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Sarwar R, Hassan SU. UrduAI: Writeprints for Urdu Authorship Identification. ACM T ASIAN LOW-RESO 2022. [DOI: 10.1145/3476467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
The authorship identification task aims at identifying the original author of an anonymous text sample from a set of candidate authors. It has several application domains such as digital text forensics and information retrieval. These application domains are not limited to a specific language. However, most of the authorship identification studies are focused on English and limited attention has been paid to Urdu. However, existing Urdu authorship identification solutions drop accuracy as the number of training samples per candidate author reduces and when the number of candidate authors increases. Consequently, these solutions are inapplicable to real-world cases. Moreover, due to the unavailability of reliable POS taggers or sentence segmenters, all existing authorship identification studies on Urdu text are limited to the word n-grams features only. To overcome these limitations, we formulate a stylometric feature space, which is not limited to the word n-grams feature only. Based on this feature space, we use an authorship identification solution that transforms each text sample into a point set, retrieves candidate text samples, and relies on the nearest neighbors classifier to predict the original author of the anonymous text sample. To evaluate our solution, we create a significantly larger corpus than existing studies and conduct several experimental studies that show that our solution can overcome the limitations of existing studies and report an accuracy level of 94.03%, which is higher than all previous authorship identification works.
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Affiliation(s)
- Raheem Sarwar
- Research Group in Computational Linguistics, Research Institute of Information and Language Processing, University of Wolverhampton, Wolverhampton, Midlands, United Kingdom
| | - Saeed-Ul Hassan
- Department of Computer Science, Information Technology University, Lahore, Punjab, Pakistan
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Nwankwo TV, Odiachi RA, Anene IA. Black articles matter: exploring relative deprivation and implicit bias in library and information science research publications of Africa and other continents. LIBRARY HI TECH 2021. [DOI: 10.1108/lht-05-2021-0164] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
PurposeThe purpose of this paper is to explore relative deprivation and implicit bias in library and information science research publications of Africa and other continents.Design/methodology/approachResearch design used for this study is descriptive survey research. Specifically, the study will adopt both web content analysis and survey to collect data. The content analysis covers the whole continents of the world: Africa, Asia, Eastern Europe, Latin America, Middle East, Northern America, Pacific Region and Western Europe; using the Webometrics World Ranking of Universities and the SCImago/Scopus Journal Ranking. Library and information science was used as the search and control parameter. The scopes covered by the research are: 1. Ascertaining the visible publishing and assessment standards of top library and information science (LIS) journals, which was evaluated using Kleinert and Wager (2010)'s study.FindingsIt was found out among others that editors making fair and unbiased decisions as policy is seen in 33% of the journals, which is very poor. All the structural disparities, such as presence ranking, impact ranking, excellence ranking, etc. were favouring Europe and the Americas mainly. As much as rejection is getting to these respondents, research generally is also suffering by missing out on some untapped knowledge and ideas from these deprived populations. Many authors are losing faith in their capabilities and are now afraid of venturing into tedious research exercises because it will most likely be rejected either ways.Research limitations/implicationsIt is an established fact that social media gains research impact and attracts international collaborations. In support, studies such as Hassan et al. (2019) reported the fact that tweet mentions of articles with positive sentiment to more visibility and citations. They claim that cited articles in either positive or neutral tweets have a more significant impact than those not cited at all or cited in negative tweets. In addition, Hassan et al. (2020) equally highlighted tweet coupling as a social media methodology useful for clustering scientific publications. Despite the fact that social media have these influences on research and publications visibility and presence, the context of the present research did cover this scope of study. The study focused mainly on sources from Scopus as well as results from responses. Further studies can be carried out on this area.Originality/valueResearch studies linking “Black Articles Matter” to relative deprivation and implicit bias in research publications, especially in library and information discipline, are very rare. Also, the scope of approach of the study is quite different and interesting.
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Understanding and predicting the dissemination of scientific papers on social media: a two-step simultaneous equation modeling–artificial neural network approach. Scientometrics 2021. [DOI: 10.1007/s11192-021-04051-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Sarwar R, Zia A, Nawaz R, Fayoumi A, Aljohani NR, Hassan SU. Webometrics: evolution of social media presence of universities. Scientometrics 2021. [DOI: 10.1007/s11192-020-03804-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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