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In response to questions by Buxton and Meadows, there was an examination of the occurrence of title words in the abstracts, first paragraphs, last paragraphs and cited titles of research papers in chemistry, economics, history, mathematics and philosophy for the 1960 and 1970 eras. Title word occurrence in first paragraphs varied little among disciplines. Last paragraphs tended to have most frequent occurrence of title words in history and philosophy, and cited titles had most frequent occurrence in chemistry and mathematics. There was no significant difference between chemistry and mathematics of occurrence in abstracts; abstracts were not available for the other disciplines. Among disciplines taken as a whole, the best reflection of title word occurrence was the collection of abstracts, followed in order by first paragraphs, last paragraphs and cited titles. First and last paragraphs together provided 70% to 80% of the title words. For most disciplines, longer than average titles did demonstrate a higher frequency of title word occurrence in first and last paragraphs than did titles in general. The results implied that indexing based on extraction of title words could employ similar procedures from discipline to discipline. Nevertheless, sensitive information retrieval systems should be prepared for changes in the vocabulary of fields like history and philosophy to occur possibly more slowly than in fields like mathematics and chemistry.

Scientific impact is traditionally assessed with citation-based metrics. Recently, altmetric indices have been introduced to measure scientific impact both within academia and among the general public. However, little research has investigated the association between the linguistic features of research article titles and received online attention. To address this issue, the authors examined in the present study the relationship between a series of title features and altmetric attention scores.

The data included 8,658 titles of Science articles. The authors extracted six features from the title corpus (i.e. mean word length, lexical sophistication, lexical density, title length, syntactic dependency length and sentiment score). The authors performed Spearman’s rank analyses to analyze the correlations between these features and online impact. The authors then conducted a stepwise backward multiple regression to identify predictors for the articles' online impact.

The correlation analyses revealed weak but significant correlations between all six title features and the altmetric attention scores. The regression analysis showed that four linguistic features of titles (mean word length, lexical sophistication, title length and sentiment score) have modest predictive effects on the online impact of research articles.

In the internet era with the widespread use of social media and online platforms, it is becoming increasingly important for researchers to adapt to the changing context of research evaluation. This study identifies several linguistic features that deserve scholars’ attention in the writing of article titles. It also has practical implications for academic administrators and pedagogical implications for instructors of academic writing courses.

The various indexes published by the H.W. Wilson Company are now helping a fourth generation of library users to find articles in hundreds of periodicals and journals. One Wilson index in particular, the Social Sciences Index (SSI), has undergone major revamping in recent years. It was originally called the International Index (1907–1965). In April, 1965 it became the Social Sciences and Humanities Index; then in April, 1974 it split to form separate indexes for each area, simultaneously expanding the scope of coverage of both parts.

Librarians increasingly encounter decisions related to the use and/or purchase of an expanding body of bibliographic databases. This article examines the coverage of anthropology literatures in major academic indexes widely available in electronic format. Eight databases were selected for comparison, including three subject‐specific indexes, two multidisciplinary social sciences indexes, and three general academic indexes. Indexes were compared for their coverage of a core list of 135 anthropology journals as well as journals relevant to anthropology in other social science disciplines. In addition to journal coverage, several index characteristics were also compared: years of coverage; timeliness; extent of indexing; record structure; search software; and availability of controlled vocabulary, abstracts and full text. It is concluded that each database has relative merits and weaknesses and that these multiple factors must be considered within the context of local conditions in order to determine which database products are appropriate for meeting local information needs.

Historical newspaper collections provide a wealth of information about the past. Although the digitization of these collections significantly improves their accessibility, a large portion of digitized historical newspaper collections, such as those of KBR, the Royal Library of Belgium, are not yet searchable at article-level. However, recent developments in AI-based research methods, such as document layout analysis, have the potential for further enriching the metadata to improve the searchability of these historical newspaper collections. This paper aims to discuss the aforementioned issue.

In this paper, the authors explore how existing computer vision and machine learning approaches can be used to improve access to digitized historical newspapers. To do this, the authors propose a workflow, using computer vision and machine learning approaches to (1) provide article-level access to digitized historical newspaper collections using document layout analysis, (2) extract specific types of articles (e.g. feuilletons – literary supplements from Le Peuple from 1938), (3) conduct image similarity analysis using (un)supervised classification methods and (4) perform named entity recognition (NER) to link the extracted information to open data.

The results show that the proposed workflow improves the accessibility and searchability of digitized historical newspapers, and also contributes to the building of corpora for digital humanities research. The AI-based methods enable automatic extraction of feuilletons, clustering of similar images and dynamic linking of related articles.

The proposed workflow enables automatic extraction of articles, including detection of a specific type of article, such as a feuilleton or literary supplement. This is particularly valuable for humanities researchers as it improves the searchability of these collections and enables corpora to be built around specific themes. Article-level access to, and improved searchability of, KBR's digitized newspapers are demonstrated through the online tool (https://tw06v072.ugent.be/kbr/).

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 determine the percentage of reference errors and type of errors in four library and information science (LIS) journals.

Research articles from issues published in 2007 were selected for analysis. The references were compared to online freely available tables of contents. The errors identified were categorised into six elements: journal title; author(s); article title; publication year; volume; and page numbers.

The highest percentage of reference errors was 49.1 percent (Information and Management). The overall error rates were: author (56 percent), page number (22 percent), article title (15 percent), volume (3 percent), publication year (2 percent) and journal title (2 percent).

The tables of contents (ToCs) used to compare the article references may not have correctly recorded the article details. Not all journal references could be reviewed as not all journal ToCs are available online. This one year, 2007, may not accurately reflect the citation accuracy of the journal in other years. This study did not differentiate between errors that would prevent the location of the article and those that could still be located with perseverance.

Error rates in these LIS journals are considerable. The current method of authors being responsible for the references is not resulting in accurate bibliographic information.

Based on the findings, possible solutions are suggested that could improve the accuracy of references.

In the age of social media, when publishers are vying for consumer attention, click-baits have become very common. Not only viral websites but also mainstream publishers, such as news channels, use click-baits for generating traffic. Therefore, click-bait detection and prediction of click-bait virality have become important challenges for social media platforms to keep the platform click-bait free and give a better user experience. The purpose of this study is to try exploring how the contents of the social media posts and the article can be used to explain and predict social media posts and the virality of a click-bait.

This study has used 17,745 tweets from Twitter with 4,370 click-baits from top 27 publishers and applied econometric along with machine learning methods to explain and predict click-baitiness and click-bait virality.

This study finds that language formality, readability, sentiment scores and proper noun usage of social media posts and various parts of the target article plays differential and important roles in click-baitiness and click-bait virality.

The paper contributes toward the literature of dark behavior in social media at large and click-bait prediction and explanation in particular. It focuses on the differential roles of the social media post, the article shared and the source in explaining click-baitiness and click-bait virality via psycho-linguistic framework. The paper also provides explanability to the econometric and machine learning predictive models, thus performing methodological contribution too.

The paper helps social media managers create a mechanism to detect click-baits and also predict which ones of them can become viral so that corrective measures can be taken.

To the best of the authors’ knowledge, this is one of the first papers which focus on both explaining and predicting click-baitiness and click-bait virality.

UnCover, a multidisciplinary article access database, was released in December 1988 to members of CARL (Colorado Alliance of Research Libraries). Since its release, access to UnCover has been acquired by additional libraries through a gateway connection. UnCover is made possible by the cooperation of eight of the CARL libraries, which presently send their journals to CARL Systems Inc., where they are checked in and their table of contents entered into the UnCover database (See Table 1). The journals are returned to their libraries within 24 hours. The diversity of the universities' academic programs and the many interests of the public library clients have resulted in the creation of this large database containing journal citations on virtually every subject (See Table 2). As of June 1990, UnCover contains nearly 10,000 journal titles and over 900,000 article titles.

This article discusses the possibility of the automation of sophisticated subject indexing of medical journal articles. Approaches to subject descriptor assignment in information retrieval research are usually either based upon the manual descriptors in the database or generation of search parameters from the text of the article. The principles of the Medline indexing system are described, followed by a summary of a pilot project, based upon the Amed database. The results suggest that a more extended study, based upon Medline, should encompass various components: Extraction of ‘concept strings’ from titles and abstracts of records, based upon linguistic features characteristic of medical literature. Use of the Unified Medical Language System (UMLS) for identification of controlled vocabulary descriptors. Coordination of descriptors, utilising features of the Medline indexing system. The emphasis should be on system manipulation of data, based upon input, available resources and specifically designed rules.