CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
Implementation of Feature Selection Using Correlation Matrix in Python

Ahmad Farhan AlShammari
Random Articles
Reseach Article

Framework for Participative and Collaborative Governance using Social Media Mining Techniques

by Nazura Javed, Muralidhara B.L.
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 123 - Number 8
Year of Publication: 2015
Authors: Nazura Javed, Muralidhara B.L.
10.5120/ijca2015905435

Nazura Javed, Muralidhara B.L. . Framework for Participative and Collaborative Governance using Social Media Mining Techniques. International Journal of Computer Applications. 123, 8 ( August 2015), 36-41. DOI=10.5120/ijca2015905435

@article{ 10.5120/ijca2015905435,
author = { Nazura Javed, Muralidhara B.L. },
title = { Framework for Participative and Collaborative Governance using Social Media Mining Techniques },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 123 },
number = { 8 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 36-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume123/number8/21982-2015905435/ },
doi = { 10.5120/ijca2015905435 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:13:10.154739+05:30
%A Nazura Javed
%A Muralidhara B.L.
%T Framework for Participative and Collaborative Governance using Social Media Mining Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 123
%N 8
%P 36-41
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Social media and networking sites have broken the barriers in communication and brought about a revolution in information access, dissemination and communication. Given the inclusive and collaborative nature of social media, political leaders and government organizations too have attempted to harness it for building support, gauging popularity and analyzing the opinions of citizens. This paper, proposes a framework which focuses on the application of text mining techniques on social media content for achieving participative, collaborative and inclusive governance. Most of the political entities and government departments have twitter accounts. Though the tweet text is limited to 140 characters, the accompanying metadata and hashtag (#tag) embedded content make it a potent source for extracting entities, concepts and topics. Web Crawling for content pertaining to these entities and compilation of the same, results in a corpus suitable for text mining. Applying Natural Language Processing (NLP) and mining techniques like Part-of-Speech (POS) tagging, classification, clustering to this generated corpus enables categorizing and summarizing: a) sentiments b) queries and c) grievances. Summarized view of the content makes it amenable for decision making and formulation of responses. A Knowledgebase (KB) is created and perpetually updated with the concepts, entities, summaries, queries and responses. The continued process of creation and enhancement contributes to machine learning. The scope of this paper is limited to proposing a conceptual framework for social media mining facilitating informed decision making for better governance.

References
  1. Magro, M. J. 2012. A review of social media use in e-government.Administrative Sciences, 2(2), 148-161.
  2. Bertot, J. C., Jaeger, P. T., & Grimes, J. M. 2010. Using ICTs to create a culture of transparency: E-government and social media as openness and anti-corruption tools for societies. Government information quarterly, 27(3), 264-271.
  3. Unsworth, K., & Townes, A. 2012. Social media and E‐Government: A case study assessing Twitter use in the implementation of the open government directive. Proceedings of the American Society for Information Science and Technology, 49(1), 1-3.
  4. Arpit, B. 2012. E-Government and Social Media as Openness and Anti-Corruption Strategy. Research Journal of Management Sciences, 1(1), 48-52.
  5. Megele C 2012. How to move from local e-government to collaborative e-governance, the guardian, http://www.theguardian.com/local-government-network/ 2012/may/08/local-government-e-citizenship. Accessed 12 December 2013
  6. Zhou, K., Gui-Rong, X., Yang, Q., & Yu, Y. 2010. Learning with positive and unlabeled examples using topic-sensitive PLSA. Knowledge and Data Engineering, IEEE Transactions on, 22(1), 46-58.
  7. Kavanaugh, A. L., Fox, E. A., Sheetz, S. D., Yang, S., Li, L. T., Shoemaker, D. J., ... & Xie, L. 2012. Social media use by government: From the routine to the critical. Government Information Quarterly, 29(4), 480-491.
  8. Xie, Y., Chen, Z., Zhang, K., Cheng, Y., Honbo, D. K., Agrawal, A., & Choudhary, A. N. 2014. MuSES: Multilingual Sentiment Elicitation System for Social Media Data. Intelligent Systems, IEEE, 29(4), 34-42.
  9. Bao, S., Xu, S., Zhang, L., Yan, R., Su, Z., Han, D., & Yu, Y. 2012. Mining social emotions from affective text. Knowledge and Data Engineering, IEEE Transactions on, 24(9), 1658-1670.
  10. Mostafa, M. M. 2013. More than words: Social networks’ text mining for consumer brand sentiments. Expert Systems with Applications, 40(10), 4241-4251.
  11. Giannopoulos, G., Koniaris, M., Weber, I., Jaimes, A., & Sellis, T. 2014. Algorithms and criteria for diversification of news article comments. Journal of Intelligent Information Systems, 44(1), 1-47.
  12. Lloret, E., Balahur, A., Gómez, J. M., Montoyo, A., & Palomar, M. 2012. Towards a unified framework for opinion retrieval, mining and summarization. Journal of Intelligent Information Systems, 39(3), 711-747.
  13. Bosco, C., Patti, V., & Bolioli, A. 2013. Developing corpora for sentiment analysis: The case of irony and senti-tut. IEEE Intelligent Systems, (2), 55-63.
  14. He, W., Zha, S., & Li, L. 2013. Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33(3), 464-472.
  15. Han, Bo, and Timothy Baldwin. 2011. Lexical normalisation of short text messages: Makn sens a# twitter  Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1. Association for Computational Linguistics, 2011.
  16. Chai, X., Deshpande, O., Garera, N., Gattani, A., Lam, W., Lamba, D. S., ... & Doan, A. 2013. Social Media Analytics: The Kosmix Story. IEEE Data Eng. Bull., 36(3), 4-12.
  17. Boella, G., Di Caro, L., Ruggeri, A., & Robaldo, L. 2014. Learning from syntax generalizations for automatic semantic annotation. Journal of Intelligent Information Systems, 43(2), 231-246.
  18. Nguyen, L. T., Wu, P., Chan, W., Peng, W., & Zhang, Y. 2012, August. Predicting collective sentiment dynamics from time-series socia media. InProceedings of the first international workshop on issues of sentiment discovery and opinion mining (p. 6). ACM.
  19. O'Hara, K. 2013. Social machine politics are here to stay. Internet Computing, IEEE, 17(2), 87-90.
  20. Stieglitz, S., & Dang-Xuan, L. 2013. Social media and political communication: a social media analytics framework. Social Network Analysis and Mining, 3(4), 1277-1291.
  21. Boutet, A., Kim, H., & Yoneki, E. 2013. What’s in Twitter, I know what parties are popular and who you are supporting now!. Social Network Analysis and Mining, 3(4), 1379-1391.
  22. Gainous, J., Marlowe, A. D., & Wagner, K. M. 2013. Traditional cleavages or a new world: Does online social networking bridge the political participation divide?. International Journal of Politics, Culture, and Society, 26(2), 145-158.
  23. Vergeer, M., & Hermans, L. 2013. Campaigning on Twitter: microblogging and online social networking as campaign tools in the 2010 general elections in the Netherlands. Journal of Computer‐Mediated Communication, 18(4), 399-419.
  24. Grubmüller, V., Götsch, K., & Krieger, B. 2013. Social media analytics for future oriented policy making. European Journal of Futures Research, 1(1), 1-9.
  25. Charalabidis, Y., Loukis, E., Spiliotopoulou, L., & Diamantopoulou, V. 2013. A Framework for Utilizing Web 2.0 Social Media for Participative Governance. InEuropean, Mediterranean & Middle Eastern Conference On Information System 2013, 01 (Vol. 4).
  26. Berry M.W., Castellanos M. 2007 Survey of Text Mining: Clustering, Classification and Retrieval Springer, Second Edition September 30, 2007
  27. Russell M. A. 2013 Mining the Social Web O’Reilly, ISBN:978-1-449-36761-9
Index Terms

Computer Science
Information Sciences

Keywords

Framework Social media Text Mining Political Machine learning

Powered by PhDFocusTM