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The goal of evidence-based medicine is helping clinical professionals to obtain relevant and valid information to make appropriate clinical decisions. Therefore, to meet the information needs of health professionals, information retrieval systems should facilitate access relevant and also methodologically valid.

The current research uses the quasi-experimental method of one group pre-test - post-test in order to achieve the research goals. The research community was made up all the clinical trial articles available in the Cochrane database until the end of 2018.

The results of the present study showed that among the research variables, the words of the reviewers' comments along with the abstract had the greatest impact on the ranking. These results showed that the Cochrane reviewers' comments can be a suitable tool for use in ranking systems based on relevance and validity. Also, the results of this research showed that it is possible to rank the methodological validity and combine it with relevance by using the internal and external elements of the articles.

This research distinguished the types of words used in the text of scientific articles and its effect on improving the ranking based on relevance and methodological validity. In retrieval systems based on natural language, more emphasis has been placed on the totality of the text and less attention has been paid to the contribution of vocabulary types in explaining the relevance of articles. The findings of this research highlighted, once again, the difference of vocabulary types in explaining relevance and methodological validity and revealed the necessity of determining the target vocabulary set in all types of searches.

Methodological validity is one of the aspects of quality. Methodological elements are parts of the text of articles that deal with research methodology. The purpose of this study was to determine the contribution and role of methodological elements in explaining the relationship between evidence and questions.

This semi-experimental study employed a one-group pretest–posttest design. The research population consisted of clinical trial articles included in the meta-analysis of Cochrane systematic review articles. The sampling method employed was purposeful, whereby systematic review articles containing at least 50 related clinical trial articles retrieved by the retrieval system were selected as the research sample.

The results of the paired t-test showed that the difference in the average nDCG score across all four groups was negative at all points of accuracy. The highest average difference (−0.064) was observed for the basic and standard methodological elements in the abstract at accuracy point 10 (the tenth document in the retrieved results), while the lowest average difference (−0.021) was observed for the basic methodological elements in the abstract at the 50th and 70th accuracy points.

The findings of this research showed that methodological elements, whether independently or to expand the abstract, do not affect the ranking of relevance results or may even have a negative effect. In other words, the occurrence of methodological elements in the text or their weighting can reduce relevant results.

The purpose of this study was to systematically examine dimensions, aspects, and levels of search stopping in the literature in order to identify gaps in the field.

Research reports on the subject published between 1961-2020 were identified in databases. The contents of 60 studies found were thematically analysed in terms of basic, organizing, and comprehensive concepts.

The reviewed studies have identified two stopping levels, namely session-level stopping and query-level/ result- summary- level stopping. At the latter level, the search session does not stop permanently, but the user continues searching after refining the query. The studies also referred to another stopping method labeled as “search- engine- result- page- level stopping” in which the user leaves the result page without clicking on any result after making a general inference of the search results. Then, the user may or may not continue searching after refining the query. Here, we are not dealing with another level of stopping, but only the time of making decision to stop is changed. Most of the 60 studies we identified dealt with session-level stopping. Identifying stopping rules, examining the application of stopping rules in various contexts, identifying the factors affecting search stopping or query stopping, checking search- engine- result- page- level stopping, examining the depth of users' search, distinguishing between stopping due to satisfaction or frustration, and exploring search stopping signs were dimensions of search stopping studied in the literature.  Stopping rules included satisfaction and frustration, magnitude threshold, difference threshold, representational stability, mental list, and single criterion. Also, factors affecting stopping search included time constraints, search task structure, information scent, user interface features, importance of the task to the users, motivation, domain knowledge, interest in the topic, need for closure and need for cognition.

Our review showed that studies each dealt with some specific aspect(s) of search stopping behavior and sought to identify the rules or factors affecting it. Research gaps we identified include not investigating the impact of variables such as search language, task difficulty, and individual differences on stopping behavior, the detailed differentiation of the signs of good abandonment from bad ones, and the use of eye tracking technology and recording electrical activity of the brain (Electroencephalography) to better understand the stopping behavior.

Quantitative citation analysis fails to take into account different citation motivations which may be neutral, confirmative, or negational. It necessitates devising new methods or techniques to evaluate cited documents based on the attitudes of their citing articles, to increase the accuracy of the results of the quantitative approach. The challenge is believed to be partially answered by content analysis of citations, including citation opinion mining. It is based on extracting and analyzing the sentiment words occurring in citations, or citation contexts, i.e., a word window surrounding any given citation within a citing paper. However, there exist few pieces of evidence on the degree of divergence or convergence of the results of the quantitative and content-based approaches. To provide further research evidence, the present study investigated the correlation between the results of these two citation-analysis approaches.

Using a citation analysis method with the quantitative and opinion-mining approaches, this communication explored a sample consisting of 524 medical papers. Their bibliographic information and citations were extracted from PubMed and CoLil, respectively. 3663 citations were identified, of which 3639 contexts were available through CoLil. The citations were processed using the KNIME data mining platform. The opinion scores of the words were extracted from SentiWords. The citations opinions were measured in terms of the polarity and strength of the average opinion scores of their words. The data were then analyzed by Spearman Correlation.

The citations were revealed to carry numerous sentiment words. They were mostly positive in their polarity, however, the number of citations with negative polarity was also considerable. The citation counts were found to be directly and strongly correlated to the absolute count of the opinionated citations. However, they were revealed to be indirectly and weakly correlated to the relative count of opinionated citations, i.e., the number of opinionated citations normalized by the total count of the citations. Furthermore, the citation counts had an insignificant correlation with the relative frequency of positive citations, while displaying significant direct relationships with negative and neutral ones. Moreover, they were indirectly associated with the average opinion scores.

In general, the findings of this research showed that as the number of citations increases, the relative number of opinionated contexts decreases. They were dominated by a positive polarity which is in line with previous studies revealing a confirmative motivation in citation behavior. The predominance of positive opinions implied explicit and implicit confirmation motives of researchers reflected in the citing works: the citer may explicitly express her opinion about the cited article and its features, or implicitly express her approval by citing it (i.e., using algorithms, methods, tools, findings and etc.).According to the findings, as citation counts increase, the cited papers witness a reduction in their opinionated contexts’ relative numbers, while experiencing an increase in their negative and neutral ones. Consequently, the content-based citation analysis with an opinion-mining approach may be able to adjust the results of the quantitative approach. However, this finding and its generalizability should be treated with caution. Because the sample of the current research was not selected randomly. Given the differences between disciplines and scientific communities in their citation behavior, it is necessary to replicate the research in various contexts to support the results. Moreover, in the dictionary-based opinion mining method applied in the present study, the big challenge is precisely detecting negative opinions. As in negative citations, the negative opinions of citers may be mingled with their reports of negative objective findings. In other words, the method cannot precisely distinguish these two types of opinion contexts, i.e., the negative attitudes of the citers and their narration of negative findings. Moreover, citations are of social nature. Therefore, negative citations are mostly hidden and indirect, and their lexical identification is hence quite difficult. Thus, advanced methods using, for example, machine learning algorithms, are required to detect and analyze any possible implicit and indirect negative opinions which the direct natural language method may fail to capture.

Despite the novelty in methodologies, User behavior study based on brain activity during information-seeking stages has become popular among information science researchers. This paper reviews scientific publications in which information-seeking behavior has been studied along with recorded brain activity to shed light on research status, challenges, and suggestions for future studies. Based on Kitchenham & Charters (2007) framework, a complete web search was performed in English and Persian scientific databases, and 22 publications in English were found as the final result, from 2007 to 2020. Review results demonstrate that exploring the user status (10 papers) and brain wave activity during information-seeking episodes (12 papers) were the most dominant subjective approaches in the field of user behavior studies. Cognitive load was found as an effective cognitive component on user status. With eye movement measurement and brain waves frequency study, 3 factors were found effective on cognitive load level generated during information searching and processing: searching media type, information representation, and text reading style. Brain wave activity and pupil dilation analysis were the most important measures in user status during search stages, and alpha and theta band waves were demonstrated as an index for cognitive load measurement during the information searching process. A correlation among eye data, search behavior, task complexity based on user experience, and cognitive style – as another effective factor on user status- led to results in different information searching behavior demonstrations. Also, 3 main stages were analyzed in the information-seeking process, based on brain wave activity: information exploring and query formulation, query reformulation and selection, relevance judgment, and decision making. Results showed a difference between brain activity areas, and differences in pupil dilation change level and alpha/beta frequency level during different search episodes. For future research, some suggestions were offered based on reviews. Finding relations between correlations among cognitive styles, task features, and domain knowledge during information searching process, personalized information retrieval improvement, more collaboration between information science and neurocognitive specialists, research in more user affective status like aggression and fatigue during the search process, using more economic methods and portable devices aiming to reduce research costs and expenses, facilitating larger sample studies and designing standard tasks were considered as a suggestion. Finally, some challenges were found based on reviewed studies. Some concepts like relevance feedback in information retrieval need more investigation. Also, it is necessary to investigate and explore user affections during the search process with multiple approaches.

The level of desired and objective experiences of students during the collaborative information seeking is explored.

The statistical population in this survey research included all graduate students of Shiraz University in 2020. The sample consisted of 302 students from different faculties, selected according to the Morgan table. Data gatherin too was a researcher-made questionnaire designed by reviewing the literature and  extracting relevant items. extracted.  To assess the face and the content validity of the questionnaire.  The draft of the questionnaire was sent to seven LIS experts, and finalized according to their recommendations. The reliability of the instrument attained Cronbach's alpha coefficient with a value of 0.91.

The sample t-test and comparison of means showed that the average of collaborative information seeking behavior in the study is more than the expected average (70%). So it can be said that the tendency of postgraduate students towards collaborative information seeking is high. Also, the results on the subjects' experiences showed that there was no significant difference between the observed and expected frequencies "experience of searching information on the web separately and then discussing with a group about the results" and "Experience searching the web separately with a separate computer or mobile phone". This means that students who had group web search experience (71.2%) as well as students with collaborative information seeking experience in homework and projects (74.2%) were more than those without such experience. Also, the number of students whose group work lasted between one week to one month (35.4%) and students who needed different sources of information (82.8) was higher. On the other hand, significantly more students experience searching the web as a group in a common place, in different places, communicating through online chat or telephone conversation. Those communicate with a shared computer or mobile phone and use collaborative information software less than those who had never experienced this type of search.

The results showed that the tendency for collaborative information seeking is high. Paying attention to this segment of the scientific community can lead to more knowledge of collaborative information seeking experience. Also, recognizing the tendency of this group to participate in collaborative information seeking behavior can help the policy makers of the higher education system to plan, considering solving possible problems such as little use of students 'collaborative search tools and the existence of the necessary platforms for students' collaborative work in educational and research center and strengthen these behaviors.

Informed peer-review university ranking systems like REF involve peer review to achieve high-quality university performance evaluations while considering bibliometric facts. Despite its advantages and methodological success, it is not possible to implement the review-based exercise in all countries due to unaffordable costs, vulnerability to bias, and differences in cultural, economic, managerial, and infrastructural conditions. As an alternative, some science systems prefer to consider using the results of international university rankings to gauge the academic performance of their higher education institutions. Using a different methodology, the international ranking systems tend to maintain their efficiency by heavily relying on the bibliometric information extracted from databases, the performance data gathered from official authorities, and academic and employer surveys. This gives rise to the question of to what extent these two types of systems are convergent in their results. If their different methodologies lead to similar results, they could be interpreted to have similar effectiveness in their evaluation of academic performance. Therefore, by relying on the results of the international ranking systems, one can avoid the shortcomings of the review-based method, and maintain both the efficiency and effectiveness of the evaluation systems. To reveal the convergence of the results obtained from the methods, the present study explores a sample of British universities evaluated by REF (2014) to investigate the correlation between their scores in REF and the world’s prestigious university rankings.

Using a quantitative content analysis method, the present study concentrates on a collection of 150 British universities evaluated simultaneously by REF (2014) and at least one of the world's prestigious ranking systems including QS, THE, Leiden and ARWU. Due to the small size of the population, all REF members are examined without sampling. The evaluation results of these universities are extracted from these systems and entered into a checklist. Also, the subject fields and disciplines covered by the universities are collected to investigate their probable effects on the results. The universities’ subject coverage similarity is calculated using the Cosine similarity measure and K Nearest Neighbor technique in the KNIME data mining platform. Finally, correlation and regression analyses are applied to analyze the data.

UK universities’ scores in REF are found to be significantly correlated to theirs in international ranking systems. They are highly correlated to QS and THE’s, while being moderately associated with ARWU’s, and weakly-to-strongly correlated to Leiden’s. The regression analyses show no significant effects of subject coverage on the overall scores, except for medical tendency’s effect on QS. However, the subject coverage affects some dimension scores. While it does not significantly predict any of the dimensions of ARWU, it partially predicts the Citations and Industry Income dimensions in THE, with medical and technical, and engineering subjects respectively having the highest positive predicting power. In QS, the subject coverage partially predicts the dimensions of Academic reputation, Faculty/student ratio, International faculty ratio, International student ratio, and Citation Per faculty. Medical science has the highest positive effect on the dimensions of Academic reputation, Faculty/student ratio, and Citations per faculty. Moreover, basic sciences have the highest negative effect on the International student ratio and International faculty ratio. Also, the subject coverage can predict Leiden scores for the dimensions PP (top 10%) (i.e. the proportion of a university’s publications that, compared with other publications in the same field and the same year, belong to the top 10% most frequently cited), MCS (i.e. the average number of citations of the publications of a university), MNCS (i.e. the average number of citations of the publications of a university, normalized for field and publication year), PP(collab) (i.e. the proportion of a university’s publications that have been co-authored with one or more other organizations), PP(int collab) (i.e. the proportion of a university’s publications that have been co-authored by two or more countries), PP(industry) (i.e. the proportion of a university’s publications that have been co-authored with one or more industrial organizations) and PP(>1000 km) (i.e. the proportion of a university’s publications with a geographical collaboration distance of more than 1000 km). Medical science has the highest positive predictive power for the scores in all the mentioned dimensions except for PP(industry).

The results of REF, as a peer-review-based university ranking informed with bibliometric data, are highly correlated to those of the international evaluations based on performance statistics and enriched by surveys, while being moderately correlated to those performed by just performance statistics. The subject coverage impact on the rankings challenges the application of the results in comparing universities with different subject coverages.

The purpose of this study is to identify the impact of trust on collaborative information seeking behaviors of postgraduate students of Shiraz University.

A quantitive approach was applied and survey method was used to analyze the the impact of trust on collaborative information seeking behaviors. The statistical population included all graduate students of Shiraz University in 1399. The sample of this research was randomly calculated from 302 students of different faculties based on Morgan table.

The results showed that the tendency to collaborative information seeking behavior can be predicted by the variable of trust (40. 5%). Among the sub-components of trust, the perceived trustworthiness sub-component had the greatest effect (32. 9%) on the tendency to collaborative information seeking behavior, followed by cooperative behaviors (31.8 percent) and the propensity to trust (9 percent), are placed.

Despite the potential for trust in predicting collaborative activities, no research has been conducted that has examined the role of this in relation to collaborative information seeking behaviors. The results of the research can lead to a better understanding of the factors affecting collaborative information seeking behaviors.

Several factors are involved in the production and presentation of recommender systems.The aim of this study was to investigate the effect of the two challenges lack and sparsity of data on the effectiveness of the proposed results of the RICeST Journal Finder. The corpus includes more than 15,000 articles from technical and engineering publications in the period 2013 to 2017, which have been collected from their website. Textual elements of these articles were extracted, normalized and processed, and a research body database was created. Based on the number of collected articles, by using Cochran's formula, 400 basic articles that previously published in related to the topic of each journal were selected in a random-proportional method. Title and abstract of these articles as a query, in order to receive the system Journals suggested, to print the article in two stages of before and after improving the two challenges of lack and sparsity of data in the test corpus. The suggested results in each step were saved in Excel. Finally, the effectiveness of the system results in each stage was determined by Leave-one-out Cross-Validation method and based on the accuracy criterion in k.The relative abundance of categories showed that, in the current situation, the target journal was suggested in only 26% of searches in the first 3 ranks. After enriching, normalizing and processing the data and thus improving the lack of data challenge, although 30% of the results were still rated above 10; But the accuracy of the results in the first 3 ranks increased by 15%. Also, after thematically categorizing the data with the aim of improving the sparsity challenge, 30% increase in the accuracy of the system results in the first 3 ranks compared to the previous step was achieved. The results of this study showed that enriching the database, improving the processing process and thematic classification of data in RICeST journal finder can reduce the two challengs lack and sparsity of data and increase the effectiveness of the proposed results of this systems.

To identify the most important factors affecting query formulation.

The design of this study was qualitative. The factors were extracted and categorized through a systematic review and an analytical meta-analysis in six steps. The steps included: determining keywords, creating inclusion criteria and deleting articles, background search, extracting lists A, B and C, descriptive analysis, and thematic analysis. In this regard, based on a search cycle and using a number of keywords approved by experts, articles related to the factors affecting the query formulation were studied. After descriptive analysis of these resources, contents were extracted according to the topic. The research population included all published articles on the factors affecting the query formulation in the period from 1991 to 2021. These resources were searched in reliable databases such as Scopus, Springer, Emerald, Web of Science, Elsevier, Eric, ProQuest, Ebsco, Cite Seer, Information Research, LISA, LISTA and Wiley. After extracting articles, collecting and analysing qualitative data, these analyses were used to determine the factors affecting the query formulation.

Findings showed that the factors affecting the query formulation itself originate from several fields such as user and retrieval task, which can be classified into two general categories of user-related factors and task-related factors. User-related factors included subject knowledge, knowledge of how to use the search system, search experience, language, demographic features, contextual factors, cognitive style, and cognitive load. Task-related factors included the type of search task and task difficulty. These factors change the formulation process in different ways that can be considered both positive and negative, as some of them may help to improve the quality of the formulation, others may prevent retrieval to be performed correctly.

Identifying factors affecting the query formulation and analysing the relationships between them can improve the quality of information literacy training by experts in this field. Also, it helps web designers create search tools tailored to the user's needs. Since formulation is an important part of information retrieval behaviours, recognizing the influencing factors can help discovering information retrieval patterns. Understanding how to formulate a query under the influence of various factors and recognizing these factors, can help increase the quality of search and retrieve more relevant results and improve human-computer interaction.

This study aims to compare the researchers' views in two fields about the importance of criteria for submitting an article to the journal and the degree of thematic relevance of the proposed results of the RICeST Journal Finder.

The research is a survey in terms of applied purpose and the data collection method and was done in two steps; First, due to the lack of a standard questionnaire, while studying the literature, important and common criteria for the researchers of the two fields were extracted and a researcher-made questionnaire consisting of 13 criteria was prepared. The face and content validity of the questionnaire was done by 10 experts in information science and epistemology. Then, in order to identify the importance of the criteria as well as the thematic relevance of the system's proposed results, subject matter experts (reviewers) in two fields from all over Iran were used.

Criteria of Peer review, thematic relevance of the article with the thematic scope of the journal, and having an impact factor from the perspective of researchers in both groups, were the most important and the age of the journal was the least. Measuring the thematic relevance of the results suggested by the system using the opinions of experts showed that in more than 85% of the queries, the proposed publication for the article is completely relevant. There is no statistically significant difference between the opinions of experts in these two areas. It is necessary to explain that the evaluation of the thematic relevance of the results was done after improving the existing challenges in the RICeST journal finder.

There are many Journals in various scientific fields, so authors are facing challenges to find the most appropriate journal to publish their research findings. The results showed that the importance of the criteria of selecting the journal, from the viewpoint of national researchers, is consistent with the findings of international studies. However, considering the mental variables of researchers in different conditions, it is not possible to consider a single factor category to choose a journal to publish a manuscript; However, the possibility of refining the results, based on the priority criteria of the researchers has been introduced the use of Journal finder systems as an auxiliary tool that can be found more quickly and easily to a list of related publications. Among the other factors that can be examined objectively and are important, regardless of the author's priorities and limitations, is the thematic connection of the manuscript with the journal to which the manuscript is to be sent and published, which is the basis of the performance of the Journal finder systems. In general, according to the obtained results, the authors can use the RICeST journal finder at the national level to obtain relevant journals to publish the manuscript.

During health crises, such as Coronavirus disease 2019 (COVID-19) pandemic, the spread of fake news and rumors makes it difficult to distinguish real news. Besides, despite dangers, some endanger public safety by avoiding vaccination. It is not clear to what extent the individuals understand the dangers, and are influenced by the fake news. To elucidate the causes of this risky behavior, this study examined the role of fake news detection and risk perception in vaccine acceptance among students of Shiraz University, Shiraz, Iran.

Research method was survey, and research tool was a combined electronic questionnaire, including vaccine acceptance questionnaire of Montagni et al., fake news detection, developed by the researcher, and the risk perception questionnaire of Champion. The sampling method was voluntary. 382 students from Shiraz University (academic year of 2021-2022) answered the questionnaire. Data were analyzed through chi-square and binary logistic regression tests.

72.0% of students were vaccine acceptant, and 62.8% had a moderate risk perception regarding the disease. Although the students' demographic characteristics and risk perception showed no significant correlation with the vaccine acceptance, their ability to detect fake news had a significant positive correlation. In effect, the participants with higher fake news detection scores were 0.8 times less likely to be classed in the vaccine hesitant group, compared to the vaccine acceptance ones.

Acceptance of fake news about COVID-19 vaccine may reduce the vaccine acceptance. This finding highlights the key role of fake news detection literacy in controlling the behavior of individuals, and thus the safety of society.

This study aimed to identify the main features of Open Access tweeted by Twitter users and to analyze their opinions about them. A sample of English tweets about Open Access was identified and analyzed using a quantitative content analysis method with opinion mining approach. To do so, based on a wide literature review, the main features of Open Access discussed in the literature were identified and used to compose the search formula. This step retrieved 874270 tweets. The data were, then, cleaned using exact match search in Excel, resulting in a sample consisting of 165320 relevant tweets. The sample was, then, classified into four thematic groups including “costs and financial resources”, “publication and resources”, “models” and “validity and quality”. To analyze the tweets’ contents and calculate their opinion scores, the KNIME and SentiStrength applications were used, respectively. The data were analyzed using descriptive and inferential statistics in Excel and SPSS. The results showed that the concepts related to the Open Access “publication and resources” have been tweeted the most. The group being set aside given its all-inclusive nature, the “credibility and quality” as well as the “costs and financial resources” are the two main issues discussed by Twitter users. According to the opinion mining results, more than half of the tweets were neutral and the positive opinions are of slightly higher frequency compared to the negative ones. The “credibility and quality” and the “costs and financial resources” received more negative tweets. Thus, in line with previous surveys conducted to explore the concerns of researchers and stakeholders about Open Access, the present study provided an almost similar picture for the social space. Since Open Access to scientific information is a necessity for the development of knowledge-based societies, the results highlight the need to take serious steps to strengthen its positive points and devise solutions to the problems and challenges leading to the negative feedbacks.

The present study aimed to investigate the correlation between universities scores in Leiden and Nature index as two simple ranking systems based on bibliometrics with two complex systems, Times and QS.

 Using a documentary research method, the present study investigates an available sample consisted of the top universities commonly ranked in Leiden, Nature index, Times and QS university ranking systems during 2012-2017. Spearman's correlation analysis was used to identify the statistical relationship between the results of these systems.

 The findings showed significant and positive statistical correlations between university scores in Leiden and Nature index on the one hand and those of Times and QS ranking systems on the other hand. However, the results of the Nature Index and Leiden ranking system showed to be more strongly correlated with Times’ than with QS’.

Conclustion: 

There exist degrees of alignment between the results of the simple and complex university ranking systems, brought about by their similarities in dimensions and indicators, especially in those related to research productivity. However, the associations are not so strong to lead to identical ranking results.

Scientific communities have always been concerned about validity of open-access articles. Given the challenges of quantitative citation analysis in evaluating scientific articles, content-based citation analysis, including opinion mining of citances, can bring about more transparent results about their validity. In view of this, the present study compared the opinions contained in citances about open-access and non-open-access articles.

We used a quantitative content analysis method with citation and opinion analysis approaches. The citances, bibliographic, and bibliometric data were extracted from Colil and PubMed databases. Opinion scores were assigned to the citances through SentiWords. After processing the titles, abstracts, and citances, Cosine similarity of Term Frequency-Inverse Document Frequency (TF-IDF) values were calculated. The open-access and non-open-access articles were then paired by their similarities in abstracts, titles, and citances. The data were analyzed using Friedman test and Spearman correlation.

There was no significant difference between the open-access and non-open-access articles in terms of their opinion scores, despite a significant difference in citation advantages. The pairs’ citance and textual similarities had no significant correlation with their opinion distance.

Although the open-access studies had citation advantage over their similar non-open-access peers, they showed no significant opinion distance. Besides, similar texts did not necessarily follow the same opinion patterns. Consequently, to complete the results of quantitative citation analysis, the content-based citation analysis is emphasized

Keyword and phrase extraction is a prerequisite of many natural language processing tasks. However, a review on the related Persian and English literature showed that a few studies have already been done on how to extract keywords and phrases from Persian texts. Thus, Aiming to shed light on the research status of Keyword and phrase extraction from Persian texts, the present study reviews the Persian and English publications which have assessed their research ideas over Persian texts. We also focus on each of the studies to challenge their methodologies, implementations and evaluation methods and measures.To our knowledge, a total number of 14 Persian and 6 English papers exist which have worked on the extraction of Persian keywords and phrases. Investigating on the papers revealed that they were mostly based on statistical and linguistic information. A majority of the papers suffered from the lack of either appropriate methodologies or lucid explanation of their research ideas. They generally used non-standard datasets and vague or problematic metrics to evaluate the experimental systems. Generally speaking, except for 3 papers that appropriately reported their proposed methods, the other papers lacked reproducibility and generalizability. Hence, their results cannot be confidently used as a benchmark in evaluating future works, and their proposed ideas cannot be employed in developing applications for extraction of key words and phrases from Persian texts.

Considering the development of social web, its capacities in publishing research findings, researchers’ tendency in utilizing social web to increase their visibility, beside the importance to answer the question of how to measure the research social impact, the present study attempts to provide new insights into the amount of scientific and social users' attention to climate variability articles and do co-occurrence analysis of the key terms. This is to provide scholars with a knowledge visualization in the field and help them identify central or neglected issues.

The current applied research was conducted through altmetrics method along with the use of co-word analysis. The climate variability articles analyzed using descriptive and analytic statistics in Excel, VoS viewer and SPSS version 20.

The results showed that a number of 2755 articles in the field received 89129 citations over the past 41 years. The most cited article published in 2000, received 1458 citations. The growth rate of the publications in the field was 2366 percent with the largest number of annual publications in 2017. The United States, China and Germany contributed the largest number of articles, respectively, while Iran ranked 43. From among 2556 articles with DOIs, a number of 1447 items were available from Altmetric explorer and received a total of 8832 social citations in 14 social Medias. Mendeley (92358 reads) and Twitter (6480 tweets) were the most used social media, respectively, followed by news outlet, blogs, and policy documents. The most active users of Twitter, Facebook, news media and policy documents are from the US. The altmetrics attention score of the most influential papers is 651 and for an Iranian paper was 82, published in 2016 and 2015 respectively. Spearman test showed that there was a significant though weak relationship between citation and altmetric score. The most productive, most socially and academic cited subject categories was multidisciplinary sciences. Visualizing climate variability articles revealed that sea surface temperature, global warming, southern oscillation, and El Niño were the most frequently used key terms in the articles.

The value of this research lies in providing a picture of four decades of research in the field and in the clarification of the extent to which social network users pay attention to climate variability issues and how much is the power of altmetrics in showing the impact of science on society and research evaluation.

The present survey aims to study the scientific texts and social texts linked each other by citations in order to investigate the influence of scientific terms on social texts. The social text is expected to be a bridge between scholars and users by citing scientific texts. By indexing scientific and social text and measuring its similarity by means of an inclusion index, it is possible to see the influence of scientific terms on social texts. The basis of the statistical data of the present study is the scientific papers on climate change issues published in the journals covered by the Web of Science, which have the role of the research community. Sample members consisted of 9876 scientific and social texts (7912 social texts cited 1964 scientific texts) that were extracted and stored in .txt files with the help of Python web scraping using beautiful soup package. The pdf files were extracted manually and converted to a txt file. The indexed terms of the scientific and social texts and the common terms between them, were identified and categorized according to their length and document types separately using the automatic negative indexing model and regular expressions. Measuring the penetration of scientific terms on social texts using the inclusion index, showed that the average penetration rate was 27%. There was a positive, significant and strong correlation (0.76) between the number of common terms and academic penetration. The results of chi-square test showed that there is a significant relationship at 1% level between the term length and the average penetration rate. There is also a difference between the level of penetration and the type of social documents type, including news, blogs, policy documents, Wikipedia, and peer reviews. The highest percentage of penetration is related to policy documents and then Wikipedia. Obtaining 22 citations and 40 social citations on average per paper, and the strong correlation between citation and altmetric score, indicates the citation influence among the academic and non-academic community. Collectively, with the power of altmetrics like policy documents, Wikipedia and blogs and their permeability, they can be used to further promote scientific findings. Publishing scientific findings in these sources, even in a different language from scientific authors, can contributed to greater awareness of society and the general public.

The present study investigated the challenges of transliteration of Persian names to English ones, and the impact of these challenges on the recall of web of science’s retrieved results. This study is applied in terms of objectives and quantitative (i.e., survey) in terms of data collection procedure. The population included the names of Iranian authors who had published an English article in Web of Science between 2010 and 2017. First of all, 3110873 names were retrieved. It reduced to 11,242 after homogenization process. Data was collected through Bibexcel and Excel software and Web of Science database. The findings showed that there were three types of challenges (i.e., vowels, consonants, and omitted or double consonants) in transliteration of Persian names to English ones; they resulted in non-homogenous and different written forms. In addition, the results of Chi-square test showed that the frequency of all forms of the mentioned challenges had a significant effect on the recall of the retrieved results. Considering the importance of retrieving the names in retrieval studies as well as research studies’ evaluation, some solutions, such as the using Boolean operators and wildcards between the prevailing typographical forms, creating Persian soundex, and equipping the database with the thesaurus, are suggested

The social web provides a platform for publicizing open peer review reports. In this sphere, journal readers, authors, editors, and reviewers can involve in multilateral discussions on the reviewed papers and share their comments and viewpoints on the merits and probable pitfalls of papers. Open peer review comments may, hence, reflect the features of their mother articles. To identify this potential, the present study investigates to what extent similar comments accurately predict similar papers.

Applying natural language processing techniques, it analyzes the contents of a sample of papers in medicine and life sciences and the comments received by them. To do so, a test collection is built from the papers openly published on F1000Research, an open access publishing platform that adheres to an open peer reviewing process by transparently providing the public with peer review reports, authors’ responses, and users’ comments. The test collection consists of 2212 papers and their comments. 100 papers are randomly selected as seed documents that serve as queries. The similarities between the comments and the contents of the papers are calculated using Cosine similarity of TF-IDF values. The TF-IDF values are calculated for both unigrams and bigrams extracted from the contents and comments. The correlation between the content and comment similarities is analyzed using Spearman correlation, given the non-normality of the data distributions. The accuracy of prediction of the papers’ content similarity by the similarity of their comments is tested using Receiver Operating Characteristic (ROC) curves.

The results of the Spearman correlation revealed a significant correlation between the content and comment similarities. This signifies that similar papers are more likely to receive similar comments and vice versa. The ROC curves show that similar comments can significantly identify similar papers, either at unigram or bigram level. The prediction is highly accurate.

Similar comments are effective in representing similar papers. In other words, similar comments are expected to present similar papers. This finding has implications for interactive information retrieval systems, where users are interested in reading experts’ comments on a given paper before viewing or downloading the paper itself. The findings also may pave the path towards new studies about the application of the comments in such spheres as information retrieval, evaluation or classification, where content similarity is of importance.

Books are significant scholarly publications which contribute a large part to the diffusion of scholarly findings and aid general knowledge promotion. The major goal of this research was to determine the relationship between teaching ranks of textbooks used in World Top Rank Universities according to Times Higher Education and indicators extracted from the citation, PageRank, and HITS.

The current study is applied research and utilizes descriptive correlation method. This research was conducted using citation analysis. The population involved 1869 textbooks from the collections of indexed books in Scopus. The books were identified through matching 168,866 book titles published between 1953- 2017 which were indexed in Scopus and recorded in Open Syllabus Project as teaching resources of World Top Rank Universities. The Library of Congress Classification was used to determine book subjects. The citations count of each book was exported from Scopus. PageRank and HITS algorithms were calculated by books citation networks using the Sci2Tool program.

The results showed that there was a weak relationship among all three levels of teaching ranks with citation counts. Besides, The correlation between teaching ranks and PageRank were moderate and significant. Of the two ranking scores associated with the HITS algorithm, the correlations between teaching ranks and authority scores were significantly weak, but they were only significant at sub-classification levels with Hub scores. Furthermore, teaching ranks had a significantly moderate correlation with both PageRank and Authority scores in Social Sciences (H), Philosophy, Psychology, Religion (B), Politics (J), Law (K) and World History (D).

Pagerank and authority scores of books calculated based upon the book citation network can be used in determining the teaching impact of books in some fields. Hence, this research suggests international citation indexes to utilize these indicators for books or to make the calculation of such indicators feasible for users. This is the first research conducted to examine the relationship between teaching rank of books with citation network indicators calculated by PageRank and HITS algorithms.

The purpose of this study is to investigate the citation status of Iranian publication in the National Journal Set (NJS) and the identification of Core journals in groups of Citation Core Journals, Publishing core journals, and Mathew core journals.

The present study conducted using citation analysis method. Data analysis was performed using Excel and SPSS software. The sample consists of scientific articles from five top universities of Iran, which are indexed during the years 2011 through 2014 at the Scopus Citation Database.

The results showed that the National Journal Set Includes 994 titles, of which 182 were headed to the Publishing core journals, 107 titles for the group of Citation Core Journals, 193 titles were Negative Mathew Core journals and 14 titles belong to Positive Mathew Core journals. All Iranian journals during the time period of the study had 942 negative matthew citations and 73 positive matthew citations, meaning that they lost 942 potential citations.

The findings of this study showed that the Iranian articles in the period of study loss many of their expected citations and did not have proper performance in the National Journal Set.