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1.
BMC Med Educ ; 22(1): 525, 2022 Jul 03.
Artículo en Inglés | MEDLINE | ID: mdl-35786406

RESUMEN

BACKGROUND: In recent years, social networking sites (SNSs) have evolved beyond connection and networking to become a powerful instructional tool. There is still a dearth of knowledge on the professional use of SNSs for education particularly among students from diverse backgrounds. This study examined the extent and pattern of SNSs usage for education across six institutions and then conducted an interventional workshop to fortify and regulate the educational use of SNSs. METHODS: This multicenter study was done in two phases. In the first phase, an online cross-sectional survey using a validated inventory was administered to determine the prevalence, extent, and preferences of SNSs usage by undergraduate students in medicine, health sciences and dentistry across five centers. Later, the second phase of the study was undertaken in a 75-min guided live workshop about the appropriate use of SNSs in academia. Additionally, pre- and post-test surveys were conducted to assess the impact and outcome of workshop. RESULTS: Of the 1722 respondents, 1553 (90%) reported using SNSs with the frequency of once a month to three to five times per day for education and to stay in touch with others. Most students agreed with the benefits of SNSs for education mainly in terms of information gathering, networking and collaboration. Twitter, Instagram, and Pinterest were noted as the most preferred SNSs for education. Nevertheless, 63% perceived that proper instruction was required for the efficient use of SNSs. Following the guided workshop, there was a significant improvement in web technology understanding, digital professionalism, skills and knowledge on the productive use of SNSs. Students rated the efficient for conceptual learning, connection to community practice, e-portfolio, and collaborative learning as the top four major teaching and learning strategies, respectively, in the post-workshop survey. CONCLUSION: Our study demonstrates that SNSs can be used as learning tools in medical education. However, SNSs usage should be regulated and guided for a more collegial and coherent learning climate in the digital realm. We urge medical educators to integrate SNSs into their courses for a technologically advanced and impactful curriculum.


Asunto(s)
Red Social , Estudiantes , Estudios Transversales , Escolaridad , Humanos , Profesionalismo
2.
Comput Intell Neurosci ; 2022: 4709146, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35814537

RESUMEN

With the popularity of the Internet and the advancement of information technology, more and more people are accepting the teaching and sharing of knowledge through the digitalization of information. The widespread adoption of 5G technology has pushed online learning even further into the mainstream. However, because online teaching does not have the drawback of being intuitive like classroom teaching, teachers' assessments of students' learning situations are less accurate. As a result, how to effectively evaluate students' academic performance in the context of 5G wireless network technology is a pressing issue that must be investigated. By processing these heterogeneous large-scale learning records and integrating multiple perspectives to analyze this learning record information to identify students' learning behaviors, this study proposes an integrated analysis algorithm based on artificial intelligence information technology. The possible learning outcomes of students are predicted based on their current learning situation, so teachers can provide auxiliary teaching strategies to students who may have learning difficulties based on the predicted information. The method proposed in this article uses information technology to predict students' grades, and the analysis shows that the method is very effective. In this article, different grades of classification methods are used to analyze and predict the whole students. All grade classification methods are effective in describing decision rules. No matter what grades classification method is used, the error rate of students' grades distribution is predicted to be below 40%.


Asunto(s)
Inteligencia Artificial , Estudiantes , Humanos , Conocimiento , Aprendizaje , Red Social
3.
Comput Intell Neurosci ; 2022: 1359730, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35800688

RESUMEN

With the advent of the Internet and the era of big data, education is increasingly dependent on data resources to support product and business innovation, and the lack of data resources has severely limited the areas involved. As a general information filtering method, personalized recommendation systems analyze the historical interaction data between users and items to build user interest models in an environment of "information overload", allowing users to discover and recommend information that interests them. However, the explosive growth of information in the network makes users wander in the sea of information, and it is increasingly difficult to find the information they really need, i.e., information overload. This has given rise to personalized recommendation systems, which currently have more mature applications in industries such as e-commerce, music services, and movie services. To this end, this paper studies and implements a customized educational resource recommendation system that can handle big data. The results show that the values of different similarity calculations all fluctuate with the gradual increase of the number of nearest neighbors, and the algorithm in this paper is maximum at the number of neighbors around 60; then, it is inferred that applying the calculation method to the recommendation algorithm will improve the recommendation accuracy. Therefore, education uses the concept of big data to process the huge amount of education data and find some correlations and laws in education, so as to realize "teaching according to the material, teaching according to the material".


Asunto(s)
Algoritmos , Macrodatos , Comercio , Películas Cinematográficas , Red Social
4.
Comput Intell Neurosci ; 2022: 1926794, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35800694

RESUMEN

The rapid iteration of information technology makes the development of online social networks increasingly rapid, and its corresponding network scale is also increasingly large and complex. The corresponding algorithms to deal with social networks and their corresponding related problems are also increasing. The corresponding privacy protection algorithms such as encryption algorithm, access control strategy algorithm, and differential privacy protection algorithm have been studied and analyzed, but these algorithms do not completely solve the problem of privacy disclosure. Based on this, this article first searches and accurately filters the relevant information and content of online social networks based on the deep convolution neural network algorithm, so as to realize the perception and protection of users' safe content. For the corresponding graphics and data, this article introduces the compressed sensing technology to randomly disturb the corresponding graphics and data. At the level of tracking network information leakage algorithm, this article proposes a network information leakage-tracking algorithm based on digital fingerprint, which mainly uses relevant plug-ins to realize the unique identification processing of users, uses the uniqueness of digital fingerprint to realize the tracking processing of leakers, and formulates the corresponding coding scheme based on the social network topology, and at the same time, the network information leakage-tracking algorithm proposed in this article also has high efficiency in the corresponding digital coding efficiency and scalability. In order to verify the advantages of the online social network information leakage-tracking algorithm based on deep learning, this article compares it with the traditional algorithm. In the experimental part, this article mainly compares the accuracy index, recall index, and performance index. At the corresponding accuracy index level, it can be seen that the maximum improvement of the algorithm proposed in this article is about 10% compared with the traditional algorithm. At the corresponding recall index level, the proposed algorithm is about 5-8% higher than the traditional algorithm. Corresponding to the overall performance index, it improves the performance by about 50% compared with the traditional algorithm. The comparison results show that the proposed algorithm has higher accuracy and the corresponding source tracking is more accurate.


Asunto(s)
Aprendizaje Profundo , Algoritmos , Redes Neurales de la Computación , Privacidad , Red Social
5.
Artículo en Inglés | MEDLINE | ID: mdl-35805599

RESUMEN

Mothers, fathers, or guardians of children and adolescents who do not identify with the gender they were assigned at birth face barriers in their social network to recognize their children's gender identity. This study aimed to analyze the scientific evidence on the dynamics of primary social networks to support mothers, fathers, or guardians of transgender children and adolescents. This is a systematic review of qualitative studies guided by the PRISMA guidelines. Controlled and free vocabulary were used to survey the studies in the EMBASE, Scopus, MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycInfo, Latin American and Caribbean Literature in Health Sciences (LILACS), and Web of Science databases. A total of 21 studies composed the final sample. Primary social networks were described as fragile and conflicting family/blood relationship ties with disapproval, isolation, rejection, a lack of understanding, and feelings of exclusion were expressed. Some have lost friends, reported tension in marriage and with relatives, and were commonly treated with hostility and harassment. Social transition does take place in the mutual context of struggle and resistance which demands a support network for parents or guardians.


Asunto(s)
Personas Transgénero , Adolescente , Niño , Padre , Femenino , Identidad de Género , Humanos , Recién Nacido , Masculino , Madres , Red Social
6.
Cereb Cortex ; 32(14): 3031-3041, 2022 Jul 12.
Artículo en Inglés | MEDLINE | ID: mdl-35848863

RESUMEN

Homophily refers to the tendency to like similar others. Here, we ask if homophily extends to brain structure. Specifically: do children who like one another have more similar brain structures? We hypothesized that neuroanatomic similarity tied to friendship is most likely to pertain to brain regions that support social cognition. To test this hypothesis, we analyzed friendship network data from 1186 children in 49 classrooms. Within each classroom, we identified "friendship distance"-mutual friends, friends-of-friends, and more distantly connected or unconnected children. In total, 125 children (mean age = 7.57 years, 65 females) also had good quality neuroanatomic magnetic resonance imaging scans from which we extracted properties of the "social brain." We found that similarity of the social brain varied by friendship distance: mutual friends showed greater similarity in social brain networks compared with friends-of-friends (ß = 0.65, t = 2.03, P = 0.045) and even more remotely connected peers (ß = 0.77, t = 2.83, P = 0.006); friends-of-friends did not differ from more distantly connected peers (ß = -0.13, t = -0.53, P = 0.6). We report that mutual friends have similar "social brain" networks, adding a neuroanatomic dimension to the adage that "birds of a feather flock together."


Asunto(s)
Amigos , Grupo Paritario , Encéfalo/diagnóstico por imagen , Niño , Femenino , Humanos , Imagen por Resonancia Magnética , Red Social
7.
J Environ Public Health ; 2022: 4126217, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35859577

RESUMEN

Purpose/Significance. This paper aims to explore the influence mechanism of adolescent health information literacy on health behavior. Method/Process. 13-19 year olds are taken as the survey objects to investigate their health information literacy through a questionnaire. Health information literacy mainly includes health information needs, acquisition, evaluation, use, and behaviors. A total of 252 adolescents' data were collected in this study, and model testing was performed with the help of regression analysis and structural equation modeling. Conclusion/Results. The results of the study show that adolescents' health information needs, acquisition, evaluation, and application abilities have a positive impact on health behaviors in the social network environment. Emotional responses and individual cognition as intermediate variables play important roles between health information literacy and health behaviors. Health information needs and health information assessments have the highest impact on mental health and social health, respectively. The society should pay special attention to the influence of adolescents' health cognition and anxiety on health behavior in the context of social network.


Asunto(s)
Salud del Adolescente , Alfabetización en Salud , Adolescente , Análisis de Datos , Alfabetización en Salud/métodos , Humanos , Red Social , Encuestas y Cuestionarios , Tecnología
8.
Psicothema ; 34(3): 365-374, 2022 Aug.
Artículo en Inglés | MEDLINE | ID: mdl-35860998

RESUMEN

BACKGROUND: Previous research about use of Social Networking Sites (SNS) use during the COVID-19 lockdown has examined benefits and risks of SNS use (i.e., support through SNS, problematic SNS use and interaction about COVID-19) without comparing them. This study has two objectives: (i) to evaluate which SNS uses (problematic SNS use and interaction about COVID-19 on SNS) predict increased emotional distress, and (ii) to analyse if social support and interaction about COVID-19 mediated the relationship between time spent on SNS and increased emotional distress. METHOD: A total of 1,003 participants (75.5% women) over 18 years old took part (M = 42.33; SD = 14.32 years). Three hierarchical linear regressions were performed for the first objective and a path analysis was performed for the second. RESULTS: Results showed that negative social comparison on SNS had the highest positive regression weight, followed by interaction about COVID-19 and addictive consequences. Also, an indirect effect of time spent on SNS on anxiety, depression, and life satisfaction through interaction about COVID-19 and support through SNS was found. CONCLUSIONS: The results indicate that comparative SNS use is the best predictor of emotional distress. The mediation model proposed was confirmed, highlighting the importance of assessing specific SNS uses.


Asunto(s)
COVID-19 , Medios de Comunicación Sociales , Adolescente , Control de Enfermedades Transmisibles , Femenino , Humanos , Masculino , Medición de Riesgo , Red Social
9.
Chaos ; 32(7): 073118, 2022 Jul.
Artículo en Inglés | MEDLINE | ID: mdl-35907736

RESUMEN

In the evolution of cooperation, the individuals' payoffs are commonly random in real situations, e.g., the social networks and the economic regions, leading to unpredictable factors. Therefore, there are chances for each individual to obtain the exceeding payoff and risks to get the low payoff. In this paper, we consider that each individual's payoff follows a specific probability distribution with a fixed expectation, where the normal distribution and the exponential distribution are employed in our model. In the simulations, we perform the models on the weak prisoner's dilemmas (WPDs) and the snowdrift games (SDGs), and four types of networks, including the hexagon lattice, the square lattice, the small-world network, and the triangular lattice are considered. For the individuals' normally distributed payoff, we find that the higher standard deviation usually inhibits the cooperation for the WPDs but promotes the cooperation for the SDGs. Besides, with a higher standard deviation, the cooperation clusters are usually split for the WPDs but constructed for the SDGs. For the individuals' exponentially distributed payoff, we find that the small-world network provides the best condition for the emergence of cooperators in WPDs and SDGs. However, when playing SDGs, the small-world network allows the smallest space for the pure cooperative state while the hexagon lattice allows the largest.


Asunto(s)
Conducta Cooperativa , Teoría del Juego , Evolución Biológica , Humanos , Distribución Normal , Dilema del Prisionero , Red Social
10.
Chaos ; 32(7): 073105, 2022 Jul.
Artículo en Inglés | MEDLINE | ID: mdl-35907740

RESUMEN

Many biological phenomena or social events critically depend on how information evolves in complex networks. However, a general theory to characterize information evolution is yet absent. Consequently, numerous unknowns remain about the mechanisms underlying information evolution. Among these unknowns, a fundamental problem, being a seeming paradox, lies in the coexistence of local randomness, manifested as the stochastic distortion of information content during individual-individual diffusion, and global regularity, illustrated by specific non-random patterns of information content on the network scale. Here, we attempt to formalize information evolution and explain the coexistence of randomness and regularity in complex networks. Applying network dynamics and information theory, we discover that a certain amount of information, determined by the selectivity of networks to the input information, frequently survives from random distortion. Other information will inevitably experience distortion or dissipation, whose speeds are shaped by the diversity of information selectivity in networks. The discovered laws exist irrespective of noise, but noise accounts for disturbing them. We further demonstrate the ubiquity of our discovered laws by analyzing the emergence of neural tuning properties in the primary visual and medial temporal cortices of animal brains and the emergence of extreme opinions in social networks.


Asunto(s)
Modelos Neurológicos , Neuronas , Animales , Encéfalo , Ruido , Red Social
11.
Chaos ; 32(7): 073117, 2022 Jul.
Artículo en Inglés | MEDLINE | ID: mdl-35907745

RESUMEN

Opinion dynamics on social networks have received considerable attentions in recent years. Nevertheless, just a few works have theoretically analyzed the condition in which a certain opinion can spread in the whole structured population. In this article, we propose an evolutionary game approach for a binary opinion model to explore the conditions for an opinion's spreading. Inspired by real-life observations, we assume that an agent's choice to select an opinion is not random but is based on a score rooted from both public knowledge and the interactions with neighbors. By means of coalescing random walks, we obtain a condition in which opinion A can be favored to spread on social networks in the weak selection limit. We find that the successfully spreading condition of opinion A is closely related to the basic scores of binary opinions, the feedback scores on opinion interactions, and the structural parameters including the edge weights, the weighted degrees of vertices, and the average degree of the network. In particular, when individuals adjust their opinions based solely on the public information, the vitality of opinion A depends exclusively on the difference of basic scores of A and B. When there are no negative (positive) feedback interactions between connected individuals, we find that the success of opinion A depends on the ratio of the obtained positive (negative) feedback scores of competing opinions. To complete our study, we perform computer simulations on fully connected, small-world, and scale-free networks, respectively, which support and confirm our theoretical findings.


Asunto(s)
Actitud , Red Social , Simulación por Computador , Humanos
12.
BMC Health Serv Res ; 22(1): 955, 2022 Jul 27.
Artículo en Inglés | MEDLINE | ID: mdl-35897005

RESUMEN

BACKGROUND: Efforts have been made by health research granting agencies to bring research closer to patients' concerns. In Canada, such efforts were formalized in 2011 with the funding of the Strategy for Patient-Oriented Research (SPOR)'s research networks to address research priorities identified by patients and accelerate the translation of research findings into patient care and health care policy. Among these networks, SPOR Diabetes Action Canada (DAC) has created patient-partner circles to facilitate their integration within the network. The nature of the relationships within this atypical patient-oriented research network is systematically explored in this paper. METHODS: A cross-sectional social network study was conducted among the SPOR DAC's network members to examine inter-individual interactions, and the topics discussed the most between members. Descriptive data analyses were conducted to explore which discussion topics were discussed most among members whose primary roles were research, administration, governance, and patient representation. RESULTS: The response rate was 51.9%, providing data on 76.5% of the maximum number of connections in the network. The survey captured 2763 inter-individual relationships. Responses to a sub-question inserted in the survey show that 482 of these relationships (17,4%) existed before joining the network in collaboration on a research project. Most ties captured in the survey were yearly or quarterly, while few relationships were monthly, weekly, or daily. In measured relationships, members discussed several topics, the most frequent being scientific research, patient engagement, network coordination and governance, and operations and management. The topics associated with the most significant proportion of relationships captured in the survey were scientific research (45.4%) and patient engagement (40.7%). Management & operations and governance & coordination follow, corresponding to 24.3 and 23.9% of the captured relationships. All discussion topic subnetworks were either somewhat or highly centralized, meaning that relationships were not equally distributed among members involved in these discussions. Of the 1256 relationships involving exchanges about scientific research, 647 (51.5%) involved a researcher, 419 (33.3%) an administrator, 182 (14.5%) a patient partner, and 82 (6.5%) a member whose primary role is network governance. CONCLUSIONS: Scientific research and patient engagement were the most common topics discussed, consistent with the patient-centered research at the heart of the SPOR Diabetes Action Canada network. The study identified several relationships where a patient partner has discussed scientific research with a researcher. However, relationships involving research discussions were three times more common between a researcher and an administrator than between a researcher and a patient partner, although twice as many patient partners as administrators participated in the survey. The institutionalization of patient-partner involvement in large research networks is an evolving practice for which optimal engagement methods are still being explored.


Asunto(s)
Diabetes Mellitus , Participación del Paciente , Canadá , Estudios Transversales , Humanos , Red Social
13.
Sensors (Basel) ; 22(15)2022 Jul 25.
Artículo en Inglés | MEDLINE | ID: mdl-35898032

RESUMEN

With the wide application of social media, public opinion analysis in social networks has been unable to be met through text alone because the existing public opinion information includes data information of various modalities, such as voice, text, and facial expressions. Therefore multi-modal emotion analysis is the current focus of public opinion analysis. In addition, multi-modal emotion recognition of speech is an important factor restricting the multi-modal emotion analysis. In this paper, the emotion feature retrieval method for speech is firstly explored and the processing method of sample disequilibrium data is then analyzed. By comparing and studying the different feature fusion methods of text and speech, respectively, the multi-modal feature fusion method for sample disequilibrium data is proposed to realize multi-modal emotion recognition. Experiments are performed using two publicly available datasets (IEMOCAP and MELD), which shows that processing multi-modality data through this method can obtain good fine-grained emotion recognition results, laying a foundation for subsequent social public opinion analysis.


Asunto(s)
Opinión Pública , Voz , Emociones , Expresión Facial , Humanos , Red Social
14.
Proc Natl Acad Sci U S A ; 119(30): e2120742119, 2022 Jul 26.
Artículo en Inglés | MEDLINE | ID: mdl-35862454

RESUMEN

Targeting structurally influential individuals within social networks can enhance adoption of health interventions within populations. We tested the effectiveness of two algorithms to improve social contagion that do not require knowledge of the whole network structure. We mapped the social interactions of 2,491 women in 50 residential buildings (chawls) in Mumbai, India. The buildings, which are social units, were randomized to (1) targeting 20% of the women at random, (2) targeting friends of such randomly chosen women, (3) targeting pairs of people composed of randomly chosen women and a friend, or (4) no targeting. Both targeting algorithms, friendship nomination and pair targeting, enhanced adoption of a public health intervention related to the use of iron-fortified salt for anemia. In particular, the targeting of pairs of friends, which is relatively easily implementable in field settings, enhanced adoption of novel practices through both social influence and social reinforcement.


Asunto(s)
Promoción de la Salud , Salud Pública , Red Social , Algoritmos , Femenino , Amigos , Promoción de la Salud/métodos , Humanos , India
15.
BMC Psychiatry ; 22(1): 451, 2022 07 05.
Artículo en Inglés | MEDLINE | ID: mdl-35790935

RESUMEN

BACKGROUND: Social networks and relationships create a sense of belonging and social identity; hence, can affect mental health and the quality of life, especially in young people. The present study was conducted to determine the predicting role of social networks and internet emotional relationships on students' mental health and quality of life. METHODS: The present cross-sectional study was conducted in 2021 on 350 students at Alborz University of Medical Sciences selected by convenience sampling. Data were collected using five questionnaires: socioeconomic status, social networks, internet emotional relationships, stress, anxiety, depression scale (DASS-21), quality of life, and a checklist of demographic details. Data were analyzed in SPSS-25, PLS-3, and Lisrel-8.8. RESULTS: According to the path analysis, the DASS-21 score had the most significant positive causal association with internet emotional relationships in the direct path (B = 0.22) and the most negative association with socioeconomic status (B = - 0.09). Quality of life had the highest negative causal association with the DASS-21 score in the direct path (B = - 0.26) and the highest positive association with socioeconomic status in the indirect path (B = 0.02). The mean duration of using social networks (B ≈ - 0.07) and internet emotional relationships (B ≈ - 0.09) had the highest negative association with quality of life. CONCLUSION: The use of the internet and virtual networks, internet emotional relationships, and unfavorable socioeconomic status were associated with higher DASS-21 scores and reduced quality of life in the students. Since students are the future of countries, it is necessary for policymakers to further address this group and their concerns.


Asunto(s)
Salud Mental , Calidad de Vida , Adolescente , Estudios Transversales , Humanos , Internet , Análisis de Clases Latentes , Calidad de Vida/psicología , Red Social , Estudiantes
16.
PLoS One ; 17(7): e0271224, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35816493

RESUMEN

The massively and rapidly spreading disinformation on social network platforms poses a serious threat to public safety and social governance. Therefore, early and accurate detection of rumors in social networks is of vital importance before they spread on a large scale. Considering the small-world property of social networks, the source tweet-word graph is decomposed from the global graph of rumors, and a rumor detection method based on graph attention network of source tweet-word graph is proposed to fully learn the structure of rumor propagation and the deep representation of text contents. Specifically, the proposed model can adequately capture the contextual semantic association representation of source tweets during the propagation and extract semantic features. For the data sparseness of the early stage of information dissemination, text attention mechanism based on opinion similarity can aggregate and capture more tweet propagation structure features to help improve the efficiency of early detection of rumors. Through the analysis of the experimental results on real public datasets, the rumor detection performance of the proposed method is better than that of other baseline methods. Especially in the early rumor detection tasks, the proposed method can detect rumors with an accuracy of nearly 90% in the early stage of information dissemination. And it still has good robustness with noise interference.


Asunto(s)
Difusión de la Información , Red Social , Recolección de Datos
17.
J Med Internet Res ; 24(7): e38395, 2022 Jul 27.
Artículo en Inglés | MEDLINE | ID: mdl-35820053

RESUMEN

BACKGROUND: Crowdfunding is increasingly used to offset the financial burdens of illness and health care. In the era of the COVID-19 pandemic and associated infodemic, the role of crowdfunding to support controversial COVID-19 stances is unknown. OBJECTIVE: We sought to examine COVID-19-related crowdfunding focusing on the funding of alternative treatments not endorsed by major medical entities, including campaigns with an explicit antivaccine, antimask, or antihealth care stances. METHODS: We performed a cross-sectional analysis of GoFundMe campaigns for individuals requesting donations for COVID-19 relief. Campaigns were identified by key word and manual review to categorize campaigns into "Traditional treatments," "Alternative treatments," "Business-related," "Mandate," "First Response," and "General." For each campaign, we extracted basic narrative, engagement, and financial variables. Among those that were manually reviewed, the additional variables of "mandate type," "mandate stance," and presence of COVID-19 misinformation within the campaign narrative were also included. COVID-19 misinformation was defined as "false or misleading statements," where cited evidence could be provided to refute the claim. Descriptive statistics were used to characterize the study cohort. RESULTS: A total of 30,368 campaigns met the criteria for final analysis. After manual review, we identified 53 campaigns (0.17%) seeking funding for alternative medical treatment for COVID-19, including popularized treatments such as ivermectin (n=14, 26%), hydroxychloroquine (n=6, 11%), and vitamin D (n=4, 7.5%). Moreover, 23 (43%) of the 53 campaigns seeking support for alternative treatments contained COVID-19 misinformation. There were 80 campaigns that opposed mandating masks or vaccination, 48 (60%) of which contained COVID-19 misinformation. Alternative treatment campaigns had a lower median amount raised (US $1135) compared to traditional (US $2828) treatments (P<.001) and a lower median percentile of target achieved (11.9% vs 31.1%; P=.003). Campaigns for alternative treatments raised substantially lower amounts (US $115,000 vs US $52,715,000, respectively) and lower proportions of fundraising goals (2.1% vs 12.5%) for alternative versus conventional campaigns. The median goal for campaigns was significantly higher (US $25,000 vs US $10,000) for campaigns opposing mask or vaccine mandates relative to those in support of upholding mandates (P=.04). Campaigns seeking funding to lift mandates on health care workers reached US $622 (0.15%) out of a US $410,000 goal. CONCLUSIONS: A small minority of web-based crowdfunding campaigns for COVID-19 were directed at unproven COVID-19 treatments and support for campaigns aimed against masking or vaccine mandates. Approximately half (71/133, 53%) of these campaigns contained verifiably false or misleading information and had limited fundraising success. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1001/jamainternmed.2019.3330.


Asunto(s)
COVID-19 , Colaboración de las Masas , COVID-19/epidemiología , Comunicación , Estudios Transversales , Humanos , Pandemias , Red Social
18.
Sci Rep ; 12(1): 12707, 2022 Jul 26.
Artículo en Inglés | MEDLINE | ID: mdl-35882902

RESUMEN

Disinformation campaigns are prevalent, affecting vaccination coverage, creating uncertainty in election results, and causing supply chain disruptions, among others. Unfortunately, the problems of misinformation and disinformation are exacerbated due to the wide availability of online platforms and social networks. Naturally, these emerging disinformation networks could lead users to engage with critical infrastructure systems in harmful ways, leading to broader adverse impacts. One such example involves the spread of false pricing information, which causes drastic and sudden changes in user commodity consumption behavior, leading to shortages. Given this, it is critical to address the following related questions: (i) How can we monitor the evolution of disinformation dissemination and its projected impacts on commodity consumption? (ii) What effects do the mitigation efforts of human intermediaries have on the performance of the infrastructure network subject to disinformation campaigns? (iii) How can we manage infrastructure network operations and counter disinformation in concert to avoid shortages and satisfy user demands? To answer these questions, we develop a hybrid approach that integrates an epidemiological model of disinformation spread (based on a susceptible-infectious-recovered model, or SIR) with an efficient mixed-integer programming optimization model for infrastructure network performance. The goal of the optimization model is to determine the best protection and response actions against disinformation to minimize the general shortage of commodities at different nodes over time. The proposed model is illustrated with a case study involving a subset of the western US interconnection grid located in Los Angeles County in California.


Asunto(s)
Medios de Comunicación Sociales , Comunicación , Desinformación , Humanos , Política , Red Social
19.
Front Public Health ; 10: 899949, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35899151

RESUMEN

Several factors that follow the development of society affect physical inactivity, which primarily includes the development of technology and digitalization and the increasing choice of unhealthy lifestyle habits. However, certain shifts in the fitness industry have been noted in the last decade. The development of wearable technologies and artificial intelligence is one of the leading fitness trends and undoubtedly represents the future of the fitness industry. On the other hand, the significant influence of social media and networks affects the development and attitudes of people related to physical activity. Therefore, this review paper evaluates the advantages and disadvantages of wearable technologies and artificial intelligence, the positive and negative effects of social networks, and points out the problems accompanying these new fitness trends. The development of fitness trends follows humanity's needs, and one of the biggest challenges is incorporating these novelties in a mission to improve physical activity levels worldwide.


Asunto(s)
Inteligencia Artificial , Medios de Comunicación Sociales , Ejercicio Físico , Humanos , Conducta Sedentaria , Red Social
20.
Front Public Health ; 10: 931102, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35899153

RESUMEN

Purpose: Our objective is to pilot an advertisement-driven sampling procedure among African American (AA) breast cancer survivors living in Maryland. These pilot study methods will inform a future population-based study of AA breast cancer survivors at high risk of poor outcomes due to biological differences and social inequities. Methods: This cross-sectional study utilizes an innovative, social media-based advertisement campaign with an associated social media study page to recruit 100 AA breast cancer survivors. Participants are biologically female, aged 18 and older, identify as AA/Black, have a diagnosis of breast cancer, and reside in Maryland. A preset "Audience" was created via Meta (formerly Facebook) to automatically target potential interest in the online study via geolocation and public social media interests (estimated range = 101,000 women). Eligible participants complete an online survey including demographic and clinical characteristics, cancer screening, healthcare access, and utilization, COVID-19 impact, quality of doctor-patient communication, and preferences for future study participation. Results: Recruitment began on 5 January 2022 and remains ongoing. As of 7 June 2002: 124 completed the screener, 110/124 (88.7%) consented passively, 24/110 (21.8%) started but did not complete survey, 86/110 (78.1%) completed the survey. Conclusions: Results from this study will inform a statewide multilevel prospective population-based study to improve health behaviors, disease management, and self-efficacy of chronic disease management among AA breast cancer survivors.


Asunto(s)
Neoplasias de la Mama , COVID-19 , Supervivientes de Cáncer , Medios de Comunicación Sociales , Publicidad/métodos , Afroamericanos , Estudios Transversales , Femenino , Humanos , Proyectos Piloto , Estudios Prospectivos , Red Social
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