Search results for: Omwoyo Bosire Onyancha

Authors: Omwoyo Bosire Onyancha

Abstract:

The Journal Citation Reports (JCR) of the Thomson Reuters has, for decades, provided the data for bibliometrically assessing the impact of journals. In their criticism of the journal impact factor (JIF), a number of scholars such as Priem, Taraborelli, Groth and Neylon (2010) observe that the “JIF is often incorrectly used to assess the impact of individual articles. It is troubling that the exact details of the JIF are a trade secret, and that significant gaming is relatively easy”. The emergence of alternative metrics (Altmetrics) has introduced another dimension of re-assessing how the impact of journals (and other units such as articles and even individual researchers) can be measured. Altmetrics is premised upon the fact that research is increasingly being disseminated through social network sites such as ResearchGate, Mendeley, Twitter, Facebook, LinkedIn, and ImpactStory, among others. This paper adopts informetrics (including altmetrics) techniques to report on the findings of a study conducted to investigate and compare the social media impact of 274 South Africa Post Secondary Education (SAPSE)-accredited journals, which are recognized and accredited by the Department of Higher Education and Training (DHET) of South Africa (SA). We used multiple sources to extract data for the study, namely Altmetric.com and the Thomson Reuters’ Journal Citation Reports. Data was analyzed in order to determine South African journals’ presence and impact on social media as well as contrast the social media impact with Thomson Reuters’ citation impact. The Spearman correlation test was performed to compare the journals’ social media impact and JCR citation impact. Preliminary findings reveal that a total of 6360 articles published in 96 South African journals have received some attention in social media; the most commonly used social media platform was Twitter, followed by Mendeley, Facebook, News outlets, and CiteULike; there were 29 SA journals covered in the JCR in 2008 and this number has grown to 53 journals in 2014; the journals indexed in the Thomson Reuters performed much better, in terms of their altmetrics, than those journals that are not indexed in Thomson Reuters databases; nevertheless, there was high correlation among journals that featured in both datasets; the journals with the highest scores in Altmetric.com included the South African Medical Journal, African Journal of Marine Science, and Transactions of the Royal Society of South Africa while the journals with high impact factors in JCR were South African Medical Journal, Onderstepoort: Journal of Veterinary Research, and Sahara: Journal of Social Aspects of HIV-AIDS; and that Twitter has emerged as a strong avenue of sharing and communicating research published in the South African journals. Implications of the results of the study for the dissemination of research conducted in South Africa are offered. Discussions based on the research findings as well as conclusions and recommendations are offered in the full text paper.

Keywords: altmetrics, citation impact, journal citation reports, journal impact factor, journals, research, scholarly publishing, social media impact, South Africa

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Authors: Julius Onyancha, Valentina Plekhanova

Abstract:

One of the significant issues facing web users is the amount of noise in web data which hinders the process of finding useful information in relation to their dynamic interests. Current research works consider noise as any data that does not form part of the main web page and propose noise web data reduction tools which mainly focus on eliminating noise in relation to the content and layout of web data. This paper argues that not all data that form part of the main web page is of a user interest and not all noise data is actually noise to a given user. Therefore, learning of noise web data allocated to the user requests ensures not only reduction of noisiness level in a web user profile, but also a decrease in the loss of useful information hence improves the quality of a web user profile. Noise Web Data Learning (NWDL) tool/algorithm capable of learning noise web data in web user profile is proposed. The proposed work considers elimination of noise data in relation to dynamic user interest. In order to validate the performance of the proposed work, an experimental design setup is presented. The results obtained are compared with the current algorithms applied in noise web data reduction process. The experimental results show that the proposed work considers the dynamic change of user interest prior to elimination of noise data. The proposed work contributes towards improving the quality of a web user profile by reducing the amount of useful information eliminated as noise.

Keywords: web log data, web user profile, user interest, noise web data learning, machine learning

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