A FILTER MODEL FOR TEXT CATEGORIZATION AGAINST ONLINE HATE SPEECHES
Keywords:
Text categorization, Hate Speech, Classification Techniques, Data miningAbstract
Text classification is a method of grouping a document text into different predefined categories. This method has been applied in different areas such as classification of scientific articles, spam filtering, and classification of document genre. Text classification is a popular task in data mining because of its level of accuracy and easy application. The Internet is a common message transmission medium among many people, billions of messages move around the internet on a daily basis through different platforms on the internet such as e-mail, Facebook, Twitter, etc. Some of these messages are being transmitted with wrong motives, thus it became imperative to design a model for filtering some of these messages using data mining algorithms to sieve away the unwanted messages from circulation. In the light of this, this paper applied three data mining techniques namely: Support Vector Machine (SVM), Naïve Bayes and K-Nearest Neighbour (KNN) to develop models that can be applied to filter messages from Facebook and e-mail to counter circulation of online hate speeches on these platforms. It also compared the performance of these models against collected data to identify the state of the art text classifier. It was observed that the Naïve Bayes algorithm performed better than the other two with an accuracy of 61.5 and ROC of 0.66.
References
Olaniyan, A. (2016) Cows, Bandits and Violents Complicts: Understanding Cattle Rusttling in Northern Nigeria. Jornal of Sub-Universities Hamburg.
Chukuma O. And Francis N.O.(2014) Cattle rustling and Dialetics of Security in Northern Nigeria: International Security Sector Advisory team (ISSAT) report. International Journal of Liberal Arts and social sciences.
Badayi A. S. (2002). Towards Effective Poverty Alleviation Strategies.NCEMA Policy sAnalysis Series 3(8)1-16.
Kaimba, G. K., Njhia, K. B. and Guliye, A. (2011). ‘Effects of Cattle Rustling and Household Characteristics on Migration Decisions and Herd Size amongst Pastoralists in Baringo District in Kenya’ Pastoralism: research, Policy and Practice, Vol. 1 (18): 1 – 16. http://www.pastoralismjournal.com/content/1/1/18
Kaprom, T. (2013). “Effects of Cattle Rustling on Economic Development: A Case of Masol Location, West Pokot County’’. Unpublished M.Sc. Planning and Management Project, University of Nairobi, Kenya.
Manu, I. N., Andu, W. N., Tarla, D. N. and Agharih, W. N (2014). “Socio-economic Effect of Cattle Theft on the Pastoralists of the North-west Region of Cameroon’’. Scholarly Journal of Agricultural Science, Vol. 4(6):299 -305. Retrieved on 2nd October 2015. http://www.scholarlyjournals. com/SJAS
Ogunkoya, F. T. (2014). “Socio-economic Factors that Affect Livestock Numbers: A case Study of Smallholder Cattle and Sheep Farmers in the Free State Province of South Africa. Unpublished M. Sc. Agriculture Thesis. University of South Africa.
Olayide S, Eweka A, and Osagie B (1980). Nigeria’s Small Farmer: Problems and Prospects in Integrated Rural Development. Centre for Agricultural research In Development, University of Ibadan, Nigeria.
Saifullahi I. S, Ibrahim, A., Na-Allah, A. and L. A. Saulawa (2016). Building community Cattle Ranch and Radio- Frequency Identification (RFID) Technology as an Alternative Methods of Curtailing Cattle Rustling in Katsina State. Pastoralism: Research, Policy and Practice; (2016) 6:10 Doi 10:1186/s13570-0016-0055-z Springer.
Shehu S. A, Victor E. And Vinta M. (2018) Menance of cattle rustling and bandity in North Western Nigeria; A case study of Katsina State. IOSR Journal.
Sumner S. (2016) TVC News reports on cattle rustling in Maigora Village of Katsina State. (August, 2016).
Schneider, K. and Gugerty, M. K. (2011). Agricultural Productivity and Poverty Reduction: Linkages and Pathways. The Evans School Review, Vol. 1(1): 56 – 74
Siegmund-Shultze, M. and Rischkowsky, B. (2001). ‘Relating Households Characteristics to Urban SheepKeeping In West Africa. Agricultural Systems, 67(2001): 139 – 152.
Simelane, H. S. (2005) Cross-Border Cattle Rustling and its Socioeconomic Impact on Rural Southern SWAZILAND, 1990–2004, Journal of Contemporary African Studies, Vol. 23(2): 215-231.
Thys, E., Oueadraogo, M. Speybroeck, N, Geerts, S. (2005). Socioeconomic Determinants of Urban Household Livestock Keeping in Semi-arid Western Africa .Journal of Arid Environments, Vol. 63(2005): 475 -496.
UNCHS, (1996). An urbanizing world: global report on human settlements. Oxford University Press, New York/Oxford.
World Bank. (2007). World development report 2008: Agriculture for development. Washington, D.C.: World Bank. doi: 10.1596/978-0-8213-7233-3
World Bank.(2013). Helping Nigeria to boost Agriculture and improved food security: World Bank News report.
World Bank. (2016 &2017). Understanding Poverty: Agriculture and Food. World Bank Report 2016 & 2017: Washington, D.C.: World Bank.
World Bank. (2017). Enabling the business of agriculture; World Bank Report, third series annual report.
Xavier, I., Lin, L., Colin, T. and Steve, W. (2001).Agricultural Productivity Growth and Poverty Alleviation. Development Policy Review, 19(4), 449-466. doi:10.1111/1467-7679.00144
Published
How to Cite
Issue
Section
FUDMA Journal of Sciences
