of ensuring that the classification performance is not reduced.

Keywords:

rumor detection, text classification, ensemble learning, adversarial training

Huang,B.;Feng,R.;Yuan,J. (2023). Exploiting ensembled neural network model for social platform rumor detection. Applied and Computational Engineering,20,231-239.
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References

[1]. Shunzhi, X. (2022). Overview of rumor detection based on Neural Network, Changjiang Information & Communications, 35(01), 53-56.

[2]. Nuo, X, Wei, Z, Keyuan, S, et, al. (2022). Health Rumor Detection based on Pre-Trained Language Model, Journal of Systems Science and Mathematical Sciences, 42(10), 2582-2589.

[3]. Al-Sarem, M., Boulila, W., Al-Harby, M., Qadir, J., & Alsaeedi, A. (2019). Deep learning-based rumor detection on microblogging platforms: a systematic review. IEEE access, 7, 152788-152812.

[4]. Cao, J., Guo, J., Li, X., Jin, Z., Guo, H., & Li, J. (2018). Automatic rumor detection on microblogs: A survey. arXiv preprint arXiv:1807.03505.

[5]. Choi, D., Oh, H., Chun, S., Kwon, T., & Han, J. (2022). Preventing rumor spread with deep learning. Expert Systems with Applications, 197, 116688.

[6]. Song, C., Yang, C., Chen, H., Tu, C., Liu, Z., & Sun, M. (2019). CED: credible early detection of social media rumors. IEEE Transactions on Knowledge and Data Engineering, 33(8), 3035-3047.

[7]. Hossin, M., & Sulaiman, M. N. (2015). A review on evaluation metrics for data classification evaluations. International journal of data mining & knowledge management process, 5(2), 1.

[8]. Luan, Y., & Lin, S. (2019). Research on text classification based on CNN and LSTM. In 2019 IEEE international conference on artificial intelligence and computer applications (ICAICA), 352-355.

[9]. Dong, X., Yu, Z., Cao, W., Shi, Y., & Ma, Q. (2020). A survey on ensemble learning. Frontiers of Computer Science, 14, 241-258.

[10]. Wong, E., Rice, L., & Kolter, J. Z. (2020). Fast is better than free: Revisiting adversarial training. arXiv preprint arXiv:2001.03994.

Cite this article

Huang,B.;Feng,R.;Yuan,J. (2023). Exploiting ensembled neural network model for social platform rumor detection. Applied and Computational Engineering,20,231-239.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-031-8(Print) / 978-1-83558-032-5(Online)
Editor：Roman Bauer, Marwan Omar, Alan Wang
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.20
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Shunzhi, X. (2022). Overview of rumor detection based on Neural Network, Changjiang Information & Communications, 35(01), 53-56.

[2]. Nuo, X, Wei, Z, Keyuan, S, et, al. (2022). Health Rumor Detection based on Pre-Trained Language Model, Journal of Systems Science and Mathematical Sciences, 42(10), 2582-2589.

[3]. Al-Sarem, M., Boulila, W., Al-Harby, M., Qadir, J., & Alsaeedi, A. (2019). Deep learning-based rumor detection on microblogging platforms: a systematic review. IEEE access, 7, 152788-152812.

[4]. Cao, J., Guo, J., Li, X., Jin, Z., Guo, H., & Li, J. (2018). Automatic rumor detection on microblogs: A survey. arXiv preprint arXiv:1807.03505.

[5]. Choi, D., Oh, H., Chun, S., Kwon, T., & Han, J. (2022). Preventing rumor spread with deep learning. Expert Systems with Applications, 197, 116688.

[6]. Song, C., Yang, C., Chen, H., Tu, C., Liu, Z., & Sun, M. (2019). CED: credible early detection of social media rumors. IEEE Transactions on Knowledge and Data Engineering, 33(8), 3035-3047.

[7]. Hossin, M., & Sulaiman, M. N. (2015). A review on evaluation metrics for data classification evaluations. International journal of data mining & knowledge management process, 5(2), 1.

[8]. Luan, Y., & Lin, S. (2019). Research on text classification based on CNN and LSTM. In 2019 IEEE international conference on artificial intelligence and computer applications (ICAICA), 352-355.

[9]. Dong, X., Yu, Z., Cao, W., Shi, Y., & Ma, Q. (2020). A survey on ensemble learning. Frontiers of Computer Science, 14, 241-258.

[10]. Wong, E., Rice, L., & Kolter, J. Z. (2020). Fast is better than free: Revisiting adversarial training. arXiv preprint arXiv:2001.03994.

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