CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Identifying the Topic-Specific Influential Users in Twitter

by May Shalaby, Ahmed Rafea
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 18
Year of Publication: 2018
Authors: May Shalaby, Ahmed Rafea
10.5120/ijca2018916316

May Shalaby, Ahmed Rafea . Identifying the Topic-Specific Influential Users in Twitter. International Journal of Computer Applications. 179, 18 ( Feb 2018), 34-39. DOI=10.5120/ijca2018916316

@article{ 10.5120/ijca2018916316,
author = { May Shalaby, Ahmed Rafea },
title = { Identifying the Topic-Specific Influential Users in Twitter },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2018 },
volume = { 179 },
number = { 18 },
month = { Feb },
year = { 2018 },
issn = { 0975-8887 },
pages = { 34-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number18/28970-2018916316/ },
doi = { 10.5120/ijca2018916316 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:55:47.362251+05:30
%A May Shalaby
%A Ahmed Rafea
%T Identifying the Topic-Specific Influential Users in Twitter
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 18
%P 34-39
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Social Influence can be described as the ability to have an effect on the thoughts or actions of others. Influential members in online communities are becoming the new media to market products and sway opinions. Also, their guidance and recommendations can save some people the search time and assist their selective decision making. The objective of this research is to detect the influential users in a specific topic on Twitter. From a collection of tweets matching a specified query, the influential users are to be detected in an online fashion. In order to address this, the issue of which set of features can best lead us to the topic-specific influential users is investigated along with how these features can be expressed in a model to produce a list of ranked influential users.

References
  1. Agarwal, N., Liu, H., Tang, L. and Yu, P.S., 2008, February. Identifying the influential bloggers in a community. In Proceedings of the 2008 international conference on web search and data mining (pp. 207-218). ACM.
  2. Akritidis, L., Katsaros, D. and Bozanis, P., 2009, September. Identifying influential bloggers: Time does matter. In Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology-Volume 01 (pp. 76-83). IEEE Computer Society.
  3. Akritidis, L., Katsaros, D. and Bozanis, P., 2011. Identifying the productive and influential bloggers in a community. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41(5), pp.759-764.
  4. Zhou, H. and Zeng, D., 2009, June. Finding leaders from opinion networks. In Intelligence and Security Informatics, 2009. ISI'09. IEEE International Conference on (pp. 266-268). IEEE.
  5. Weng, J., Lim, E.P., Jiang, J. and He, Q., 2010, February. Twitterrank: finding topic-sensitive influential twitterers. In Proceedings of the third ACM international conference on Web search and data mining (pp. 261-270). ACM.
  6. Yao, Y., Li, B. and Peng, L., 2015, October. Evaluating User Influence Based on the Properties of User in Social Networks. In Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), 2015 IEEE International Conference on (pp. 884-888). IEEE.
  7. Xiao, C., Xue, Y., Li, Z., Luo, X. and Qin, Z., 2015, December. Measuring User influence based on multiple metrics on YouTube. In Parallel Architectures, Algorithms and Programming (PAAP), 2015 Seventh International Symposium on (pp. 177-182). IEEE.
  8. Bakshy, E., Hofman, J.M., Mason, W.A. and Watts, D.J., 2011, February. Everyone's an influencer: quantifying influence on twitter. In Proceedings of the fourth ACM international conference on Web search and data mining (pp. 65-74). ACM.
  9. Romero, D.M., Galuba, W., Asur, S. and Huberman, B.A., 2011, September. Influence and passivity in social media. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 18-33). Springer Berlin Heidelberg.
  10. Bigonha, C., Cardoso, T.N., Moro, M.M., Almeida, V.A. and Gonçalves, M.A., 2010. Detecting evangelists and detractors on twitter. In Brazilian Symposium on Multimedia and the Web (pp. 107-114).
  11. Ya-ting, L. and Jing-min, C., 2011, April. The social network analysis of political blogs in people: Based on centrality. In Consumer Electronics, Communications and Networks (CECNet), 2011 International Conference on (pp. 5441-5444). IEEE.
  12. Chang, C.C. and Lin, C.J., 2011. LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), p.27.
  13. Quercia, D., Ellis, J., Capra, L. and Crowcroft, J., 2011, October. In the mood for being influential on twitter. In Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on (pp. 307-314). IEEE.
Index Terms

Computer Science
Information Sciences

Keywords

Twitter Feature Selection

Powered by PhDFocusTM