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With the rise of alternate discovery services, such as Google Scholar, in conjunction with the increase in open access content, researchers have the option to bypass academic libraries when they search for and retrieve scholarly information. This state of affairs implies that academic libraries exist in competition with these alternate services and with the patrons who use them, and as a result, may be disintermediated from the scholarly information seeking and retrieval process. Drawing from decision and game theory, bounded rationality, information seeking theory, citation theory, and social computing theory, this study investigates how academic librarians are responding as competitors to changing scholarly information seeking and collecting practices. Bibliographic data was collected in 2010 from a systematic random sample of references on CiteULike.org and analyzed with three years of bibliometric data collected from Google Scholar. Findings suggest that although scholars may choose to bypass libraries when they seek scholarly information, academic libraries continue to provide a majority of scholarly documentation needs through open access and institutional repositories. Overall, the results indicate that academic librarians are playing the scholarly communication game competitively.

Purpose – As researchers need partners to collaborate with, this study aims to provide author recommendation for academic researchers for potential collaboration, conference planning, and compilation of scientific working groups with the help of social information. Hereby the chapter analyzes and compares different similarity metrics in information and computer science.

Methodology/approach – The study uses data from the multidiscipline information services Web of Science and Scopus as well as the social bookmarking service CiteULike to measure author similarity and recommend researchers to unique target researchers. The similarity approach is based on author co-citation, bibliographic coupling of authors and collaborative filtering methods. The developed clusters and graphs are then evaluated by these target researchers.

Findings – The analysis shows, for example, that different methods for social recommendation complement each other and that the researchers evaluated user- and tag-based data from a social bookmarking system positively.

Research limitations/implications – The present study, providing author recommendation for six target physicists, is supposed to be a starting point for further approaches on social academic author recommendation.

Practical implications – The chapter investigates in recommendation methods and similarity algorithm models as basis for an implementation of a social recommendation system for researchers in academics and knowledge-intensive organizations.

Originality/value of chapter – The comparison of different similarity measurements and the user evaluation provide new insights into the construction of social data mining and the investigation of personalized recommendation.

The purpose of this study is to examine the growth patterns of tag vocabulary in collaborative tagging systems to verify the sustainability and stabilization of tag distributions. Both sustainability and stabilization are essential to the mining and categorization of information driven by tagging behaviors.

The study was based on time series data of CiteULike from November 2004 to April 2010. Power law distributions were detected to reveal statistical regularities and tagging patterns. Logistic regression analysis with time‐dependent covariates was conducted to identify the factors affecting the growth of distinct tags for articles. The significance of the effects and the time taken for a given article to reach its tagging maturity were also explored.

Time series plots and trend analysis illustrated the continuous growth of the tagging system. Exploratory analysis of power law distribution fittings indicated a sign of system stability known as scale invariance. Logistic regression results demonstrated that for a particular article, the number of users who tagged the article, the initial date when the article was tagged, and the life span of the article are statistically significant to the ratio of the distinct tag number to the total tag number for a given article. These results confirmed that the distinct tag ratio of an article gives rise to a stable pattern.

Though extensive work has been done on the patterns of tag vocabulary, it is not clear how the growth of distinctive tags behaves in relation to the total number of tag applications, considering time‐dependent covariates such as the number of users, and the longevity of an article. This paper sets to complement the literature on the existing methodology and investigate this property in detail.

The purpose of this paper is to study the presence of highly cited papers of Nature in social media websites and tools. It also tries to examine the correlation between altmetric and bibliometric indicators.

This descriptive study was carried out using altmetric indicators. The research sample consisted of 1,000 most-cited articles in Nature. In February 2019, the bibliographic information of these articles was extracted from the Scopus database. Then, the titles of all articles were manually searched on Google, and by referring to the article in the journal website and altmetric institution, the data related to social media presence and altmetric score of articles were collected. The data were analyzed using Microsoft Excel and SPSS.

According to the results of the study, from 1,000 articles, 989 of them (98.9 per cent) were mentioned at least once in different social media websites and tools. The most used altmetric source in highly cited articles was Mendeley (98.9 per cent), followed by Citeulike (79.8 per cent) and Wikipedia (69.4 per cent). Most Tweets, blog posts, Facebook posts, news stories, readers in Mendeley, Citeulike and Connotea and Wikipedia citations belonged to the article titled “Mastering the game of Go with deep neural networks and tree search”. The highest altmetric score was 3,135 which belonged to this paper. Most tweeters and articles’ readers were from the USA. The membership type of the tweeters was public membership. In terms of fields of study, most readers were PhD students in Agricultural and Biological Sciences. Finally, the results of Spearman’s Correlation revealed positive significant statistical correlation between all altmetric indicators and received citations of highly cited articles (p-value = 0.0001).

The results of this study can help researchers, editors and editorial boards of journals better understand the importance and benefits of using social media and tools to publish articles.

Altmetrics is a relatively new field, and in particular, there are not many studies related to the presence of articles in various social media until now. Accordingly, in this study, a comprehensive altmetric analysis was carried out on 1000 most-cited articles of one of the world's most reliable journals.

The purpose of this paper is to investigate the behaviour preferences and patterns of the organisation of information by taggers, including usage of tags, tag categories and implicit patterns embedded in social tags.

The sample was 4,390 social tags (1,777 unique) from 1,661 articles published in 16 library and information science journals selected from CiteULike between February and March 2011. Using application profiles, a tag category model served as a framework to develop two sets of hybrid tag categories for analysing the distribution of tag categories and their implicit patterns.

The frequency of tag categories was consistent with that of individual tags and obeyed a power law distribution. In total, six implicit patterns embedded in tags – syntactical, semantic, mnemonic, genre, contextual hybrid relations and split term – were discovered.

Although this study focused solely on investigating taggers' behaviour preferences and patterns, the results of this study may shed light on tagging practice, query formulation and construction of controlled vocabularies.

A set of hybrid tag categories consisting of title, function, content and topic‐related categories is proposed to delineate the distribution of social tags and taggers' behaviour preferences, and implicit patterns embedded in tags are generalised. These patterns may be useful for tagging practice, query formulation and construction of controlled vocabularies.

This paper aims to explore the characteristics of the co-occurrence network of online tags and propose new approaches of applying social network analysis by utilising social tagging in order to organise data.

The authors collected online resources labelled “tag” from 7 November 2004 to 31 October 2011 from the CiteULike website, comprising 684 papers and their URLs, titles and data on tagging (users, times, and tags). They examined the co-occurrence network of online tags by using the analyses of social networks, including the analysis of coherence, the analysis of centricity and core to periphery categorical analysis.

Some features of the co-occurrence of online tags are as follows: the internet is subject to the “small world” phenomenon, as well as being “scale-free”. The structure of the internet reflects stable areas of core knowledge. In addition to five possible applications of social network analysis, social tagging has the greatest significance in organising online resources.

This research finds that co-occurrence of tags online is an effective way to organise and index data. Some suggestions are provided on the organisation of online resources.

The purpose of this paper is to contribute to the understanding of altmetrics in different disciplines of social science: first, by investigating the current richness and future potential of altmetrics in the selected social science disciplines and then by evaluating the validity of altmetrics as indicators of research impact in each discipline through correlation analysis.

This study uses three approaches to understand the current richness and future potential of ten altmetric measures in nine selected disciplines: first, investigate the distribution and trend of altmetric data; second, verify the relationship between citation rate and altmetric presence of the discipline using Pearson correlation; and third, perform word frequency analysis on tweets to understand different altmetric presence in different disciplines. In addition, this study uses Spearman and sign test to find the correlation between altmetrics and citation counts for the articles that receive altmetric mention(s) to test the validity of altmetrics as indicators of research impact.

There is a steady increase in the number of articles that receive altmetric mentions in all disciplines studied. In general, disciplines with higher citation rates have higher altmetric presence. At the same time, altmetrics are also an effective complement to citation in disciplines with low citation rates. There is a moderate correlation with Mendeley and significant but weak correlations with Tweets and CiteULike in seven disciplines. Altmetrics appear effective as a predictor of citation counts in seven out of nine disciplines studied. However, there is low presence and lack of correlation with citation count in business-finance and law disciplines.

This paper furthers the understanding of altmetrics in social science disciplines. It reveals the disciplines where altmetrics are most effective, potentially useful, and fairly applicable. In addition, it presents evidence that altmetrics are an effective complement to citation in disciplines with low citation rates.

This paper aims to assess the impact of research in the field of scientometrics by using the altmetrics (social media metrics) approach.

This is an applied study which uses scientometric and altmetrics methods. The research population consists of the studies and their citations published in the two core journals (Scientometrics and Journal of Informetrics) in a period of five years (included 1,738 papers and 11,504 citations). Collecting and extracting the studies directly was carried from Springer and ScienceDirect databases. The Altmetric Explorer, a service provided by Altmetric.com, was used to collect data on studies from various sources (www.altmetric.com/). The research studies with the altmetric scores were identified (included 830 papers). The altmetric scores represent the quantity and quality of attention that the study has received on social media. The association between altmetric scores and citation indicators was investigated by using correlation tests.

The findings indicated a significant, positive and weak statistical relationship between the number of citations of the studies published in the field of scientometrics and the altmetric scores of these studies, as well as the number of readers of these studies in the two social networks (Mendeley and Citeulike) with the number of their citations. In this study, there was no statistically significant relationship between the number of citations of the studies and the number of readers on Twitter. In sum, the above findings suggest that some social networks and their indices can be representations of the impact of scientific papers, similar citations. However, owing to the weakness of the correlation coefficients, the replacement of these two categories of indicators is not recommended, but it is possible to use the altmetrics indicators as complementary scientometrics indicators in evaluating the impact of research.

Investigating the impact of research on social media can reflect the social impact of research and can also be useful for libraries, universities, and research organizations in planning, budgeting, and resource allocation processes.

The purpose of this paper is to report on a comparative and analytical examination of ten social tagging systems' interfaces and their features and functionalities. The specific objective of the study was to examine the ways in which the user interfaces of social tagging systems encourage and provide users with features to assign, explore, browse and make use of tags during their interaction with social tagging sites.

The user interface features and functionalities of ten social tagging sites (six social bookmarking and four social media sharing sites) are examined. A categorisation of tag‐related features is developed for analysis. The sites are selected based on such criteria as popularity, variety of site type, and inclusion of tagging features and content type.

The findings of this study show that there is an emerging interface design paradigm with respect to social tagging sites that reflects a particular focus on exploratory search and browsing features and services. Some of the key areas discussed are: user tagging features; exploratory and tag browsing features; and interface layout.

The findings of this study of the user interface features of social tagging sites provide a comprehensive picture of the possible and potential features that can be incorporated into new social tagging systems. Based on the evidence found in the examined social tagging interfaces, recommendations are made on the design of tag posting, tag use, tag browsing, tag lists and tag clouds. The design recommendations offer ideas for the development of more sophisticated exploratory and interactive user interfaces for social tagging systems.

This is the first paper that reports on a comparative and exploratory examination of social tagging user interface features and functionalities.