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Abstract

The potential of social media to give insight into the dynamic evolution of public conversations, and into their reactive and constitutive role in political activities, has to date been underdeveloped. While topic modeling can give static insight into the structure of a conversation, and keyword volume tracking can show how engagement with a specific idea varies over time, there is need for a method of analysis able to understand how conversations about societal values evolve and react to events in the world by incorporating new ideas and relating them to existing themes. In this article, we propose a method for analyzing social media messages that formalizes the structure of public conversations and allows the sociologist to study the evolution of public discourse in a rigorous, replicable, and data-driven fashion. This approach may be useful to those studying the social construction of meaning, the origins of factionalism and internecine conflict, or boundary-setting and group-identification exercises and has potential implications.

Keywords

social media framing public conversation analysis tools visualization

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References

Agarwal

S. D.

Bennett

W. L.

Johnson

C. N.

Walker

2014. “A Model of Crowd Enabled Organization: Theory and Methods for Understanding the Role of Twitter in The Occupy Protests.” International Journal of Communication 8:646–72.

Bail

C. A.

2015. “Taming Big Data: Using App Technology to Study Organizational Behavior on Social Media.” Sociological Methods & Research 46:1–29. doi: 0049124115587825.

Becker

Naaman

Gravano

2011. “Beyond Trending Topics: Real-world Event Identification on Twitter.” ICWSM. 17-21 July 2011, Barcelona, Spain.

Benford

R. D.

Snow

D. A.

2000. “Framing Processes and Social Movements: An Overview and Assessment.” Annual Review Sociology 26:611–39.

Cogan

Andrews

2012. “Reconstruction and Analysis of Twitter Conversation Graphs.” Proceedings of the First ACM International Workshop on Hot Topics on Interdisciplinary Social Networks Research. August 12-16, 2012, Beijing, China.

Collins

A. M.

Quillian

M. R.

1969. “Retrieval Time from Semantic Memory.” Journal of Verbal Learning and Verbal Behavior 8:240–47.

DiGrazia

. “Using Internet Search Data to Produce State-level Measures: The Case of Tea Party Mobilization.” Sociological Methods & Research. Prepublished October 14, 2015. doi: 0049124115610348.

Gregori

Lenzini

Mainardi

2013. “Parallel K-clique Community Detection on Large-scale Networks.” IEEE Transactions on Parallel and Distributed Systems 24:1651–60.

Huberty

. 2013. “Multi-cycle Forecasting of Congressional Elections with Social Media.” Proceedings of the 2nd Workshop on Politics, Elections and Data. Oct 27 - Nov 1, 2013, San Francisco, CA.

10.

Marres

Gerlitz

2016. “Interface Methods: Renegotiating Relations between Digital Research, STS and Sociology.” The Sociological Review 64:21–46.

11.

Mayer

R. E

. 1995. “The Search for Insight: Grappling with Gestalt Psychology’s Unanswered Questions.” P. 3–32 in The Nature of Insight, edited by Sternberg

R. J.

Davidson

J. E.

12.

McCormick

T. H.

Lee

Cesare

Shojaie

Spiro

E. S.

“Using Twitter for Demographic and Social Science Research: Tools for Data Collection and Processing.” Sociological Methods & Research. Prepublished October 9, 2015. doi: 0049124115605339.

13.

Morales

A. J.

Borondo

Losada

J. C.

Benito

R. M.

2015. “Measuring Political Polarization: Twitter Shows the Two Sides of Venezuela.” Chaos: An Interdisciplinary Journal of Nonlinear Science 25:033114.1-9.

14.

Palla

Barabási

Vicsek

2007. “Quantifying Social Group Evolution.” Nature 446:664–667.

15.

Porter

M. A.

Onnela

J.-P.

Mucha

P. J.

2009. “Communities in Networks.” American Mathematical Society 56:0–26.

16.

Ritter

Cherry

Dolan

2010. “Unsupervised Modeling of Twitter Conversations.” Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL, June 1–6, 2010, Los Angeles CA. pp. 172–80.

17.

Schilling

M. A.

2005. “A “Small-world” Network Model of Cognitive Insight.” Creativity Research Journal 17:131–54.

18.

Smith

Rainie

2014. “Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters.” Pew Research Center 20:1–56.

19.

Tounaka

. “How to Analyze 50 Billion Records in Less than a Second without Hadoop or Big Iron.” Computing Resources in Boston and Beyond. Massachusetts Institute of Technology, Cambridge, MA. August 2, 2013.

20.

Tulving

. 1972. “Episodic and Semantic Memory.” P.381–403 in Organization of Memory, edited by Tulving

and Donaldson

Academic Press, New York.

21.

Tumasjan

Sprenger

Sandner

Welpe

2010. “Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment.” ICWSM. May 23-26 2010, Washington DC.

22.

Woods

W. A

. 1975. “What’s in a Link: Foundations for Semantic Networks.” Representation and understanding: Studies in cognitive science 1975:35–82.

23.

Zachary

W. W.

1977. “An Information Flow Model for Conflict and Fission in Small Groups.” Journal of Anthropological Research 33:452–73.

24.

Zubiaga

Spina

Fresno

Martínez

2011. “Classifying Trending Topics: A Typology of Conversation Triggers on Twitter.” Proceedings of the 20th ACM international conference on Information and knowledge management. October 24-28, 2011, Glasgow, UK. pp. 2461–4.

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