Scientists increasingly use Twitter for communication about science. The microblogging service has been heralded for its potential to foster public engagement with science; thus, measuring how engaging, that is dialogue-oriented, tweet content is, has become a relevant research object. Tweet content designed in an engaging, dialogue-oriented way is also supposed to link to user interaction (e.g. liking, retweeting). The present study analyzed content-related and functional indicators of engagement in scientists' tweet content, applying content analysis to original tweets ( = 2884) of 212 communication scholars. Findings show that communication scholars tweet mostly about scientific topics, with, however, low levels of engagement. User interaction, nevertheless, correlated with content-related and functional indicators of engagement. The findings are discussed in light of their implications for public engagement with science.
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http://dx.doi.org/10.1177/09636625231166552 | DOI Listing |
J Adolesc Young Adult Oncol
December 2024
Department of Human Development and Family Sciences, University of Connecticut, Storrs, Connecticut, USA.
Over a half million children are living with cancer in the United States. Social media platforms offer unique opportunities for cancer communication by public health organizations as well as health care providers, scientists, patients, and caregivers. Given the dearth of research on childhood cancer communication, the present study aimed to examine the nature of tweets on the social media platform X (formerly Twitter) that used the hashtag #childhoodcancer, the types of these tweets that attracted the most retweets, the types of users tweeting about childhood cancer (e.
View Article and Find Full Text PDFFront Psychol
December 2024
School of Journalism and Communication, Shandong University, Jinan, China.
During the Russia-Ukraine war, Ukrainian President Volodymyr Zelenskyy has strategically used social media to appeal for international support. This reflects a broader trend of political figures relying on digital platforms to shape public opinion and influence global narratives during crises. This work uses three main analysis methods, content, sentiment and social network analysis.
View Article and Find Full Text PDFHeliyon
December 2024
Department of Computer Science, Forman Christian College (A Chartered University), Lahore, Pakistan.
Hate speech constitutes a major problem on microblogging platforms, with automatic detection being a growing research area. Most existing works focus on analyzing the content of social media posts. Our study shifts focus to predicting which users are likely to become targets of hate speech.
View Article and Find Full Text PDFObjective: Social media content created by users with different personality traits presents various sentiment tendencies, easily leading to irrational public opinion. This study aims to explore the relationships between users' personality traits and sentiment tendencies of user-generated content (UGC).
Method: We crawled 18,686 tweets of 1, 215 users from Twitter to figure out the relationships between personality traits and sentiment tendencies.
Front Big Data
November 2024
Tobacco Control Research Group, Department for Health, University of Bath, Bath, United Kingdom.
Background: Accurate sentiment analysis and intent categorization of tobacco and e-cigarette-related social media content are critical for public health research, yet they necessitate specialized natural language processing approaches.
Objective: To compare pre-trained and fine-tuned Flan-T5 models for intent classification and sentiment analysis of tobacco and e-cigarette tweets, demonstrating the effectiveness of pre-training a lightweight large language model for domain specific tasks.
Methods: Three Flan-T5 classification models were developed: (1) tobacco intent, (2) e-cigarette intent, and (3) sentiment analysis.
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