Geoinformatics and social media: New big data challenge

Big Data: Techniques and Technologies in Geoinformatics

Volumn , Issue , 2014, Pages 207-232

  Croitoru, Arie ^a   Crooks, Andrew ^a   Radzikowski, Jacek ^a   Stefanidis, Anthony ^a   Vatsavai, Ranga R ^b   Wayant, Nicole ^c  

^a GEORGE MASON UNIVERSITY   (United States)

^b OAK RIDGE NATIONAL LABORATORY   (United States)

^c US ARMY ENGINEER RESEARCH AND DEVELOPMENT CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIG DATA; INFORMATION DISSEMINATION; MULTIMEDIA SERVICES; UBIQUITOUS COMPUTING;

DIGITAL INTERACTIONS; GENERAL PUBLICS; GEO-INFORMATICS; INFORMATION EXCHANGES; MULTIMEDIA CONTENT SHARING; SOCIAL MEDIA SERVICES; SOCIAL NETWORKING SERVICES; TECHNOLOGICAL ADVANCEMENT;

SOCIAL NETWORKING (ONLINE);

EID: 84949764360     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/b16524     Document Type: Chapter

References (85)

1. Achrekar, H., Gandhe, A., Lazarus, R., Yu, S.H., and Liu, B. (2011) Predicting flu trends using twitter data, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Shanghai, China, pp. 702-707.
2. Ames, M. and Naaman, M. (2007) Why we tag: Motivations for annotation in mobile and online media, Proceedings ACM SIGCHI Conference on Human Factors in Computing Systems, San Jose, CA, pp. 971-980.
3. Arthur, C. (2008) How Twitter and Flickr recorded the Mumbai terror attacks, The Guardian, Available at http://bit.ly/MIMz (accessed on June 3, 2013).
4. BBC (2011) iPhone tracks users’ movements, Available at http://bbc.in/e81tgt (accessed on June 3, 2013).
5. Bliss, C.A., Kloumann, I.M., Harris, K.D., Danforth, C.M., and Dodds, P.S. (2012) Twitter reciprocal reply networks exhibit assortativity with respect to happiness, Journal of Computational Science, 3(5): 388-397.
6. Bollobas, B., Riordan, O., Spencer, J., and Tusnady, G. (2001) The degree sequence of a scale-free random graph process, Random Structures & Algorithms, 18(3): 279-290.
7. Borgatti, S.P. (2005) Centrality and network flow, Social Networks, 27: 55-71.
8. Bremmer, I. (2010) Democracy in cyberspace: What information technology can and cannot do, Foreign Affairs, 89(6): 86-92.
9. Castillo, C., Mendoza, M., and Poblete, B. (2011) Information credibility on Twitter, Proceedings of the 20th International Conference on World Wide Web, Hyderabad, India, pp. 675-684.
10. Christensen, C. (2011) Twitter revolutions? Addressing social media and dissent, The Communication Review, 14(3): 155-157.
11. ComputerWeekly (2013) What does a petabyte look like? Available at http://bit.ly/Ywtnqg (accessed on October 29, 2013).
12. Croitoru, A. and Arazy, O. (2007) Introducing location-based mass collaboration systems, Proceeding of the 17th Workshop on Information Technologies and Systems, Montreal, Quebec, Canada, pp. 37-42.
13. Croitoru, A., Stefanidis, A., Radzikowski, J., Crooks, A.T., Stahl, J., and Wayant, N. (2012) Towards a collaborative GeoSocial analysis workbench, COM-Geo, Washington, DC.
14. Crooks, A.T., Croitoru, A., Stefanidis, A., and Radzikowski, J. (2013) #Earthquake: Twitter as a distributed sensor system, Transactions in GIS, 17(1): 124-147.
15. De Longueville, B., Smith, R.S., and 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, Proceedings of the 2009 International Workshop on Location Based Social Networks, Seattle, WA, pp. 73-80.
16. Duggan, M. and Brenner, J. (2012) The Demographics of Social Media Users-2012, Washington, DC: Pew Research Center, Available at http://bit.ly/XORHo0.
17. Engadget (2013) DARPA’s 1.8-gigapixel cam touts surveillance from 20,000 feet (video), Available at http://engt.co/UyGFUF (accessed on October 29, 2013).
18. Financial Times (2010) Coke sees ‘phenomenal’ result from twitter ads, Financial Times, June 25, Available at http://on.ft.com/13jvSkp (accessed on June 3, 2013).
19. Forbes (2012) Twitter’s Dick Costolo: Twitter mobile ad revenue beats desktop on some days, Available at http://onforb.es/KgTWYP (accessed on January 19, 2013).
20. Freeman, L.C. (1979) Centrality in social networks conceptual clarification, Social Networks, 1(3): 215-239.
21. Friedland, G. and Sommer, R. (2010) Cybercasing the joint: On the privacy implications of geotagging, Proceedings of the Fifth USENIX Workshop on Hot Topics in Security (HotSec 10), Washington, DC.
22. Fritz, S., MacCallum, I., Schill, C., Perger, C., Grillmayer, R., Achard, F., Kraxner, F., and Obersteiner, M. (2009) Geo-wiki.org: The use of crowdsourcing to improve global land cover, Remote Sensing, 1(3): 345-354.
23. Gerrard, G. and Thompson, R. (2011) Two million cameras in the UK, CCTV Image, 42: 10-12.
24. Golovinskiy, A., Kim, V.G., and Funkhouser, T. (2009) Shape-based recognition of 3D point clouds in urban environments, Proceedings of IEEE International Conference on Computer Vision, Kyoto, Japan, pp. 2154-2161.
25. Goodchild, M.F. (2007) Citizens as sensors: The world of volunteered geography, GeoJournal, 69(4): 211-221.
26. Graham, M., Poorthuis, A., and Zook, M. (2012) Digital trails of the UK floods-How well do tweets match observations? The Guardian, Available at http://bit.ly/TnOvwu (accessed on May 12, 2013).
27. Gupta, A., Lamba, H., Kumaraguru, P., and Joshi, A. (2013) Faking sandy: Characterizing and identifying fake images on twitter during hurricane sandy, Second International Workshop on Privacy and Security in Online Social Media, Rio de Janeiro, Brazil.
28. Hagerstrand, T. (1967) Innovation Diffusion as a Spatial Process, Chicago, IL: The University of Chicago Press.
29. Haklay, M., Basiouka, S., Antoniou, V., and Ather, A. (2010) How many volunteers does it take to map an area well? The validity of Linus’ law to volunteered geographic information, The Cartographic Journal, 47(4): 315-322.
30. Hall, D. (1996) Stemming algorithms-A case study for detailed evaluation, Journal of the American Society for Information Science, 47(1): 70-84.
31. Hecht, B. and Moxley, E. (2009) Terabytes of Tobler: Evaluating the first law in a massive, domain-neutral representation of world knowledge, in Hornsby, K.S., Claramunt, C., Denis, M., and Ligozat, G. (eds.), Spatial Information Theory: Proceedings of the Ninth International Conference, COSIT 2009, Berlin, Germany: Springer, pp. 88-105.
32. Howard, P.N., Duffy, A., Freelon, D., Hussain, M., Mari, W., and Mazaid, M. (2011) Opening closed regimes: What was the role of social media during the Arab spring? Working Paper 2011.1. Project on Information Technology and Political Islam, University of Washington’s Department of Communication, Seattle, WA.
33. Huang, J., Thornton, K., and Efthimiadis, E. (2010) Conversational tagging in Twitter, Proceedings of the 21st ACM Conference on Hypertext and Hypermedia, Toronto, Ontario, Canada, pp. 173-178.
34. Huang, Z. (2006) Link prediction based on graph topology: The predictive value of the generalized clustering coefficient, Workshop on Link Analysis: Dynamics and Static of Large Networks (LinkKDD2006), Philadelphia, PA.
35. InfoQ (2012) Petabyte scale data at Facebook, Available at http://www.infoq.com/presentations/Data-Facebook (accessed on October 29, 2013).
36. Instagram (2013) Instagram stats, Available at http://instagram.com/press/ (accessed on October 29, 2013).
37. Kalnikaitė, V., Sellen, A., Whittaker, S., and Kirk, D. (2010) Now let me see where I was: Understanding how lifelogs mediate memory, Proceedings of the 28th International Conference on Human Factors in Computing Systems, Atlanta, GA, pp. 2045-2054.
38. Kang, U., Papadimitriou, S., Sun, J., and Tong, H. (2011) Centralities in large networks: Algorithms and observations, SIAM International Conference on Data Mining, Mesa, AZ, pp. 119-130.
39. Kaplan, A.M. and Haenlein, M. (2010) Users of the world unite! The challenges and opportunities of social media, Business Horizons, 53(1): 59-68.
40. Katz, L. (1953) A new index derived from sociometric data analysis, Psychometrika, 18: 39-43.
41. Laube, P., Dennis, T., Forer, P., and Walker, M. (2007) Movement beyond the snapshot-Dynamic analysis of geospatial lifelines, Computers, Environment and Urban Systems, 31(5): 481-501.
42. Lieberman, M.D., Samet, H., and Sankaranarayanan, J. (2010) Geotagging with local Lexicons to build indexes for textually-specified spatial data, Proceedings of the IEEE 26th International Conference on Data Engineering, Long Beach, CA, pp. 201-212.
43. Lyons, K. and Lessard, L. (2012) S-FIT: A technique for integrating social features in existing information systems, Proceedings of the 2012 iConference, New York, pp. 263-270.
44. Maiden, M. (2012) NASA Earth Science Data Systems Program, Southeastern Universities Research Association (SURA) Information Technology Committee Meeting, Washington, DC, Available at http://www.sura.org/.
45. Marcus, A., Bernstein, M.S., Badar, O., Karger, D., Madden, S., and Miller, R.C. (2011) Processing and visualizing the data in tweets, SIGMOD Record, 40(4): 21-27.
46. Mocanu, D., Baronchelli, A., Perra, N., Gonçalves, B., Zhang, Q., and Vespignani, A. (2013) The twitter of Babel: Mapping world languages through microblogging platforms, PLoS One, 8(4): e61981. doi: 10.1371/journal.pone.0061981.
47. New York Times (2011) Spotlight again falls on web tools and change, New York Times, Available at http://www.nytimes.com/2011/01/30/weekinreview/30shane.html?_r=1&hp (accessed on July 23, 2012).
48. Nielsen (2012) State of the media: The social media report, Available at http://slidesha.re/Zrwu6V (accessed on June 1, 2013).
49. Norris, C., McCahill, M., and Wood, D. (2004) The growth of CCTV: A global perspective on the international diffusion of video surveillance in publicly accessible space, Surveillance & Society, 2(2/3): 110-135.
50. Page, L., Brin, S., Motwani, R., and Winograd, T. (1999) The PageRank citation ranking: Bringing order to the web, Technical report. Stanford InfoLab, Stanford, CA.
51. Quercia, D., Capra, L., and Crowcroft, J. (2012) The social world of twitter: Topics, geography, and emotions, Proceedings of the Sixth International Conference on Weblogs and Social Media, Dublin, Ireland, pp. 298-305.
52. Rahm, E. and Do, H.H. (2000) Data cleaning: Problems and current approaches, IEEE Data Engineering Bulletin, 24(4): 3-13.
53. Raymond, E.S. (1999). The Cathedral and the Bazaar: Musings on Linux and Open Source by an Accidental Revolutionary, Cambridge, MA: O’Reilly.
54. Reinhardt, W., Varlemann, T., Moi, M., and Wilke, A. (2010) Modeling, obtaining and storing data from social media tools with artefact-actor-networks, Proceedings of ABIS 2010: The 18th International Workshop on Personalization and Recommendation on the Web and Beyond, Kassel, Germany.
55. Romero, D., Meeder, B., and Kleinberg, J. (2011) Differences in the mechanics of information diffusion across topics: Idioms, political hashtags, and complex contagion on twitter, Proceedings of the 20th International Conference on World Wide Web, Hyderabad, India, pp. 695-704.
56. Sahito, F., Latif, A., and Slany, W. (2011) Weaving twitter stream into linked data: A proof of concept framework, Proceedings of the Seventh International Conference on Emerging Technologies, Islamabad, Pakistan, pp. 1-6.
57. Sakaki, T., Okazaki, M., and Matsuo, Y. (2010) Earthquake shakes twitter users: Real-time event detection by social sensors, Proceedings of the 19th International Conference on World Wide Web, Raleigh, NC, pp. 851-860.
58. Sapiro, G. (2011) Images everywhere: Looking for models: Technical perspective, Communications of the ACM, 54(5): 108-108.
59. Scott, J. (2012) Social Network Analysis (3rd edn.), London, U.K.: SAGE Publications.
60. Sharma, P., Khurana, U., Shneiderman, B., Scharrenbroich, M., and Locke, J. (2011) Speeding up network layout and centrality measures for social computing goals, in Greenberg, A.M., Kennedy, W.G., and Bos, N.D. (eds.), Social Computing, Behavioral-Cultural Modeling and Prediction (Proceedings of the Sixth International Conference, SBP 2013, Washington, DC, April 2-5, 2013), Berlin, Germany: Springer-Verlag, pp. 244-251.
61. Shimojo, A., Kamada, S., Matsumoto, S., and Nakamura, M. (2010) On integrating heterogeneous lifelog services, Proceedings of the 12th International Conference on Information Integration and Web-based Applications & Services, New York, pp. 263-272.
62. Smith, A. (2011) Why Americans use social media: Social networking sites are appealing as a way to maintain contact with close ties and reconnect with old friends, Pew Research Center, Washington, DC, Available at http://bit.ly/rLCsA6 (accessed on June 3, 2013).
63. Standby Task Force (2011) The security and ethics of live mapping in repressive regimes and hostile environments, Available at http://blog.standbytaskforce.com/?p = 259 (accessed on May 27, 2013).
64. Stefanidis, A., Cotnoir, A., Croitoru, A., Crooks, A.T., Radzikowski, J., and Rice, M. (2013a) Statehood 2.0: Mapping nation-states and their satellite communities through social media content, Cartography and Geographic Information Science, 40(2): 116-129.
65. Stefanidis, A. and Nittel, S. (2004) GeoSensor Networks, Boca Raton, FL: CRC Press.
66. Stefanidis, T., Crooks, A.T., and Radzikowski, J. (2013b) Harvesting ambient geospatial information from social media feeds, GeoJournal, 78(2): 319-338.
67. Sui, D. and Goodchild, M. (2011) The convergence of GIS and social media: Challenges for GIScience, International Journal of Geographical Information Science, 25(11): 1737-1748.
68. Sun, A., Bhowmick, S.S., Nguyen, N., Tran, K., and Bai, G. (2011a) Tag-based social image retrieval: An empirical evaluation, Journal of the American Society for Information Science and Technology, 62(12): 2364-2381.
69. Sun, Y. and Han, J. (2012) Mining heterogeneous information networks: Principles and methodologies, Synthesis Lectures on Data Mining and Knowledge Discovery, 3(2): 1-159.
70. Sun, Y., Han, J., Yan, X., Yu, P.S., and Wu, T. (2011b) Pathsim: Meta path-based top-k similarity search in heterogeneous information networks, Proceedings of the 37th Very Large Data Base Conference, Seattle, WA.
71. TechAmerica (2012) Demystifying big data: A practical guide to transforming the business of government, TechAmerica Foundation, Federal Big Data Commission, Washington, DC, Available at http://bit.ly/11CpPDc (accessed on May 27, 2013).
72. TechCrunch (2012) How big is Facebook’s data? 2.5 billion pieces of content and 500+ terabytes ingested every day, Available at http://tcrn.ch/NhjAVz (accessed on October 29, 2013).
73. Tobler, W. (1970) A computer movie simulating urban growth in the Detroit region, Economic Geography, 46(2): 234-240.
74. TomTom (2011) This is what we really do with your data, Available at http://www.tomtom.com/page/facts (accessed on June 3, 2013).
75. Tonkin, T., Pfeiffer, H.D., and Tourte, G. (2012) Twitter, information sharing and the London riots? Bulletin of the American Society for Information Science and Technology, 38(2): 49-57.
76. Tumasjan, A., Sprenger, T., Sandner, P., and Welpe, I. (2011) Election forecasts with twitter: How 140 characters reflect the political landscape, Social Science Computer Review, 29(4): 402-418.
77. Twitter (2011) 200 million tweets per day, Available at http://bit.ly/laY1Jx (accessed on January 19, 2013).
78. Vatsavai, R.R. and Bhaduri, B. (2013) Geospatial analytics for big spatiotemporal data: Algorithms, applications, and challenges, NSF Workshop on Big Data and Extreme-Scale Computing, Charleston, SC.
79. Wasserman, S. and Faust, K. (1994). Social Network Analysis: Methods and Applications, New York, NY: Cambridge University Press.
80. Wayant, N., Crooks, A.T., Stefanidis, A., Croitoru, A., Radzikowski, J., Stahl, J., and Shine, J. (2012) Spatiotemporal clustering of social media feeds for activity summarization, GI Science (Seventh International Conference for Geographical Information Science), Columbus, OH.
81. West, G. (2013) Big data needs a big theory to go with it, Scientific American, May 15, Available at http://bit.ly/15wqygI (accessed on May 20, 2013).
82. Wohn, D.Y., Ellison, N.B., Khan, M.L., Fewins-Bliss, R., and Gray, R. (2013) The role of social media in shaping first-generation high school students’ college aspirations: A social capital lens, Computers & Education, 63: 424-436.
83. YouTube (2013) YouTube statistics, Available at http://bit.ly/XQlBYW (accessed on October 29, 2013).
84. Zhao, S., Zhong, L., Wickramasuriya, J., and Vasudevan, V. (2011) Human as real-time sensors of social and physical events: A case study of Twitter and sports games, Technical report TR0620-2011, Rice University and Motorola Labs, Houston, TX, arXiv: 1106.4300v1.
85. Zook, M., Graham, M., Shelton, T., and Gorman, S. (2010) Volunteered geographic information and crowdsourcing disaster relief: A case study of the Haitian earthquake, World Medical & Health Policy, 2(2): 7-33.