Social activity versus academic activity: a case study of computer scientists on Twitter

ABSTRACT

In this work, we study social and academic network activities of researchers from Computer Science. Using a recently proposed framework, we map the researchers to their Twitter accounts and link them to their publications. This enables us to create two types of networks: first, networks that reflect social activities on Twitter, namely the researchers' follow, retweet and mention networks and second, networks that reflect academic activities, that is the co-authorship and citation networks. Based on these datasets, we (i) compare the social activities of researchers with their academic activities, (ii) investigate the consistency and similarity of communities within the social and academic activity networks, and (iii) investigate the information flow between different areas of Computer Science in and between both types of networks. Our findings show that if co-authors interact on Twitter, their relationship is reciprocal, increasing with the numbers of papers they co-authored. In general, the social and the academic activities are not correlated. In terms of community analysis, we found that the three social activity networks are most consistent with each other, with the highest consistency between the retweet and mention network. A study of information flow revealed that in the follow network, researchers from Data Management, Human-Computer Interaction, and Artificial Intelligence act as a source of information for other areas in Computer Science.

References

1. V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008, 2008.
2. K. W. Boyack, R. Klavans, and K. Börner. Mapping the backbone of science. Scientometrics, 64:351--374, 2005.
3. A. Clauset, M. E. J. Newman, and C. Moore. Finding community structure in very large networks. Physical Review E, 70:066111, 2004.
4. L. Danon, A. Díaz-Guilera, J. Duch, and A. Arenas. Comparing community structure identification. J. Stat. Mech., 9:8, 2005.
5. L. De Vocht, S. Softic, A. Dimou, R. Verborgh, E. Mannens, M. Ebner, and R. Van de Walle. Visualizing collaborations and online social interactions at scientific conferences for scholarly networking. In Proc. WWW, pages 1053--1054, 2015.
6. S. Dongen. Performance criteria for graph clustering and markov cluster experiments. Technical report, CWI (Centre for Mathematics and Computer Science), Amsterdam, The Netherlands, 2000.
7. M. Ebner and W. Reinhardt. Social networking in scientific conferences -- Twitter as tool for strengthen a scientific community. In Proc. EC-TEL, Berlin/Heidelberg, Oct. 2009. Springer.
8. A. T. Hadgu and R. Jäschke. Identifying and analyzing researchers on Twitter. In Proc. Web Science, pages 23--32. ACM, 2014.
9. L. Hubert and P. Arabie. Comparing partitions. Journal of Classification, 2(1):193--218, 1985.
10. S. Jung and A. Segev. Analyzing future communities in growing citation networks. Knowledge-Based Systems, 69(0):34--44, 2014.
11. J. Letierce, A. Passant, J. Breslin, and S. Decker. Understanding how Twitter is used to widely spread scientific messages. In Proc. Web Science, 2010.
12. J. Letierce, A. Passant, J. G. Breslin, and S. Decker. Using Twitter during an academic conference: The #iswc2009 use-case. In W. W. Cohen and S. Gosling, editors, ICWSM. The AAAI Press, 2010.
13. M. Mahrt, K. Weller, and I. Peters. Twitter in scholarly communication. In Twitter and Society, pages 399--410. Peter Lang, New York, 2014.
14. A. Mazarakis and I. Peters. Tweets and scientific conferences: The use case of the science 2.0 conference. In Proceedings of the 2nd European Conference on Social Media 2015 (ECSM 2015), 2015.
15. M. Meilă. Comparing clusterings by the variation of information. In Learning Theory and Kernel Machines, pages 173--187. Springer, 2003.
16. M. E. J. Newman. Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23):8577--8582, 2006.
17. W. Rand. Objective criteria for the evaluation of clustering methods. Journal of the American Statistical Association, 66(336):846--850, 1971.
18. X. Shi, B. L. Tseng, and L. A. Adamic. Information diffusion in computer science citation networks. CoRR, abs/0905.2, 2009.
19. J. Tang, J. Zhang, L. Yao, J. Li, L. Z. 0007, and Z. Su. Arnetminer: extraction and mining of academic social networks. In Proc. KDD, pages 990--998. ACM, 2008.
20. K. Weller, E. Dröge, and C. Puschmann. Citation analysis in twitter: Approaches for defining and measuring information flows within tweets during scientific conferences. In M. Rowe, M. Stankovic, A.-S. Dadzie, and M. Hardey, editors, Making Sense of Microposts (#MSM2011), pages 1--12, May 2011.

• Information systems

  • Information systems applications

    • Data mining