arXiv subjects

Saeed-Ul Hassan

Publications and source records attributed to Saeed-Ul Hassan.

7 recordsLinked to original sources

arXiv2022

Exploring the Research Landscape of Pakistan: A Data-driven Analysis of Scopus Indexed Scientific Literature

Scientific contribution and research performance of a university, research group, or institute needs to be evaluated all the more with the increasing volume and fast-developing disciplines of research. The need of the time is to develop tools for strategic planning and management that will help research bodies to rank and benchmark themselves against international standards. This will enable them to invest appropriately in research areas of promising strength and gain maximally from them, thus fulfilling the ultimate purpose of positive impact of research on society. Our tool is capable of rating and benchmarking universities as well as research institutes in not only the major disciplines and sub-disciplines, but at the finest level of niche areas of science and technology too with the help of its innovative bibliometric indicators based on publications and citation analysis. The tool accepts inputs like discipline/subject area, university, and country and time window while using data retrieved from bibliography database, Scopus, to benchmark and rate the research body under consideration. We have evaluated that there are many niche subject areas in which small or medium size universities are performing good in comparison to the large universities. Most of these subject areas are of more significance in the present day and the future. Government and funds allocating bodies should take this factor in account that investing the right money at right place will give far better results than we they are having right now

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arXiv2022

A Bibliometric Perspective of Social Science Scientific Communities of Pakistan and India

In this study, we use research publication data from the field of social science to identify collaboration networks among social science research communities of India and Pakistan. We have used Scopus database to extract information of social science journals for both countries India and Pakistan. Study of this data is significant as both countries have common social issues and many of common social values. Keywords analysis has been done to see common research areas in both communities like poverty, education, the issue of gender etc. Despite having many of the common social issues, collaboration among social science research communities of both countries is not strong.

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arXiv2022

Assessment of Sustainable Funding Impact by Exploiting Research Performance Indicators and Semantic Techniques

This paper deploys bibliometric indices and semantic techniques for understanding to what extent research grants are likely to impact publications, research direction, and co-authorship rate of principal investigators. The novelty of this paper lies within the fact that it includes semantic analysis in the research funding evaluation process in order to effectively study short-term and long-term funding impact in terms of publication outputs. Our dataset consists of researchers that receive research grants from the National ICT Research and Development funding program of Pakistan. We show a number of interesting case studies to conclude that bibliometric-based quantitative assessment combined with semantics can lead to building better sustainable pathways to deploy evaluation frameworks for research funding effectively.

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arXiv2020

A Decade of In-text Citation Analysis based on Natural Language Processing and Machine Learning Techniques: An overview of empirical studies

Citation analysis is one of the most frequently used methods in research evaluation. We are seeing significant growth in citation analysis through bibliometric metadata, primarily due to the availability of citation databases such as the Web of Science, Scopus, Google Scholar, Microsoft Academic, and Dimensions. Due to better access to full-text publication corpora in recent years, information scientists have gone far beyond traditional bibliometrics by tapping into advancements in full-text data processing techniques to measure the impact of scientific publications in contextual terms. This has led to technical developments in citation context and content analysis, citation classifications, citation sentiment analysis, citation summarisation, and citation-based recommendation. This article aims to narratively review the studies on these developments. Its primary focus is on publications that have used natural language processing and machine learning techniques to analyse citations.

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arXiv2020

Exploiting Tweet Sentiments in Altmetrics Large-Scale Data

This article aims to exploit social exchanges on scientific literature, specifically tweets, to analyse social media users' sentiments towards publications within a research field. First, we employ the SentiStrength tool, extended with newly created lexicon terms, to classify the sentiments of 6,482,260 tweets associated with 1,083,535 publications provided by Altmetric.com. Then, we propose harmonic means-based statistical measures to generate a specialized lexicon, using positive and negative sentiment scores and frequency metrics. Next, we adopt a novel article-level summarization approach to domain-level sentiment analysis to gauge the opinion of social media users on Twitter about the scientific literature. Last, we propose and employ an aspect-based analytical approach to mine users' expressions relating to various aspects of the article, such as tweets on its title, abstract, methodology, conclusion, or results section. We show that research communities exhibit dissimilar sentiments towards their respective fields. The analysis of the field-wise distribution of article aspects shows that in Medicine, Economics, Business & Decision Sciences, tweet aspects are focused on the results section. In contrast, Physics & Astronomy, Materials Sciences, and Computer Science these aspects are focused on the methodology section. Overall, the study helps us to understand the sentiments of online social exchanges of the scientific community on scientific literature. Specifically, such a fine-grained analysis may help research communities in improving their social media exchanges about the scientific articles to disseminate their scientific findings effectively and to further increase their societal impact.

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arXiv2017

Measuring Social Media Activity of Scientific Literature: An Exhaustive Comparison of Scopus and Novel Altmetrics Big Data

This paper measures social media activity of 15 broad scientific disciplines indexed in Scopus database using Altmetric.com data. First, the presence of Altmetric.com data in Scopus database is investigated, overall and across disciplines. Second, the correlation between the bibliometric and altmetric indices is examined using Spearman correlation. Third, a zero-truncated negative binomial model is used to determine the association of various factors with increasing or decreasing citations. Lastly, the effectiveness of altmetric indices to identify publications with high citation impact is comprehensively evaluated by deploying Area Under the Curve (AUC) - an application of receiver operating characteristic. Results indicate a rapid increase in the presence of Altmetric.com data in Scopus database from 10.19% in 2011 to 20.46% in 2015. A zero-truncated negative binomial model is implemented to measure the extent to which different bibliometric and altmetric factors contribute to citation counts. Blog count appears to be the most important factor increasing the number of citations by 38.6% in the field of Health Professions and Nursing, followed by Twitter count increasing the number of citations by 8% in the field of Physics and Astronomy. Interestingly, both Blog count and Twitter count always show positive increase in the number of citations across all fields. While there was a positive weak correlation between bibliometric and altmetric indices, the results show that altmetric indices can be a good indicator to discriminate highly cited publications, with an encouragingly AUC= 0.725 between highly cited publications and total altmetric count. Overall, findings suggest that altmetrics could better distinguish highly cited publications.

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arXiv2016

Altmetrics of "altmetrics" using Google Scholar, Twitter, Mendeley, Facebook, Google-plus, CiteULike, Blogs and Wiki

We measure the impact of "altmetrics" field by deploying altmetrics indicators using the data from Google Scholar, Twitter, Mendeley, Facebook, Google-plus, CiteULike, Blogs and Wiki during 2010- 2014. To capture the social impact of scientific publications, we propose an index called alt-index, analogues to h-index. Across the deployed indices, our results have shown high correlation among the indicators that capture social impact. While we observe medium Pearson's correlation (\r{ho}= .247) among the alt-index and h-index, a relatively high correlation is observed between social citations and scholarly citations (\r{ho}= .646). Interestingly, we find high turnover of social citations in the field compared with the traditional scholarly citations, i.e. social citations are 42.2 % more than traditional citations. The social mediums such as Twitter and Mendeley appear to be the most effective channels of social impact followed by Facebook and Google-plus. Overall, altmetrics appears to be working well in the field of "altmetrics".

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