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Sunday, September 28 • 10:41 - 11:01
"Social Media Rumors as Improvised Public Opinions: A Semantic Network Analysis of Twitter during Korean Saber Rattling 2013"

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Background:  Understanding public opinion is one of the most challenging tasks for communication/political scholars (Herbst, 1991). In contrary to an institutionalized and top-down construction of public opinion climate, most notably via “polling” systems, recent social media sites manifest citizens’ improvised opinion sharing, thus provide alternative indicators of spontaneous, bottom-up opinion climates. In particular, “rumors” that spread in social media represent “affect-laden” collective reactions to uncertainties (Peterson & Gist, 1951) and reveal publics’ hopes, fears, anxieties and/or hostilities (Oh, Kwon, & Rao, 2010; Oh, Manish, & Rao, 2013). Social media archives help to revive textual studies of rumors by allowing an easy access to otherwise ephemeral and time-sensitive rumor stories. Among different types of rumors, hostile rumors (Knapp, 1944; Allport & Postman, 1947; Garrett, 2011) reflect negative subcultural opinion climates, of which understanding may contribute to identify structures of social relations embedded in a society and subconscious sources of intergroup hostility.

Objective: This paper aims to explore semantic structures of hostile rumors spread in social media. By leveraging Twitter messages shared during an event of South-North Korea saber rattling in 2013, the paper highlights topical clusters of hostile rumors in comparison with non-rumor messages. The goal is to conduct a preliminary analysis to show how online rumormongering mirrors socio-historical antecedents of political schism, and how such antecedents are intertwined with a collective process of uncertainty reductions under a social crisis. 

Methods: The study analyzes 2,500 unique tweets that were quota-sampled based on retweet frequencies. First, manual content analyses were conducted to distinguish hostile rumors from non-rumors. A series of z-tests was then conducted to identify which words/concepts occurred more prominently in one group than the other. Semantic network analyses (Yuan, Feng, & Danowski, 2013; Doerfel & Barnett, 1999) were conducted based on the co-occurrence matrices among these identified words/concepts. To address semantic structures in each text network, the Clauset-Newman-Moore clustering algorithm was employed.

Results: Semantic network analyses revealed four topical clusters from rumor messages, and three clusters from non-rumor messages. In the non-rumor network, the major themes were (a) government’s military strategies to the threat (Cluster 1), (b) international organizational response (Cluster 2), and (c) North Korea’s international relations (Cluster 3). In the rumor network, the emerged themes were (a) connections between particular politicians/events and North Korea (Cluster 1), (b) social and religious entities’ responses to the threat (Cluster 2), (c) Cold-war metaphors (Cluster 3), and (d) satires about Congress (Cluster 4). The comparisons between semantic structures reveal that non-rumor messages mainly discusses about institutional, formal measures to reduce uncertainty. On the other hand, hostile rumors uncover a hidden side of public mind, including skepticism about government’s readiness and intergroup polarity deeply rooted in ideology of the Cold war era (See Figures).

Conclusions: Social media data gives an unprecedented opportunity for political/communication scholars to explore improvised public opinion climates, especially under a crisis. In this preliminary study, we conducted semantic network analyses of Twitter messages during Korean threat situation 2013 to explore topical differences between rumors and non-rumor messages. Textual studies of online rumors can help in understanding informal process of collective interpretation of the situation and revealing subconscious public mind in a socio-historical context. While this study relied on manual content analysis to identify rumors, machine-learning detection of rumors will help scale up the scope of analysis.

Allport, G. W. & Postman, L. (1947). An analysis of rumor. The Public Opinion Quarterly, 10(4), 501-517

Doerfel, M. L., & Barnett, G. A. (1999). A semantic network analysis of the International Communication Association. Human Communication Research, 25(4), 589-603.

Garrett, R. K. (2011). Troubling consequences of online political rumoring. Human Communication Research, 37, 255-274.

Herbst, S. (1991). Classical democracy, polls, and public opinion: Theoretical frameworks for studying the development of public sentiment. Communication Theory, 1(3), 225-238.

Knapp, R. (1944). A psychology of rumor, The Public Opinion Quarterly 8, 22–37.

Oh, O., Kwon, K. H., & Rao, H. R. (2010). An exploration of social media in extreme events: Rumor theory and twitter during the Haiti earthquake 2010.

Oh, O., Agrawal, M., & Rao, H. R. (2013). Community intelligence and social media services: A Rumor theoretic analysis of Tweets during social crises. Management Information Systems Quarterly, 37(2), 407-426.

Peterson, W. A., & Gist, N. P. (1951). Rumor and public opinion. American Journal of Sociology, 159-167.

Yuan, E. J., Feng, M., & Danowski, J. A. (2013). “Privacy” in Semantic Networks on Chinese Social Media: The Case of Sina Weibo. Journal of Communication, 63(6), 1011-1031.



C. Chris Bang

University at Buffalo - SUNY
avatar for K. Hazel Kwon

K. Hazel Kwon

Assistant Professor, Arizona State University

Sunday September 28, 2014 10:41 - 11:01
TRS 1-149 Ted Rogers School of Management

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