计算机科学 ›› 2016, Vol. 43 ›› Issue (10): 1-8.doi: 10.11896/j.issn.1002-137X.2016.10.001

• 目次 •    下一篇



  1. 中国科学院信息工程研究所信息安全国家重点实验室 北京 100093,中国科学院信息工程研究所信息安全国家重点实验室 北京 100093,中国科学院信息工程研究所信息安全国家重点实验室 北京 100093,中国科学院信息工程研究所信息安全国家重点实验室 北京 100093,中兴通讯股份有限公司 深圳 518057,上海交通大学电子工程系 上海 200240
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:

Specific Content Monitoring on Social Networks Based on Social Computing and Deep Learning

CAO Xiao-chun, JING Li-hua, WANG Rui, ZHANG Rui, DONG Zhen-jiang and XIONG Hong-kai   

  • Online:2018-12-01 Published:2018-12-01

摘要: 社交网络极大地方便了人们的生活,加速了信息的共享,但同时也被用于不良和敏感信息的传播,内容安全问题亟待解决。针对此类问题,提出了一套基于社会计算和深度学习的社交网络特定内容监控体系,首先基于成对监督信息实现以内容为导向的半监督社区发现,找到所关心的特定人群;然后对所挖掘的特定人群进行实时监控并获取其发布的内容,对图像和视频进行实时自动内容识别;同时针对实网数据误报多的问题提出面向多负类的误判修正方法,以达到收集实时信息,净化网络环境,在一定程度上预防犯罪的目的。

关键词: 社会计算,社区发现,深度学习,图像识别,社交网络

Abstract: Social networks provide great convenience to people’s daily life and information sharing.Unfortunately,these conveniences are accompanied with content security problems,where the social networks are frequently employed to disseminate malicious or sensitive information.This paper proposed a content security solution,which builds a system to monitor specific content based on social computing and deep learning.To search for the specific people,the system achieves a content-sensitive semi-supervised community discovery method with pairwise constraint.By monitoring the discovered people and obtaining their published content,the system performs an automatic detection procedure to identify the content of the published images and videos.In addition,an error correction method was proposed to reduce the false positives when processing the real network data.Experimental results demonstrate that the proposed system gives decent performances under various circumstances.

Key words: Social computing,Community discovery,Deep learning,Image recognition,Social networks

[1] Digital in 2016.We Are Social Singapore.http://www.slideshare.net/wearesocialsg/digital-in-2016
[2] Allahverdyan A E,Steeg G V,Galstyan A.Community Detection with and without Prior Information[J].Europhysics Letters,2009,90(1):983-995
[3] Liu Dong,Bai Hong-yu,Li Hui-jia,et al.Semi-supervised community detection using label propagation[J].International Journal of Modern Physics B,2014,28(29)
[4] Steeg G V,Galstyan A,Allahverdyan A E.Statistical mechanics of semi-supervised clustering in sparse graphs[J].Journal of Statistical Mechanics:Theory and Experiment,2011,8(8)
[5] Ma X,Gao L,Yong X,et al.Semi-supervised clustering algo-rithm for community structure detection in complex networks[J].Physica A Statistical Mechanics & Its Applications,2010,389(1):187-197
[6] Cheng J,Leng M,Li L,et al.Active semi-supervised community detection based on must-link and cannot-link constraints[J].Plos One,2014,9(10)
[7] Yang Liang,Cao Xiao-chun,Jin Di,et al.A Unified Semi-Supervised Community Detection Framework Using Latent Space Graph Regularization[J].IEEE Transactions on Cybernetics,2015,45(11):2585-2598
[8] Yang Liang,Jin Di,Wang Xiao,et al.Active Link Selection for Efficient Semi-supervised Community Detection[J].Scientific Reports,2015,5
[9] Leavitt A,Burchard E,Fisher D,et al.The Influentials:NewApproaches for Analyzing Influence on Twitter[J].Web Ecology Project,2009,4(2):1-18
[10] Sarma A D,Gollapudi S,Panigrahy R.Ranking mechanisms in twitter-like forums[C]∥Proceedings of the Third ACM International Conference on Web Search and Data Mining.2010:21-30
[11] Cha M,Haddadi H,Benevenuto F,et al.Measuring User Influen-ce in Twitter:The Million Follower Fallacy[C]∥Proceedings of International AAAI Conference on Weblogs and Social.2010:10-17
[12] Wang Rui,Jin Yong-sheng.An empirical study on the relationship between the followers’ number and influence of microblogging[C]∥The International Conference on E-Business and E-Government.2010
[13] Nagmoti,Rinkesh,Teredesai A,et al.Ranking approaches formicroblog search[C]∥IEEE/WIC/ACM International Confe-rence on Web Intelligence & Intelligent Agent Technology.2010
[14] Rao Ai-bing,Srihari R K,Zhang Zhong-fei.Spatial color histograms for content-based image retrieval[C]∥IEEE International Conference on Tools with Artificial Intelligence.1999:183-186
[15] Ojala T,Pietikainen M,Maenpaa T.Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(7):971-987
[16] Harris C,Stephens M.A combined corner and edge detector[C]∥Alvey Vision Conference.1988:147-151
[17] Grauman K,Darrell T.Pyramid match kernels:Discriminativeclassification with sets of image features[C]∥IEEE International Conference on Computer Vision.2005:1458-1465
[18] Wang J,Yang J,Yu K,et al.Locality-constrained linear coding for image classification[J].IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2010,119(5):3360-3367
[19] Bo L,Lai K,Ren X,et al.Object recognition with hierarchicalkernel descriptors[J].IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2011,42(7):1729-1736
[20] Hubel,David H,Wiesel T N.Receptive fields of single neurones in the cat’s striate cortex[J].The Journal of physiology,1959,148(3):574-591
[21] Lecun Y,Bottou L,Bengio Y,et al.Gradient-based learning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324
[22] Bengio Y.Learning deep architectures for AI[J].Foundationsand trends in Machine Learning,2009,2(1):1-127
[23] Krizhevsky,Alex,Sutskever I,et al.ImageNet Classificationwith Deep Convolutional Neural Networks[C]∥Advances in Neural Information Processing Systems,2012:1097-1105
[24] Deng J,Dong W,Socher R,et al.Imagenet:A large-scale hierarchical image database[C]∥IEEE Conference on Computer Vision and Pattern Recognition.2009:248-255
[25] Szegedy C,Liu W,Jia Y,et al.Going deeper with convolutions[C]∥IEEE Conference on Computer Vision and Pattern Recognition.2015:1-9
[26] Simonyan K,Zisserman A.Very deep convolutional networksfor large-scale image recognition[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2015
[27] He K,Zhang X,Ren S,et al.Deep Residual Learning for Image Recognition[C]∥IEEE Computer Society Conference on Computer Vision and Pattern Recognition.2015
[28] Girshick R,Donahue J,Darrell T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]∥IEEE Conference on Computer Vision and Pattern Recognition.2014:580-587
[29] He K,Zhang X,Ren S,et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(9):1904-1916
[30] Girshick R.Fast R-CNN[C]∥IEEE International Conferenceon Computer Vision.2015:1440-1448
[31] Ren S,He K,Girshick R,et al.Faster R-CNN:Towards real-time object detection with region proposal networks[C]∥Advances in Neural Information Processing Systems.2015
[32] Turchenko V,Luczak A.Caffe:Convolutional architecture forfast feature embedding[C]∥Proceedings of the 22nd ACM International Conference on Multimedia.2014:675-678

No related articles found!
Full text



[1] 编辑部. 新网站开通,欢迎大家订阅![J]. 计算机科学, 2018, 1(1): 1 .
[2] 雷丽晖,王静. 可能性测度下的LTL模型检测并行化研究[J]. 计算机科学, 2018, 45(4): 71 -75, 88 .
[3] 夏庆勋,庄毅. 一种基于局部性原理的远程验证机制[J]. 计算机科学, 2018, 45(4): 148 -151, 162 .
[4] 厉柏伸,李领治,孙涌,朱艳琴. 基于伪梯度提升决策树的内网防御算法[J]. 计算机科学, 2018, 45(4): 157 -162 .
[5] 王欢,张云峰,张艳. 一种基于CFDs规则的修复序列快速判定方法[J]. 计算机科学, 2018, 45(3): 311 -316 .
[6] 孙启,金燕,何琨,徐凌轩. 用于求解混合车辆路径问题的混合进化算法[J]. 计算机科学, 2018, 45(4): 76 -82 .
[7] 张佳男,肖鸣宇. 带权混合支配问题的近似算法研究[J]. 计算机科学, 2018, 45(4): 83 -88 .
[8] 伍建辉,黄中祥,李武,吴健辉,彭鑫,张生. 城市道路建设时序决策的鲁棒优化[J]. 计算机科学, 2018, 45(4): 89 -93 .
[9] 刘琴. 计算机取证过程中基于约束的数据质量问题研究[J]. 计算机科学, 2018, 45(4): 169 -172 .
[10] 钟菲,杨斌. 基于主成分分析网络的车牌检测方法[J]. 计算机科学, 2018, 45(3): 268 -273 .