基于C-GRU的微博谣言事件检测方法

doi:10.6040/j.issn.1672-3961.0.2018.189

摘要/Abstract

摘要：

提出基于卷积-门控循环单元(convolution-gated recurrent unit, C-GRU)的微博谣言事件检测模型。结合卷积神经网络(convolutional neural networks, CNN)和门控循环单元(gated recurrent unit, GRU)的优点,将微博事件博文句向量化,通过CNN中的卷积层学习微博窗口的特征表示,将微博窗口特征按时间顺序拼接成窗口特征序列,将窗口特征序列输入GRU中学习序列特征表示进行谣言事件检测。在真实数据集上的试验结果表明,相比基于传统机器学习方法、CNN和GRU的谣言检测模型,该模型有更好的谣言识别能力。

关键词: 谣言事件检测, 深度学习, 卷积-门控循环单元, 窗口特征序列

Abstract:

A microblog rumor events detection model based on convolution-gated recurrent unit(C-GRU) was proposed. Combining the advantages of CNN and GRU, the microblog event′s posts was vectorized. By learning the features representation of the microblog windows through the convolution layer of CNN, the features of microblog windows was spliced into a sequence of window feature according to the time order, and the sequence of window feature was put into the GRU to learn feature representation of sequence for rumor events detection. Experimental results from real data sets showed that this model had better ability to rumor detection than other models based on traditional machine learning, CNN or RNN.

Key words: rumor events detection, deep learning, convolution-gated recurrent unit, window feature sequence

中图分类号:

TP391.1

李力钊, 蔡国永, 潘角. 基于C-GRU的微博谣言事件检测方法[J]. 山东大学学报 (工学版), 2019, 49(2): 102-106.

Lizhao LI, Guoyong CAI, Jiao PAN. A microblog rumor events detection method based on C-GRU[J]. Journal of Shandong University(Engineering Science), 2019, 49(2): 102-106.

图/表 6

图1

图2

图3

图4

表1

表2

参考文献 21

1	霍恩比. 牛津高阶英语词典[M]. 9版北京: 商务印书馆, 2018.
2	QAZVINIAN V, ROSENGREN E, RADEV D R, et al. Rumor has it: identifying misinformation in microblogs[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. Scotland, UK: Association for Computational Linguistics, 2011: 1589-1599.
3	HASSAN A, QAZVINIAN V, RADEV D. What's with the attitude?: identifying sentences with attitude in online discussions[C]//Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing. Massachusetts, USA: Association for Computational Linguistics. ACM, 2010: 1245-1255.
4	MA Ben , LIN Dazhen , CAO Donglin . Content representation for microblog rumor detection[M]. Advances in Computational Intelligence Systems. Lancaster, UK: Springer International Publishing, 2017: 245- 251.
5	CASTILLO C, MENDOZA M, POBLET B. Information credibility on Twitter[C]//Proceedings of the 20th international conference on World wide web. Hyderabad, India: ACM, 2011: 675-684.
6	MORRIS M R, COUNTS S, ROSEWAY A, et al. Tweeting is believing? understanding microblog credibility perceptions[C]//Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work. Washington, USA: ACM, 2012: 441-450.
7	LIANG Gang , HE Wenbo , XU Chun , et al. Rumor identification in microblogging systems based on users' behavior[J]. IEEE Transactions on Computational Social Systems, 2015, 2 (3): 99- 108. doi: 10.1109/TCSS.2016.2517458
8	MENDOZA M, POBLETE B, CASTILLO C. Twitter under crisis: can we trust what we RT?[C]//Proceedings of the First Workshop on Social Media Analytics. Washington, USA: ACM, 2010: 71-79.
9	CAI Guoyong, BI Mengying, LIU Jianxing. A novel rumor detection method based on labeled cascade propagation tree[C]//Proceedings of the 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery. Changsha, China: ACM, 2017.
10	BAO Yuanyuan, YI Chengqi, XUE Yibo, et al. A new rumor propagation model and control strategy on social networks[C]//Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Ontario, Canada: ACM, 2013: 1472-1473.
11	KWON S, CHA M, JUNG K, et al. Prominent features of rumor propagation in online social media[C]//Data Mining (ICDM), 2013 IEEE 13th International Conference. Dallas, TX, USA: IEEE, 2013: 1103-1108.
12	MA Jing, GAO Wei, WEI Zhongyu, et al. Detect rumors using time series of social context information on microblogging websites[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. Melbourne, Australia: ACM, 2015: 1751-1754.
13	毛二松, 陈刚, 刘欣, 等. 基于深层特征和集成分类器的微博谣言检测研究[J]. 计算机应用研究, 2016, (11): 3369- 3373.
	MAO Ersong , CHEN Gang , LIU Xin , et al. Research on detecting micro-blog rumors based on deep features and ensemble classifier[J]. Application Research of Computers, 2016, (11): 3369- 3373.
14	MA Jing, GAO Wei, MITRA P, et al. Detecting rumors from microblogs with recurrent neural networks[C]//Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. New York, USA: AAAI Press, 2016: 3818-3824.
15	CHEN Tong, WU Lin, LI Xue, et al. Call attention to rumors: deep attention based recurrent neural networks for early rumor detection[J]. arXiv Preprint, 2017. https://arxiv.org/pdf/1704.05973.pdf
16	RUCHANSKY N, SEO S, Liu Y. CSI: a hybrid deep model for fake news[C]. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. Singapore: ACM, 2017: 797-806.
17	刘政, 卫志华, 张韧弦. 基于卷积神经网络的谣言检测[J]. 计算机应用, 2017, 37 (11): 3053- 3056.
	LIU Zheng , WEI Zhihua , ZHANG Renxian . Rumor detection based on convolution neural network[J]. Journal of Computer Applications, 2017, 37 (11): 3053- 3056.
18	ZHOU Chunting, SUN Chonglin, LIU Zhiyuan, et al. A C-LSTM neural network for text classification[J]. Computer Science, 2015. https://arxiv.org/pdf/1704.05973.pdf?tdsourcetag=s_pcqq_aiomsg.
19	CHO K, MERRIENBOER B V, BAHDANAU D, et al. On the properties of neural machine translation: Encoder-decoder approaches[C]//Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation. Doha, Qatar: ACL, 2014, 103-111.
20	KINGMA D, BA J. ADAM: A Method for stochastic optimization[C]//Proceedings of the 3rd International Conference on Learning Representation. San Diego, USA: ICLR, 2015
21	YANG Fan, YU Xiaohui, LIU Yang, et al. Automatic detection of rumor on Sina Weibo[C]//Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics. Beijing, China: ACM, 2012.

相关文章 15

[1]	周晓昕,廖祝华,刘毅志,赵肄江,方艺洁. 融合历史与当前交通流量的信号控制方法[J]. 山东大学学报 (工学版), 2023, 53(4): 48-55.
[2]	于畅,伍星,邓秋菊. 基于深度学习的多视角螺钉缺失智能检测算法[J]. 山东大学学报 (工学版), 2023, 53(4): 104-112.
[3]	宋佳芮,陈艳平,王凯,黄瑞章,秦永彬. 基于Affix-Attention的命名实体识别语义补充方法[J]. 山东大学学报 (工学版), 2023, 53(2): 70-76.
[4]	袁钺,王艳丽,刘勘. 基于空洞卷积块架构的命名实体识别模型[J]. 山东大学学报 (工学版), 2022, 52(6): 105-114.
[5]	李旭涛,杨寒玉,卢业飞,张玮. 基于深度学习的遥感图像道路分割[J]. 山东大学学报 (工学版), 2022, 52(6): 139-145.
[6]	孟令灿,聂秀山,张雪. 基于遮挡目标去除的公交车拥挤度分类算法[J]. 山东大学学报 (工学版), 2022, 52(4): 83-88.
[7]	杨霄,袭肖明,李维翠,杨璐. 基于层次化双重注意力网络的乳腺多模态图像分类[J]. 山东大学学报 (工学版), 2022, 52(3): 34-41.
[8]	王心哲,邓棋文,王际潮,范剑超. 深度语义分割MRF模型的海洋筏式养殖信息提取[J]. 山东大学学报 (工学版), 2022, 52(2): 89-98.
[9]	蒋桐雨, 陈帆, 和红杰. 基于非对称U型金字塔重建的轻量级人脸超分辨率网络[J]. 山东大学学报 (工学版), 2022, 52(1): 1-8.
[10]	吴建清,宋修广. 同步定位与建图技术发展综述[J]. 山东大学学报 (工学版), 2021, 51(5): 16-31.
[11]	杨修远,彭韬,杨亮,林鸿飞. 基于知识蒸馏的自适应多领域情感分析[J]. 山东大学学报 (工学版), 2021, 51(3): 15-21.
[12]	柴庆发,孙守晶,邱吉福,陈明,魏振,丛伟. 气象灾害条件下电网应急物资预测方法[J]. 山东大学学报 (工学版), 2021, 51(3): 76-83.
[13]	廖锦萍,莫毓昌,YAN Ke. 基于C-LSTM的短期用电预测模型和应用[J]. 山东大学学报 (工学版), 2021, 51(2): 90-97.
[14]	刘帅,王磊,丁旭涛. 基于Bi-LSTM的脑电情绪识别[J]. 山东大学学报 (工学版), 2020, 50(4): 35-39.
[15]	蔡国永,贺歆灏,储阳阳. 基于空间注意力和卷积神经网络的视觉情感分析[J]. 山东大学学报 (工学版), 2020, 50(4): 8-13.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed

方法	A_c/%
SVM-RBF	79.75
DTC	81.25
RNN	87.25
1-LSTM	89.75
1-GRU	90.25
2-GRU	90.75
CNN	95.25
C-GRU	95.75

过滤器长度	滤器个数	A_c/%
2	180	92.50
3	180	93.25
4	180	94.25
5	180	93.75
2, 3	90	94.00
3, 4	90	94.50
4, 5	90	94.75
2, 3, 4	60	94.25
3, 4, 5	60	95.75
3, 4, 5	50	95.50
3, 4, 5	70	90.25