4 Conclusion
We firstly use the CNN neural network that incorporates the attention mechanism.
Compared with the single neural network model, it can better explore the deep user
characteristics and extract significant fine-grained user and content relation features.
Then, LSTM is used to mine the coarse-grained features of long memory information,
so as to realize the purpose of rumor recognition. The method is simulated in the deep
learning framework—TensorFlow. The experiment proves the feasibility of the proposed model strategy, and the CNN integrates attention mechanism that combines with
LSTM network model and fully exploits the characteristics between the user and the
content relationship, which can effectively improve the accuracy and time efficiency of
rumor detection.
Acknowledgements. This work is supported by Xinjiang Joint Fund of National Science Fund
of China (U1703261).
References
1. Qazvinian V, Rosengren E, Radev DR et al (2011) Rumor has it: identifying misinformation
in microblogs. In: Proceedings of the conference on empirical methods in natural language
processing, Edinburgh, pp 1589–1599
2. Zhang Q, Zhang S, Dong J et al (2015) Automatic detection of rumor on social network. In:
Natural language processing and chinese computing, Springer, Cham, pp 113–122
3. Ruchansky N, Seo S, Liu Y (2017) CSI: a hybrid deep model for fake news detection
4. Zhou C, Sun C, Liu Z et al (2015) A C-LSTM neural network for text classification. arXiv
preprint arXiv:1511.08630
5. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):
1735–1780
6. Abedinia O, Amjady N, Zareipour H (2016) A new feature selection technique for load and
price forecast of electrical power systems. IEEE Trans Power Syst 32(1):62–74
7. Wang P, Li W, Gao Z et al (2017) Scene flow to action map: a new representation for
RGB-D based action recognition with convolutional neural networks
8. Yang S, Shu K, Wang S et al (2019) Unsupervised fake news detection on social media: a
generative approach. In: Proceedings of 33rd AAAI conference on artificial intelligence
9. Gao L, Guo Z, Zhang H et al (2017) Video captioning with attention-based LSTM and
semantic consistency. IEEE Trans Multimedia 19(9):2045–2055
10. Kwon S, Cha M, Jung K (2017) Rumor detection over varying time windows. PLoS ONE
12(1):e0168344
11. Ma J, Gao W, Wong KF (2018) Detect rumor and stance jointly by neural multi-task
learning. In: Companion of the web conference 2018, International World Wide Web
Conferences Steering Committee, pp 585–593
434
Z. Zhou et al.
We firstly use the CNN neural network that incorporates the attention mechanism.
Compared with the single neural network model, it can better explore the deep user
characteristics and extract significant fine-grained user and content relation features.
Then, LSTM is used to mine the coarse-grained features of long memory information,
so as to realize the purpose of rumor recognition. The method is simulated in the deep
learning framework—TensorFlow. The experiment proves the feasibility of the proposed model strategy, and the CNN integrates attention mechanism that combines with
LSTM network model and fully exploits the characteristics between the user and the
content relationship, which can effectively improve the accuracy and time efficiency of
rumor detection.
Acknowledgements. This work is supported by Xinjiang Joint Fund of National Science Fund
of China (U1703261).
References
1. Qazvinian V, Rosengren E, Radev DR et al (2011) Rumor has it: identifying misinformation
in microblogs. In: Proceedings of the conference on empirical methods in natural language
processing, Edinburgh, pp 1589–1599
2. Zhang Q, Zhang S, Dong J et al (2015) Automatic detection of rumor on social network. In:
Natural language processing and chinese computing, Springer, Cham, pp 113–122
3. Ruchansky N, Seo S, Liu Y (2017) CSI: a hybrid deep model for fake news detection
4. Zhou C, Sun C, Liu Z et al (2015) A C-LSTM neural network for text classification. arXiv
preprint arXiv:1511.08630
5. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):
1735–1780
6. Abedinia O, Amjady N, Zareipour H (2016) A new feature selection technique for load and
price forecast of electrical power systems. IEEE Trans Power Syst 32(1):62–74
7. Wang P, Li W, Gao Z et al (2017) Scene flow to action map: a new representation for
RGB-D based action recognition with convolutional neural networks
8. Yang S, Shu K, Wang S et al (2019) Unsupervised fake news detection on social media: a
generative approach. In: Proceedings of 33rd AAAI conference on artificial intelligence
9. Gao L, Guo Z, Zhang H et al (2017) Video captioning with attention-based LSTM and
semantic consistency. IEEE Trans Multimedia 19(9):2045–2055
10. Kwon S, Cha M, Jung K (2017) Rumor detection over varying time windows. PLoS ONE
12(1):e0168344
11. Ma J, Gao W, Wong KF (2018) Detect rumor and stance jointly by neural multi-task
learning. In: Companion of the web conference 2018, International World Wide Web
Conferences Steering Committee, pp 585–593
434
Z. Zhou et al.