Understanding the Selectional Preferences of the Twitter Mentions Network
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.
[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] J. Yang and S. Counts, “Predicting the speed, scale, and range of
information diffusion in twitter.” ICWSM, vol. 10, pp. 355–358, 2010.
[9] D. R. Cox and D. Oakes, Analysis of survival data. CRC Press, 1984,
vol. 21.
[10] 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.
[11] 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.
[12] T. Maheshwari, A. N. Reganti, U. Kumar, T. Chakraborty, and A. Das,
“Semantic interpretation of social network communities.” in AAAI, 2017,
pp. 4967–4968.
[13] 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.”
[14] L. Inquiry and W. Count, http://liwc.wpengine.com/.
[15] Sensicon, https://hlt-nlp.fbk.eu/technologies/sensicon.
[16] J. Leskovec and A. Krevl, “SNAP Datasets: Stanford large network
dataset collection,” http://snap.stanford.edu/data, Jun. 2014.
[17] A. Argando˜na, “Fostering values in organizations,” Journal of Business
Ethics, vol. 45, no. 1, pp. 15–28, 2003.
[18] 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.
[19] G. Hofstede, “Cultures and organizations: software of the mind london,”
UK: McGraw-Hill, 1991.
[20] M. Rokeach, The nature of human values. Free press, 1973.
[21] 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.
[22] C. J. Clawson and D. E. Vinson, “Human values: a historical and
interdisciplinary analysis,” NA-Advances in Consumer Research Volume
05, 1978.
[23] 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.
[24] I. Documentation, http://igraph.org/r/doc/reciprocity.html.
[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] 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.
[8] J. Yang and S. Counts, “Predicting the speed, scale, and range of
information diffusion in twitter.” ICWSM, vol. 10, pp. 355–358, 2010.
[9] D. R. Cox and D. Oakes, Analysis of survival data. CRC Press, 1984,
vol. 21.
[10] 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.
[11] 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.
[12] T. Maheshwari, A. N. Reganti, U. Kumar, T. Chakraborty, and A. Das,
“Semantic interpretation of social network communities.” in AAAI, 2017,
pp. 4967–4968.
[13] 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.”
[14] L. Inquiry and W. Count, http://liwc.wpengine.com/.
[15] Sensicon, https://hlt-nlp.fbk.eu/technologies/sensicon.
[16] J. Leskovec and A. Krevl, “SNAP Datasets: Stanford large network
dataset collection,” http://snap.stanford.edu/data, Jun. 2014.
[17] A. Argando˜na, “Fostering values in organizations,” Journal of Business
Ethics, vol. 45, no. 1, pp. 15–28, 2003.
[18] 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.
[19] G. Hofstede, “Cultures and organizations: software of the mind london,”
UK: McGraw-Hill, 1991.
[20] M. Rokeach, The nature of human values. Free press, 1973.
[21] 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.
[22] C. J. Clawson and D. E. Vinson, “Human values: a historical and
interdisciplinary analysis,” NA-Advances in Consumer Research Volume
05, 1978.
[23] 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.
[24] I. Documentation, http://igraph.org/r/doc/reciprocity.html.
@article{"International Journal of Information, Control and Computer Sciences:77821", author = "R. Sudhesh Solomon and P. Y. K. L. Srinivas and Abhay Narayan and Amitava Das", title = "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.", keywords = "Social network analysis, information diffusion,
personality and values, Twitter Mentions Network.", volume = "12", number = "8", pages = "638-6", }
