Monitoring environmental quality by sniffing social media

Sustainability (Switzerland)

Volumn 9, Issue 2, 2017, Pages

  Wang, Zhibo ^a,b   Ke, Lei ^a   Cui, Xiaohui ^a   Yin, Qi ^a   Liao, Longfei ^a   Gao, Lu ^a   Wang, Zhenyu ^a  

^a WUHAN UNIVERSITY   (China)

^b EAST CHINA UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

Environment monitoring; Environmental quality; Social media; Support Vector Machine (SVM)

Indexed keywords

ALGORITHM; ATMOSPHERIC POLLUTION; ENVIRONMENTAL DEGRADATION; ENVIRONMENTAL MONITORING; ENVIRONMENTAL QUALITY; FOOD QUALITY; PUBLIC ATTITUDE; SOCIAL MEDIA; SUPPORT VECTOR MACHINE;

CHINA;

EID: 85013408489     PISSN: None     EISSN: 20711050     Source Type: Journal    
DOI: 10.3390/su9020085     Document Type: Article

References (24)

1. Cui, X.; Yang, N.;Wang, Z.; Hu, C.; Zhu, W.; Li, H.; Ji, Y.; Liu, C. Chinese social media analysis for disease surveillance. Pers. Ubiquitous Comput. 2015, 19, 1125-1132.
2. Wang, Z.; Cui, X.; Gao, L.; Yin, Q.; Ke, L.; Zhang, S. A hybrid model of sentimental entity recognition on mobile social media. EURASIP J. Wirel. Commun. Netw. 2016.
3. The Growing Impact of Social Media. Available online: http://www.sociallyawareblog.com/2012/11/21/time-americans-spendper-month-on-social-media-sites/(accessed on 22 December 2016).
4. Honicky, R.J.; Brewer, E.A.; Paulos, E.; White, R.M. N-Smarts: Networked Suite of Mobile Atmospheric Real-Time Sensors. In Proceedings of the Second ACM SIGCOMMWorkshop on Networked Systems for Developing Regions, Seattle, WA, USA, 17-22 August 2008.
5. Aoki, P.M.; Honicky, R.J.; Mainwaring, A.; Myers, C.; Paulos, E.; Subramanian, S.;Woodruff, A. A Vehicle for Research: Using Street Sweepers to Explore the Landscape of Environmental Community Action. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Boston, MA, USA, 4-9 April 2009.
6. Xu, J.M.; Bhargava, A.; Nowak, R.; Zhu, X. Socioscope: Spatio-Temporal Signal Recovery from Social Media. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Bristol, UK, 24-28 September 2012.
7. Zheng, Y.; Liu, F.; Hsieh, H.P. U-Air: When Urban Air Quality Inference Meets Big Data. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Chicago, IL, USA, 11-14 August 2013.
8. Chen, J.; Chen, H.; Zheng, G.; Pan, J.Z.; Wu, H.; Zhang, N. Big Smog Meets Web Science: Smog Disaster Analysis Based on Social Media and Device Data on the Web. In Proceedings of the 23rd International Conference onWorldWideWeb, Seoul, Korea, 7-11 April 2014.
9. Mei, S.; Li, H.; Fan, J.; Zhu, X.; Dyer, C.R. Inferring Air Pollution by Sniffing Social Media. In Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Beijing, China, 17-20 August 2014.
10. Kay, S.; Zhao, B.; Sui, D. Can social media clear the air? A case study of the air pollution problem in Chinese cities. Prof. Geogr. 2015, 67, 351-363.
11. Sammarco, M.; Tse, R.; Pau, G.; Marfia, G. Using geosocial search for urban air pollution monitoring. Pervasive Mob. Comput. 2016.
12. Tse, R.; Xiao, Y.; Pau, G.; Fdida, S.; Roccetti, M.; Marfia, G. Sensing pollution on online social networks: A transportation perspective. Mob. Netw. Appl. 2016, 21, 688-707.
13. Xu, L.; Lin, H.; Pan, Y.; Ren, H.; Chen, J. Constructing the affective lexicon ontology. J. China Soc. Sci. Tech. Inf. 2008, 2, 6.
14. Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn. 1995, 20, 273-297.
15. Burges, C.J.C. A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 1998, 2, 121-167.
16. Platt, J.C. Fast training of support vector machines using sequential minimal optimization. In Advances in Kernel Methods; MIT Press: Cambridge, MA, USA, 1999; pp. 185-208.
17. Schuldt, C.; Laptev, I.; Caputo, B. Recognizing Human Actions: A Local SVM Approach. In Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, Cambridge, UK, 23-26 August 2004.
18. Rakotomamonjy, A. Variable selection using SVM-based criteria. J. Mach. Learn. Res. 2003, 3, 1357-1370.
19. Duan, K.; Keerthi, S.S.; Poo, A.N. Evaluation of simple performance measures for tuning SVM hyperparameters. Neurocomputing 2003, 51, 41-59.
20. Platt, J. Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Available online: https://www.microsoft.com/en-us/research/publication/sequential-minimal-optimization-a-fastalgorithm-for-training-support-vector-machines/(accessed on 22 December 2016).
21. Forney, G.D. The Viterbi Algorithm. Available online: http://www.systems.caltech.edu/EE/Courses/EE127/EE127A/handout/ForneyViterbi.pdf (accessed on 22 December 2016).
22. Mihalcea, R.; Tarau, P. TextRank: Bringing Order into Texts. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, Bacelona, Spain, 21-26 July 2004.
23. Xu, L.; Lin, H.; Pan, Y. Construction of ontology emotional vocabulary. J. China Soc. Sci. Tech. Inf. 2008, 27, 180-185.
24. Joachims, T. Making large scale SVM learning practical. In Advances in Kernel Methods; MIT Press: Cambridge, MA, USA, 1999.