A Visual Approach to the Empirical Analysis of Social Influence

Chiara Francalanci, Ajaz Hussain

2014

Abstract

This paper starts from the observation that social networks follow a power-law degree distribution of nodes, with a few hub nodes and a long tail of peripheral nodes. While there exist consolidated approaches supporting the identification and characterization of hub nodes, research on the analysis of the multi-layered distribution of peripheral nodes is limited. In social media, hub nodes represent social influencers. However, the literature provides evidence of the multi-layered structure of influence networks, emphasizing the distinction between influencers and influence. The latter seems to spread following multi-hop paths across nodes in peripheral network layers. This paper proposes a visual approach to the graphical representation and exploration of peripheral layers and clusters to exploit underlying concept of k-shell decomposition analysis. The core concept of our approach is to partition the node set of a graph into hub and peripheral nodes. Then, a power-law based modified force-directed method is applied to clearly display local multi-layered neighbourhood clusters around hub nodes. Our approach is tested on a large sample of tweets from the tourism domain. Empirical results indicate that peripheral nodes have a greater probability of being retweeted and, thus, play a critical role in determining the influence of content. Our visualization technique helps us highlight peripheral nodes and, thus, seems an interesting tool to the visual analysis of social influence.

References

  1. Abello, J. and Queyroi, F. (2013). Fixed points of graph peeling. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pages 256-263. ACM.
  2. Alvarez-Hamelin, J. I., Dall'Asta, L., Barrat, A., and Vespignani, A. (2006). Large scale networks fingerprinting and visualization using the k-core decomposition. Advances in neural information processing systems, 18:41.
  3. Andersen, R., Chung, F., and Lu, L. (2004). Drawing power law graphs using local/global decomposition. Twelfth Annual Symposium on Graph Drawing.
  4. Andersen, R., Chung, F., and Lu, L. (2007). Drawing power law graphs using a local global decomposition. Algorithmica, 47(4):397.
  5. Anger, I. and Kittl, C. (2011). Measuring influence on twitter. In Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies, page 31. ACM.
  6. Anholt, S. (2006). Competitive identity: The new brand management for nations, cities and regions. Palgrave Macmillan.
  7. Arbuckle, J. L. (2011). Ibm spss amos 20 users guide. Amos Development Corporation, SPSS Inc.
  8. Asur, S., Huberman, B. A., Szabo, G., and Wang, C. (2011). Trends in social media: Persistence and decay. In ICWSM.
  9. Bagozzi, R. P. and Fornell, C. (1982). Theoretical concepts, measurements, and meaning. A second generation of multivariate analysis, 2(2):5-23.
  10. Bakshy, E., Hofman, J. M., Mason, W. A., and Watts, D. J. (2011). Everyone's an influencer: quantifying influence on twitter. In Proceedings of the fourth ACM international conference on Web search and data mining, pages 65-74. ACM.
  11. Barbagallo, D. (2010). A data quality based methodology to improve sentiment analyses. PhD thesis, Politecnico di Milano, Milan, Italy.
  12. Barbagallo, D., Bruni, L., Francalanci, C., and Giacomazzi, P. (2012). An empirical study on the relationship between twitter sentiment and influence in the tourism domain. In Information and Communication Technologies in Tourism 2012, pages 506-516. Springer.
  13. Benevenuto, F., Cha, M., Gummadi, K., and Haddadi, H. (2010). Measuring user influence in twitter: The million follower fallacy. In International AAAI Conference on Weblogs and Social (ICWSM10), pages pp. 10-17.
  14. Bigonha, C., Cardoso, T. N., Moro, M. M., Gonc¸alves, M. A., and Almeida, V. A. (2012). Sentiment-based influence detection on twitter. Journal of the Brazilian Computer Society, 18(3):169-183.
  15. Blitzer, J., Dredze, M., and Pereira, F. (2007). Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In ACL, volume 7, pages 440-447.
  16. Boutin, F., Thievre, J., and Hascoet, M. (2006). Focusbased filtering + clustering technique for power-law networks with small world phenomenon. SPIE-IS & T Electronic Imaging, 6060.
  17. Boyd, D., Golde, S., and Lotan, G. (2010). Tweet, tweet, retweet: Conversational aspects of retweeting on twitter. IEEE, pages pp. 1-10.
  18. Bruni, L. (2010). A methodology framework to understand and leverage the impact of content on social media influence. PhD thesis, Politecnico di Milano, Milan, Italy.
  19. Bruni, L., Francalanci, C., Giacomazzi, P., Merlo, F., and Poli, A. (2013). The relationship among volumes, specificity, and influence of social media information. In Proceedings of International Conference on Information Systems.
  20. Bullock, H. E., Harlow, L. L., and Mulaik, S. A. (1994). Causation issues in structural equation modeling research. Structural Equation Modeling: A Multidisciplinary Journal, 1(3):253-267.
  21. Carmi, S., Havlin, S., Kirkpatrick, S., Shavitt, Y., and Shir, E. (2007). A model of internet topology using k-shell decomposition. Proceedings of the National Academy of Sciences, 104(27):11150-11154.
  22. Chan, D., Chua, K., Leckie, C., and Parhar, A. (2004). Visualisation of power-law network topologies. In Networks, 2003. ICON2003. The 11th IEEE International Conference on, pages 69-74. IEEE.
  23. Chung, B. (2007). An analysis of success and failure factors for ERP systems in engineering and construction firms. ProQuest.
  24. Dehkharghani, R., Mercan, H., Javeed, A., and Saygin, Y. (2014). Sentimental causal rule discovery from twitter. Expert Systems with Applications, 41(10):4950- 4958.
  25. Freeman, L. C. (1979). Centrality in social networks conceptual clarification. Social networks, 1(3):215-239.
  26. Fruchterman, T. and Reingold, E. (1991). Graph drawing by force-directed placement. Software: Practice and experience, 21(11):1129-1164.
  27. Godbole, N., Srinivasaiah, M., and Skiena, S. (2007). Large-scale sentiment analysis for news and blogs. ICWSM, 7.
  28. Hao, M., Rohrdantz, C., Janetzko, H., Dayal, U., Keim, D. A., Haug, L., and Hsu, M.-C. (2011). Visual sentiment analysis on twitter data streams. In Visual Analytics Science and Technology (VAST), 2011 IEEE Conference on, pages 277-278. IEEE.
  29. Hossain, L., Wu, A., and Chung, K. K. (2006). Actor centrality correlates to project based coordination. In Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work, pages 363- 372. ACM.
  30. Hussain, A., Latif, K., Rextin, A., Hayat, A., and Alam, M. (2014). Scalable Visualization of Semantic Nets using Power-Law Graphs. Applied Mathematics & Information Sciences, 8(1):355-367.
  31. Kitsak, M., Gallos, L. K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H. E., and Makse, H. A. (2010). Identification of influential spreaders in complex networks. Nature Physics, 6(11):888-893.
  32. Klotz, C., Ross, A., Clark, E., and Martell, C. (2014). Tweet!-and i can tell how many followers you have. In Recent Advances in Information and Communication Technology, pages 245-253. Springer.
  33. Koch, R. (1999). The 80/20 principle: the secret to achieving more with less. Crown Business.
  34. Kwak, H., Lee, C., Park, H., and Moon, S. (2010). What is twitter, a social network or a news media? In Proceedings of the 19th international conference on World wide web, pages 591-600. ACM.
  35. Metra, I. (2014). Influence based exploration of twitter social network. PhD thesis, Politecnico di Milano, Milan, Italy.
  36. Naaman, M., Boase, J., and Lai, C.-H. (2010). Is it really about me?: message content in social awareness streams. In Proceedings of the 2010 ACM conference on Computer supported cooperative work, pages 189- 192. ACM.
  37. Newman, M. E. (2005). Power laws, pareto distributions and zipf's law. Contemporary physics, 46(5):323- 351.
  38. Pallant, J. (2010). SPSS survival manual: A step by step guide to data analysis using SPSS. McGraw-Hill International.
  39. Perline, R. (2005). Strong, weak and false inverse power laws. Statistical Science, pages 68-88.
  40. Renoust, B., Melanc¸on, G., and Viaud, M.-L. (2013). Assessing group cohesion in homophily networks. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pages 149-155. ACM.
  41. Sparrowe, R. T., Liden, R. C., Wayne, S. J., and Kraimer, M. L. (2001). Social networks and the performance of individuals and groups. Academy of management journal, 44(2):316-325.
  42. Suh, B., Hong, L., Pirolli, P., and Chi, E. H. (2010). Want to be retweeted? large scale analytics on factors impacting retweet in twitter network. In Social computing (socialcom), 2010 ieee second international conference on, pages 177-184. IEEE.
  43. Xu, X., Yuruk, N., Feng, Z., and Schweiger, T. (2007). Scan: a structural clustering algorithm for networks. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 824-833. ACM.
Download


Paper Citation


in Harvard Style

Francalanci C. and Hussain A. (2014). A Visual Approach to the Empirical Analysis of Social Influence . In Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA, ISBN 978-989-758-035-2, pages 319-330. DOI: 10.5220/0004992803190330


in Bibtex Style

@conference{data14,
author={Chiara Francalanci and Ajaz Hussain},
title={A Visual Approach to the Empirical Analysis of Social Influence},
booktitle={Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA,},
year={2014},
pages={319-330},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004992803190330},
isbn={978-989-758-035-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: DATA,
TI - A Visual Approach to the Empirical Analysis of Social Influence
SN - 978-989-758-035-2
AU - Francalanci C.
AU - Hussain A.
PY - 2014
SP - 319
EP - 330
DO - 10.5220/0004992803190330