Search results

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.

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.

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.

The purpose of this study is to compare violent and non-violent extremists in terms of their age when they first perpetrate an extremist act, and to understand how this relates to other factors underlying extremist behaviours. While the end goal of many extremists may be functionally similar, the pathways into extremism vary, and the literature has demonstrated that a “one-size-fits-all” explanation does not exist. Motivational drivers are complex and dynamic; therefore, attempting to identify a terrorist “profile” has limited applied efficacy.

This study applied a temporal approach (“crime script analysis” or CSA) to identify, map and compare the sequential stages (or “scenes”) in the life histories of violent and non-violent extremists who have committed acts of extremism across different age groups. Crime scripts comprising mainly qualitative data for 40 male extremists (20 violent, 20 non-violent “cases”) were developed, and CSA was conducted according to the age at which they committed their first extremist offence.

Results demonstrated key temporal, developmental differences between the pathways of extremists who commit their first offence at different ages. One key difference was that for both the violent and non-violent extremists, those under 30 used the internet as a main means of joining networks and spreading information, whereas the over 30s made more personal, community links.

This research can aid identification of potential environmental triggers and potential increased susceptibility to triggers across certain age groups.