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10 - High-dimensional network analytics: mapping topic networks in Twitter data during the Arab Spring

from Part III - Big data over social networks

Published online by Cambridge University Press:  18 December 2015

Kathleen M. Carley
Affiliation:
Carnegie Mellon University, USA
Wei Wei
Affiliation:
Carnegie Mellon University, USA
Kenneth Joseph
Affiliation:
Carnegie Mellon University, USA
Shuguang Cui
Affiliation:
Texas A & M University
Alfred O. Hero, III
Affiliation:
University of Michigan, Ann Arbor
Zhi-Quan Luo
Affiliation:
University of Minnesota
José M. F. Moura
Affiliation:
Carnegie Mellon University, Pennsylvania
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Summary

Social change is often reflected in social talk. The capability to track who is talking about what, where, and with whom, as well as changes in the topics of concern by region, may provide insight into emerging crises and guidance on how to mitigate other crises. Network analytics have been proven successful at analyzing such data. However, such talk is increasingly carried out in social media at dramatically higher volumes than previously analyzed. A high-dimensional network approach for assessing this talk and identifying not just what is being talked about, but the locality and change in that talk and the associated groups, as well as their structure, is presented. This approach is applied to data captured with respect to the Arab Spring. The results provide insight into the co-evolution of topics and groups across the region during this period of dramatic social change.

Introduction

The wave of revolutions in the Arab world, commonly referred to as the Arab Spring, was a period of major social change. As protests and demonstrations broke out in country after country, questions arose as to what mechanisms supported the diffusion of ideas and actions, promoting or inhibiting violence, and thus enabling successful regime change. New communication technologies and social media were touted as critical to these revolutions. The belief in the power of the Internet was such that in some cases embattled leaders turned off access, e.g. Egypt and Syria [1]. In all cases, as these countries moved from a pre-revolutionary state to a revolutionary state the “talk” changed. Where Wikileaks and sports were topics of interest prior to the onset of the protests, discussion moved towards issues such as liberation, government overthrow, and insurgency once the revolution began. At the same time, groups formed and disbanded, and alliances among diverse actors altered the way they went about their activities.

Throughout the Arab Spring, discussion of the transition and issues potentially related to the transition, such as economic conditions, injustices, and civil rights were discussed in the traditional and social media. Various actors, purportedly, used these media to engage discussions to foment or counter rebellion. These media contain information about the set of actors, the set of topics, and the connections among actors and topics. Herein we use the term “topic” to refer to a general idea or issue around which a set of diverse words and sentiments coalesce.

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Publisher: Cambridge University Press
Print publication year: 2016

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References

[1] “Internet ‘cut off across Syria,”’ BBC News. [Online]. Available: http://www.bbc.co.uk/news/technology-20546302. [Accessed: 21-Apr-2014].
[2] K. M., Carley, “Dynamic network analysis,” in Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers, 2003, pp. 133–145.Google Scholar
[3] W. R., Louis and R., Owen, A Revolutionary Year: the Middle East in 1958, IB Tauris, 2002.Google Scholar
[4] K., Selvik and S., Stenslie, Stability and Change in the Modern Middle East, IB Tauris, 2011.Google Scholar
[5] M. N., Barnett and E., Goldberg, “Dialogues in Arab politics,” Comp. Polit. Stud., vol. 33, no. 2, pp. 271–272, 2000.Google Scholar
[6] P. N., Howard and M. R., Parks, “Social media and political change: capacity, constraint, and consequence,” J. Commun., vol. 62, no. 2, pp. 359–362, 2012.Google Scholar
[7] G., Lotan, E., Graeff, M., Ananny, et al., “The revolutions were tweeted: information flows during the 2011 Tunisian and Egyptian revolutions,” Int. J. Commun., vol. 5, pp. 1375–1405, 2011.Google Scholar
[8] S., Meraz and Z., Papacharissi, “Networked gatekeeping and networked framing on #Egypt,” Int. J. Press., vol. 18, no. 2, pp. 138–166, April 2013.Google Scholar
[9] A., Bruns, T., Highfield, and J., Burgess, “The Arab Spring and socialmedia: audiences English and Arabic Twitter users and their networks,” Am. Behav. Sci., vol. 57, no. 7, pp. 871–898, 2013.Google Scholar
[10] K., Starbird and L., Palen, “(How) will the revolution be retweeted?: information diffusion and the 2011 Egyptian uprising,” in Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, 2012, pp. 7–16.Google Scholar
[11] Y., Takhteyev, A., Gruzd, and B., Wellman, “Geography of Twitter networks,” Soc. Netw., vol. 34, no. 1, pp. 73–81, Jan. 2012.Google Scholar
[12] E., Gilbert, “Predicting tie strength in a new medium,” in Proceedings of the ACM 2012 conference on Computer Supported CooperativeWork, NewYork, NY, USA, 2012, pp. 1047–1056.Google Scholar
[13] H.K, wak, C., Lee, H., Park, and S., Moon, “What is Twitter, a social network or a newsmedia?,” in Proceedings of the 19th International Conference on World Wide Web, New York, NY, USA, 2010, pp. 591–600.Google Scholar
[14] K., Leetaru, “Culturomics 2.0: Forecasting large-scale human behavior using global news media tone in time and space,” First Monday, vol. 16, no. 9, 2011.Google Scholar
[15] K., Joseph, K. M., Carley, D., Filonuk, G. P., Morgan, and J., Pfeffer, “Arab Spring: from newspaper data to forecasting,” Soc. Netw. Anal. Min., vol. 4, no. 1, pp. 1–17, Dec. 2014.Google Scholar
[16] Kathleen M., Carley, Jürgen, Pfeffer, Fred, Morstatter, and Huan, Liu, “Embassies burning: toward a near real time assessment of social media using geo-temporal dynamic network analytics, social network analysis and mining,” in the press, 2014.
[17] D. M., Blei, A. Y., Ng, and M. I., Jordan, “Latent dirichlet allocation,” J. Mach. Learn. Res., vol. 3, pp. 993–1022, Mar. 2003.Google Scholar
[18] S. C., Deerwester, S. T., Dumais, T.K., Landauer, G.W., Furnas, and R. A., Harshman, “Indexing by latent semantic analysis,” JASIS, vol. 41, no. 6, pp. 391–407, 1990.Google Scholar
[19] F., Morstatter, J., Pfeffer, H., Liu, and K. M., Carley, “Is the sample good enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose,” in The 7th International Conference on Weblogs and Social Media (ICWSM-13), Boston, MA. Retrieved from http://www.public. asu. edu/˜fmorstat/paperpdfs/icwsm2013. pdf, 2013.
[20] K., Joseph, P. M., Landwehr, and K. M., Carley, “Two 1%s don't make a whole: comparing simultaneous samples from Twitter's Streaming API,” in Social Computing, Behavioral- Cultural Modeling and Prediction, W. G., Kennedy, N., Agarwal, and S. J., Yang, Eds., Springer International Publishing, 2014, pp. 75–83.Google Scholar
[21] A., Ritter, S., Clark, and O., Etzioni, “Named entity recognition in tweets: an experimental study,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2011, pp. 1524–1534.
[22] J., Eisenstein, B. O’, Connor, N. A., Smith, and E. P., Xing, “A latent variable model for geographic lexical variation,” in Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, Stroudsburg, PA, USA, 2010, pp. 1277–1287.Google Scholar
[23] K., Taghva, R., Elkoury, and J. Coombs, J.Arabic stemming without a root dictionary,” Information Science Research Institute, University of Nevada, Las Vegas, USA. 2005.Google Scholar
[24] S., Bird, “NLTK: the natural language toolkit,” in Proceedings of the COLING/ACL on Interactive Presentation Sessions, Association for Computational Linguistics, 2006.
[25] Z., Tufekci, “Big questions for social media big data: representativeness, validity and other methodological pitfalls,” in ICWSM ’14: Proceedings of the 8th International AAAI Conference on Weblogs and Social Media, 2014.
[26] L., Hong and B. D., Davison, “Empirical study of topic modeling in twitter,” in Proceedings of the First Workshop on Social Media Analytics, 2010, pp. 80–88.
[27] D., Ramage, S., Dumais, and D., Liebling, “Characterizing microblogs with topic models,” in ICWSM, 2010.
[28] L., Hong, and B.D., Davison, “Empirical study of topic modeling in twitter,” in Proceedings of the First Workshop on Social Media Analytics, ACM, 2010, pp. 80–88.Google Scholar
[29] H. M., Wallach, I., Murray, R., Salakhutdinov, and D., Mimno, “Evaluation methods for topic models,” in Proceedings of the 26th Annual International Conference on Machine Learning, New York, NY, USA, 2009, pp. 1105–1112.Google Scholar
[30] D., Boyd and K., Crawford, “Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon,” Inf. Commun. Soc., vol. 15, no. 5, pp. 662–679, 2012.Google Scholar
[31] V. D., Blondel, J.-L., Guillaume, R., Lambiotte, and E., Lefebvre, “Fast unfolding of communities in large networks,” J. Stat. Mech. Theory Exp., vol. 2008, no. 10, p. P10008, 2008.Google Scholar
[32] M. E. J., Newman, “Modularity and community structure in networks,” Proc. Natl. Acad. Sci., vol. 103, no. 23, pp. 8577–8582, Jun. 2006.Google Scholar

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