Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 2/2017

07.12.2016 | Regular Paper

A survey on location estimation techniques for events detected in Twitter

verfasst von: Ozer Ozdikis, Halit Oğuztüzün, Pinar Karagoz

Erschienen in: Knowledge and Information Systems | Ausgabe 2/2017

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Detection of events using voluntarily generated content in microblogs has been the objective of numerous recent studies. One essential challenge tackled in these studies is estimating the locations of events. In this paper, we review the state-of-the-art location estimation techniques used in the localization of events detected in microblogs, particularly in Twitter, which is one of the most popular microblogging platforms worldwide. We analyze these techniques with respect to the targeted event type, granularity of estimated locations, location-related features selected as sources of spatial evidence, and the method used to make aggregate decisions based on the extracted evidence. We discuss the strengths and advantages of alternative solutions to various problems related to location estimation, as well as their preconditions and limitations. We examine the most widely used evaluation methods to analyze the accuracy of estimations and present the results reported in the literature. We also discuss our findings and highlight important research challenges that may need further attention.
Nächster Artikel The (black) art of runtime evaluation: Are we comparing algorithms or implementations?

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Fußnoten
Literatur
1.
Abdelhaq H, Sengstock C, Gertz M (2013) EvenTweet: online localized event detection from twitter. Proc VLDB Endow 6(12):1326–1329CrossRef
2.
3.
Achrekar H, Gandhe A, Lazarus R, Yu SH, Liu B (2013) Online social networks flu trend tracker: a novel sensory approach to predict flu trends. In: Gabriel J, Schier J et al (eds) Biomedical engineering systems and technologies, communications in computer and information science, vol 357. Springer, Berlin Heidelberg, pp 353–368
4.
Aggarwal CC (2013) A survey of stream clustering algorithms. In: Data clustering: algorithms and applications. CRC Press, Florida, USA, pp 231–258
5.
Aggarwal CC, Zhai C (2012) A survey of text clustering algorithms. In: Aggarwal CC, Zhai C (eds) Mining text data. Springer, New York, pp 77–128CrossRef
6.
Ajao O, Hong J, Liu W (2015) A survey of location inference techniques on Twitter. J Inf Sci 41(6):855–864CrossRef
7.
Allan J (ed) (2002) Topic Detection and Tracking: Event-based Information Organization. Kluwer Academic Publishers
8.
Amitay E, Har’El N, Sivan R, Soffer A (2004) Web-a-where: Geotagging web content. In: Proceedings of the 27th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 273–280
9.
Anantharam P, Barnaghi P, Thirunarayan K, Sheth A (2015) Extracting city traffic events from social streams. ACM Trans Intell Syst Technol 6(4):43:1–43:27CrossRef
10.
Ao J, Zhang P, Cao Y (2014) Estimating the locations of emergency events from Twitter streams. Proc ITQM 2014:731–739
11.
Arulampalam MS, Maskell S, Gordon N, Clapp T (2002) A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process 50(2):174–188CrossRef
12.
Atefeh F, Khreich W (2015) A survey of techniques for event detection in Twitter. Comput Intell 31(1):132–164MathSciNetCrossRef
13.
Boettcher A, Lee D (2012) EventRadar: a real-time local event detection scheme using Twitter stream. In: Proceedings of the 2012 IEEE international conference on green computing and communications, IEEE Computer Society, GREENCOM ’12, pp 358–367
14.
Calvo T, Kolesárová A, Komorníková M, Mesiar R (2002) Aggregation operators: properties, classes and construction methods. Aggregation operators: new trends and applications. Physica-Verlag GmbH, Heidelberg, pp 3–104CrossRef
15.
Cheng T, Wicks T (2014) Event detection using Twitter: a spatio-temporal approach. PLoS ONE 9(6):1–10
16.
Cheng Z, Caverlee J, Lee K (2010) You are where you tweet: a content-based approach to geo-locating Twitter users. In: Proceedings of the 19th ACM international conference on information and knowledge management, ACM, pp 759–768
17.
Cordeiro M, Gama J (2016) Online social networks event detection: a survey. In: Michaelis S, Piatkowski N, Stolpe M (eds) Solving large scale learning tasks. Challenges and Algorithms. Springer, Cham, pp 1–41
18.
Crooks A, Croitoru A, Stefanidis A, Radzikowski J (2013) # Earthquake: Twitter as a distributed sensor system. Trans GIS 17(1):124–147CrossRef
19.
De Longueville B, Smith RS, Luraschi G (2009) OMG, from here, I can see the flames!: a use case of mining location based social networks to acquire spatio-temporal data on forest fires. In: Proceedings of international workshop on location based social networks, ACM, LBSN ’09, pp 73–80
20.
21.
Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd international conference on knowledge discovery and data mining, AAAI Press, pp 226–231
22.
Feng W, Zhang C, Zhang W, Han J, Wang J, Aggarwal C, Huang J (2015) STREAMCUBE: Hierarchical spatio-temporal hashtag clustering for event exploration over the Twitter stream. In: International conference on data engineering (ICDE), pp 1561–1572
23.
Fox D, Hightower J, Liao L, Schulz D, Borriello G (2003) Bayesian filtering for location estimation. Pervasive Computing, IEEE 2(3):24–33CrossRef
24.
Garg M, Kumar M (2016) Review on event detection techniques in social multimedia. Online Inf Rev 40(3):347–361CrossRef
25.
Gelernter J, Mushegian N (2011) Geo-parsing messages from microtext. Trans GIS 15(6):753–773CrossRef
26.
Giridhar P, Abdelzaher T, George J, Kaplan L (2015a) On quality of event localization from social network feeds. In: Pervasive computing and communication workshops (PerCom Workshops), pp 75–80
27.
Giridhar P, Wang S, Abdelzaher T, George J, Kaplan L, Ganti R (2015b) Joint localization of events and sources in social networks. In: Distributed computing in sensor systems (DCOSS), pp 179–188
28.
Goodchild MF (2007) Citizens as sensors: the world of volunteered geography. GeoJournal 69(4):211–221CrossRef
29.
Hamed AA, Ayer AA, Clark EM, Irons EA, Taylor GT, Zia A (2015) Measuring climate change on Twitter using Google’s algorithm: perception and events. Int J Web Inf Syst 11(4):527–544CrossRef
30.
Han J, Kamber M, Tung AKH (2001) Spatial clustering methods in data mining: A survey. In: Miller HJ, Han J (eds) Geographic data mining and knowledge discovery, research monographs in GIS. Taylor & Francis Inc, Bristol
31.
Hecht B, Hong L, Suh B, Chi EH (2011) Tweets from Justin Bieber’s heart: The dynamics of the location field in user profiles. In: Proceedings of CHI conference on human factors in computing systems, ACM, pp 237–246
32.
Heravi BR, Morrison D, Khare P, Marchand-Maillet S (2014) Where is the news breaking? Towards a location-based event detection framework for journalists. In: Proceedings of the 20th international conference on multimedia modeling, Volume 8326, Springer, pp 192–204
33.
Hill LL (2006) Georeferencing: the geographic associations of information. The MIT Press, Cambridge
34.
Hua T, Chen F, Zhao L, Lu CT, Ramakrishnan N (2013) STED: Semi-supervised targeted-interest event detection in Twitter. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, KDD ’13, pp 1466–1469
35.
Huang Y, Liu Z, Nguyen P (2015) Location-based event search in social texts. In: International conference on computing, networking and communications (ICNC), pp 668–672
36.
Imran M, Castillo C, Diaz F, Vieweg S (2015) Processing social media messages in mass emergency: a survey. ACM Comput Surv 47(4):67:1–67:38CrossRef
37.
Jin P, Lin S, Zhang Q (2014) Spatiotemporal Information for the Web. Encyclopedia of social network analysis and mining. Springer, Berlin, pp 1997–2010
38.
Kalman RE (1960) A new approach to linear filtering and prediction problems. Trans ASME J Basic Eng 82(Series D):35–45CrossRef
39.
Kleinberg J (2002) Bursty and hierarchical structure in streams. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, KDD ’02, pp 91–101
40.
Kulldorff M (1999) Spatial scan statistics: models, calculations, and applications. Scan statistics and applications. Birkhäuser Boston, Boston
41.
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, ACM, WWW ’10, pp 591–600
42.
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, ACM, LBSN ’10, pp 1–10
43.
44.
Li C, Weng J, He Q, Yao Y, Datta A, Sun A, Lee BS (2012a) TwiNER: Named entity recognition in targeted Twitter stream. In: Proceedings of the 35th international ACM SIGIR conference on research and development in information retrieval, ACM, pp 721–730
45.
Li R, Lei KH, Khadiwala R, Chang KC (2012b) TEDAS: A Twitter-based event detection and analysis system. In: IEEE international conference on data engineering (ICDE), pp 1273–1276
46.
Lingad J, Karimi S, Yin J (2013) Location extraction from disaster-related microblogs. In: Proceedings of WWW companion, pp 1017–1020
47.
Lu Y, Hu X, Wang F, Kumar S, Liu H, Maciejewski R (2015) Visualizing social media sentiment in disaster scenarios. In: Proceedings of the 24th international conference on world wide web, ACM, New York, NY, USA, WWW ’15 Companion, pp 1211–1215
48.
MacEachren A, Jaiswal A, Robinson A, Pezanowski S, Savelyev A, Mitra P, Zhang X, Blanford J (2011) SensePlace2: GeoTwitter analytics support for situational awareness. In: IEEE conference on visual analytics science and technology (VAST), pp 181–190
49.
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 SIGCHI conference on human factors in computing systems, ACM, CHI’11, pp 227–236
50.
Middleton S, Middleton L, Modafferi S (2014) Real-time crisis mapping of natural disasters using social media. Intell Syst IEEE 29(2):9–17CrossRef
51.
Musaev A, Wang D, Pu C (2015) LITMUS: a multi-service composition system for landslide detection. IEEE Trans Serv Comput 8(5):715–726CrossRef
52.
Nagar R, Yuan Q, Freifeld CC, Santillana M, Nojima A, Chunara R, Brownstein SJ (2014) A case study of the New York City 2012–2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives. J Med Internet Res 16(10):e236CrossRef
53.
Neill DB (2012) Fast subset scan for spatial pattern detection. J R Stat Soc: Ser B (Stat Methodol) 74(2):337–360MathSciNetCrossRef
54.
Nguyen HL, Woon YK, Ng WK (2014) A survey on data stream clustering and classification. Knowl Inf Syst 45(3):535–569CrossRef
55.
Ozdikis O, Senkul P, Oguztuzun H (2012) Semantic expansion of hashtags for enhanced event detection in Twitter. In: Proceedings of VLDB 2012 workshop on online social systems (WOSS)
56.
Ozdikis O, Oguztuzun H, Karagoz P (2013) Evidential location estimation for events detected in Twitter. In: Proceedings of the 7th workshop on geographic information retrieval, ACM, GIR ’13, pp 9–16
57.
Ozdikis O, Oğuztüzün H, Karagoz P (2016) Evidential estimation of event locations in microblogs using the Dempster–Shafer theory. Inf Process Manag 52(6):1227–1246CrossRef
58.
Padmanabhan A, Wang S, Cao G, Hwang M, Zhang Z, Gao Y, Soltani K, Liu Y (2014) FluMapper: a cyberGIS application for interactive analysis of massive location-based social media. Concurr Comput: Pract Exp 26(13):2253–2265CrossRef
59.
Panteras G, Wise S, Lu X, Croitoru A, Crooks A, Stefanidis A (2015) Triangulating social multimedia content for event localization using Flickr and Twitter. Trans GIS 19(5):694–715CrossRef
60.
Paradesi SM (2011) Geotagging tweets using their content. In: Proceedings of FLAIRS, AAAI Press
61.
Power R, Robinson B, Colton J, Cameron M (2014) Emergency situation awareness: Twitter case studies. In: Hanachi C, Bnaben F, Charoy F (eds) Information systems for crisis response and management in mediterranean countries, lecture notes in business information processing, vol 196, Springer, pp 218–231
62.
Rill S, Reinel D, Scheidt J, Zicari RV (2014) Politwi: early detection of emerging political topics on Twitter and the impact on concept-level sentiment analysis. Knowl-Based Syst 69:24–33CrossRef
63.
Ritter A, Clark S, Mausam, Etzioni O (2011) Named entity recognition in tweets: an experimental study. In: Proceedings of the conference on empirical methods in natural language processing, association for computational linguistics, pp 1524–1534
64.
Roick O, Heuser S (2013) Location based social networks—definition, current state of the art and research agenda. Trans GIS 17(5):763–784
65.
Sakai T, Tamura K (2014) Identifying bursty areas of emergency topics in geotagged tweets using density-based spatiotemporal clustering algorithm. In: 7th International workshop on computational intelligence and applications (IWCIA), pp 95–100
66.
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, ACM, WWW ’10, pp 851–860
67.
Sakaki T, Okazaki M, Matsuo Y (2013) Tweet analysis for real-time event detection and earthquake reporting system development. IEEE Trans Knowl Data Eng 25(4):919–931CrossRef
68.
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, ACM, pp 42–51
69.
Dos Santos ADP, Wives LK, Alvares LO (2012) Location-based events detection on micro-blogs. CoRR abs/1210.4008
70.
Shafer G (1976) A mathematical theory of evidence. Princeton University Press, PrincetonMATH
71.
Silva JA, Faria ER, Barros RC, Hruschka ER, Carvalho ACPLFd, Gama J (2013) Data stream clustering: a survey. ACM Comput Surv 46(1):13:1–13:31CrossRefMATH
72.
Silverman BW (1986) Density estimation for statistics and data analysis. Chapman & Hall, LondonCrossRefMATH
73.
Stefanidis A, Crooks A, Radzikowski J (2013) Harvesting ambient geospatial information from social media feeds. GeoJournal 78(2):319–338CrossRef
74.
Steiger E, de Albuquerque JP, Zipf A (2015) An advanced systematic literature review on spatiotemporal analyses of Twitter data. Trans GIS 19(6):809–834CrossRef
75.
Tamura K, Ichimura T (2013) Density-based spatiotemporal clustering algorithm for extracting bursty areas from georeferenced documents. In: IEEE international conference on systems, man, and cybernetics (SMC), pp 2079–2084
76.
Tamura K, Kitakami H (2013) Detecting location-based enumerating bursts in georeferenced micro-posts. In: International conference on advanced applied informatics, pp 389–394
77.
Teitler BE, Lieberman MD, Panozzo D, Sankaranarayanan J, Samet H, Sperling J (2008) NewsStand: A new view on news. In: Proceedings of the 16th ACM SIGSPATIAL international conference on advances in geographic information systems, ACM, pp 1–10
78.
Unankard S, Li X, Sharaf M (2015) Emerging event detection in social networks with location sensitivity. World Wide Web 18(5):1393–1417CrossRef
79.
Vieweg S, Hughes AL, Starbird K, Palen L (2010) Microblogging during two natural hazards events: What Twitter may contribute to situational awareness. In: Proceedings of the SIGCHI conference on human factors in computing systems, ACM, CHI ’10, pp 1079–1088
80.
Wang D, Amin MTA, Abdelzaher T, Roth D, Voss CR, Kaplan LM, Tratz S, Laoudi J, Briesch D (2014) Provenance-assisted classification in social networks. IEEE J Sel Top Signal Process 8(4):624–637CrossRef
81.
Wanner F, Stoffel A, Jäckle D, Kwon BC, Weiler A, Keim DA (2014) State-of-the-art report of visual analysis for event detection in text data streams. In: Borgo R, Maciejewski R, Viola I (eds). EuroVis - STARs, The Eurographics Association
82.
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, ACM, CIKM ’11, pp 2541–2544
83.
Welch G, Bishop G (1995) An introduction to the Kalman filter. Technical report, Chapel Hill, NC, USA
84.
Weng J, Lee B (2011) Event detection in Twitter. In: Proceedings of the 5th international conference on weblogs and social media
85.
Wu X, Kumar V, Ross Quinlan J, Ghosh J, Yang Q, Motoda H, McLachlan GJ, Ng A, Liu B, Yu PS, Zhou ZH, Steinbach M, Hand DJ, Steinberg D (2008) Top 10 algorithms in data mining. Knowl Inf Syst 14(1):1–37CrossRef
86.
Yin J, Lampert A, Cameron M, Robinson B, Power R (2012) Using social media to enhance emergency situation awareness. Intell Syst IEEE 27(6):52–59CrossRef
87.
Yuan Q, Cong G, Ma Z, Sun A, Thalmann NM (2013) Who, Where, When and What: Discover spatio-temporal topics for Twitter users. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, ACM, KDD ’13, pp 605–613
88.
Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: An efficient data clustering method for very large databases. In: Proceedings of the 1996 ACM SIGMOD international conference on management of data, ACM, New York, NY, USA, SIGMOD ’96, pp 103–114
Metadaten
Titel
A survey on location estimation techniques for events detected in Twitter
verfasst von
Ozer Ozdikis
Halit Oğuztüzün
Pinar Karagoz
Publikationsdatum
07.12.2016
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 2/2017
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-016-1007-z

Weitere Artikel der Ausgabe 2/2017

Knowledge and Information Systems 2/2017 Zur Ausgabe

Regular Paper

Properties of information sets and information processing with an application to face recognition

Regular Paper

-LGP: an improved version of linear genetic programming evaluated in the Ant Trail problem

Regular Paper

FIU-Miner (a fast, integrated, and user-friendly system for data mining) and its applications

Regular Paper

Learning extremely shared middle-level image representation for scene classification

Regular Paper

Evaluating intelligent knowledge systems: experiences with a user-adaptive assistant agent

Regular Paper

Prequential AUC: properties of the area under the ROC curve for data streams with concept drift