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Twitter as a social tool allows people to express their views, emotions or communicate information within brevity of 140 character limit. It has provided an opportunity to researchers to tab users’ expressions on social or political issues, be it natural calamity, elections and alike. The purpose of this paper is to assess how people explored Twitter to express their views regarding state assembly elections of Jammu and Kashmir (India).

The authors performed content analysis of 4,537 tweets that were posted by 1,420 different Twitter users over a period of 78 days (October 30, 2014 through January 15, 2015).

Users were found to be active on the days of polling while post-polling period witnessed a huge influx in particular on the day of voting and declaration of results. Nearly 94 percent users have posted around 50 percent of tweets and there were only 81 handles which posted remaining 50 percent tweets. In additions to people, news agencies, anonymous groups and social/political groups have expressed their views on this event. Nearly one-fourth tweets were retweeted and one-fourth tweets were marked favorite. Users have mostly providing news updates or personnel commentaries about the election process.

The study is first of its kind using Twitter to represent the sentiments of people during floods.

The study aims to showcase public sentiments via social media, Twitter, during 2014 floods of Jammu and Kashmir, India.

The study is based on content analysis of tweets related to Kashmir floods. Search was performed with “#kashmirfloods” and was confined to tweets posted from 4 September 2014 through 3 November 2014. A naturalistic approach was applied to examine the content and classify tweets into 5 major and 25 sub categories. Data as such collected were tabulated in SPSS 21 for analysis.

During the study period, individuals, news channels, and organisations posted a total of 36,697 tweets related to Kashmir floods. It all started with an outburst of tweets which goes on declining (exponentially) with every passing day. People express themselves in a number of ways with informational tweets used more during the time of disaster. Individuals expressing their sentiments outscore other types of sentiments with text-based tweets ranking high. About 44 per cent of tweets were retweeted, and nearly 31 per cent tweets were marked favourite. Comparatively, more number of informational and help tweets were retweeted or marked favourite. Contextual richness of tweet (i.e. number of embedded expressions) enhances its visibility by means of getting liked and/or retweeted. A statistically significant positive association is observed between the number of expressions in a tweet and the number of times it is liked (favourite) or retweeted.

Twitter plays a pivotal role during natural calamities like Kashmir floods to connect people in the hour of need and help. It provides a platform where the plight of people is heard across the globe and which encourages people to unite and overcome hurdles together.

This study examines the sentiments of people expressed during Jammu and Kashmir (India) Floods 2014 on social media – Twitter.

Sharing and obtaining information over social media has enabled people to express their opinions regarding any event. Since the tweets regarding the Russia-Ukraine war were extensively publicized on social media, this study aims to analyse the temporal sentiments people express through tweets related to the war.

Relevant hashtag related to the Russia-Ukraine war was identified, and tweets were downloaded using Twitter API, which were later migrated to Orange Data mining software. Pre-processing techniques like transformation, tokenization, and filtering were applied to the extracted tweets. VADER (Valence Aware Dictionary for Sentiment Reasoning) sentiment analysis module of Orange software was used to categorize tweets into positive, negative and neutral ones based on the tweet polarity. For ascertaining the key and co-occurring terms and phrases in tweets and also to visualize the keyword clusters, VOSviewer, a data visualization software, was made use of.

An increase in the number of tweets is witnessed in the initial days, while a decline is observed over time. Most tweets are negative in nature, followed by positive and neutral ones. It is also ascertained that tweets from verified accounts are more impactful than unverified ones. russiaukrainewar, ukraine, russia, false, war, nato, zelensky and stoprussia are the dominant co-occurring keywords. Ukraine, Russia and Putin are the top hashtags for sentiment representation. India, the USA and the UK contribute the highest tweets.

The study tries to explore the public sentiments expressed over Twitter related to Russia-Ukraine war.

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.

With 43.2 million coronavirus cases and 525,000 deaths in 2022, India ranked second worldwide, after the United States (84.6 million cases and 1 million deaths), according to the latest available June 2022 COVID-19 impact data.

Amid people’s growing mistrust in the government, India’s news media enhanced the nation’s distinguished designation as the world’s largest and most populous democracy. India’s news media inform, educate, empower, and entertain a surging population of 1.4 billion people, which is roughly one-sixth of the world’s people.

Drawing upon the media agendamelding theoretical framework, we conducted a case study research into interplay between two prominent democratic institutions, the media and the government, to analyze the role of the COVID-19 pandemic in redefining India’s networked society.

India’s COVID-19 pandemic aggravated internecine tensions between media and government relating to four key freedom issues: (1) world’s largest COVID-19 lockdown affecting 1.3 billion Indians from March 25, 2020 to August 2020 with extensions and five-phased re-openings, to restrict the spread of COVID-19; (2) Internet shutdowns; (3) media censorship during the 1975–1977 “Emergency”; and (4) unabated murders of journalists in India.

Although the COVID-19 pandemic caused deleterious problems debilitating the tensions between the media and the government, India’s journalists thrived by speaking truth to power. This study delineates key aspects of India’s media agendamelding that explicates how the people of India form their media agendas. India’s news audiences meld media messages from newspapers, television, and social media to form a picture of the issues, insights, and ideas that define their lives and times in the 21st century digital age.

This study aims to understand the impact of content orientation, media type, and information richness on fan engagement in multi-sport global events.

The authors conducted a content analysis on Twitter posts recording over two million user impressions from the official account managed by the International Olympic Committee for India during the Tokyo Olympic Games 2020. A multivariate Poisson model using the Bayesian approach was used for analyzing data.

This study found that fan engagement is likely to be higher for player-oriented content as opposed to team-oriented content. Also, the usage of photos to enhance engagement worked better than any other media type. Finally, the results revealed that the inclusion of hashtags has a positive effect on fan engagement for tweet comments but not for like count and retweet count.

The study highlights the differences in player versus team-oriented posts in global multi-sport competitions. The findings have significant implications for practicing sport managers by informing them about key elements that drive fans to engage in online communication.

The purpose of this paper is to test if there are biases in lexical sentiment analysis accuracy between reviews authored by males and females.

This paper uses data sets of TripAdvisor reviews of hotels and restaurants in the UK written by UK residents to contrast the accuracy of lexical sentiment analysis for males and females.

Male sentiment is harder to detect because it is less explicit. There was no evidence that this problem could be solved by gender-specific lexical sentiment analysis.

Only one lexical sentiment analysis algorithm was used.

Care should be taken when drawing conclusions about gender differences from automatic sentiment analysis results. When comparing opinions for product aspects that appeal differently to men and women, female sentiments are likely to be overrepresented, biasing the results.

This is the first evidence that lexical sentiment analysis is less able to detect the opinions of one gender than another.

Due to the size and importance of social media, user-generated content analysis is becoming a key factor for companies and brands across the world. By using Twitter messages’ content, the purpose of this paper is to identify which elements of the messages enable tweet diffusion and facilitate eWOM.

In total, 30,082 tweets collected from 10,120 Twitter users were classified based on four assorted brands. By comparing with multiple regression techniques high vs low purchase involvement and hedonic vs utilitarian products and using the theory of heuristic-systematic processing of information, the authors examine the causes of tweet diffusion.

The authors illustrate how the elements of a tweet (hashtags, mentions, links, sentiment or tweet length) influence its diffusion and popularity.

This study validated the use of information processing theories in the social media field. The study showed a picture on how different Twitter elements influence eWOM and message diffusion under several purchase involvement situations.

The results of this study can help social media brand community managers of all types of companies on how to write their Twitter messages to obtain greater dissemination and popularity.

The study offers a unique deep brand analysis which helps brands and companies to understand their social media popularity in detail. Depending on product category, companies can achieve maximum social impact on Twitter by focusing on the interactivity items that will work best for their products or brands.