Comparative analysis of Mechanisms for Categorization and Moderation of User Generated Text Contents on a Social E-Governance Forum

Imeobong Frank Inyang, Simeon Ozuomba, Chinedu Pascal Ezenkwu


This paper presents a comparative analysis of two mechanisms for an automated categorization and moderation of User Generated Text Contents (UGTCs) on a social e-governance forum. Posts on the forum are categorized into “relevant”, “irrelevant but interesting” and “must be removed”. Relevant posts are those posts that are capable of supporting government decisions; irrelevant but interesting category consists of posts that are not relevant but can entertain or enlighten other users; must be removed posts consists of abusive or obscene posts. Two classifiers, Support Vector Machine (SVM) with One-Vs-The-Rest technique and Multinomial Naive Bayes were trained, evaluated and compared using Scikit-learn. The results show that SVM with an accuracy score of 96% on test set performs better than Naive Bayes with 88.6% accuracy score on the same test set.


Moderation; Ranking; UGC; UGTC; web 2.0; Sentiment analysis; Social e-governance

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Keohane, R.O., & Nye, J.S.Jr. (2002). Governance in a globalization world. Power and governance in a partially globalized world, 193-218.

Kettl, D.F. (2015). The transformation of governance: Public administration for the twenty-first century. JHU Press.

OJO, J. S. (2014). E-governance: An imperative for sustainable grass root development in Nigeria. Journal of Public Administration and Policy Research, 6(4), 77-89.

Palvia, S.C.J., & Sharma, S.S. (2007). E-Government and E-Governance: Definitions/Domain Framework and Status around the World. In International Conference on E-governance., 5 International Conference on EGovernance, Foundations of E-Government, 1-12.

Cvijikj, I.P. and Michahelles, F. (2012) Understanding the user generated content and interactions on a Facebook brand page, Int. J. Social and Humanistic Computing, Vol. 2, No. 1-2, 118–140.

Ochoa, X., Duval, E. (2008). Quantitative analysis of user-generated content on the web. Proceedings of WebEvolve2008: web science workshop at WWW2008, 1-8.

Khadilkar, A., Pai, T., Ghadiali, S. (2012). How to De-Risk the Creation and Moderation of User-Generated Content, Available at : Accessed on: 10th October 2016.

ABC Managing Director (2011). Moderating User Generated Content, 9, Available at: Accessed on: 10th October 2016.

Interactive advertising bureau Australia (2013) Best Practice for User CommentModeration: Including commentary for organisations using social media platforms. Available at:, Accessed on: 10th October 2016.

Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis lectures on human language technologies, 5(1), 1-167.

Kumar, S. A., &Vijayalakshmi, M. N. (2012). Inference of Naïve Baye’s Technique on Student Assessment Data. In Global Trends in Information Systems and Software Applications, Volume 270 of the series Communications in Computer and Information Science, 186-191.


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