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Annals of Computer Science and Information Systems, Volume 15

Proceedings of the 2018 Federated Conference on Computer Science and Information Systems

Do Actions Speak Louder Than Words? Predicting Influence in Twitter using Language and Action Features

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DOI: http://dx.doi.org/10.15439/2018F304

Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 457–464 ()

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Abstract. This work explores the connection between language, personality, and influence in a social media network. It clusters users based on two types of features: account activity features and stream content (word) features and compares the usefulness of these different types of features in categorizing users according to their influence and leadership potential in the network. Results of clustering using different sets of features are examined to answer questions about distribution of Twitter users from the influence perspective. These results are compared against distributions of personality traits obtained from previous research on personality types and established assessment tools that measure leadership aptitude and style. Experiments with different clustering algorithms are described and their performance and cluster outputs are reported.

References

  1. “DISC Instrument Validation Manual,” http://www.coachannette.com/pdfs/DISCValidityManual.pdf, 2004.
  2. “DISC Classic®Validation Report,” https://www.inscape-exchange.com/downloads/marketing_support/researchreports/DiSCClassicValidationResearchReport.pdf.
  3. G. Beamish, “How chief executives learn and what behaviour factors distinguish them from other people,” in Industrial and Commercial Training. Emerald Group Publishing Limited, 2005, vol. 37, pp. 138–144.
  4. P. Blignaut and A. Naude, “The influence of temperament style on a student’s choice of and performance in a computer programming course,” Comput. Hum. Behav., vol. 24, no. 3, pp. 1010–1020, May 2008. [Online]. Available: http://dx.doi.org/10.1016/j.chb.2007.03.005
  5. S. Adali, F. Sisenda, and M. M. Ismail, “Actions speak as loud as words: predicting relationships from social behavior data,” in Proceedings of the 21st international conference on World Wide Web, ser. WWW ’12, New York, NY, USA, 2012, pp. 689–698. [Online]. Available: http://dx.doi.org/10.1145/2187836.2187930
  6. E. Gilbert and K. Karahalios, “Predicting tie strength with social media,” in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ser. CHI ’09. New York, NY, USA: ACM, 2009, pp. 211–220. [Online]. Available: http://doi.acm.org/10.1145/1518701.1518736
  7. J. Golbeck, C. Robles, M. Edmondson, and K. Turner, “Predicting Personality from Twitter,” in Privacy, security, risk and trust (passat), 2011 ieee third international conference on and 2011 ieee third international conference on social computing (socialcom). IEEE, 2011, pp. 149–156. [Online]. Available: http://dx.doi.org/10.1109/passat/socialcom.2011.33
  8. 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 ICMLA (2). IEEE, 2012, pp. 386–393. [Online]. Available: http://dblp.uni-trier.de/db/conf/icmla/icmla2012-2.html#SumnerBBP12
  9. J. Duck, “Making the connection: Improving virtual team performance through behavioral assessment profiling and behavioral cues,” in Developments in Business Simulation and Experiential Learning, vol. 33, 2006, pp. 358–359.
  10. R. Li, S. Wang, H. Deng, R. Wang, and K. C.-C. Chang, “Towards social user profiling: unified and discriminative influence model for inferring home locations.” in KDD, Q. Yang, D. Agarwal, and J. Pei, Eds. ACM, 2012, pp. 1023–1031. [Online]. Available: http://dblp.uni-trier.de/db/conf/kdd/kdd2012.html#LiWDWC12
  11. A. Altman and M. Tennenholtz, “Ranking systems: The pagerank axioms,” in Proceedings of the 6th ACM Conference on Electronic Commerce, ser. EC ’05. New York, NY, USA: ACM, 2005, pp. 1–8. [Online]. Available: http://doi.acm.org/10.1145/1064009.1064010
  12. Y. R. Tausczik and J. W. Pennebaker, “The psychological meaning of words: LIWC and computerized text analysis methods,” Journal of Language and Social Psychology, 2010. [Online]. Available: http://homepage.psy.utexas.edu/homepage/students/Tausczik/Yla/index.html