Event Detection Using Twitter Platform

Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 21)


Online Social Network (OSN) has evolved through a radical transformation in the way user communicate with each other in the Web 2.0 environment. User communicate over OSN through a connected network structure, forming a group of individuals who interacts among themselves. Interaction among users, within a community or inter-community, facilitates in the formation and exchange of huge User-Generated Content (UGC) across the OSN platforms. UGC is an important source for researchers to extract relevant insights related to events of significance e.g. earthquake, product review, emerging topics, etc. In this chapter, a comprehensive survey of event detection techniques for OSN is done. First, the types of OSN based on information flow (service oriented, sharing services, Social Network Sharing News, Location Based Social Network and community building Social networks) and then the various categories of events (natural or manmade disaster events, public opinion events & emerging events) are studied. Second, events were categorized based on four dimensions—thematic, temporal, spatial and network structure. An extensive survey of dimension-wise event detection techniques is carried out and the research gaps are identified. Third, Twitter platform was taken as a case study due to its popularity among users as well as researchers. An in-depth survey of event detection techniques with respect to different dimensions applicable to Twitter data for disaster event management, detection of emerging events and prediction of emerging events is performed and respective research challenges are enlisted. Finally, an exclusive study is conducted for Twitter platform based data collection and event detection & analysis tools. The suggested open challenges will give researchers/readers ample scope to work upon.


Web 2.0 Online social networks Twitter Event detection Disaster events Emerging trends Public opinion events Data collection tools Event detection tools 


  1. 1.
    Goswami A, Kumar A (2016) A survey of event detection techniques in online social networks. Soc Netw Anal Mining 6(1):107CrossRefGoogle Scholar
  2. 2.
    Cisco VNI (2017) Cisco visual networking index: forecast and methodology, 2016—2021. Accessed 6 June 2017
  3. 3.
    Youtube (2016) Accessed Dec 2016
  4. 4.
    Facebook (2016) Accessed Dec 2016
  5. 5.
    Twitter (2016) Accessed Dec 2016
  6. 6.
    Deitrick W, Hu W (2013) Mutually enhancing community detection and sentiment analysis on Twitter networks. J Data Anal Inf Process 1:19–29Google Scholar
  7. 7.
    Chen Z, Kalashnikov DV, Mehrotra S (2009) Exploiting context analysis for combining multiple entity resolution systems. In: Proceedings of the 2009 ACM SIGMOD international conference on management of data, pp 207–218. ACMGoogle Scholar
  8. 8.
    Goswami A, Kumar A (2017) Challenges in the analysis of online social networks: a data collection tool perspective. Wirel Pers Commun 97(3):4015–4061CrossRefGoogle Scholar
  9. 9.
    Wassaerman S, Faust K (1994) Social network analysis in the social and behavioural sciences. Social network analysis: methods and applications. Cambridge University Press, CambridgeGoogle Scholar
  10. 10.
    Mislove A, Marcon M, Gummadi KP, Druschel P, Bhattacharjee B (2007) Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM conference on internet measurement, pp 29–42. ACMGoogle Scholar
  11. 11.
    Flake GW, Lawrence S, Giles CL, Coetzee FM (2002) Self-organization and identification of web communities. Computer 35(3):66–70CrossRefGoogle Scholar
  12. 12.
    Flake GW, Tarjan RE, Tsioutsiouliklis K (2004) Graph clustering and minimum cut trees. Internet Math 1(4):385–408MathSciNetCrossRefGoogle Scholar
  13. 13.
    Girvan M, Newman ME (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826MathSciNetCrossRefGoogle Scholar
  14. 14.
    Hopcroft J, Khan O, Kulis B, Selman B (2003) Natural communities in large linked networks. In: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 541–546. ACMGoogle Scholar
  15. 15.
    Newman ME (2004) Detecting community structure in networks. Eur Phys J B Condens Matter Complex Syst 38(2):321–330CrossRefGoogle Scholar
  16. 16.
    Kaplan AM, Haenlein M (2010) Users of the world, unite! The challenges and opportunities of social media. Bus Horiz 53(1):59–68CrossRefGoogle Scholar
  17. 17.
    Moriarty GL (2010) Psychology 2.0: harnessing social networking, user-generated content, and crowdsourcing. J Psychol Issues Organ Culture 1(2):29–39CrossRefGoogle Scholar
  18. 18.
    Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Human Lang Technol 5(1):1–167CrossRefGoogle Scholar
  19. 19.
    Shelley GA, Bernard HR, Killworth PD (1990) Information flow in social networks. J Quant Anthropol 2(3)Google Scholar
  20. 20.
    Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135CrossRefGoogle Scholar
  21. 21.
    Asur S, Huberman B (2010) Predicting the future with social network. In: 2010 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (WIIAT), vol 1Google Scholar
  22. 22.
    Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Identifying influencers on twitter. In: Fourth ACM international conference on web search and data mining (WSDM)Google Scholar
  23. 23.
    Wen-ying SC, Hunt YM, Beckjord EB, Moser RP, Hesse BW (2009) Social media use in the United States: implications for health communication. J Med Internet Res 11(4):e48CrossRefGoogle Scholar
  24. 24.
    Zuber M (2014) A survey of data mining techniques for social network analysis. Int J Res Comput Eng Electron 3(6)Google Scholar
  25. 25.
    Shin H, Byun C, Lee H (2015) The influence of social media: Twitter usage pattern during the 2014 super bowl game. Life 10(3)CrossRefGoogle Scholar
  26. 26.
    Site of SEO Company. SEO Positive. Accessed 31 Dec 2014
  27. 27.
    Fraser M, Dutta S (2010) Throwing sheep in the boardroom: how online social networking will transform your life, work and world. WileyGoogle Scholar
  28. 28.
    Daum Communications Corp. Daum blog.
  29. 29.
    Daum Communications Corp. Tistory.
  30. 30.
    Google. Blogger.
  31. 31.
    MySpace Inc.
  32. 32.
    NHN Corp. Naver blog.
  33. 33.
    SK Communications Corp. Cyworld.
  34. 34.
    Lim SH, Kim SW, Kim S, Park S (2011) Construction of a blog network based on information diffusion. In: Proceedings of the 2011 ACM symposium on applied computing, pp 937–941. ACMGoogle Scholar
  35. 35. Accessed Dec 2015
  36. 36.
    Boyd, Ellison (2008) Social network sites: definition, history & scholarship. J Comput Med Commun 13:210–230Google Scholar
  37. 37.
    LinkedIn. Accessed Dec 2015
  38. 38.
    Elftown. Accessed 20 Dec 2017
  39. 39.
    Ravelry. Accessed Dec 2015
  40. 40.
    Saha S, Paul G (2013) On effective sharing of user generated content. In: Proceedings of the 11th Asia Pacific conference on computer human interaction, pp 114–118. ACMGoogle Scholar
  41. 41.
    Flikr. Accessed Dec 2015
  42. 42.
    Podcast Alley. Accessed Dec 2015
  43. 43.
    Wong LLY, Burkell J (2017) Motivations for sharing news on social media. Soc Media Soc 17:1Google Scholar
  44. 44.
    Hermida A (2014) Tell everyone: why we share and why it matters. Doubleday Canada, TorontoGoogle Scholar
  45. 45.
    Digg. Accessed Dec 2015
  46. 46.
    Reddit. Accessed Dec 2017
  47. 47.
    Zheng Y (2011) Location-based social networks: users. Computing with spatial trajectories. Springer, New York, NY, pp 243–276CrossRefGoogle Scholar
  48. 48.
    Chorley MJ, Whitaker RM, Allen SM (2015) Personality and location-based social networks. Comput Hum Behav 46:45–56CrossRefGoogle Scholar
  49. 49.
    Foursquare. Accessed Dec 2015
  50. 50.
    Google Groups. Accessed Dec 2015
  51. 51.
    Allan J, Papka R, Lavrenko V (1998) On-line new event detection and tracking. In: Proceedings of the 21st annual international ACM SIGIR conference on research and development in information retrieval, pp 37–45. ACMGoogle Scholar
  52. 52.
    Dou W, Wang X, Skau D, Ribarsky W, Zhou MX (2012) Leadline: interactive visual analysis of text data through event identification and exploration. In: 2012 IEEE conference visual analytics science and technology (VAST), pp 93–102Google Scholar
  53. 53.
    Setzer A (2002) Temporal information in newswire articles: an annotation scheme and corpus study, Doctoral dissertation, University of SheffieldGoogle Scholar
  54. 54.
    Chen L, Roy A (2009) Event detection from flickr data through wavelet-based spatial analysis. In: Proceedings of the 18th ACM conference on information and knowledge management, pp 523–532. ACMGoogle Scholar
  55. 55.
    Zhou X, Chen L (2014) Event detection over twitter social media streams. VLDB J Int J Very Large Data Bases 23(3):381–400MathSciNetCrossRefGoogle Scholar
  56. 56.
    Madani A, Boussaid O, Zegour DE (2014) What’s happening: a survey of tweets event detection. In: Proceedings of 3rd INNOVGoogle Scholar
  57. 57.
    Baas S, Ramasamy S, DePryck JD, Battista F (2008) Disaster risk management systems analysis: a guide book, vol 3. Food and Agriculture Organization of the United NationsGoogle Scholar
  58. 58.
    Srikanth A. Social media can solve many problems during natural disastersGoogle Scholar
  59. 59.
    Freberg K (2011) Crisis information curators & digital relief coordinators via social media: Japan Tsunami catastrophe brief report 2011. Presented to the National Center for Food Protection and Defense. Minneapolis, MNGoogle Scholar
  60. 60.
    Pohl D, Bouchachia A, Hellwagner H (2012) Automatic sub-event detection in emergency management using social media. In: Proceedings of the 21st international conference on world wide web, pp 683–686. ACMGoogle Scholar
  61. 61.
    Bailey NJ, Bevington JS, Lewis HG, Swinerd GG, Atkinson PM, Crowther R, Holland D (2007) From Buncefield to Tunguska: hazard and disaster modelling at the University of SouthamptonGoogle Scholar
  62. 62.
    Santos ADPD, Wives LK, Alvares LO (2012) Location-based events detection on micro-blogs. arXiv:1210.4008
  63. 63.
    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. ACMGoogle Scholar
  64. 64.
    Lee R, Sumiya K (2010) Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection. In: Proceedings of the 2nd ACM SIGSPATIAL international workshop on location based social networks, pp 1–10. ACMGoogle Scholar
  65. 65.
    Iyengar A, Finin T, Joshi A (2011) Content-based prediction of temporal boundaries for events in Twitter. In: 2011 IEEE third international conference on privacy, security, risk and trust (PASSAT) and 2011 IEEE third inernational conference on social computing (SocialCom), pp 186–191. IEEEGoogle Scholar
  66. 66.
    Samadzadegan F, Rastiveisi H (2008) Automatic detection and classification of damaged buildings, using high resolution satellite imagery and vector data. In: Proceedings of the international archives of the photogrammetry, remote sensing and spatial information sciences, vol 37, pp 415–420Google Scholar
  67. 67.
    Velev D, Zlateva P (2012) Use of social media in natural disaster management. Int Proc Econ Dev Res 39:41–45Google Scholar
  68. 68.
    Palen L (2008) Online social media in crisis events. Educ Q 31(3):76–78MathSciNetGoogle Scholar
  69. 69.
    Chae J, Thom D, Bosch H, Jang Y, Maciejewski R, Ebert DS, Ertl T (2012) Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition. In: 2012 IEEE conference on visual analytics science and technology (VAST), pp 143–152. IEEEGoogle Scholar
  70. 70.
    Mathioudakis M, Koudas N (2010) Twittermonitor: trend detection over the twitter stream. In: Proceedings of the ACM SIGMOD international conference on management of data, pp 1155–1158Google Scholar
  71. 71.
    Childs H (1939) By public opinion I mean. Public Opin Q 3(2):327–336. Scholar
  72. 72.
    Stromback J (2012) The media and their use of opinion polls: reflecting and shaping public opinion. In: Holtz-Bacha C, Strömbäck J (eds) Opinion polls and the media: reflecting and shaping public opinion. Palgrave Macmillan, Basingtstoke, pp 1–23Google Scholar
  73. 73.
    Gallup GH (1939) Public opinion in a democracy. Herbert L. Baker foundation, Princeton University Press, Princeton, NJGoogle Scholar
  74. 74.
    Bourdieu P (1979) Public opinion does not exist. In: Mattelart A, Siegelaub S (eds) News and the empowerment of citizens. International General, New York, pp 124–130Google Scholar
  75. 75.
    Herbst S (1993) Numbered voices: how opinion polling has shaped American politics. University of Chicago Press, ChicagoGoogle Scholar
  76. 76.
    Moon N (1999) Opinion polls: History, theory and practice. University of Manchester Press, ManchesterGoogle Scholar
  77. 77.
    Tork H (2011) Event detection. Thesis, LIAAD-INESC TECGoogle Scholar
  78. 78.
    Neill DB, Wong WK (2009) Tutorial on event detection. In: proceedings of KDD 2009Google Scholar
  79. 79.
    Neill DB, Gorr WL (2007) Detecting and preventing emerging epidemics of crime. Adv Dis Surveill 4:13Google Scholar
  80. 80.
    Dereszynski E, Dietterich T (2007) Probabilistic models for anomaly detection in remote sensor data streams. In: Proceedings of the 23rd conference on uncertainty in artificial intelligence (UAI-2007), pp 75–82Google Scholar
  81. 81.
    Papka R, Allan J (1998) On-line new event detection using single pass clustering. UMass Computer ScienceGoogle Scholar
  82. 82.
    CNN Library (2015) Mumbai terror attacks fast facts. Accessed Jan 2016
  83. 83.
    Zhao Q, Mitra P (2007) Event detection and visualization for social text streams. In: International conference of weblogs and social media. ICWSMGoogle Scholar
  84. 84.
    Garofalakis M, Gehrke J, Rastogi R (eds) (2016) Data stream management: processing high-speed data streams. SpringerGoogle Scholar
  85. 85.
    Brants T, Chen F, Farahat A (2003) A system for new event detection. In: Proceedings of the 26th annual international ACM SIGIR conference on research and development in informaion retrieval, pp. 330–337. ACMGoogle Scholar
  86. 86.
    Kumaran G, Allan J (2004) Text classification and named entities for new event detection. In: Proceedings of the 27th annual international ACM SIGIR conference on research and development in information retrieval, pp 297–304. ACMGoogle Scholar
  87. 87.
    Cutting, DR, Karger DR, Pedersen JO (1993) Constant interaction-time scatter/gather browsing of very large document collections. In: Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval, pp 126–134. ACMGoogle Scholar
  88. 88.
    McCallum AK (1996) Bow: a toolkit for statistical language modeling, text retrieval, classification and clustering.
  89. 89.
    Aggarwal CC, Zhai C (2012) A survey of text clustering algorithms. Mining text data. Springer, Boston, MA, pp 77–128CrossRefGoogle Scholar
  90. 90.
    Fisher DH (1987) Knowledge acquisition via incremental conceptual clustering. Mach Learn 2(2):139–172Google Scholar
  91. 91.
    Sahoo N, Callan J, Krishnan R, Duncan G, Padman R (2006) Incremental hierarchical clustering of text documents. In: Proceedings of the 15th ACM international conference on information and knowledge management, pp 357–366. ACMGoogle Scholar
  92. 92.
    Zhuang X, Huang Y, Palaniappan K, Zhao Y (1996) Gaussian mixture density modeling, decomposition, and applications. IEEE Trans Image Process 5(9):1293–1302CrossRefGoogle Scholar
  93. 93.
    Fung GPC, Yu JX, Yu PS, Lu H (2005) Parameter free bursty events detection in text streams. In: Proceedings of the 31st international conference on very large data bases, pp 181–192. VLDB EndowmentGoogle Scholar
  94. 94.
    Fung GPC, Yu JX, Lu H, Yu PS (2006) Text classification without negative examples revisit. IEEE Trans Knowl Data Eng 18(1):6–20CrossRefGoogle Scholar
  95. 95.
    Li X, Liu B (2003) Learning to classify texts using positive and unlabeled data. In: Proceedings of IJCAI, vol 3, no 2003, pp 587–592Google Scholar
  96. 96.
    He Q, Chang K, Lim EP (2007) Analyzing feature trajectories for event detection. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, pp 207–214. ACMGoogle Scholar
  97. 97.
    Sankaranarayanan J, Samet H, Teitler BE, Lieberman MD, Sperling J (2009) Twitterstand: news in tweets. In: Proceedings of the 17th acm sigspatial international conference on advances in geographic information systems, pp 42–51. ACMGoogle Scholar
  98. 98.
    Mitchell TM (1997) Machine learning, vol 45, no 37. McGraw Hill, Burr Ridge, IL, pp 870–877Google Scholar
  99. 99.
    Vapnik V (2013) The nature of statistical learning theory. Springer science & business mediaGoogle Scholar
  100. 100.
    Cha Y, Cho J (2012) Social-network analysis using topic models. In: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval, pp 565–574. ACMGoogle Scholar
  101. 101.
    Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022Google Scholar
  102. 102.
    Breiman L (2001) Random forests. Mach Learn 45(1):5–32CrossRefGoogle Scholar
  103. 103.
    Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Trans Pattern Anal Mach Intell 12(10):993–1001CrossRefGoogle Scholar
  104. 104.
    Schapire RE (2003) The boosting approach to machine learning: an overview. Nonlinear estimation and classification. Springer, New York, pp 149–171CrossRefGoogle Scholar
  105. 105.
    Sewell M (2011) Ensemble learning. Technical report, Department of Computer Science, University College London.
  106. 106.
    Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 1189–1232Google Scholar
  107. 107.
    Freund Y, Schapire RE (1995) A desicion-theoretic generalization of on-line learning and an application to boosting. In: European conference on computational learning theory. Springer, Berlin, Heidelberg, pp 23–37Google Scholar
  108. 108.
    Lafferty J, McCallum A, Pereira FC (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence dataGoogle Scholar
  109. 109.
    Gu H, Xie X, Lv Q, Ruan Y, Shang L (2011) Etree: effective and efficient event modeling for real-time online social media networks. In: 2011 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (WI-IAT), vol 1, pp 300–307. IEEEGoogle Scholar
  110. 110.
    Nallapati R, Feng A, Peng F, Allan J (2004) Event threading within news topics. In: Proceedings of the thirteenth ACM international conference on information and knowledge management, pp 446–453. ACMGoogle Scholar
  111. 111.
    Weng J, Lee BS (2011) Event detection in twitter. In: Proceedings of ICWSM, vol 11, pp 401–408Google Scholar
  112. 112.
    Kaiser G (2010) A friendly guide to wavelets. Springer Science & Business MediaGoogle Scholar
  113. 113.
    Allan J, Carbonell JG, Doddington G, Yamron J, Yang Y (1998) Topic detection and tracking pilot study final reportGoogle Scholar
  114. 114.
    Chen CC, Chen MC, Chen MS (2005) LIPED: HMM-based life profiles for adaptive event detection. In: Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery in data mining, pp 556–561. ACMGoogle Scholar
  115. 115.
    Yang Y, Pierce T, Carbonell J (1998) A study of retrospective and on-line event detection. In: Proceedings of the 21st annual international ACM SIGIR conference on research and development in information retrieval, pp 28–36. ACMGoogle Scholar
  116. 116.
    Chen CC, Chen MC (2008) TSCAN: a novel method for topic summarization and content anatomy. In: Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval, pp 579–586. ACMGoogle Scholar
  117. 117.
    Metzler D, Cai C, Hovy E (2012) Structured event retrieval over microblog archives. In: Proceedings of the 2012 conference of the North American chapter of the association for computational linguistics: human language technologies, pp 646–655. Association for Computational LinguisticsGoogle Scholar
  118. 118.
    Tang X, Yang C, Gong X (2011) A spectral analysis approach for social media community detection. Soc Inform 127–134Google Scholar
  119. 119.
    Klinger R, Tomanek K (2007) Classical probabilistic models and conditional random fields. TU, Algorithm EngineeringGoogle Scholar
  120. 120.
    Wong WK, Neill DB (2009) Tutorial on event detection KDD 2009. Age 9:30Google Scholar
  121. 121.
    Dong G, Li J (1999) Efficient mining of emerging patterns: discovering trends and differences. In: Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining, pp 43–52. ACMGoogle Scholar
  122. 122.
    Bay SD, Pazzani MJ (1999) Detecting change in categorical data: mining contrast sets. In: Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining, pp 302–306. ACMGoogle Scholar
  123. 123.
    Wong WK, Moore A, Cooper G, Wagner M (2005) What’s strange about recent events (WSARE): an algorithm for the early detection of disease outbreaks. J Mach Learn Res 6:1961–1998Google Scholar
  124. 124.
    Allamanis M, Scellato S, Mascolo C (2012) Evolution of a location-based online social network: analysis and models. In: Proceedings of the 2012 ACM conference on internet measurement conference, pp 145–158. ACMGoogle Scholar
  125. 125.
    Wittie MP, Pejovic V, Deek L, Almeroth KC, Zhao BY (2010) Exploiting locality of interest in online social networks. In: Proceedings of the 6th international conference, p 25. ACMGoogle Scholar
  126. 126.
    Karagiannis T, Gkantsidis C, Narayanan D, Rowstron A (2010) Hermes: clustering users in large-scale e-mail services. In: Proceedings of the 1st ACM symposium on cloud computing, pp 89–100. ACMGoogle Scholar
  127. 127.
    Backstrom L, Sun E, Marlow C (2010) Find me if you can: improving geographical prediction with social and spatial proximity. In: Proceedings of the 19th international conference on world wide web, pp 61–70. ACMGoogle Scholar
  128. 128.
    Gionis A, Indyk P, Motwani R (1999) Similarity search in high dimensions via hashing. In: Proceedings of VLDB, vol 99, no 6, pp 518–529Google Scholar
  129. 129.
    Fox V, Hightower J, Liao L, Schulz D, Borriello G (2003) Bayesian filtering for location estimation. IEEE Pervasive Comput 2(3):24–33CrossRefGoogle Scholar
  130. 130.
    Kulldorff M (1997) A spatial scan statistic. Commun Stat Theor Methods 26(6):1481–1496MathSciNetCrossRefGoogle Scholar
  131. 131.
    Neff ND, Naus JI (1980) The distribution of the size of the maximum cluster of points on a line. Amer Mathematical SocietyGoogle Scholar
  132. 132.
    Kulldorff M, Athas WF, Feurer EJ, Miller BA, Key CR (1998) Evaluating cluster alarms: a space-time scan statistic and brain cancer in Los Alamos, New Mexico. Am J Public Health 88(9):1377–1380CrossRefGoogle Scholar
  133. 133.
    Kulldorff M, Mostashari F, Duczmal L, Katherine Yih W, Kleinman K, Platt R (2007) Multivariate scan statistics for disease surveillance. Stat Med 26(8):1824–1833MathSciNetCrossRefGoogle Scholar
  134. 134.
    Agrawal R, Lin KI, Sawhney HS, Shim K (1995) Fast similarity search in the presence of noise, scaling, and translation in times-series databases. In: Proceedings of the 21st international conference on very large databases, pp 490–501Google Scholar
  135. 135.
    Chan FP, Fu AC, Yu C (2003) Haar wavelets for efficient similarity search of time-series: with and without time warping. IEEE Trans Knowl Data Eng 15(3):686–705CrossRefGoogle Scholar
  136. 136.
    Aach J, Church GM (2001) Aligning gene expression time series with time warping algorithms. Bioinformatics 17(6):495–508CrossRefGoogle Scholar
  137. 137.
    Bar-Joseph Z, Gerber G, Gifford DK, Jaakkola TS, Simon I (2002) A new approach to analyzing gene expression time series data. In: Proceedings of the sixth annual international conference on computational biology, pp 39–48. ACMGoogle Scholar
  138. 138.
    Berndt DJ, Clifford J (1994) Using dynamic time warping to find patterns in time series. In: KDD workshop, vol 10, no 16, pp 359–370Google Scholar
  139. 139.
    Keogh EJ (2002) Exact indexing of dynamic time warping. In: Proceedings of VLDB, pp 406–417CrossRefGoogle Scholar
  140. 140.
  141. 141.
    Kerman MC et al (2009) Event detection challenges, methods, and applications in natural and artificial systems. In: Proceedings of 14th international command and control research and technology symposium: C2 and agilityGoogle Scholar
  142. 142.
    Dash D, Margineantu D, Wong WK (2007) Machine learning algorithms for event detection. Spec Issue Mach Learn J. Accessed 14 Mar 2008 (Springer)Google Scholar
  143. 143.
    Balazinska M (2007) Event detection in mobile sensor networks. In: National science foundation (NSF) workshop on data management for mobile sensor networks (MobiSensors) 2007Google Scholar
  144. 144.
    Ihler A, Hutchins J, Smyth P (2006) Adaptive event detection with time-varying poisson processes. In: The twelfth international conference on knowledge discovery and data mining (Association for Computing Machinery)Google Scholar
  145. 145.
    Mendoza M, Poblete B, Castillo C (2010) Twitter under crisis: can we trust what we RT? In: Proceedings of the first workshop on social media analytics, pp 71–79. ACMGoogle Scholar
  146. 146.
    Huberman BA, Romero DM, Wu F (2008) Social networks that matter: Twitter under the microscope. arXiv:0812.1045
  147. 147.
    Internet live stats. Accessed Feb 2018
  148. 148.
    Vieweg S (2010) Microblogged contributions to the emergency arena: discovery, interpretation and implications. In: Proceedings of computer supported collaborative work, pp 515–516Google Scholar
  149. 149.
    Kwak H, Lee C, Park H, Moon S (2010) What is Twitter, a social network or a news media? In: Proceedings of the 19th international conference on world wide web, pp 591–600. ACMGoogle Scholar
  150. 150.
    Qu Y, Huang C, Zhang P, Zhang J (2011) Microblogging after a major disaster in China: a case study of the 2010 Yushu earthquake. In: Proceedings of the ACM 2011 conference on computer supported cooperative work, pp 25–34. ACMGoogle Scholar
  151. 151.
    Long R, Wang H, Chen Y, Jin O, Yu Y (2011) Towards effective event detection, tracking and summarization on microblog data. In: International conference on web-age information management. Springer, Berlin, Heidelberg, pp 652–663CrossRefGoogle Scholar
  152. 152.
    Gao H, Barbier G, Goolsby R (2011) Harnessing the crowdsourcing power of social media for disaster relief. IEEE Intell Syst 26(3):10–14CrossRefGoogle Scholar
  153. 153.
    Hightower J, Borriello G (2001) Location systems for ubiquitous computing. Computer 34(8):57–66CrossRefGoogle Scholar
  154. 154.
    Ashktorab Z, Brown C, Nandi M, Culotta A (2014) Tweedr: mining twitter to inform disaster response. In: Proceedings of ISCRAMGoogle Scholar
  155. 155.
    Kumar S, Barbier G, Abbasi MA, Liu H (2011) TweetTracker: an analysis tool for humanitarian and disaster relief. In: International conference on web and social media (ICWSM), 5 Jul 2011Google Scholar
  156. 156.
    Cheong F, Cheong C (2011) Social media data mining: a social network analysis of tweets during the 2010–2011 Australian floods. In: Proceedings of PACIS, vol 11, pp 46–46Google Scholar
  157. 157.
    Hong L, Davison BD (2010) Empirical study of topic modeling in twitter. In: Proceedings of the first workshop on social media analytics, pp 80–88. ACMGoogle Scholar
  158. 158.
    Kireyev K, Palen L, Anderson K (2009) Applications of topics models to analysis of disaster-related twitter data. In: NIPS workshop on applications for topic models: text and beyond, vol 1, Canada, WhistlerGoogle Scholar
  159. 159.
    Ramage D, Dumais ST, Liebling DJ (2010) Characterizing microblogs with topic models. In: Proceedings of ICWSM, vol 10, no 1, p 16Google Scholar
  160. 160.
    Zhao WX, Jiang J, Weng J, He J, Lim EP, Yan H, Li X (2011) Comparing twitter and traditional media using topic models. In: European conference on information retrieval, pp 338–349. Springer, Berlin, HeidelbergGoogle Scholar
  161. 161.
    Cataldi M, Di Caro L, Schifanella C (2010) Emerging topic detection on twitter based on temporal and social terms evaluation. In: Proceedings of the tenth international workshop on multimedia data mining, p 4. ACMGoogle Scholar
  162. 162.
    Popescu AM, Pennacchiotti M (2010) Detecting controversial events from twitter. In: Proceedings of the 19th ACM international conference on information and knowledge management, pp 1873–1876. ACMGoogle Scholar
  163. 163.
    Luo G, Tang C, Yu PS (2007) Resource-adaptive real-time new event detection. In: Proceedings of the 2007 ACM SIGMOD international conference on management of data, pp 497–508. ACMGoogle Scholar
  164. 164.
    Petrović S, Osborne M, Lavrenko V (2010) Streaming first story detection with application to twitter. In: Human language technologies: The 2010 annual conference of the North American chapter of the association for computational linguistics, pp 181–189. Association for Computational LinguisticsGoogle Scholar
  165. 165.
    McCreadie R, Macdonald C, Ounis I, Osborne M, Petrovic S (2013) Scalable distributed event detection for twitter. In: 2013 IEEE international conference on big data, pp 543–549 (2013)Google Scholar
  166. 166.
    Zhao J, Wang X, Ma Z (2014) Towards events detection from microblog messages. Int J Hybrid Inf Technol 7(1):201–210CrossRefGoogle Scholar
  167. 167.
    Huang J, Iwaihara M (2011) Realtime social sensing of support rate for microblogging. Database systems for adanced applications. Springer, Berlin, Heidelberg, pp 357–368CrossRefGoogle Scholar
  168. 168.
    Popescu AM, Pennacchiotti M, Paranjpe D (2011) Extracting events and event descriptions from twitter. In: Proceedings of the 20th international conference companion on world wide web, pp 105–106. ACMGoogle Scholar
  169. 169.
    Chakrabarti D, Punera K (2011) Event summarization using tweets. In: Proceedings of ICWSM, vol 11, pp 66–73Google Scholar
  170. 170.
    Gong Y, Liu X (2001) Generic text summarization using relevance measure and latent semantic analysis. In: Proceedings of the 24th annual international ACM SIGIR conference on research and development in information retrieval, pp 19–25. ACMGoogle Scholar
  171. 171.
    Shamma D, Kennedy L, Churchill E (2010) Tweetgeist: can the twitter timeline reveal the structure of broadcast events. In: Proceedings of CSCW Horizons, pp 589–593Google Scholar
  172. 172.
    Gindl S, Weichselbraun A, Scharl A (2010) Cross-domain contextualisation of sentiment lexicons.Google Scholar
  173. 173.
    Pang B, Lee L, Vaithyanathan S (2002) Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on empirical methods in natural language processing, vol 10, pp 79–86Google Scholar
  174. 174.
    Go A, Huang L, Bhayani R (2009) Twitter sentiment analysis. Entropy 17 (2009)Google Scholar
  175. 175.
    Rao N, Srinivas S, Prashanth M (2015) Real time opinion mining of twitter data. Int J Comput Sci Inf Technol 6(3):2923–2927,Google Scholar
  176. 176.
    Sarlan A, Nadam C, Basri S (2014) Twitter sentiment analysis. In: International conference on information technology and multimedia (ICIMU), Putrajaya, Malaysia, 18–20 Nov 2014Google Scholar
  177. 177.
    Akshat B, Arora P, Kapre SMN, Singh M, Varma V (2012) Mining sentiments from tweets. In: Proceedings of the 3rd workshop on computational approaches to subjectivity and sentiment analysis. Association for Computational Linguistics, pp 11–18, Jeju, Republic of KoreaGoogle Scholar
  178. 178.
    Bahrainian S-A, Denge A (2013) Sentiment analysis and summarization of Twitter data. In: IEEE 16th international conference on computational science and engineeringGoogle Scholar
  179. 179.
    Jheser G, Poblete B (2013) On-line relevant anomaly detection in the twitter stream: an efficient bursty keyword detection model. In: Proceedings of the ACM SIGKDD workshop on outlier detection and description, pp 31–39. ACMGoogle Scholar
  180. 180.
    Sayyadi H, Hurst M, Maykov A (2009) Event detection and tracking in social streams. In: Proceedings of ICWSMGoogle Scholar
  181. 181.
    Lin, CX, Zhao B, Mei Q, Han J (2010) PET: a statistical model for popular events tracking in social communities. In: Proceedings of the 16th ACM SIGKDD, pp 929–938. ACMGoogle Scholar
  182. 182.
    Kleinberg J (2003) Bursty and hierarchical structure in streams. Data Min Knowl Disc 7(4):373–397MathSciNetCrossRefGoogle Scholar
  183. 183.
    Leskovec J, Backstrom L, Kleinberg J (2009) Meme-tracking and the dynamics of the news cycle. In: Proceedings of the 15th ACM SIGKDD, pp 497–506. ACMGoogle Scholar
  184. 184.
    Swan R, Allan J (2000) Automatic generation of overview timelines. In: Proceedings of the 23rd annual international ACM SIGIR conference on research and development in information retrievalGoogle Scholar
  185. 185.
    Naaman M, Becker H, Gravano L (2011) Hip and trendy: characterizing emerging trends on Twitter. J Am Soc Inf Sci Technol 62, no. 5 (2011)CrossRefGoogle Scholar
  186. 186. Accessed 20 Dec 2017
  187. 187.
    #TAGS. Accessed Dec 2017
  188. 188.
    Truthy. Accessed 20 Dec 2017
  189. 189.
    Tweet Archivist. Accessed Dec 2017
  190. 190.
    TweetStats, Accessed December, 2017
  191. 191.
    Twiangulate. Accessed Dec 2017
  192. 192.
    Twitonomy. Accessed Sept 2014
  193. 193.
    Tweetnest. Accessed Sept 2014
  194. 194.
  195. 195.
    Chorus. Accessed Dec 2017
  196. 196.
    Discovertext. Accessed Dec 2017
  197. 197.
    Followthehashtag. Accessed Dec 2017
  198. 198.
    Trendsmap. Accessed Dec 2017
  199. 199.
    Osborne M, Moran S, McCreadie R, Von Lunen A, Sykora MD, Cano E, Jackson T (2014) Real-time detection, tracking, and monitoring of automatically discovered events in social media. In: Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations, pp 37–42. ACL 2014Google Scholar
  200. 200.
    Xie W, Zhu F, Jiang J, Lim EP, Wang K (2013) Topicsketch: realtime bursty topic detection from twitter. In: 2013 IEEE 13th international conference on data mining, pp 837–846Google Scholar
  201. 201.
    Morstatter F, Kumar S, Liu H, Maciejewski R (2013) Understanding twitter data with tweetxplorer. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1482–1485Google Scholar
  202. 202.
    Li R, Lei KH, Khadiwala R, Chang KCC (2012) TEDAS: a twitterbased event detection and analysis system. In: 2012 IEEE 28th international conference on data engineering (ICDE), pp 1273–1276Google Scholar
  203. 203.
    Li C, Sun A, Datta A (2012) Twevent: segment-based event detection from tweets. In: Proceedings of the 21st ACM international conference on information and knowledge management, pp 155–164Google Scholar
  204. 204.
    Abel F, Hauff C, Houben GJ, Stronkman R, Tao K (2012) Twitcident: fighting fire with information from social web streams. In: Proceedings of the 21st international ACM conference companion on world wide web, pp 305–308.
  205. 205.
    Marcus A, Bernstein MS, Badar O, Karger DR, Madden S, Miller RC (2011) Twitinfo: aggregating and visualizing microblogs for event exploration. In: Proceedings of the ACM SIGCHI conference on Human factors in computing systems, pp 227–236Google Scholar
  206. 206.
    MacEachren AM, Jaiswal A, Robinson AC, Pezanowski S, Savelyev A, Mitra P, Blanford J (2011) Senseplace2: geotwitter analytics support for situational awareness. In: 2011 IEEE conference visual analytics science and technology (VAST), pp 181–190Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Symbiosis Centre for Information Technology (SCIT), Symbiosis International (Deemed University) (SIU)PuneIndia

Personalised recommendations