This study conducts an analysis on topics of the most diffused tweets and retweeting dynamics of crisis information amid Covid-19 to provide insights into how Twitter is used by the public and how crisis information is diffused on Twitter amid this pandemic. Results show that Twitter is first and foremost used as a news seeking and sharing platform with more than of the most diffused tweets being related to news and comments on crisis updates. As for the retweeting dynamics, our results show an almost immediate response from Twitter users, with some first retweets occurring as quickly as within 2 s and the vast majority of them done within 10 min. Nearly of the retweeting processes could have of their retweets finished within 24 h, indicating a 1-day information value of tweets. Distribution of retweeting behaviors could be modeled by Power law, Weibull, and Log normal in this study, but still there are original tweets whose retweeting distributions left unexplained. Results of retweeting community analysis show that following retweeters contribute to nearly of the retweets. In addition, the retweeting contribution of verified Twitter users is significantly different from that of unverified users. A similar significant difference is also found in their rates of verified retweeters, and it has been shown that verified Twitter users enjoy seven times as high value as that of unverified users. In other words, users with the same verification status are more likely to get together to diffuse crisis information.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7809642 | PMC |
http://dx.doi.org/10.1007/s11069-020-04497-5 | DOI Listing |
Sci Rep
January 2025
Department of Sociology and Social Research, University of Trento, Trento, Italy.
This study investigates the dissemination of archaeological information on Twitter/X through the lens of cultural evolution. By analysing 132,230 tweets containing the hashtag #archaeology from 2021 to 2023, we examine how content and context-related factors influence retweeting behaviour. Our findings reveal that tweets with positive sentiment and non-threatening language are more likely to be shared, contrasting with the common negativity bias observed on social media.
View Article and Find Full Text PDFJ Med Internet Res
December 2024
School of Information, University of Michigan, Ann Arbor, MI, United States.
Background: Toxicity on social media, encompassing behaviors such as harassment, bullying, hate speech, and the dissemination of misinformation, has become a pressing social concern in the digital age. Its prevalence intensifies during periods of social crises and unrest, eroding a sense of safety and community. Such toxic environments can adversely impact the mental well-being of those exposed and further deepen societal divisions and polarization.
View Article and Find Full Text PDFSci Rep
July 2024
Grupo de Sistemas Complejos, ETSIAAB, Universidad Politécnica de Madrid, Av. Puerta de Hierro 2-4, 28040, Madrid, Spain.
In this article, we present the findings of a comprehensive longitudinal social network analysis conducted on Twitter across four consecutive election campaigns in Spain, spanning from 2015 to 2019. Our focus is on the discernible trend of increasing partisan and ideological homogeneity within interpersonal exchanges on this social media platform, alongside high levels of networking efficiency measured through average retweeting. This diachronic study allows us to observe how dynamics of party competition might contribute to perpetuating and strengthening network ideological and partisan homophily, creating 'epistemic bubbles' in Twitter, yet showing a greater resistance to transforming them into 'partisan echo-chambers.
View Article and Find Full Text PDFSoc Sci Res
May 2024
Department of Sociology University of Massachusetts, 200 Hicks Way, 738 Thompson Hall, Amherst, MA, 01003, USA. Electronic address:
This study explores why some fake news publishers are able to propagate misinformation while others receive little attention on social media. Using COVID-19 vaccine tweets as a case study, this study combined the relational niche framework with pooled and multilevel models that address the unobserved heterogeneity. The results showed that, as expected, ties to accounts with more followers were associated with more fake news tweets, retweets, and likes.
View Article and Find Full Text PDFF1000Res
April 2024
Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.
Background: Sentiments and opinions regarding COVID-19 and the COVID-19 vaccination on Indonesian-language Twitter are scarcely reported in one comprehensive study, and thus were aimed at our study. We also analyzed fake news and facts, and Twitter engagement to understand people's perceptions and beliefs that determine public health literacy.
Methods: We collected 3,489,367 tweets data from January 2020 to August 2021.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!