Embassies burning: toward a near-real-time assessment of social media using geo-temporal dynamic network analytics

  • Kathleen M. Carley
  • Jürgen Pfeffer
  • Fred Morstatter
  • Huan Liu
Original Article

Abstract

Effective crisis response requires rapid assessment of a situation in order to form actionable plans. Social media and traditional media are critical to this assessment. This paper describes a rapid ethnographic approach for extracting information from Twitter and news media and then assessing that information using dynamic network analysis techniques. Text mining high-dimensional network analytics and visualization are combined to provide an integrated approach to assessing large dynamic networks. This approach was used as the Benghazi consulate and the Egyptian embassy were attacked in 2012. This near-real-time assessment was set against a backdrop of ongoing data collection associated with the Arab Spring countries. This ongoing collection provided a baseline for Libya and Egypt against which the new data could be assessed. Herein, the outcome of that near-real-time assessment, the tools used, the lessons learned, and the results discovered are described. The same approach was used in other crisis events including SuperStorm Sandy, the Kenyan elections, from which examples are also drawn. We find that to be effective such analytics require the use of multiple media, deep dives into specific secondary issues, and a high-level assessment of not just who is doing what, but who is providing what information. Finally, we show the criticality of baseline data for interpreting the behavior during a crisis.

Keywords

Social media Dynamic network analysis Social change Social geography Social network analysis Big data 

References

  1. Arcenaux N, Weiss AS (2010) Seems stupid until you try it: press coverage of Twitter, 2006–9. New Media Soc 12:1262–1279. http://nms.sagepub.com/content/12/8/1262.full.pdf+html
  2. Campbell DG (2011) Egypt unshackled: using social media to @#:) the system. Cambria Books, AmherstGoogle Scholar
  3. Carley KM (2003) Dynamic network analysis. In: Breiger R, Carley K, Pattison P (eds) Dynamic social network modeling and analysis: workshop summary and papers. Committee on Human Factors, National Research Council, National Research Council, Session 2, p 133–145Google Scholar
  4. Carley KM (2013) ORA: a toolkit for dynamic network analysis and visualization. Encyclopedia of social network analysis and mining. Springer, BerlinGoogle Scholar
  5. Carley KM, Pfeffer J (2012) Dynamic network analysis (DNA) and ORA. In: Schmorrow DD, Nicholson DM (eds) Advances in design for cross-cultural activities part I. CRC Press, pp 265–274 Google Scholar
  6. Carley KM, Columbus D, Azoulay A (2012) AutoMap user’s guide 2012, Carnegie Mellon University, School of Computer Science, Institute for Software Research, Technical Report, CMU-ISR-12-106Google Scholar
  7. Carley KM, Bigrigg MW, Diallo B (2012b) Data-to-model: a mixed initiative approach for rapid ethnographic assessment. Comput Math Organ Theory 18(3):300–327. doi:10.1007/s10588-012-9125-y CrossRefGoogle Scholar
  8. Carley KM, Reminga J, Storrick J, Columbus D (2013a) ORA user’s guide 2013. Carnegie Mellon University, School of Computer Science, Institute for Software Research, Pittsburgh, PA, Technical Report CMUISR-13-108Google Scholar
  9. Carley KM, Columbus D, Landwehr P (2013b) AutoMap user’s guide 2013. Carnegie Mellon University, School of Computer Science, Institute for Software Research, Technical Report, CMU-ISR-13-105Google Scholar
  10. Freeman LC (1979) Centrality in social networks: conceptual clarification. Soc Netw 1(3):215–239CrossRefGoogle Scholar
  11. Goolsby R (2009) Lifting elephants: Twitter and blogging in global perspective. Social computing and behavioral modeling. Springer, Berlin, pp 1–6Google Scholar
  12. Goolsby R (2010) Social media as crisis platform: the future of community maps/crisis maps. ACM Trans Intell Syst Technol 1(1):1–11CrossRefGoogle Scholar
  13. Harvard Humanitarian Initiative (2011) Disaster relief 2.0: the future of information sharing in humanitarian emergencies. Harvard Humanitarian Initiative, UN Office for the Coordination of Humanitarian Affairs, United Nations Foundation. http://www.unfoundation.org/news-and-media/publications-and-speeches/disaster-relief-2-report.html
  14. Kirkpatrick DD (2012) Libya attack brings challenges for U.S. The New York Times, 12 Sept 2012Google Scholar
  15. Kumar S, Barbier G, Abbasi MA, Liu H (2011) Relief, TweetTracker: an analysis tool for humanitarian and disaster relief. In: Fifth International AAAI Conference on Weblogs and Social Media, ICWSM, 2011, pp. 661–662Google Scholar
  16. Kumar S, Morstatter F, Huan L (2013) Twitter data analytics. Springer, BerlinGoogle Scholar
  17. Landwehr P, Carley KM (2014) Social media in disaster relief. In: Chu WW (ed) Data mining and knowledge discovery for big data, vol 1. Springer, Berlin, pp 225–257 (forthcoming)CrossRefGoogle Scholar
  18. Lewis TG (2011) Network science: theory and applications. Wiley, New YorkGoogle Scholar
  19. L.S. (2011) What’s in a tweet. Retrieved 9 25, 2012, from The economist - digital verbosity: http://www.economist.com/node/21531066. 29 Sept 2011
  20. Madrigal AC (2013) It wasn’t Sunil Tripathi: the anatomy of a misinformation disaster. The Atlantic. http://www.theatlantic.com/technology/archive/2013/04/it-wasnt-sunil-tripathi-the-anatomy-of-a-misinformation-disaster/275155/
  21. Martin E (2013) Reflections on the recent Boston crisis. Reddit Blog. http://blog.reddit.com/2013/04/reflections-on-recent-boston-crisis.html
  22. Morstatter F, Pfeffer J, Liu H, Carley KM (2013) Is the sample good enough? comparing data from twitter’s streaming api with twitter’s firehose. In: International AAAI Conference on Weblogs and Social Media (ICWSM), 2013Google Scholar
  23. Munro R, Manning CD (2012) Short message communications: users, topics, and in-language processing. In: Proceedings of the 2nd ACM Symposium on Computing. Dev. ACM, Atlanta, Georgia, pp 1–10. http://delivery.acm.org/10.1145/2170000/2160607/a4-munro.pdf?ip=128.2.161.39&id=2160607&acc=ACTIVE%20SERVICE&key=C2716FEBFA981EF1D8ED4F16102DA82BADDD11EBB600FF97&CFID=253750387&CFTOKEN=15558566&__acm__=1381759522_7937ccf38c0da291923b6ec170b7c9c2
  24. Pfeffer J, Carley KM (2012a) Rapid modeling and analyzing networks extracted from pre-structured news articles. Comput Math Organ Theory 18(3):280–299CrossRefGoogle Scholar
  25. Pfeffer J, Carley KM (2012b) Social networks, social media, social change. In: Schmorrow DD, Nicholson DM (eds) Advances in design for cross-cultural activities part II. CRC Press, Boca Raton, pp 273–282Google Scholar
  26. Shklovski I, Palen L, Sutton J (2008) Finding community through information and communication technology in disaster response. In Proceedings ACM 2008 Conference on Computer Supported Cooperative Work (CSCW), San Diego, CA, USA, pp 127–136. http://dl.acm.org/citation.cfm?id=1460563.1460584
  27. Sinnappan S, Farrell C, Stewart E (2010) Priceless tweets! A study on Twitter messages posted during crisis: black saturday. In: Australasian conference on information systemsGoogle Scholar
  28. Thelwall Michael Arijan (2004) Link analysis: an information science approach. Elsevier, UKGoogle Scholar
  29. Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, Cambridge, p 1994CrossRefGoogle Scholar
  30. Weinstein A (2013) Everybody named the wrong Boston suspects last night and promptly forgot. Gawker. http://gawker.com/5995058/how-the-fuck-did-everybody-focus-on-two-named-boston-suspects-last-night-then-forget-about-them-this-morning

Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  • Kathleen M. Carley
    • 1
    • 2
  • Jürgen Pfeffer
    • 1
  • Fred Morstatter
    • 3
  • Huan Liu
    • 3
  1. 1.Institute for Software ResearchCarnegie Mellon UniversityPittsburghUSA
  2. 2.Netanomics Inc.PittsburghUSA
  3. 3.Computer Science and EngineeringArizona State UniversityTempeUSA

Personalised recommendations