An Empirical Study of Social Media Exchanges about a Controversial Topic: Confirmation Bias and Participant Characteristics
Keywords:
confirmation bias, online sentiment, behavioral characteristics, natural language processingAbstract
There has been a significant amount of research into social media commentary influences on human behaviors, ranging from its role in affecting political elections to predicting corporate revenues; however, to this point, the factors and influences of social media have not been completely explained and it is not entirely clear whether social media influences or simply confirms preconceptions. Moreover, with sentiment analysis, much of the research has relied on human expert interpretation of the sentiments and semantics written in various social media. It has also tended to be interpretive rather than predictive in nature. In our study, we wanted to know if social media conversations were reflective or influencers of human behavior. Using a social media mining technology we were able to determine sentiments, sentiment intensity, and the characteristics of participants. We found strong evidence of confirmation bias, but that bias was influenced by personal characteristics, and in some cases, whether the sentiments were strongly positive or strongly negative.
References
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Terry, D.J. & Hogg, M.A. (1996). Group norms and the attitude-behavior relationship: A role for group identification, Personality and Social Psychology, 22(1), 776–793.
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Argamon, S., Bloom, K., Esuli, A., & Sebastiani, F. (2009). Automatically determining attitude type and force for sentiment analysis. In Z. Vetulani and H. Uszkoreit (Eds.), LTC/LNAI, pp. 218-231. Springer-Verlag Berlin Heidelberg.
Baeza-Yates, R. & Ribeiro-Neto, B. (1999). Modern information retrieval. NY Pearson/Addison Wesley.
Baird, C. H., & Parasnis, G. (2011). From social media to social customer relationship management. Strategy & Leadership, 39(5), 30-37.
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(1), 191–215.
Bates, T. C. (2015). The glass is half full and half empty: A population-representative twin study testing if optimism and pessimism are distinct systems. The Journal of Positive Psychology, 10(6), 533–542.
Bayrami, M., Abad, T. H. N., Ghoradel, J. A., Daneshfar, S., Heshmati, R., & Moslemifar, M. (2012). The role of positive and negative affectivity, optimism, pessimism, and information processing styles in student psychological adjustment. Procedia - Social and Behavioral Sciences, 46(1), 306-310.
Beck, L., & Ajzen, I. (1991). Predicting dishonest actions using the theory of planned behavior, Journal of Research in Personality, 25(1), 285–301.
Calvin, W. H. (1996). How brains think, NY, Harper Collins.
Carpenter, B. (2004). Phrasal queries with LingPipe and Lucene. In Proceedings of the 13th Meeting of the Text Retrieval Conference (TREC). Gaithersburg, Maryland, USA.
Chen, X., van der Lans, R., & Phan T. Q. (2017). Uncovering the importance of relationship characteristics in social networks: Implications for seeding strategies. Journal of Marketing Research, 54(2), 187-201.
Clore, G. L., & Huntsinger, J. R. (2007). How emotions inform judgment and regulate thought. Trends in Cognitive Sciences, 11(9), 393-399.
Danescu-Niculescu-Mizil, C; Gamon, M.; & Dumais, S. (2011). Mark my words! Linguistic style accommodation in social media. Proceedings of the 20th ACM International Conference on the World Wide Web, April, 745-754, Hyderabad, India.
Darley, J. M., & Gross, P. H. (1983). A hypothesis-confirming bias in labeling effects. Journal of Personality and Social Psychology, 44(1), 20-33.
Evans. J., Over, D. E. , & Manktelow, K. I. (1993). Reasoning, decision making and rationality. Cognition, 49 (1/2), 165-187.
Forbes, L. P., & Vespoli, E. M. (2013). Does social media influence consumer buying behavior? An investigation of recommendations and purchases. Journal of Business and Economics Research, 11(2), 107-112.
Gosling, S. D., Augustine, A. A., Vazire, S., Holtzman, N., & Gaddis, S. (2011). Manifestations of personality in online social networks: Self-reported Facebook-related behaviors and observable profile information. Cyberpsychology, Behavior, and Social Networking, 14(9), 483-488.
Greenwald, A. G., McGhee, D. E., & Schwartz, J. L.K. (1998). Measuring individual differences in implicit cognition: The implicit association test. Journal of Personality and Social Psychology, 74(6), 1464–1480.
Hanna, R. Rohm, A., & Crittenden, V. L. (2011). We are all connected: The power of the social ecosystem. Business Horizons, 54(3), 265–273.
Hayes, J., & Allinson, C. W. (1998). Cognitive style and the theory and practice of individual and collective learning in organizations. Human Relations, 51(1), 847–871.
Hollingsworth, L. C. (2015). Optimism and pessimism in guessing future outcomes: A study of conflict and security expectancies. Proceedings from the First International Conference on Security and Privacy Issues with Social Media, ISPI’2015 November, 41-49, Lyon, France.
Kahneman, D. & Tversky, A. (1973). On the psychology of prediction. Psychological Review, 80(4), 237–251. doi:10.1037/h0034747.
Kaplan, A. M., & Haenlien, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53, 59-68.
Landauer, T. K., Foltz, P. W., & Laham, D. (2009). An introduction to latent semantic analysis. Discourse Processes, 25, 259-284.
Larsen, R. J., Diener, E. & Emmons, R. A. (1986). Affect intensity and reactions to daily life events. Journal of Personality and Social Psychology, 51(4), 803-814.
Lee, H., & Oh, H. J. (2017). Normative mechanism of rumor dissemination on Twitter. Cyberpsychology, Behavior, and Social Networking, 20(3), 164-171.
Murphy, R. O., Ackermann, K. A., & Handgraaf, M. J. J. (2011). Measuring social value orientation. Judgment and Decision Making, 6(8), 771-781.
Myers, J. L., Well, A. D., & Lorch, R. F. (2010). Research design and statistical analysis. NY: Rourtledge.
Pang, B. & Lee, L. (2004). A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts. Proceedings of the Association for Computational Linguistics (ACL), July, 271–278, Barcelona, Spain.
Reisberg, D. (1996). Cognition: Exploring the science of mind. NY: WW Norton & Co.
Romero, D. M., Galuba, W., Asur, S., & Huberman, B. A. (2011). Influence and passivity in social media. In: Gunopulos D., Hofmann T., Malerba D., Vazirgiannis M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2011. Lecture Notes in Computer Science, vol. 6913. Springer, Berlin, Heidelberg.
Salanick, G.J. & Pfeffer, J. (1978). A social information processing approach to job attitudes and task design, Administrative Science Quarterly, 23(1), 224–253.
Scheier, M.F., Carver, C.S., & Bridges, M.W. (2001). Optimism, pessimism, and psychological well-being. In E.C. Chang (Ed.), Optimism and pessimism: Implications for theory, research, and practice (pp. 189-216). Washington, DC: American Psychological Association.
Sharma, R., Gupta, M. Agarwal, A, & Bhattacharyya, P. (2015). Adjective intensity and sentiment analysis. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 2520–2526, Lisbon, Portugal, 17-21 September 2015, The Association for Computational Linguistics.
Splunk Inc. (2017). Using SPLUNK user behavior analytics: Automating early breach detection and continuous threat monitoring. Tech Brief White Paper. Retrieved from the WWW (7/7/17): https://www.splunk.com/pdfs/technical-briefs/using-splunk-user-behavior-analytics.pdf
Stark, E., Baldwin, A. S., Hertel, A. W., & Rothman, A. J. (2017). The role of rational and experiential processing in influencing the framing effect. The Journal of Social Psychology, 157(3), 308-321.
Sternberg, R. J. (1980). Sketch of a componential sub-theory of human intelligence. Behavioral and Brain Sciences, 3(1), 568–585.
Sternberg, R. J. (1997). Thinking styles. NY: Cambridge University Press.
Suzuki, S. (1997). Cultural transmission in international organizations: impact of interpersonal communication, patterns in intergroup contexts, Human Communication Research, 24(1), 147–180.
Swann, W. B. (1997). The trouble with change: Self-verification and allegiance to the self. Psychological Science, 8(3), 177-180.
Terry, D.J. & Hogg, M.A. (1996). Group norms and the attitude-behavior relationship: A role for group identification, Personality and Social Psychology, 22(1), 776–793.
Terry, D.J., Hogg, M.A. & White, K.M. (1999). The theory of planned behavior: Self-identity, social identity and group norms, British Journal of Psychological Society, 38(1), 225–244.
van den Bos, K., Maas, M., Waldring, I. E. U., Waldring, G. R. S. (2003). Toward understanding the psychology of reactions to perceived fairness: The role of affect intensity. Social Justice Research, 16(2), 151-168.
Workman, M., Kahnweiler, W., & Bommer, W. (2003). The effects of cognitive style and media richness on commitment to telework and virtual teams, Journal of Vocational Behavior, 63(1), 199-219.
Workman, M., Phelps, D., & Hare, R. C. (2013). A study of performative hactivist subcultures and threats to businesses. Information Security Journal, 22(4), 187-200.
Zadeh, A. (2015). Micro-opinion sentiment intensity analysis and summarization in online videos. Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, Seattle, Washington, Nov 9-13, 587-591.
Zafarani, R., Ali Abbasi, M., & Liu, H. (2014). Social Media Mining. Cambridge University Press.
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