Buying coke online in Jerez de la Frontera

__________________________

📍 Verified store!

📍 Guarantees! Quality! Reviews!

__________________________

▼▼ ▼▼ ▼▼ ▼▼ ▼▼ ▼▼ ▼▼

▲▲ ▲▲ ▲▲ ▲▲ ▲▲ ▲▲ ▲▲

Buying coke online in Jerez de la Frontera

The page you requested could not be found. Try refining your search, or use the navigation above to locate the post. No Results Found The page you requested could not be found.

Identify yourself

Buying coke online in Jerez de la Frontera

Official websites use. Share sensitive information only on official, secure websites. Although at present there is broad agreement among researchers, health professionals, and policy makers on the need to control and combat health misinformation, the magnitude of this problem is still unknown. Consequently, it is fundamental to discover both the most prevalent health topics and the social media platforms from which these topics are initially framed and subsequently disseminated. This systematic review aimed to identify the main health misinformation topics and their prevalence on different social media platforms, focusing on methodological quality and the diverse solutions that are being implemented to address this public health concern. We defined health misinformation as a health-related claim that is based on anecdotal evidence, false, or misleading owing to the lack of existing scientific knowledge. We included 1 articles that focused on health misinformation in social media, including those in which the authors discussed the consequences or purposes of health misinformation and 2 studies that described empirical findings regarding the measurement of health misinformation on these platforms. A total of 69 studies were identified as eligible, and they covered a wide range of health topics and social media platforms. Health misinformation was most prevalent in studies related to smoking products and drugs such as opioids and marijuana. The prevalence of health misinformation was the highest on Twitter and on issues related to smoking products and drugs. However, misinformation on major public health issues, such as vaccines and diseases, was also high. Our study offers a comprehensive characterization of the dominant health misinformation topics and a comprehensive description of their prevalence on different social media platforms, which can guide future studies and help in the development of evidence-based digital policy action plans. Keywords: social media, health misinformation, infodemiology, infodemics, social networks, poor quality information, social contagion. Over the last two decades, internet users have been increasingly using social media to seek and share health information \[ 1 \]. These social platforms have gained wider participation among health information consumers from all social groups regardless of gender or age \[ 2 \]. Health professionals and organizations are also using this medium to disseminate health-related knowledge on healthy habits and medical information for disease prevention, as it represents an unprecedented opportunity to increase health literacy, self-efficacy, and treatment adherence among populations \[ 3 - 9 \]. However, these public tools have also opened the door to unprecedented social and health risks \[ 10 , 11 \]. Although these platforms have demonstrated usefulness for health promotion \[ 7 , 12 \], recent studies have suggested that false or misleading health information may spread more easily than scientific knowledge through social media \[ 13 , 14 \]. Therefore, it is necessary to understand how health misinformation spreads and how it can affect decision-making and health behaviors \[ 15 \]. Using a broad term that can include the wide variety of definitions in scientific literature, we here define health misinformation as a health-related claim that is based on anecdotal evidence, false, or misleading owing to the lack of existing scientific knowledge \[ 1 \]. This general definition would consider, on the one hand, information that is false but not created with the intention of causing harm ie, misinformation and, on the other, information that is false or based on reality but deliberately created to harm a particular person, social group, institution, or country ie, disinformation and malinformation. The fundamental role of health misinformation on social media has been recently highlighted by the COVID pandemic, as well as the need for quality and veracity of health messages in order to manage the present public health crisis and the subsequent infodemic. In fact, at present, the propagation of health misinformation through social media has become a major public health concern \[ 17 \]. The lack of control over health information on social media is used as evidence for the current demand to regulate the quality and public availability of online information \[ 18 \]. In fact, although today there is broad agreement among health professionals and policy makers on the need to control health misinformation, there is still little evidence about the effects that the dissemination of false or misleading health messages through social media could have on public health in the near future. Although recent studies are exploring innovative ways to effectively combat health misinformation online \[ 19 - 22 \], additional research is needed to characterize and capture this complex social phenomenon \[ 23 \]. More specifically, four knowledge gaps have been detected from the field of public health \[ 1 \]. First, we have to identify the dominant health misinformation trends and specifically assess their prevalence on different social platforms. Second, we need to understand the interactive mechanisms and factors that make it possible to progressively spread health misinformation through social media eg, vaccination myths, miracle diets, alternative treatments based on anecdotal evidence, and misleading advertisements on health products. Factors, such as the sources of misinformation, structure and dynamics of online communities, idiosyncrasies of social media channels, motivation and profile of people seeking health information, content and framing of health messages, and context in which misinformation is shared, are critical to understanding the dynamics of health misinformation through these platforms. For instance, although the role of social bots in spreading misinformation through social media platforms during political campaigns and election periods is widely recognized, health debates on social media are also affected by social bots \[ 24 \]. At present, social bots are used to promote certain products in order to increase company profits, as well as to benefit certain ideological positions or contradict health evidence eg, in the case of vaccines \[ 25 \]. Third, a key challenge in epidemiology and public health research is to determine not only the effective impact of these tools in the dissemination of health misinformation but also their impact on the development and reproduction of unhealthy or dangerous behaviors. Finally, regarding health interventions, we need to know which strategies are the best in fighting and reducing the negative impact of health misinformation without reducing the inherent communicative potential to propagate health information with these same tools. In line with the abovementioned gaps, a recent report represents one of the first steps forward in the comparative study of health misinformation on social media \[ 16 \]. Through a systematic review of the literature, this study offers a general characterization of the main topics, areas of research, methods, and techniques used for the study of health misinformation. However, despite the commendable effort made to compose a comprehensible image of this highly complex phenomenon, the lack of objective indicators that make it possible to measure the problem of health misinformation is still evident today. Taking into account this wide set of considerations, this systematic review aimed to specifically address the knowledge gap. In order to guide future studies in this field of knowledge, our objective was to identify and compare the prevalence of health misinformation topics on social media platforms, with specific attention paid to the methodological quality of the studies and the diverse analytical techniques that are being implemented to address this public health concern. Studies were included if 1 the objectives were to address the study of health misinformation on social media, search systematically for health misinformation, and explicitly discuss the impact, consequences, or purposes of misinformation; 2 the results were based on empirical results and the study used quantitative, qualitative, and computational methods; and 3 the research was specifically focused on social media platforms eg, Twitter, Facebook, Instagram, Flickr, Sina Weibo, VK, YouTube, Reddit, Myspace, Pinterest, and WhatsApp. For comparability, we included studies written in English that were published after until March Articles were excluded if they addressed health information quality in general or if they partially mentioned the existence of health misinformation without providing empirical findings. We did not include studies that dealt with content posted on other social media platforms. During the screening process, papers with a lack of methodological quality were also excluded. Based on previous findings \[ 16 \], the query searched for MeSH terms and keywords in the entire body of the manuscript related to the following three basic analytical dimensions that articulated our research objective: 1 social media, 2 health, and 3 misinformation. Based on the results obtained through this initial search, we added some keywords that having been extracted from the articles that met the inclusion criteria were specifically focused on the issue of health misinformation on social media. This initial search retrieved records. Additionally, this search strategy was adapted for its use in Scopus records and Web of Science records. A full description of the search terms can be found in Multimedia Appendix 1. In total, we collected research articles. After removing duplicates, we screened articles and retrieved potentially eligible articles. Discrepancies were shared and resolved by mutual agreement. Finally, a total of 69 articles were included in this systematic review Figure 1. In order to evaluate the quality of the selected studies and given the wide variety of methodologies and approaches found in the articles, we composed an extraction form based on previous work \[ 27 - 29 \]. Following this coding scheme, we extracted the following four different fields of information: 1 descriptive information 27 items , 2 search strategy evaluation eight items , 3 information evaluation six items , and 4 the quality and rigor of methodology and reporting 15 items for either quantitative or qualitative studies Multimedia Appendix 1. Questions in field 2, which have been used in previous studies \[ 27 \], assessed the quality of information provided to demonstrate how well reported, systematic, and comprehensive the search strategy was S score. The items in field 3 measured how rigorous the evaluation was E score for health-related misinformation \[ 27 \]. Field 4 contained items designed for the general evaluation of quality in the research process, whether quantitative \[ 28 \] or qualitative \[ 29 \]. This Q-score approach takes into account general aspects of the research and reporting, such as the study, methodology, and quality of the discussion. The purpose of these questions is to guarantee the quality of the selected studies. Furthermore, in order to be able to compare the methods used in the selected studies, the studies were classified into several categories. In general, these studies analyzed different dimensions of the information published on social media. This category considered other dimensions in addition to content, such as readability, accuracy, usefulness, and sources of information. These studies focused on measuring how misinformation spreads on social media, the relationship between the quality of information and its popularity on these social platforms, the relationship between users and opinions, echochambers effects, and opinion formation. Table 1 classifies the studies by topic and social media platform \[ 30 - 97 \]. It also includes the prevalence of health misinformation posts. The quality assessment results for the S score, E score, and Q score are reported in Multimedia Appendix 3. Figure 2 shows the prevalence of health misinformation grouped by different topics and social media typology. Studies are ordered according to the percentage of health misinformation posts found in the studies selected. These works were also classified according to the type of social media under study. While all topics were present on all the different social media platforms, we found some differences in their prevalence. On one hand, vaccines, drugs, and pandemics were more prevalent topics on microblogging platforms ie, Twitter or MySpace. On the other hand, on media sharing platforms ie, YouTube, Instagram, or Pinterest and social network platforms ie, Facebook, VK, or WhatsApp , noncommunicable diseases and treatments were the most prevalent topics. Overall, health misinformation was most prevalent in studies related to smoking products, such as hookah and water pipes \[ 33 , 59 , 71 \], e-cigarettes, and drugs, such as opioids and marijuana \[ 45 , 70 , 97 \]. Health misinformation about vaccines was also very common. However, studies reported different levels of health misinformation depending on the type of vaccine studied, with the human papilloma virus HPV vaccine being the most affected \[ 67 , 68 \]. Health misinformation related to diets or pro—eating disorder arguments were moderate in comparison to the aforementioned topics \[ 35 , 93 \]. Studies focused on diseases ie, noncommunicable diseases and pandemics also reported moderate misinformation rates \[ 56 , 85 \], especially in the case of cancer \[ 76 , 96 \]. Finally, the lowest levels of health misinformation were observed in studies evaluating the presence of health misinformation regarding medical treatments. Although first-aid information on burns or information on dental implants was limited in quantity and quality, the prevalence of misinformation for these topics was low. Surgical treatment misinformation was the least prevalent. This was due to the fact that the content related to surgical treatments mainly came from official accounts, which made the online information complete and reliable. Regarding the methods used in the different studies, there were some differences between the diverse social media platforms. Figure 3 shows the different methods applied in the studies classified by the type of social media platform and ordered by the percentage of misinformation posts. Papers focused on YouTube also followed a similar trend, and they were centered on the HPV debate and on the public discussion on vaccine side effects and risks for specific population groups eg, autism in children. Regarding Facebook, all studies were particularly focused on vaccination decision-making. Most authors studied differences in language use, the effect of a heterogeneous community structure in the propagation of health misinformation, and the role played by fake profiles or bots in the spread of poor quality, doubtful, or ambiguous health content. In line with these concerns, authors pointed out the need to further study the circumstances surrounding those who adopt these arguments \[ 49 \], and whether alternative strategies to education could improve the fight against antivaccine content \[ 51 \]. Authors also recommended paying close attention to social media as these tools are assumed to play a fundamental role in the propagation of misinformation. For instance, the role played by the echochamber or the heterogeneous community structure on Twitter has been shown to skew the information to which users are exposed in relation to HPV vaccines \[ 49 \]. Furthermore, governmental organizations could also use social media platforms to reach a greater number of people \[ 39 , 55 \]. According to topic, regarding drug and opioid use, studies investigated the dissemination of misinformation through social media platforms \[ 32 , 45 , 46 , 70 , 97 \], the consumption of misinformation related to these products, drug abuse, and the sale of online medical products \[ 61 , 66 \]. These studies highlighted the risk, especially for young people, caused by the high rate of misinformation related to the dissemination of drug practice and misuse predominantly marijuana and opioids \[ 45 \]. In addition, social media platforms were identified as a potential source of illegal promotion of the sale of controlled substances directly to consumers \[ 66 \]. Most drug-related messages on social media were potentially misleading or false claims that lacked credible evidence to support them \[ 32 \]. Other studies pointed to social media as a potential source of information that illegally promotes the sale of controlled prescription drugs directly to consumers \[ 66 \]. In the case of cannabinoids, there was often content that described, encouraged, promoted \[ 54 \], or even normalized the consumption of illicit substances \[ 70 \]. Regarding e-cigarettes, studies pointed out the high prevalence of misinformation denying health damage \[ 95 \]. In this sense, it is worth noting the importance of sources of misinformation. While in the case of vaccines, the source of health misinformation was mainly individuals or groups of people with a particular interest eg, antivaccine movement , social media was found to be frequently contaminated by misinformation from bots ie, software applications that autonomously run tasks such as spreading positive discourse about e-cigarettes and other tobacco products \[ 78 \]. In fact, these fake accounts may influence the online conversation in favor of e-cigarettes given the scientific appearance of profiles \[ 78 \]. Some of the claims found in this study denied the harmfulness of e-cigarettes. In line with these findings, other studies pointed to the high percentage of messages favoring e-cigarettes as an aid to quitting smoking \[ 95 \]. These studies aimed to explore the misperceptions of drug abuse or alternative forms of tobacco consumption. The authors evaluated the truthfulness of claims about drugs. These studies analyzed the popularity of messages based on whether they promoted illegal access to drugs online and the interaction of users with this content. Most of the studies focused on the objective evaluation of information quality on YouTube \[ 38 , 56 , 57 , 69 , 72 , 74 , 76 , 80 , 85 \]. The authors analyzed the usefulness and accuracy of the information. The main objective of these studies was to analyze which are the most common misinformation topics. Some studies evaluated the potential of this platform as a source of information specially for health students or self-directed education among the general public. Unfortunately, the general tone of research findings was that YouTube is not an advisable source for health professionals or health information seekers. Regarding diabetes, the probability of finding misleading videos was high \[ 56 \]. Misleading videos promoted cures for diabetes, negated scientific arguments, or provided treatments with no scientific basis. Furthermore, misleading videos related to diabetes were found to be more popular than those with evidence-based health information \[ 74 \], which increased the probability of consuming low-quality health content. The same misinformation pattern was detected for other chronic diseases such as hypertension \[ 72 \], prostate cancer \[ 76 \], and epilepsy \[ 80 \]. All these studies analyzed how online platforms were used by both health information seekers and health and governmental authorities during the pandemic period. These studies were focused on analysis of the issues of misinformation. These studies focused on the study of the prevalence of health misinformation. These studies identified social media as a public forum for free discussion and indicated that this freedom might lead to rumors on anecdotal evidence and misunderstandings regarding pandemics. Consequently, although social media was described as a forum for sharing health-related knowledge, these tools are also recognized by researchers and health professionals as a source of misinformation that needs to be controlled by health experts \[ 83 , 84 \]. Therefore, while social media serves as a place where people commonly share their experiences and concerns, these platforms can be potentially used by health professionals to fight against false beliefs on communicable diseases eg, as it is happening today during the COVID pandemic. Accordingly, social media platforms have been found to be powerful tools for health promotion among governmental institutions and health-related workers, and new instruments that, for instance, are being used to increase health surveillance and intervention against false beliefs and misinformation \[ 31 , 89 \]. This set of studies identified pro—eating disorder groups and discourses within social media \[ 35 \], and how pro—eating disorder information was shared and spread on these platforms \[ 91 \]. Anorexia was the most studied eating disorder along with bulimia. Furthermore, discourses promoting fitness or recovery after an eating disorder were often compared with those issued by pro—eating disorder groups \[ 41 , 62 , 92 , 93 \]. In general, the authors agreed on the relevance of pro—eating disorder online groups, the mutual support among members, and the way they reinforce their opinions and health behaviors \[ 35 \]. The authors focused on analyzing the existing connections between individuals in the pro—eating disorder community and their engagement, or comparing the cohesion of these communities with other communities, such as the fitness community, that promote healthier habits. Furthermore, only one study used content analysis techniques. The authors classified the posts according to the following categories: proana, antiana, and prorecovery. Pro—eating disorder pages tended to identify themselves with body-associated pictures owing to the importance they attributed to motivational aspects of pro—eating disorder communities \[ 92 \]. The pro—eating disorder claims contained practices about weight loss, wanting a certain body type or characteristic of a body part, eating disorders, binge eating, and purging \[ 62 \]. Regarding eating disorders on social media, paying attention to community structure is important according to authors. Although it is widely acknowledged that communities can be positive by providing social support, such as recovery and well-being, certain groups on social media may also reaffirm the pro—eating disorder identity \[ 35 \]. In this case, the echochamber effect might explain why information campaigns are limited in scope and often encourage polarization of opinion, and can even reinforce existing divides in pro—eating disorder opinions \[ 88 \]. In this sense, the fundamental goal of these studies was aimed at assessing the quality and accuracy of the information. As in the case of noncommunicable diseases, professionals scanned social networks, especially YouTube, and evaluated the quality of online health content as an adequate instrument for self-care or for health student training. There were specific cases where information was particularly limited in quality and quantity, such as dental implants and first-aid information on burns \[ 30 , 44 \]. However, most surgical treatments or tools were found to have a sufficient level of quality information on YouTube \[ 52 , 81 \]. In relation to this topic, it is worth pointing out the source of the misinformation. In this particular case, most of the posts were published by private companies. They used the platforms to promote their medical products. Therefore, the amount of misinformation was considerably low compared with other topics, such as eating disorders and vaccines, that are closely linked to the general public. In general, the videos were accurate, were well presented, and framed treatments in a useful way for both health workers and health information seekers. A full description of the objectives and main conclusions of the reviewed articles is presented in Multimedia Appendix 4. This work represents, to our knowledge, the first effort aimed at finding objective and comparable measures to quantify the extent of health misinformation in the social media ecosystem. Our study offers an initial characterization of dominant health misinformation topics and specifically assesses their prevalence on different social platforms. Therefore, our systematic review provides new insights on the following unanswered question that has been recurrently highlighted in studies of health misinformation on social media: How prevalent is health misinformation for different topics on different social platform types ie, microblogging, media sharing, and social networks? We found that health misinformation on social media is generally linked to the following six topical domains: 1 vaccines, 2 diets and eating disorders, 3 drugs and new tobacco products, 4 pandemics and communicable diseases, 5 noncommunicable diseases, and 6 medical treatments and health interventions. With regard to vaccines, we found some interesting results throughout the different studies. Furthermore, we found that the complex and heterogeneous community structure of these online groups must be taken into account. For instance, those more exposed to antivaccine information tend to spread more negative concerns about vaccines ie, misinformation or opinions related to vaccine hesitancy than users exposed to positive or neutral opinions \[ 49 \]. Moreover, fake profiles tend to amplify the debate and discussion, thereby undermining the possible public consensus on the effectiveness and safety of vaccines, especially in the case of HPV; measles, mumps, and rubella MMR ; and influenza \[ 23 \]. As observed in our review, health topics were omnipresent over all social media platforms included in our study; however, the health misinformation prevalence for each topic varied depending on platform characteristics. Therefore, the potential effect on population health was ambivalent, that is, we found both positive and negative effects depending on the topic and on the group of health information seekers. For instance, content related to eating disorders was frequently hidden or not so evident to the general public, since pro—eating disorder communities use their own codes to reach specific audiences eg, younger groups \[ 98 \]. To provide a simple example, it is worth mentioning the usage of nicknames, such as proana for proanorexia and promia for probulimia, as a way to reach people with these health conditions and make it easier for people to talk openly about their eating disorders. More positively, these tools have been useful in prevention campaigns during health crises. For example, during the H1N1, Ebola, and Zika pandemics, and, even more recently, with the ongoing COVID pandemic, platforms, such as Twitter, have been valuable instruments for spreading evidence-based health knowledge, expert recommendations, and educative content aimed at avoiding the propagation of rumors, risk behaviors, and diseases \[ 31 , 89 \]. Throughout our review, we found different types of misinformation claims depending on the topic. Concerning vaccines, misinformation was often framed with a scientific appearance against scientific evidence \[ 53 \]. Drug-related misinformation promoted the consumption and abuse of these substances \[ 66 \]. However, these statements lacked scientific evidence to support them \[ 32 \]. As with vaccines, false accounts that influenced the online conversation did so with a scientific appearance in favor of e-cigarettes \[ 82 \]. In this sense, most accounts tended to promote the use and abuse of these items. With beauty as the final goal, misinformation about eating disorders promoted changes in the eating habits of social media users \[ 91 \]. Furthermore, we found that social media facilitated the development of pro—eating disorder online communities \[ 35 \]. In general, the results indicated that this type of content promoted unhealthy practices while normalizing eating disorders. Misinformation on this topic involved rumors, misunderstandings, and doubts arising from a lack of scientific knowledge \[ 31 \]. The statements were within the framework of the health emergency arising from the pandemic. In line with these findings, we noted findings related to noncommunicable diseases. Messages that focused on this topic promoted cures for chronic diseases or for conditions with no cure through fallacies or urban legends \[ 85 \]. In this study, we focused on analysis of the results obtained and the conclusions of the authors. Some of our findings are in line with those obtained in recent works \[ 16 \]. The reviewed studies indicate, on one hand, the difficulty in characterizing and evaluating the quality of health information on social media \[ 1 \] and, on the other, the conceptual fuzziness that can result from the convergence of multiple disciplines trying to apprehend the multidisciplinary and complex phenomenon of health misinformation on social media. This research field is being studied by health and social scientists \[ 70 , 73 \], as well as by researchers from the fields of computer science, mathematics, sociophysics, etc \[ 99 , \]. Therefore, we must understand that the inherent multidisciplinary and methodological diversity of studies and the highly dynamic world of social media are a perfect match for making it more difficult to identify comprehensive and transversal solutions to the problem of health misinformation. In fact, as we have found, misinformation on vaccines, drugs, and new smoking products is more prevalent on media-sharing platforms eg, YouTube and microblogging applications eg, Twitter , while misinformation on noncommunicable diseases is particularly prevalent on media sharing platforms where users can widely describe disease symptoms, medical treatments, and therapies \[ 76 , 85 \]. Finally, we should mention that the current results are limited to the availability and quality of social media data. Although the digitalization of social life offers researchers an unprecedented amount of health and social information that can be used to understand human behaviors and health outcomes, accessing this online data is becoming increasingly difficult, and some measures have to be taken to mitigate bias \[ 40 , 43 , 67 , 79 \]. Over the last few years, new concerns around privacy have emerged and led governments to tighten regulations around data access and storage \[ , \]. Consequently, in response to these new directives, as well as scandals involving data sharing and data breaches such as the Cambridge Analytica case, social media companies are developing new controls and barriers to data in their platforms. This is why free access to application programming interfaces APIs is becoming increasingly difficult and the range of social data accessible via APIs is gradually decreasing. These difficulties in accessing data are also determining which platforms are most frequently used by researchers, which are not used, and which will be used in the near future. The present study has some limitations. First, the conceptual definition of health misinformation is one limitation. In any case, taking into account that we were facing a new field of study, we considered a broad definition in order to be more inclusive and operative in the selection of studies. Therefore, we included as many papers as possible for the review in order to perform an analysis of the largest number of possible topics. Second, from a methodological perspective, our findings are limited to research published in English language journals and do not cover all the social media platforms that exist. Besides, we discovered some technical limitations when conducting this systematic review. Owing to the newness of this research topic, our study revealed difficulties in comparing different research studies characterized by specific theoretical approaches, working definitions, methodologies, data collection processes, and analytical techniques. Some studies selected involved observational designs using survey methods and textual analysis , whereas others were based on the application of automatic or semiautomatic computational procedures with the aim of classifying and analyzing health misinformation on social media. Finally, taking into account the particular features of each type of social media ie, microblogging service, video sharing service, or social network and the progressive barriers in accessing social media data, we need to consider the information and selection bias when studying health misinformation on these platforms. According to these biases, we should ponder which users are behind these tools and how we can extrapolate specific findings ie, applied to certain groups and social media platforms to a broader social context. Despite the limitations described above, it is necessary to mention the strengths of our work. First, we believe that this study represents one of the first steps in advancing research involving health misinformation on social media. Unlike previous work, we offer some measures that can serve as guidance and a comparative baseline for subsequent studies. In addition, our study highlights the need to redirect future research toward social media platforms, which, perhaps due to the difficulties of automatic data collection, are currently being neglected by researchers. Our study also highlights the need for both researchers and health professionals to explore the possibility of using these digital tools for health promotion and the need for them to progressively colonize the social media ecosystem with the ultimate goal of combating the waves of health misinformation that recurrently flood our societies. Health misinformation was most common on Twitter and on issues related to smoking products and drugs. Although we should be aware of the difficulties inherent in the dynamic magnitude of online opinion flows, our systematic review offers a comprehensive comparative framework that identifies subsequent action areas in the study of health misinformation on social media. Despite the abovementioned limitations, our research presents some advances when compared with previous studies. Our study provides 1 an overview of the prevalence of health misinformation identified on different social media platforms; 2 a methodological characterization of studies focused on health misinformation; and 3 a comprehensive description of the current research lines and knowledge gaps in this research field. According to the studies reviewed, the greatest challenge lies in the difficulty of characterizing and evaluating the quality of the information on social media. Knowing the prevalence of health misinformation and the methods used for its study, as well as the present knowledge gaps in this field will help us to guide future studies and, specifically, to develop evidence-based digital policy action plans aimed at combating this public health problem through different social media platforms. This section collects any data citations, data availability statements, or supplementary materials included in this article. As a library, NLM provides access to scientific literature. J Med Internet Res. Find articles by Victor Suarez-Lledo. Find articles by Javier Alvarez-Galvez. Contributed equally. Open in a new tab. Summary of the prevalence of misinformation by topic and social media platform. Conflicts of Interest: None declared. Similar articles. Add to Collections. Create a new collection. Add to an existing collection. Choose a collection Unable to load your collection due to an error Please try again. Add Cancel. Abukaraky et al \[ 30 \]. Ahmed et al \[ 31 \]. Al Khaja et al \[ 32 \]. Allem et al \[ 33 \]. Allem et al \[ 34 \]. Arseniev-Koehler et al \[ 35 \]. Basch et al \[ 36 \]. Becker et al \[ 37 \]. Biggs et al \[ 38 \]. Blankenship et al \[ 39 \]. Bora et al \[ 40 \]. Branley et al \[ 41 \]. Briones et al \[ 42 \]. Broniatowski et al \[ 23 \]. Buchanan et al \[ 43 \]. Butler et al \[ 44 \]. Cavazos-Rehg et al \[ 45 \]. Chary et al \[ 46 \]. Chew et al \[ 47 \]. Covolo et al \[ 48 \]. Dunn et al \[ 49 \]. Dunn et al \[ 50 \]. Ekram et al \[ 51 \]. Erdem et al \[ 52 \]. Faasse et al \[ 53 \]. Fullwood et al \[ 54 \]. Garg et al \[ 55 \]. Gimenez-Perez et al \[ 56 \]. Goobie et al \[ 57 \]. Guidry et al \[ 58 \]. Guidry et al \[ 59 \]. Guidry et al \[ 60 \]. Hanson et al \[ 61 \]. Harris et al \[ 62 \]. Haymes et al \[ 63 \]. Helmi et al \[ 64 \]. Kang et al \[ 65 \]. Katsuki et al \[ 66 \]. Keelan et al \[ 67 \]. Keim-Malpass et al \[ 68 \]. Krauss et al \[ 70 \]. Krauss et al \[ 71 \]. Kumar et al \[ 72 \]. Laestadius et al \[ 73 \]. Leong et al \[ 74 \]. Lewis et al \[ 75 \]. Loeb et al \[ 76 \]. Love et al \[ 77 \]. Martinez et al \[ 78 \]. Massey et al \[ 79 \]. McNeil et al \[ 80 \]. Menon et al \[ 81 \]. Merianos et al \[ 82 \]. Meylakhs et al \[ 83 \]. Morin et al \[ 84 \]. Mueller et al \[ 85 \]. Porat et al \[ 86 \]. Radzikowski et al \[ 87 \]. Schmidt et al \[ 88 \]. Seltzer et al \[ 89 \]. Seymour et al \[ 90 \]. Syed-Abdul et al \[ 91 \]. Teufel et al \[ 92 \]. Tiggermann et al \[ 93 \]. Tuells et al \[ 94 \]. Waszak et al \[ 96 \]. Yang et al \[ 97 \].

Buying coke online in Jerez de la Frontera