Identifying Twitter users who repost unreliable news sources with linguistic information.
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ABSTRACT:
SUBMITTER: Mu Y
PROVIDER: S-EPMC7924477 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Online Social Networks generate a prodigious wealth of real-time information at an incessant rate. In this paper we study the empirical data that crawled from Twitter to describe the topology and information spreading dynamics of Online Social Networks. We propose a measurement with three measures to state the efforts of users on Twitter to get their information spreading, based on the unique mechanisms for information retransmission on Twitter. It is noticed that small fraction of users with special performance on participation can gain great influence, while most other users play a role as middleware during the information propagation. Thus a community analysis is performed and four categories of users are found with different kinds of participation that cause the information dissemination dynamics. These suggest that exiting topological measures alone may reflect little about the influence of individuals and provide new insights for information spreading.
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Project description:BackgroundMisinformation about Lyme disease and other tick-transmitted pathogens circulates frequently on the internet and can compete with, or even overshadow, science-based guidance on tick-borne disease (TBD) prevention.ObjectiveWe surveyed internet users connected to academic tick-related resources to identify trusted sources of Lyme disease prevention information, explore confidence in tick bite prevention information, and examine associations of these responses with answers to commonly disputed issues.MethodsThe survey was conducted through social media and website pages for Western Connecticut State University Tickborne Disease Prevention Laboratory and the University of Rhode Island TickEncounter Resource Center.ResultsRespondents (N=1190) were predominantly female (903/1190, 76.3%), middle-aged (574/1182, 48.6%), and resided in New England states (663/1190, 55.7%). In total 984 of 1186 (83%) respondents identified conventional experts (eg, the Centers for Disease Control [CDC] or other government health agencies, physicians who follow Infectious Diseases Society of America guidelines for Lyme disease treatment guidelines, and academics) as trustworthy TBD prevention resources. However, nearly one-fourth of respondents would first consult personal contacts and web-based communities regarding prevention information before consulting conventional expert sources. The opinions of public health experts and physicians were rated among the top motivators underlying personal prevention decisions; yet, more than 50% of participants revealed distrustful attitudes toward, or were uncertain about, CDC-supported statements related to time to transmission of Lyme disease (708/1190, 59.5%), the safety of diethyltoluamide-based repellents for children (604/1183, 51.1%), and recommended use of antibiotic prophylaxis (773/1181, 65.4%). Multimodal regression models revealed that participants from high-Lyme-disease-incidence states were more likely to first seek TBD prevention information from personal networks and nontraditional sources before approaching conventional sources of TBD prevention information. We found that those reporting high rates of social media usage were more than twice as likely to first seek traditional expert sources of prevention information but were overall more likely to reject CDC-promoted Lyme disease information, in particular the established time to transmission of Lyme disease bacteria. Models also predicted that those participants who disagreed with the conventional scientific view on the antibiotic prophylaxis prevention statement were less likely to be confident in their ability to protect themselves from a tick bite. Overall, uncertainty in one's ability to protect oneself against tick bites was strongly associated with uncertainty about beliefs in CDC-promoted TBD prevention information. Self-reported trust in experts and frequency of social media use suggest that these platforms may provide opportunities to engage directly with the public about TBD prevention practices.ConclusionsUsing strategies to improve public trust and provide information where the public engages on social media may improve prevention communication and adoption of best practices.
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Project description:Fake news can have a significant negative impact on society because of the growing use of mobile devices and the worldwide increase in Internet access. It is therefore essential to develop a simple mathematical model to understand the online dissemination of fake news. In this study, we propose a point process model of the spread of fake news on Twitter. The proposed model describes the spread of a fake news item as a two-stage process: initially, fake news spreads as a piece of ordinary news; then, when most users start recognizing the falsity of the news item, that itself spreads as another news story. We validate this model using two datasets of fake news items spread on Twitter. We show that the proposed model is superior to the current state-of-the-art methods in accurately predicting the evolution of the spread of a fake news item. Moreover, a text analysis suggests that our model appropriately infers the correction time, i.e., the moment when Twitter users start realizing the falsity of the news item. The proposed model contributes to understanding the dynamics of the spread of fake news on social media. Its ability to extract a compact representation of the spreading pattern could be useful in the detection and mitigation of fake news.
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Project description:Introduction: Human papillomavirus (HPV) vaccine coverage in Australia is 80% for females and 76% for males. Attitudes may influence coverage but surveys measuring attitudes are resource-intensive. The aim of this study was to determine whether Twitter-derived estimates of HPV vaccine information exposure were associated with differences in coverage across regions in Australia. Methods: Regional differences in information exposure were estimated from 1,103,448 Australian Twitter users and 655,690 HPV vaccine related tweets posted between 6 September 2013 and 1 September 2017. Tweets about HPV vaccines were grouped using topic modelling; an algorithm for clustering text-based data. Proportional exposure to topics across 25 regions in Australia were used as factors to model HPV vaccine coverage in females and males, and compared to models using employment and education as factors. Results: Models using topic exposure measures were more closely correlated with HPV vaccine coverage (female: Pearson's R = 0.75 [0.49 to 0.88]; male: R = 0.76 [0.51 to 0.89]) than models using employment and education as factors (female: 0.39 [-0.02 to 0.68]; male: 0.36 [-0.04 to 0.66]). In Australia, positively-framed news tended to reach more Twitter users overall, but vaccine-critical information made up higher proportions of exposures among Twitter users in low coverage regions, where distorted characterisations of safety research and vaccine-critical blogs were popular. Conclusions: Twitter-derived models of information exposure were correlated with HPV vaccine coverage in Australia. Topic exposure measures may be useful for providing timely and localised reports of the information people access and share to inform the design of targeted vaccine promotion interventions.
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Project description:IntroductionSocial media, such as Twitter, have become major channels of communication and commentary on popular culture, including conversations on our nation's leading addiction: tobacco. The current study examined Twitter conversations following two tobacco-related events in the media: (1) President Obama's doctor announcing that he had quit smoking and (2) the release of a photograph of Miley Cyrus (a former Disney child star) smoking a cigarette. With a focus on high-profile individuals whose actions can draw public attention, we aimed to characterise tobacco-related conversations as an example of tobacco-related public discourse and to present a novel methodology for studying social media.MethodsTweets were collected 11-13 November 2011 (President Obama) and 1-3 August 2011 (Miley Cyrus) and analysed for relative frequency of terms, a novel application of a linguistic methodology.ResultsThe President Obama data set (N=2749 tweets) had conversations about him quitting tobacco as well as a preponderance of information on political activity, links to websites, racialised terms and mention of marijuana. Websites and terms about Obama's smoke-free status were most central to the conversation. In the Miley Cyrus data (N=4746 tweets), terms that occurred with the greatest relative frequency were positive, emotional and supportive of quitting (eg, love, and please), with words such as 'love' most central to the conversation.ConclusionsPeople are talking about tobacco-related issues on Twitter, and semantic network analysis can be used to characterise on-line conversations. Future interventions may be able to harness social media and major current events to raise awareness of smoking-related issues.
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| S-EPMC9376898 | biostudies-literature