Commenced in January 2007
Paper Count: 32009
Understanding the Selectional Preferences of the Twitter Mentions Network
Abstract:Users in social networks either unicast or broadcast their messages. At mention is the popular way of unicasting for Twitter whereas general tweeting could be considered as broadcasting method. Understanding the information flow and dynamics within a Social Network and modeling the same is a promising and an open research area called Information Diffusion. This paper seeks an answer to a fundamental question - understanding if the at-mention network or the unicasting pattern in social media is purely random in nature or is there any user specific selectional preference? To answer the question we present an empirical analysis to understand the sociological aspects of Twitter mentions network within a social network community. To understand the sociological behavior we analyze the values (Schwartz model: Achievement, Benevolence, Conformity, Hedonism, Power, Security, Self-Direction, Stimulation, Traditional and Universalism) of all the users. Empirical results suggest that values traits are indeed salient cue to understand how the mention-based communication network functions. For example, we notice that individuals possessing similar values unicast among themselves more often than with other value type people. We also observe that traditional and self-directed people do not maintain very close relationship in the network with the people of different values traits.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1474359Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 544
 A. Guille, H. Hacid, C. Favre, and D. A. Zighed, “Information diffusion in online social networks: A survey,” ACM SIGMOD Record, vol. 42, no. 2, pp. 17–28, 2013.
 M. Kimura, K. Saito, R. Nakano, and H. Motoda, “Extracting influential nodes on a social network for information diffusion,” Data Mining and Knowledge Discovery, vol. 20, no. 1, pp. 70–97, 2010.
 M. Wani and M. Ahmad, “Survey of information diffusion over interaction networks of twitter,” International Journal of Computer Application, vol. 3, no. 4, pp. 310–313, 2014.
 M. Gomez Rodriguez, J. Leskovec, and B. Sch¨olkopf, “Structure and dynamics of information pathways in online media,” in Proceedings of the sixth ACM international conference on Web search and data mining. ACM, 2013, pp. 23–32.
 D. M. Romero, B. Meeder, and J. Kleinberg, “Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter,” in Proceedings of the 20th international conference on World wide web. ACM, 2011, pp. 695–704.
 A. Apolloni, K. Channakeshava, L. Durbeck, M. Khan, C. Kuhlman, B. Lewis, and S. Swarup, “A study of information diffusion over a realistic social network model,” in Computational Science and Engineering, 2009. CSE’09. International Conference on, vol. 4. IEEE, 2009, pp. 675–682.
 T. R. Zaman, R. Herbrich, J. Van Gael, and D. Stern, “Predicting information spreading in twitter,” in Workshop on computational social science and the wisdom of crowds, nips, vol. 104, no. 45. Citeseer, 2010, pp. 17 599–601.
 J. Yang and S. Counts, “Predicting the speed, scale, and range of information diffusion in twitter.” ICWSM, vol. 10, pp. 355–358, 2010.
 D. R. Cox and D. Oakes, Analysis of survival data. CRC Press, 1984, vol. 21.
 S. H. Schwartz and W. Bilsky, “Toward a theory of the universal content and structure of values: Extensions and cross-cultural replications.” Journal of personality and social psychology, vol. 58, no. 5, p. 878, 1990.
 S. H. Schwartz, “Universals in the content and structure of values: Theoretical advances and empirical tests in 20 countries,” Advances in experimental social psychology, vol. 25, pp. 1–65, 1992.
 T. Maheshwari, A. N. Reganti, U. Kumar, T. Chakraborty, and A. Das, “Semantic interpretation of social network communities.” in AAAI, 2017, pp. 4967–4968.
 T. Maheshwari, A. N. Reganti, S. G. A. Jamatia, U. Kumar, B. Gamb¨ack, A. Das, and S. S. I. AB, “A societal sentiment analysis: Predicting the values and ethics of individuals by analysing social media content.”
 L. Inquiry and W. Count, http://liwc.wpengine.com/.
 Sensicon, https://hlt-nlp.fbk.eu/technologies/sensicon.
 J. Leskovec and A. Krevl, “SNAP Datasets: Stanford large network dataset collection,” http://snap.stanford.edu/data, Jun. 2014.
 A. Argando˜na, “Fostering values in organizations,” Journal of Business Ethics, vol. 45, no. 1, pp. 15–28, 2003.
 B. R. Agle and C. B. Caldwell, “Understanding research on values in business: A level of analysis framework,” Business & Society, vol. 38, no. 3, pp. 326–387, 1999.
 G. Hofstede, “Cultures and organizations: software of the mind london,” UK: McGraw-Hill, 1991.
 M. Rokeach, The nature of human values. Free press, 1973.
 L. R. Kahle, S. E. Beatty, and P. Homer, “Alternative measurement approaches to consumer values: the list of values (lov) and values and life style (vals),” Journal of consumer research, vol. 13, no. 3, pp. 405–409, 1986.
 C. J. Clawson and D. E. Vinson, “Human values: a historical and interdisciplinary analysis,” NA-Advances in Consumer Research Volume 05, 1978.
 J. N. Hood, “The relationship of leadership style and ceo values to ethical practices in organizations,” Journal of Business Ethics, vol. 43, no. 4, pp. 263–273, 2003.
 I. Documentation, http://igraph.org/r/doc/reciprocity.html.