Phân loại sự kiện và dự đoán vị trí từ tweet trong quá trình thiên tai

Adedoyin-Olowe, M., Gaber, M. M., Dancausa, C. M., Stahl, F., & Gomes, J. B. (2016). A rule dynamics approach to event detection in twitter with its application to sports and politics. Expert Systems with Applications, 55, 351–360. Ajao, O., Hong, J., & Liu, W. (2015). A survey of location inference techniques on twitter. Journal of Information Science, 41(6), 855–864. Al-Saggaf, Y., & Simmons, P. (2015). Social media in saudi arabia: Exploring its use during two natural disasters. Technological Forecasting and Social Change, 95, 3–15. Atefeh, F., & Khreich, W. (2015). A survey of techniques for event detection in twitter. Computational Intelligence, 31(1), 132–164. Aulov, O., & Halem, M. (2012). Human sensor networks for improved modeling of natural disasters. Proceedings of the IEEE, 100(10), 2812–2823. Benevenuto, F., Magno, G., Rodrigues, T., & Almeida, V. (2010). Detecting spammers on twitter. Collaboration, electronic messaging, anti-abuse and spam conference (CEAS), 6, 12. Bo, H., Cook, P., & Baldwin, T. (2012). Geolocation prediction in social media data by finding location indicative words. Proceedings of COLING 2012: Technical papers, (pp. 1045–1062). Boettcher, A., & Lee, D. (2012). Eventradar: A real-time local event detection scheme using twitter stream. In IEEE International Conference on Green Computing and Communications (GreenCom), 2012 , (pp. 358–367). Burkart, C., Nolz, P. C. & Gutjahr, W. J. (2016). Modelling beneficiaries’ choice in disaster relief logistics. Annals of Operations Research, 1–21. doi:10.1007/s10479-015-2097-9. Carley, K. M., Malik, M., Landwehr, P. M., Pfeffer, J., & Kowalchuck, M. (2016). Crowd sourcing disaster management: The complex nature of twitter usage in padang Indonesia. Safety Science, 90, 48–61. Chae, B. K. (2015). Insights from hashtag# supplychain and twitter analytics: Considering twitter and twitter data for supply chain practice and research. International Journal of Production Economics, 165, 247–259. Chae, J., Thom, D., Jang, Y., Kim, S., Ertl, T., & Ebert, D. S. (2014). Public behavior response analysis in disaster events utilizing visual analytics of microblog data. Computers and Graphics, 38, 51–60. Cheng, Z., Caverlee, J., & Lee, K. (2010). You are where you tweet: a content based approach to geo-locating twitter users. In 19th ACM international conference on information and knowledge management, (pp. 759–768). ACM. Cho, E., Myers, S. A., & Leskovec, J. (2011). Friendship and mobility: user movement in location-based social networks. In Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, (pp. 1082–1090). ACM. Ciulla, F., Mocanu, D., Baronchelli, A., Gonçalves, B., Perra, N., & Vespignani, A. (2012). Beating the news using social media: The case study of american idol. EPJ Data Science, 1(1), 1. Comfort, L. K., Waugh, W. L., & Cigler, B. A. (2012). Emergency management research and practice in public administration: Emergence, evolution, expansion, and future directions. Public Administration Review, 72(4), 539–547. Dai, J., Wang, S., & Yang, X. (1994). Computerized support systems for emergency decision making. Annals of Operations Research, 51(7), 313–325. Dalvi, N., Kumar, R., & Pang, B. (2012). Object matching in tweets with spatial models. In Proceedings of the fifth ACM international conference on Web search and data mining, (pp. 43–52). ACM. Davis, J., & Goadrich, M. (2006). The relationship between precision-recall and roc curves. In Proceedings of the 23rd international conference on machine learning, (pp. 233–240). ACM. Dong, X., Mavroeidis, D., Calabrese, F., & Frossard, P. (2015). Multiscale event detection in social media. Data Mining and Knowledge Discovery, 29(5), 1374–1405. Duhamel, C., Santos, A. C., Brasil, D., Chatelet, E., & Birregah, B. (2016). Connecting a population dynamic model with a multi-period location allocation problem for postdisaster relief operations. Annals of Operations Research, 247(2), 693–713. Duong-Trung, N., Schilling, N., & Schmidt-Thieme, L. (2016). Near real-time geolocation prediction in twitter streams via matrix factorization based regression. In Proceedings of the 25th ACM international on conference on information and knowledge management, CIKM ’16, (pp. 1973–1976). New York, USA: ACM. Earle, P. S., Bowden, D. C., & Guy, M. (2011). Twitter earthquake detection: Earthquake monitoring in a social world. Annals of Geophysics, 54(6), 708–715. Eisenstein, J., O’Connor, B., Smith, N. A., & Xing, E. P. (2010). A latent variable model for geographic lexical variation. In 2010 conference on empirical methods in natural language processing, (pp. 1277–1287). Association for Computational Linguistics. Gaspar, R., Pedro, C., Panagiotopoulos, P., & Seibt, B. (2016). Beyond positive or negative: Qualitative sentiment analysis of social media reactions to unexpected stressful events. Computers in Human Behavior, 56, 179–191. Gayo-Avello, D. (2013). Nepotistic relationships in twitter and their impact on rank prestige algorithms. Information Processing and Management, 49(6), 1250–1280. Graham, M., Hale, S. A., & Gaffney, D. (2014). Where in the world are you geolocation and language identification in twitter. The Professional Geographer, 66(4), 568–578. Han, B., Cook, P., & Baldwin, T. (2014). Text-based twitter user geolocation prediction. Journal of Artificial Intelligence Research, 49, 451–500. Hara, Y. (2015). Behaviour analysis using tweet data and geo-tag data in a natural disaster. Transportation Research Procedia, 11, 399–412. Hecht, B., Hong, L., Suh, B., & Chi, E. H. (2011). Tweets from justin bieber’s heart: the dynamics of the location field in user profiles. In Proceedings of the SIGCHI conference on human factors in computing systems, (pp. 237–246). ACM. Heverin, T., & Zach, L. (2012). Use of microblogging for collective sense making during violent crises: A study of three campus shootings. Journal of the American Society for Information Science and Technology, 63(1), 34–47. Hiruta, S., Yonezawa, T. Jurmu, M., & Tokuda, H. (2012). Detection, classification and visualization of place-triggered geotagged tweets. In Proceedings of the 2012 ACM conference on ubiquitous computing, (pp. 956–963). ACM. Huang, Q., & Xiao, Y. (2015). Geographic situational awareness: mining tweets for disaster preparedness, emergency response, impact, and recovery. ISPRS International Journal of Geo-Information, 4(3), 1549–1568. IAMAI (2016). Internet and mobile association of india reprt about mobile twitter user. http://www.iamai.in/media/details/4620 Imran, M., Elbassuoni, S. M., Castillo, C., Diaz, F., & Meier, P. (2013). Extracting information nuggets from disaster-related messages in social media. Proceedings Of ISCRAM. BadenBaden, Germany. Kabra, G., & Ramesh, A. (2015). Analyzing ict issues in humanitarian supply chain management: A sap-lap linkages framework. Global Journal of Flexible Systems Management, 16(2), 157–171. Kwon, H. Y., & Kang, Y. O. (2016). Risk analysis and visualization for detecting signs of flood disaster in twitter. Spatial Information Research, 24(2), 127–139. Laylavi, F., Rajabifard, A., & Kalantari, M. (2016a). Event relatedness assessment of twitter messages for emergency response. Information Processing and Management, 53(1), 266–280. Laylavi, F., Rajabifard, A., & Kalantari, M. (2016b). A multi-element approach to location inference of twitter: A case for emergency response. ISPRS International Journal of GeoInformation, 5(5), 1–16. Lei, L., Pinedo, M., Qi, L., Wang, S., & Yang, J. (2015). Personnel scheduling and supplies provisioning in emergency relief operations. Annals of Operations Research, 235(1), 487–515. Li, F., & Du, T. C. (2014). Listen to meevaluating the influence of micro-blogs. Decision Support Systems, 62, 119–130. Li, R., Lei, K. H., Khadiwala, R., & Chang, K. C.-C. (2012). Tedas: A twitter-based event detection and analysis system. In IEEE 28th international conference on data engineering, (pp. 1273–1276). IEEE. Lin, X., Lachlan, K. A., & Spence, P. R. (2016). Exploring extreme events on social media: A comparison of user reposting/retweeting behaviors on twitter and weibo. Computers in Human Behavior, 65, 576–581. Macias, W., Hilyard, K., & Freimuth, V. (2009). Blog functions as risk and crisis communication during hurricane katrina. Journal of Computer-Mediated Communication, 15(1), 1–31. Middleton, S. E., Middleton, L., & Modafferi, S. (2014). Real-time crisis mapping of natural disasters using social media. IEEE Intelligent Systems, 29(2), 9–17. Minot, A. S., Heier, A., King, D., Simek, O., & Stanisha, N. (2015). Searching for twitter posts by location. In Proceedings of the 2015 international conference on the theory of information retrieval, (pp. 357–360). ACM. Mishra, D., Gunasekaran, A., Childe, S. J., Papadopoulos, T., Dubey, R., & Wamba, S. (2016). Vision, applications and future challenges of Internet of Things: A bibliometric study of the recent literature. Industrial Management and Data Systems, 116(7), 1331–1355. Mishra, N., & Singh, A. (2016). Use of twitter data for waste minimisation in beef supply chain. Annals of Operations Research, 1–23. doi:10.1007/s10479-016-2303-4. Morstatter, F., Pfeffer, J., Liu, H., & Carley, K. M. (2013). Is the sample good enough? comparing data from twitter’s streaming api with twitter’s firehose. arXiv preprint arXiv:1306.5204. Murthy, D., & Longwell, S. A. (2013). Twitter and disasters: The uses of twitter during the 2010 pakistan floods. Information, Communication and Society, 16(6), 837–855. Neubaum, G., Rosner, L., Rosenthal-von der Pütten, A. M., & Krämer, N. C. (2014). Psychosocial functions of social media usage in a disaster situation: A multi-methodological approach. Computers in Human Behavior, 34, 28–38. Nguyen, Q. C., Kath, S., Meng, H.-W., Li, D., Smith, K. R., VanDerslice, J. A., et al. (2016). Leveraging geotagged twitter data to examine neighborhood happiness, diet, and physical activity. Applied Geography, 73, 77–88. Oh, O., Agrawal, M., & Rao, H. R. (2013). Community intelligence and social media services: A rumor theoretic analysis of tweets during social crises. MIS Quarterly, 37(2), 407–426. Oku, K., Ueno, K., & Hattori, F. (2014). Mapping geotagged tweets to tourist spots for recommender systems. In IIAI 3rd international conference on advanced applied informatics (IIAIAAI), 2014 , (pp. 789–794). IEEE. O’Leary, D. E. (2015). Twitter mining for discovery, prediction and causality: Applications and methodologies. Intelligent Systems in Accounting, Finance and Management, 22(3), 227–247. Ozdamar, L., Ekinci, E., & Küçükyazici, B. (2004). Emergency logistics planning in natural disasters. Annals of Operations Research, 129(1), 217–245. Palen, L., Vieweg, S., & Anderson, K. M. (2010). Supporting everyday analysts in safetyand time-critical situations. The Information Society, 27(1), 52–62. Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S. J., & Fosso Wamba, S. (2017). The role of big data in explaining disaster resilience in supply chains for sustainability. Journal of Cleaner Production, 142, 1108–1118. Paul, J. A., & Hariharan, G. (2012). Location-allocation planning of stockpiles for effective disaster mitigation. Annals of Operations Research, 196(1), 469–490. Paul, M. J., & Dredze, M. (2011). You are what you tweet: Analyzing twitter for public health. ICWSM, 20, 265–272. Rodrigues, E., Assunçao, R., Pappa, G. L., Renno, D., & Meira, W, Jr. (2016). Exploring multiple evidence to infer users location in twitter. Neurocomputing, 171, 30–38. Sakaki, T., Okazaki, M., & Matsuo, Y. (2010). Earthquake shakes twitter users: real-time event detection by social sensors. In Proceedings of the 19th international conference on world wide web, (pp. 851–860). ACM. Sakaki, T., Okazaki, M., & Matsuo, Y. (2013). Tweet analysis for real-time event detection and earthquake reporting system development. IEEE Transactions on Knowledge and Data Engineering, 25(4), 919–931. Schulz, A., Hadjakos, A., Paulheim, H., Nachtwey, J., & Muhlhauser M.(2013). A multi-indicator approach for geolocalization of tweets. In Seventh international AAAI conference on weblogs and social media, (pp. 573–582). Skoric, M., Poor, N., Achananuparp, P., Lim, E.-P., & Jiang, J. (2012). Tweets and votes: A study of the 2011 singapore general election. In 45th Hawaii international conference on system science (HICSS), (pp. 2583–2591). IEEE. Steiger, E., Albuquerque, J. P., & Zipf, A. (2015). An advanced systematic literature review on spatiotemporal analyses of twitter data. Transactions in GIS, 19(6), 809–834. Sushil (2017). Theory building using sap-lap linkages: an application in the context of disaster management. Annals of Operations Research, 1–26. doi:10.1007/s10479-017-2425-3. Turoff, M., Hiltz, S. R., Banuls, V. A., & Van Den Eede, G. (2013). Multiple perspectives on planning for emergencies: An introduction to the special issue on planning and foresight for emergency preparedness and management. Technological Forecasting and Social Change, 80(9), 1647–1656. Ulku, M. A., Bell, K. M., & Wilson, S. G. (2015). Modeling the impact of donor behavior on humanitarian aid operations. Annals of Operations Research, 230(1), 153–168. Uysal, A. K., & Gunal, S. (2014). The impact of preprocessing on text classification. Information Processing and Management, 50(1), 104–112. Watanabe, K., Ochi, M., Okabe, M., & Onai, R. (2011). Jasmine: a real-time local event detection system based on geolocation information propagated to microblogs. In Proceedings of the 20th ACM international conference on information and knowledge management, (pp. 2541–2544). ACM. Weiler, A., Grossniklaus, M., & Scholl, M. H. (2016). An evaluation of the run time and task-based performance of event detection techniques for twitter. Information Systems, 62, 207–219. Wing, B. P., & Baldridge, J. (2011). Simple supervised document geolocation with geodesic grids. In 49th Annual meeting of the association for computational linguistics: Human language technologies-volume 1, (pp. 955–964). Association for Computational Linguistics. Xiao, Y., Huang, Q., & Wu, K. (2015). Understanding social media data for disaster management. Natural Hazards, 79(3), 1663–1679. Yardi, S., Romero, D., Schoenebeck, G., et al. (2010). Detecting spam in a twitter network. First Monday, 15(1). Zhang, X., Chen, X., Chen, Y., Wang, S., Li, Z., & Xia, J. (2015). Event detection and popularity prediction in microblogging. Neurocomputing, 149, 1469–1480. Zhou, X., & Chen, L. (2014). Event detection over twitter social media streams. The VLDB Journal, 23(3), 381–400.