Identifying and characterizing social media communities: a socio-semantic network approach to altmetrics
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ABSTRACT:
SUBMITTER: Arroyo-Machado W
PROVIDER: S-EPMC8507359 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveTo examine current vaccine sentiment on social media by constructing and analyzing semantic networks of vaccine information from highly shared websites of Twitter users in the United States; and to assist public health communication of vaccines.BackgroundVaccine hesitancy continues to contribute to suboptimal vaccination coverage in the United States, posing significant risk of disease outbreaks, yet remains poorly understood.MethodsWe constructed semantic networks of vaccine information from internet articles shared by Twitter users in the United States. We analyzed resulting network topology, compared semantic differences, and identified the most salient concepts within networks expressing positive, negative, and neutral vaccine sentiment.ResultsThe semantic network of positive vaccine sentiment demonstrated greater cohesiveness in discourse compared to the larger, less-connected network of negative vaccine sentiment. The positive sentiment network centered around parents and focused on communicating health risks and benefits, highlighting medical concepts such as measles, autism, HPV vaccine, vaccine-autism link, meningococcal disease, and MMR vaccine. In contrast, the negative network centered around children and focused on organizational bodies such as CDC, vaccine industry, doctors, mainstream media, pharmaceutical companies, and United States. The prevalence of negative vaccine sentiment was demonstrated through diverse messaging, framed around skepticism and distrust of government organizations that communicate scientific evidence supporting positive vaccine benefits.ConclusionSemantic network analysis of vaccine sentiment in online social media can enhance understanding of the scope and variability of current attitudes and beliefs toward vaccines. Our study synthesizes quantitative and qualitative evidence from an interdisciplinary approach to better understand complex drivers of vaccine hesitancy for public health communication, to improve vaccine confidence and vaccination coverage in the United States.
| S-EPMC5548132 | biostudies-literature
Project description:In science, sleeping beauties (SBs) denotes a special phenomenon of the diffusion of scientific knowledge based on citation trajectories, the awakening of which is also measured through changes in the citations index. However, the rapid advancement of social media has altered the mode of scientific communication and knowledge diffusion. This study aims to re-identify SBs and its Prince from the perspective of comprehensive indicators, which involves the analysis of Altmetrics indexes and Citation index, and investigate the awakening mechanism of A-SB to supplement the research on the awakening mechanism of SBs. By combining Ab index, we redefined the Prince, which makes A-SB receive high attention after a long Sleeping period and reflects the most prominent academic or social behavior that awakens and sustains the Awakening of A-SB. Then we conducted empirical research on the retrieved PLOS Biology collection and examined Prince after identifying the A-SB. The analysis and summary of the characteristics of the identified A-SB and Prince revealed the SBs' awakening mechanism under the comprehensive trajectory based on Altmetrics from the three dimensions of the influence between the indicators, the overall evolution trajectory of A-SB, and literature bibliometric attributes. In the trajectory of Delayed Recognition stage of A-SB, we define the Dogsleep of SBs, which mirrors that the instability of the Sleeping of SBs will generate a specific negative impact on Prince of A-SB and Awakening intensity. Besides, the literature bibliometric attributes cannot reflect the tendency of users to read academic papers, which again proves that the traditional citation index cannot be neglected in the awakening mechanism of A-SB. Overall, this study demonstrates the addition of the Altmetrics indexes as a useful complement, illustrating the inheritance and connection between the SBs based on the comprehensive trajectory and the SBs based on the citation diffusion trajectory.
| S-EPMC7688316 | biostudies-literature
Project description:BackgroundConsidering that adolescents spend considerable time on the Internet and social media and experience high levels of stress, it is difficult to find a study that investigates adolescent stress through a big data-based network analysis of social media. Hence, this study was designed to provide basic data to establish desirable stress coping strategies for adolescents based on a big data-based network analysis of social media for Korean adolescent stress. The purpose of this study was to (1) identify social media words that express stress in adolescents and (2) investigate the associations between those words and their types.MethodsTo analyse adolescent stress, we used social media data collected from online news and blog websites and performed semantic network analysis to understand the relationships among keywords extracted in the collected data.ResultsThe top five words used by Korean adolescents were counselling, school, suicide, depression, and activity in online news, and diet, exercise, eat, health, and obesity in blogs. As the top keywords of the blog are mainly related to diet and obesity, it reflects adolescents' high degree of interest in their bodies; the body is also a primary source of adolescent stress. In addition, blogs contained more content about the causes and symptoms of stress than online news, which focused more on stress resolution and coping. This highlights the trend that social blogging is a new channel for sharing personal information.ConclusionsThe results of this study are valuable as they were derived through a social big data analysis of data obtained from online news and blogs, providing a wide range of implications related to adolescent stress. Hence this study can contribute basic data for the stress management of adolescents and their mental health management in the future.
| S-EPMC10042152 | biostudies-literature
Project description:BackgroundOnline communities such as Reddit can provide social support for those recovering from opioid use disorder. However, it is unclear whether and how advice-seekers differ from other users. Our research addresses this gap by identifying key characteristics of r/suboxone users that predict advice-seeking behavior.ObjectiveThe objective of this analysis is to identify and describe advice-seekers on Reddit for buprenorphine-naloxone use using text annotation, social network analysis, and statistical modeling techniques.MethodsWe collected 5258 posts and their comments from Reddit between 2014 and 2019. Among 202 posts which met our inclusion criteria, we annotated each post to determine which were advice-seeking (n = 137) or not advice-seeking (n = 65). We also annotated each posting user's buprenorphine-naloxone use status (current versus formerly taking and, if currently taking, whether inducting or tapering versus other stages) and quantified their connectedness using social network analysis. To analyze the relationship between Reddit users' advice-seeking and their social connectivity and medication use status, we constructed four models which varied in their inclusion of explanatory variables for social connectedness and buprenorphine use status.ResultsThe stepwise model containing "total degree" (p = 0.002), "using: inducting/tapering" (p < 0.001), and "using: other" (p = 0.01) outperformed all other models. Reddit users with fewer connections and who are currently using buprenorphine-naloxone are more likely to seek advice than those who are well-connected and no longer using the medication, respectively. Importantly, advice-seeking behavior is most accurately predicted using a combination of network characteristics and medication use status, rather than either factor alone.ConclusionsOur findings provide insights for the clinical care of people recovering from opioid use disorder and the nature of online medical advice-seeking overall. Clinicians should be especially attentive (e.g., through frequent follow-up) to patients who are inducting or tapering buprenorphine-naloxone or signal limited social support.
| S-EPMC9141384 | biostudies-literature
Project description:BackgroundThe use of social media assists in the distribution of information about COVID-19 to the general public and health professionals. Alternative-level metrics (ie, Altmetrics) is an alternative method to traditional bibliometrics that assess the extent of dissemination of a scientific article on social media platforms.ObjectiveOur study objective was to characterize and compare traditional bibliometrics (citation count) with newer metrics (Altmetric Attention Score [AAS]) of the top 100 Altmetric-scored articles on COVID-19.MethodsThe top 100 articles with the highest AAS were identified using the Altmetric explorer in May 2020. AAS, journal name, and mentions from various social media platforms (Twitter, Facebook, Wikipedia, Reddit, Mendeley, and Dimension) were collected for each article. Citation counts were collected from the Scopus database.ResultsThe median AAS and citation count were 4922.50 and 24.00, respectively. TheNew England Journal of Medicine published the most articles (18/100, 18%). Twitter was the most frequently used social media platform with 985,429 of 1,022,975 (96.3%) mentions. Positive correlations were observed between AAS and citation count (r2=0.0973; P=.002).ConclusionsOur research characterized the top 100 COVID-19-related articles by AAS in the Altmetric database. Altmetrics could complement traditional citation count when assessing the dissemination of an article regarding COVID-19.International registered report identifier (irrid)RR2-10.2196/21408.
| S-EPMC10365589 | biostudies-literature
Project description:We investigate the dynamics of semantic organization using social media, a collective expression of human thought. We propose a novel, time-dependent semantic similarity measure (TSS), based on the social network Twitter. We show that TSS is consistent with static measures of similarity but provides high temporal resolution for the identification of real-world events and induced changes in the distributed structure of semantic relationships across the entire lexicon. Using TSS, we measured the evolution of a concept and its movement along the semantic neighborhood, driven by specific news/events. Finally, we showed that particular events may trigger a temporary reorganization of elements in the semantic network.
| S-EPMC4449913 | biostudies-literature
Project description:Online social media provides massive open-ended platforms for users of a wide variety of backgrounds, interests, and beliefs to interact and debate, facilitating countless discussions across a myriad of subjects. With numerous unique voices being lent to the ever-growing information stream, it is essential to consider how the types of conversations that result from a social media post represent the post itself. We hypothesize that the biases and predispositions of users cause them to react to different topics in different ways not necessarily entirely intended by the sender. In this paper, we introduce a set of unique features that capture patterns of discourse, allowing us to empirically explore the relationship between a topic and the conversations it induces. Utilizing "microscopic" trends to describe "macroscopic" phenomena, we set a paradigm for analyzing information dissemination through the user reactions that arise from a topic, eliminating the need to analyze the involved text of the discussions. Using a Reddit dataset, we find that our features not only enable classifiers to accurately distinguish between content genre, but also can identify more subtle semantic differences in content under a single topic as well as isolating outliers whose subject matter is substantially different from the norm.
| S-EPMC8700409 | biostudies-literature
Project description:IntroductionThe number of people with kidney disease using social media to search for medical information and peer support is increasing. IgA nephropathy (IgAN) predominantly affects young adults, demographically the biggest users of social media. This article presents an innovative analysis of social media interactions to identify unmet education and information needs of patients with IgAN.MethodsFollowing ethical approval for the study, the IgAN Support UK Facebook group (https://www.facebook.com/groups/915274415226674) granted us permission to anonymously collect and analyze 1959 posts and comments from 498 group users. An initial patient focus group and quantitative word-frequency analysis created an initial categorization matrix that was iteratively refined after serial analyses of the social media database to generate a final categorization matrix of needs. We evaluated narrative data relating to each identified category to define patient narratives relating to each area.ResultsA large number of information gaps and unanswered questions were identified relating to the following: diet, symptoms, diagnosis, treatment, and patient comorbidities. Patient-clinician communication and the presentation of information were also drawn out as cross-cutting issues. These themes differed significantly from those identified from the traditional patient focus group, highlighting the value of this novel method for interrogating social media data to understand unmet patient needs.ConclusionSocial media data are untapped and valuable resources that can be used to better understand patient information gaps, leading to the generation of targeted materials to address unmet educational needs. This innovative approach could be replicated across other health conditions.
| S-EPMC8116902 | biostudies-literature
Project description:BackgroundThe medical community has increasingly embraced social media for a variety of purposes, including trainee education, research dissemination, professional networking, and recruitment of trainees and faculty. Platform choice and usage patterns appear to vary by specialty and purpose, but few studies comprehensively assess programs' social media presence. Prior studies assessed general surgery departments' Twitter use but omitted additional social media platforms and residency-specific accounts.ObjectiveThis study sought to broadly characterize the social media footprint of U.S. general surgery residency programs.MethodsUsing a protocolized search of program websites, social media platforms (Twitter, Facebook, Instagram, LinkedIn), and internet search, cross-sectional data on social media usage in March 2020 were collected for programs, their affiliated departments, their program directors (PDs), and their assistant/associate PDs (APDs).Results318 general surgery residency programs, 313 PDs, and 296 APDs were identified. 47.2% of programs had surgery-specific accounts on ≥1 platform. 40.2% of PDs and APDs had ≥1 account on Twitter and/or LinkedIn. Program type was associated with social media adoption and Twitter utilization, with lower usage among university-affiliated and independent programs (p<0.01).ConclusionsMost general surgery residencies, especially non-university-based programs, lacked any department or residency accounts across Twitter, Facebook, and Instagram by March 2020. These findings highlight opportunities for increased social media engagement and act as a pre-pandemic baseline for future investigations of how the shift to virtual trainee education, recruitment, conferences, and clinical care affect social media use.
| S-EPMC8244871 | biostudies-literature
Project description:BackgroundThe COVID-19 infodemic has been disseminating rapidly on social media and posing a significant threat to people's health and governance systems.ObjectiveThis study aimed to investigate and analyze posts related to COVID-19 misinformation on major Chinese social media platforms in order to characterize the COVID-19 infodemic.MethodsWe collected posts related to COVID-19 misinformation published on major Chinese social media platforms from January 20 to May 28, 2020, by using PythonToolkit. We used content analysis to identify the quantity and source of prevalent posts and topic modeling to cluster themes related to the COVID-19 infodemic. Furthermore, we explored the quantity, sources, and theme characteristics of the COVID-19 infodemic over time.ResultsThe daily number of social media posts related to the COVID-19 infodemic was positively correlated with the daily number of newly confirmed (r=0.672, P<.01) and newly suspected (r=0.497, P<.01) COVID-19 cases. The COVID-19 infodemic showed a characteristic of gradual progress, which can be divided into 5 stages: incubation, outbreak, stalemate, control, and recovery. The sources of the COVID-19 infodemic can be divided into 5 types: chat platforms (1100/2745, 40.07%), video-sharing platforms (642/2745, 23.39%), news-sharing platforms (607/2745, 22.11%), health care platforms (239/2745, 8.71%), and Q&A platforms (157/2745, 5.72%), which slightly differed at each stage. The themes related to the COVID-19 infodemic were clustered into 8 categories: "conspiracy theories" (648/2745, 23.61%), "government response" (544/2745, 19.82%), "prevention action" (411/2745, 14.97%), "new cases" (365/2745, 13.30%), "transmission routes" (244/2745, 8.89%), "origin and nomenclature" (228/2745, 8.30%), "vaccines and medicines" (154/2745, 5.61%), and "symptoms and detection" (151/2745, 5.50%), which were prominently diverse at different stages. Additionally, the COVID-19 infodemic showed the characteristic of repeated fluctuations.ConclusionsOur study found that the COVID-19 infodemic on Chinese social media was characterized by gradual progress, videoization, and repeated fluctuations. Furthermore, our findings suggest that the COVID-19 infodemic is paralleled to the propagation of the COVID-19 epidemic. We have tracked the COVID-19 infodemic across Chinese social media, providing critical new insights into the characteristics of the infodemic and pointing out opportunities for preventing and controlling the COVID-19 infodemic.
| S-EPMC7869922 | biostudies-literature