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Real Time Traffic Analysis of Online Social Networks
Abstract
Social networks have been recently employed as a source of information for event detection, with particular reference to road traffic congestion and car accidents. In this paper, we present a real-time monitoring system for traffic event detection from Twitter stream analysis. The system fetches tweets from Twitter according to several search criteria; processes tweets, by applying text mining techniques; and finally performs the classification of tweets. The aim is to assign the appropriate class label to each tweet, as related to a traffic event or not. The traffic detection system was employed for real-time monitoring of several areas of the Italian road network, allowing for detection of traffic events almost in real time, often before online traffic news web sites. We employed the support vector machine as a classification model, and we achieved an accuracy value of 95.75% by solving a binary classification problem (traffic versus non-traffic tweets). We were also able to discriminate if traffic is caused by an external event or not, by solving a multiclass classification problem and obtaining an accuracy value of 88.89%.
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References
F. Atefeh and W. Khreich, “A survey of techniques for event detection in Twitter,” Comput. Intell. vol. 31, no. 1, pp. 132–164, 2015.
P. Ruchi and K. Kamalakar, “ET: Events from tweets,” in Proc. 22nd Int. Conf. World Wide Web Comput., Rio de Janeiro, Brazil, 2013, pp. 613–620.
A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee, “Measurement and analysis of online social networks,” in Proc. 7th ACM SIGCOMM Conf. Internet Meas., San Diego, CA, USA, 2007, pp. 29–42.
G. Anastasi et al., “Urban and social sensing for sustainable mobility in smart cities,” in Proc. IFIP/IEEE Int. Conf. Sustainable Internet ICT Sustainability, Palermo, Italy, 2013, pp. 1–4.
A. Rosi et al., “Social sensors and pervasive services: Approaches and perspectives,” in Proc. IEEE Int. Conf. PERCOM Workshops, Seattle, WA, USA, 2011, pp. 525–530.
T. Sakaki, M. Okazaki, and Y.Matsuo, “Tweet analysis for real-time event detection and earthquake reporting system development,” IEEE Trans. Knowl. Data Eng., vol. 25, no. 4, pp. 919–931, Apr. 2013.
K. Ramesh, “Test Suite Generation using Genetic Algorithm and Evolutionary Techniques with Dynamically Evolving Test Cases” International Journal of Innovation and Scientific Research 2 (2), 296-300.
J. Allan, Topic Detection and Tracking: Event-Based Information Organization. Norwell, MA, USA: Kluwer, 2002.
F. Atefeh and W. Khreich. A survey of techniques for event detection in twitter. Comput. Intell, 31(1):132--164, 2015.
S. Bakhshi, D. A. Shamma, L. Kennedy, and E. Gilbert. Why we filter our photos and how it impacts engagement. In Ninth International AAAI Conference on Web and Social Media, 2015.
M. Ghiassi, J. Skinner, and D. Zimbra. Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with applications, 40(16):6266--6282, 2013.
N. Hochman and L. Manovich. Zooming into an instagram city: Reading the local through social media. First Monday, 18(7), 2013.
Y. Hu, L. Manikonda, and S. Kambhampati. What we instagram: A first analysis of instagram photo content and user types. 2014.
O. Larsson and H. Moe. Studying political microblogging: Twitter users in the 2010 swedish election campaign. New Media & Society, 14(5):729--747, 2012.
Y. Li, J. Huang, and J. Luo. Using user generated online photos to estimate and monitor air pollution in major cities. In Proceedings of the 7th International Conference on Internet Multimedia Computing and Service, ICIMCS '15.
J. Mao, U. Phommasak, and H. Shioya. Detecting foggy images and estimating the haze degree factor. In Journal of Computer Science and Systems Biology 7(6):226--228, 2014.
M. Mathioudakis and N. Koudas. Twittermonitor: Trend detection over the twitter stream. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, SIGMOD '10, 2010.
A. Pak and P. Paroubek. Twitter as a corpus for sentiment analysis and opinion mining. In LREC, volume 10, pages 1320--1326, 2010.
O. Phelan, K. McCarthy, and B. Smyth. Using twitter to recommend real-time topical news. In Proceedings of the third ACM conference on Recommender systems, 2009.
P. K. Prasetyo, M. Gao, E. Lim, and C. N. Scollon. Proceedings of the 5th international conference on social informatics (socinfo). Lecture Notes in Computer Science, pages 478--491, 2013.
A. Tumasjan, T. O. Sprenger, P. G. Sandner, and I. M. Welpe. Election forecasts with twitter: How 140 characters reflect the political landscape. Social Science Computer Review, 2010.
P. Viola and M. Jones. Rapid object detection using a boosted cascade of simple features. In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, volume 1, pages I--511, 2001.
M. Walther and M. Kaisser. Geo-spatial event detection in the twitter stream. In Advances in Information Retrieval, pages 356--367. 2013.
W. Xie, F. Zhu, J. Jiang, E.-P. Lim, and K. Wang. Topicsketch: Real-time bursty topic detection from twitter. In Data Mining (ICDM), 2013 IEEE 13th International Conference on, 2013.
K. Ramesh, P. Karunanidhi, “Literature Survey on Algorithmic and Non- Algorithmic Models For Software Development Effort Estimation”, International Journal Of Engineering And Computer Science ISSN: 2319-7242.
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