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The purpose of this paper is to share the experience of a university library in response to the COVID-19 pandemic since early March 2020. The paper describes the library’s position during the crisis and illustrates the uncharted challenges that the pandemic has posed to its digital services. Furthermore, it details how the library has adapted some existing services into a digital format and explored new initiatives/practices to support the university’s full online teaching and learning since March 23, 2020.

This paper describes the library’s various digital services that are used to meet the needs of its end-users during the COVID-19 pandemic. The approaches used are the authors’ personal experiences working at an academic library, observations of the library’s responses with regards to its digital services, as well as their reflections on what can be considered for development now and in the future. It highlights the current initiatives and best practices for digital library services during a public health crisis.

This paper aims to make other university libraries aware of what the library has implemented with providing digital services to its teaching faculty and students during the pandemic. It also describes the challenges and implications for the library professionals working in-house and remotely.

This paper is of great value in providing insights and practical solutions responding to the global health crisis for other libraries that are coping with the similar challenges for digital library services.

In online social network analysis, the problem of identification and ranking of influential nodes based on their prominence has attracted immense attention from researchers and practitioners. Identification and ranking of influential nodes is a challenging problem using Twitter, as data contains heterogeneous features such as tweets, likes, mentions and retweets. The purpose of this paper is to perform correlation between various features, evaluation metrics, approaches and results to validate selection of features as well as results. In addition, the paper uses well-known techniques to find topical authority and sentiments of influential nodes that help smart city governance and to make importance decisions while understanding the various perceptions of relevant influential nodes.

The tweets fetched using Twitter API are stored in Neo4j to generate graph-based relationships between various features of Twitter data such as followers, mentions and retweets. In this paper, consensus approach based on Twitter data using heterogeneous features has been proposed based on various features such as like, mentions and retweets to generate individual list of top-k influential nodes based on each features.

The heterogeneous features are meant for integrating to accomplish identification and ranking tasks with low computational complexity, i.e. O(n), which is suitable for large-scale online social network with better accuracy than baselines.

Identified influential nodes can act as source in making public decisions and their opinion give insights to urban governance bodies such as municipal corporation as well as similar organization responsible for smart urban governance and smart city development.

In the Indian judicial system, the court considers interpretations of similar previous judgments for the present case. An essential requirement of legal practitioners is to determine the most relevant judgments from an enormous amount of judgments for preparing supportive, beneficial and favorable arguments against the opponent. It urges a strong demand to develop a Legal Document Recommendation System (LDRS) to automate the process. In existing works, traditionally preprocessed judgment corpus is processed by Doc2Vec to learn semantically rich judgment embedding space (i.e. vector space). Here, vectors of semantically relevant judgments are in close proximity, as Doc2Vec can effectively capture semantic meanings. The enormous amount of judgments produces a huge noisy corpus and vocabulary which possesses a significant challenge: traditional preprocessing cannot fully eliminate noisy data from the corpus and due to this, the Doc2Vec demands huge memory and time to learn the judgment embedding. It also adversely affects the recommendation performance in terms of correctness. This paper aims to develop an effective and efficient LDRS to support civilians and the legal fraternity.

To overcome previously mentioned challenges, this research proposes the LDRS that uses the proposed Generalized English and Indian Legal Dictionary (GEILD) which keeps the corpus of relevant dictionary words only and discards noisy elements. Accordingly, the proposed LDRS significantly reduces the corpus size, which can potentially improve the space and time efficiency of Doc2Vec.

The experimental results confirm that the proposed LDRS with GEILD yield superior performance in terms of accuracy, F1-Score, MCC-Score, with significant improvement in the space and time efficiency.

The proposed LDRS uses the customized domain-specific preprocessing and novel legal dictionary (i.e. GEILD) to precisely recommend the relevant judgments. The proposed LDRS can be incorporated with online legal search repositories/engines to enrich their functionality.

Scholarly communications, particularly, questions and answers (Q&A) present on digital scholarly platforms provide a new avenue to gain knowledge. However, several studies have raised a concern about the content anomalies in these Q&A and suggested a proper validation before utilizing them in scholarly applications such as influence analysis and content-based recommendation systems. The content anomalies are referred as disinformation in this research. The purpose of this research is firstly, to assess scholarly communications in order to identify disinformation and secondly, to help scholarly platforms determine the scholars who probably disseminate such disinformation. These scholars are referred as the probable sources of disinformation.

To identify disinformation, the proposed model deduces (1) content redundancy and contextual redundancy in questions (2) contextual nonrelevance in answers with respect to the questions and (3) quality of answers with respect to the expertise of the answering scholars. Then, the model determines the probable sources of disinformation using the statistical analysis.

The model is evaluated on ResearchGate (RG) data. Results suggest that the model efficiently identifies disinformation from scholarly communications and accurately detects the probable sources of disinformation.

Different platforms with communication portals can use this model as a regulatory mechanism to restrict the prorogation of disinformation. Scholarly platforms can use this model to generate an accurate influence assessment mechanism and also relevant recommendations for their scholars.

The existing studies majorly deal with validating the answers using statistical measures. The proposed model focuses on questions as well as answers and performs a contextual analysis using an advanced word embedding technique.