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The Usage Characteristics of Twitter, and Their Relationship with Gender, Age, and Brand Preferences
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 Title & Authors
The Usage Characteristics of Twitter, and Their Relationship with Gender, Age, and Brand Preferences
Ahn, Hyung Jun;
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With the increasing popularity of social network services (SNSs), there have been many attempts to analyze the users of SNSs. By doing so, the characteristics and preferences of the users can be understood, which can help companies provide personalized information and services that they need or are relevant for them. This study aimed to analyze the usage behavior of Korean Twitter users from various perspectives to deepen the understanding of it. For this research goal, an online survey was conducted for the users of Twitter and the data about their actual usage were collected using the open API of Twitter. Factor analysis of the data revealed five factors that explain about 69.3% of the usage variables. It was also investigated how the factors are related to gender, age, and brand preferences. The results showed that the usage behavior of Twitter is largely affected by age (p<0.001), and also by gender through an interaction effect (p<0.05). Also, the factors showed significant statistical correlations with the brand preferences of the users.
SNS;Twitter;brand preference;user characteristics;
 Cited by
P. R. Berthon, L. F. Pitt, K. Plangger, and D. Shapiro, "Marketing meets Web 2.0, social media, and creative consumers: Implications for international marketing strategy," Business Horizons, vol. 55, no. 3, pp. 261- 271, May 2012. crossref(new window)

L. R. Men and W.-H. S. Tsai, "How companies cultivate relationships with publics on social network sites: Evidence from China and the United States," Public Relations Review, vol. 38, no. 5, pp. 723-730, Dec. 2012. crossref(new window)

H. A. Schwartz, J. C. Eichstaedt, M. L. Kern, L. Dziurzynski, S. M. Ramones, M. Agrawal, A. Shah, M. Kosinski, D. Stillwell, M. E. P. Seligman, and L. H. Ungar, "Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach," PLoS ONE, vol. 8, no. 9, p. e73791, Sep. 2013. crossref(new window)

C. Sumner, A. Byers, R. Boochever, and G. J. Park, "Predicting Dark Triad Personality Traits from Twitter Usage and a Linguistic Analysis of Tweets," in 2012 11th International Conference on Machine Learning and Applications (ICMLA), 2012, vol. 2, pp. 386-393.

M. Pennacchiotti and A.-M. Popescu, "A Machine Learning Approach to Twitter User Classification.," ICWSM, vol. 11, pp. 281-288, 2011.

M. M. Mostafa, "More than words: Social networks' text mining for consumer brand sentiments," Expert Systems with Applications, vol. 40, no. 10, pp. 4241-4251, Aug. 2013. crossref(new window)

B. Pang and L. Lee, "Opinion mining and sentiment analysis," Foundations and Trends in Information Retrieval, vol. 2, no. 1-2, pp. 1-135, 2008. crossref(new window)

M. Kandias, V. Stavrou, N. Bozovic, L. Mitrou, and D. Gritzalis, "Can We Trust This User? Predicting Insider's Attitude via YouTube Usage Profiling," 2013, pp. 347- 354.

J. Golbeck, C. Robles, M. Edmondson, and K. Turner, "Predicting Personality from Twitter," in 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011, pp. 149-156.

D. Quercia, R. Lambiotte, D. Stillwell, M. Kosinski, and J. Crowcroft, "The personality of popular facebook users," in Proceedings of the ACM 2012 conference on computer supported cooperative work, 2012, pp. 955-964.

Y. Shen and R. Jin, "Learning personal + social latent factor model for social recommendation," 2012, p. 1303.

C. Biancalana, F. Gasparetti, A. Micarelli, and G. Sansonetti, "An approach to social recommendation for context-aware mobile services," ACM Transactions on Intelligent Systems and Technology, vol. 4, no. 1, pp. 1-31, Jan. 2013.

H. Ma, "An experimental study on implicit social recommendation," in Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, 2013, pp. 73-82.

H. J. Ahn, "A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem," Information Sciences, vol. 178, no. 1, pp. 37-51, 2008. crossref(new window)

P. G. Kelley and J. Cranshaw, "Conducting Research on Twitter: A Call for Guidelines and Metrics," presented at the CSCW Measuring Networked Social Privacy Workshop 2013, 2013.

S. Wu, J. M. Hofman, W. A. Mason, and D. J. Watts, "Who says what to whom on twitter," in Proceedings of the 20th international conference on World wide web, 2011, pp. 705-714.

D. H. McKnight, V. Choudhury, and C. Kacmar, "Developing and validating trust measures for e-commerce: An integrative typology," Information systems research, vol. 13, no. 3, pp. 334-359, 2002. crossref(new window)

M. Ahuja, B. Gupta, and P. Raman, "An empirical investigation of online consumer purchasing behavior," Communications of the ACM, vol. 46, no. 12, pp. 145- 151, 2003. crossref(new window)

I. Park and K. Min, "Making a List of Korean Emotion Terms and Exploring Dimensions Underlying Them," Korean Journal of Social and Personal Psychology, vol. 19, no. 1, pp. 109-129, 2005.

D. Lee, J. Yeon, I. Hwang, and S. Lee, "KKMA : A Tool for Utilizing Sejong Corpus based on Relational Database," KIISE Transactions on Computing Practices (KTCP), vol. 16, no. 11, pp. 1046-1050, 2010.

A. Smith and J. Brenner, Twitter use 2012. "Pew Internet & American Life Project Washington", DC, 2012.