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The study aims to access, monitor and visualize the scientific progress of Twitter-based research through a bibliometric analysis of scientific publications.

The data was retrieved from 2006 to February 23, 2022 using the Web of Science, a leading indexing and abstracting database. In response to the authors’ query, 6,193 items with 101,037 citations, an average citation of 16.31 and an h index of 126 were received. The “Biblioshiny” extension of the “Bibliometrics” package (www.bibliometrix.org) of R software was used to evaluate and visualize the data.

The present study highlighted the scientific progress of the field evolved over a period of time. The obtained results uncovered the publication trends, productive countries and their collaboration pattern, active authors who nurture the field by making their contribution, prolific source titles adopted by authors to publish the literature on the topic, most productive language in which literature was written, productive institutions, funding agencies that sponsor the research, influential articles, prominent keywords used in publications were also identified which will aid scientists in identifying research gaps in a particular area.

This study comprehensively illustrates the research status of Twitter-related research by conducting a bibliometric analysis. The study’s findings can assist relevant researchers in understanding the research trend, seeking scientific collaborators and funding for their research. Further, the study will act as a ready reference tool for the scientific community to identify research gaps, select research topics and appropriate platforms for submitting their scholarly endeavors.

Twitter is gaining popularity as a microblogging and social networking service to discuss various social issues. Coronavirus disease 2019 (COVID-19) has become a global pandemic and is discussed worldwide. Social media is an instant platform to deliberate various dimensions of COVID-19. The purpose of the study is to explore and analyze the public sentiments related to COVID-19 vaccines across the Twitter messages (positive, neutral, and negative) and the impact tweets make across digital social circles.

To fetch the vaccine-related posts, a manual examination of randomly selected 500 tweets was carried out to identify the popular hashtags relevant to the vaccine conversation. It was found that the hashtags “covid19vaccine” and “coronavirusvaccine” were the two popular hashtags used to discuss the communications related to COVID-19 vaccines. 23,575 global tweets available in public domain were retrieved through “Twitter Application Programming Interface” (API), using “Orange Software”, an open-source machine learning, data visualization and data mining toolkit. The study was confined to the tweets posted in English language only. The default data cleaning and preprocessing techniques available in the “Orange Software” were applied to the dataset, which include “transformation”, “tokenization” and “filtering”. The “Valence Aware Dictionary for sEntiment Reasoning” (VADER) tool was used for classification of tweets to determine the tweet sentiments (positive, neutral and negative) as well as the degree of sentiments (compound score also known as sentiment score). To assess the influence/impact of tweets account wise (verified and unverified) and sentiment wise (positive, neutral, and negative), the retweets and likes, which offer a sort of reward or acknowledgment of tweets, were used.

A gradual decline in the number of tweets over the time is observed. Majority (11,205; 47.52%) of tweets express positive sentiments, followed by neutral (7,948; 33.71%) and negative sentiments (4,422; 18.75%), respectively. The study also signifies a substantial difference between the impact of tweets tweeted by verified and unverified users. The tweets related to verified users have a higher impact both in terms of retweets (65.91%) and likes (84.62%) compared to the tweets tweeted by unverified users. Tweets expressing positive sentiments have the highest impact both in terms of likes (mean = 10.48) and retweets (mean = 3.07) compared to those that express neutral or negative sentiments.

The main limitation of the study is that the sentiments of the people expressed over one single social platform, that is, Twitter have been studied which cannot generalize the global public perceptions. There can be a variation in the results when the datasets from other social media platforms will be studied.

The study will help to know the people's sentiments and beliefs toward the COVID-19 vaccines. Sentiments that people hold about the COVID-19 vaccines are studied, which will help health policymakers understand the polarity (positive, negative, and neutral) of the tweets and thus see the public reaction and reflect the types of information people are exposed to about vaccines. The study can aid the health sectors to intensify positive messages and eliminate negative messages for an enhanced vaccination uptake. The research can also help design more operative vaccine-advocating communication by customizing messages using the obtained knowledge from the sentiments and opinions about the vaccines.

The paper focuses on an essential aspect of COVID-19 vaccines and how people express themselves (positively, neutrally and negatively) on Twitter.

The current study seeks to investigate, why and how faculty members of Indian central universities are using academic social networking sites (ASNs) for research communication and information dissemination, as well as validate and update the results of previous scholarship in this area. To achieve this, the paper uses structural equation model (SEM).

A simple random sampling method was adopted. Online survey was conducted using a well-designed questionnaire circulated via email id among 3384 faculty members of Indian Central Universities. A SEM was designed and tested with International Business Machines (IBM) Amos. Apart from this, Statistical Package for Social Sciences (SPSS) 22 and Microsoft Excel 2010 were also used for data screening and analysis.

The study explores that most of the respondents are in favour of using the ASNs/tools for their professional activities. The study also found that a large chunk of the respondents used ASNs tools during day time. Apart from it, more number of faculty members used ASNs in research work than general purpose. No significant differences were found among the disciplines in use behaviour of ASNs in scholarly communication. Three hypotheses have been accepted while two were rejected in this study.

The study was confined to the twelve central universities, and only 312 valid responses were taken into consideration in this study.

The paper demonstrates the faculty members’ use behaviour of ASNs in their research communication. The study also contributes new knowledge to methodological discussions as it is the first known study to employ SEM to interpret scholarly use of ASNs by faculty members of Indian central universities.