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Vaccine hesitancy is a long-standing issue among both the general population and health communicators. This study aims to ascertain the inclination and the reasons for vaccine hesitancy by conducting content analysis and sentiment analysis of the perspectives expressed in comments on videos related to vaccine hesitancy uploaded from India on YouTube.

The assessment of the sentiments of the vaccine-hesitant population is done using Valence Aware Dictionary and sEntiment Reasoner sentiment analysis module implemented with Python’s NLTK library to automatically determine the sentiments of the comments. Manual content analysis was performed on 60.09% viewer comments randomly selected from the total comments in 238 videos on vaccine hesitancy originated from India and labelled each comment with labels “Anti”, “Pro”, “Confused”, “Not Applicable” and “Unrelated” labels.

The study found “Mistrust-Government policies”, “Fear-health related consequences”, “Mistrust-Scientific research”, “Vaccine effectiveness and efficacy” and “Misinformation/myths” as the top five determinants for vaccine hesitancy, whereas “Religious beliefs”, “Fear-Economic consequences”, “Side Effects- short-term” and “Fear-mode of administration” found to be the lesser cited reasons for vaccine hesitancy. However, the study also investigates changes in the inclination of Indian commenters towards vaccine hesitancy and revolving issues over time.

Public health policymakers and health communicators may find the study useful in determining vaccine hesitancy factors in India.

The originality of this study lies in its approach. To date, no sentiment analysis has been conducted on the content released on YouTube by Indian content creators regarding pro- and anti-vaccination videos. This inquiry seeks to fill this research gap.

The consumption of news from social media is the new trend, still news channels are the authentic source to transmit relevant news to audiences. Social media has gradually left an impact on the audience but the news channels have upgraded and providing various news services online on social media websites. The present study aims to study the type of news videos uploaded by the top five Hindi TV news channels on their YouTube channels with an aim to see which type of videos spark interest for YouTube viewers.

By applying the techniques of content analysis, sentiment analysis and text mining the study aims to measure the average sentiments, top words and the trend of selected popular terms in the comments on uploaded news videos by the top five Hindi news channels over a period of one year.

Results of the study indicate that the news channels are uploading more news videos about crime and investigation, politics, health and protests while uploading fewer news videos covering travel, science and technology, and religion. While the viewers of the participating news channels are more interested in giving their thoughts or opinions in the form of comments on news videos concerning crime, politics, protests and health or that these videos inspire conversation on YouTube.

The findings might be of interest to content managers of news channels to understand the interest of their audience.

The study's distinctiveness resides in the approach utilised to collect data and analyse the results in order to better understand the online behaviour of news channel audiences.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-01-2022-0007

The purpose of the study is to evaluate certain characteristics of the videos of the software Koha and DSpace posted on YouTube. Since YouTube has the potential to provide the content creator to share their knowledge and experience through their content which has become much more beneficial to the information seeker. Nowadays, people search for queries or tutorial videos on YouTube very often to earn a better understanding of the term. Sentiment analysis of the viewers' opinion of the videos is another purpose of this study.

Dataset for evaluating the characteristic of the videos of Koha and DSpace was extracted by using Webometric Analyst by creating YouTube API. Once retrieval of data was completed, a manual verification was enhanced to filter out spam videos unrelated to the scope. After the confirmation of authentic relatable videos, seeking the video's id as query, the comments per video were extracted using Webometric Analyst. For opinion mining, the Parallel Dots API web service was used in Google Sheets as an addon function. The sentiment, multilingual sentiment, emotion, intention and word frequency of the viewers' opinion was examined with the help of certain default functionalities.

Webometric Analyst extracted a total of 461 and 397 videos of Koha and DSpace, respectively, uploaded on the YouTube platform. The findings of the study indicate that the growth rate of videos on Koha is decreasing, while the number of videos uploaded on DSpace is gradually increased in the last 10 years. The highest number of videos posted in 1–20 min duration category with mostly high definition (HD) with standard YouTube license and prominently in the English language. The sentiment analysis of the total extracted comments on Koha and DSpace videos found to be 2043 and 862 comments, respectively, among whom “Positive” comments are mostly found and with “Happy” emotion can be highly detected with most supportive “Feedback” intention on both Koha and DSpace videos. The top word frequency signifies that the users of both the software are using the comments section of the videos on YouTube to ask and provide troubleshooting help to each other.

The present study has some limitations too; the dataset for the study includes only those videos whose title, description or keywords sections had the query terms “Koha” or “DSpace” there are chances that some videos would have been left out from the dataset related to these software.

This is the first paper to evaluate the characteristics and sentiment of both the videos Koha and DSpace. Through this, the popularity, likeness and dislike and the impact of the contents of the videos uploaded will be disclosed, and creators can make an improvement by referring this, and the seekers will adapt to the use of correct and authentic information.