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Cryptocurrencies, which the Bitcoin is the most remarkable one, have allured substantial awareness up to now, and they have encountered enormous instability in their price. While some studies utilize conventional statistical and econometric ways to uncover the driving variables of Bitcoin's prices, experimentation on the advancement of predicting models to be used as decision support tools in investment techniques is rare. There are many different predicting cryptocurrencies' price methods that cover various purposes, such as forecasting a one-step approach that can be done through time series analysis, neural networks, and machine learning algorithms. Sometimes realizing the trend of a coin in a long run period is needed. In this paper, some machine learning algorithms are applied to find the best ones that can forecast Bitcoin price based on three other famous coins. Second, a new methodology is developed to predict Bitcoin's worth, this is also done by considering different cryptocurrencies prices (Ethereum, Zcash, and Litecoin). The results demonstrated that Zcash has the best performance in forecasting Bitcoin's price without any data on Bitcoin's fluctuations price among these three cryptocurrencies.

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Background While there is high-quality online health information, a lot of recent work has unfortunately highlighted significant issues with the health content on social media platforms (eg, fake news and misinformation), the consequences of which are severe in health care. One solution is to investigate methods that encourage users to post high-quality content. Objective Incentives have been shown to work in many domains, but until recently, there was no method to provide financial incentives easily on social media for users to generate high-quality content. This study investigates the following question: What effect does the provision of incentives have on the creation of social media health care content? Methods We analyzed 8328 health-related posts from an incentive-based platform (Steemit) and 1682 health-related posts from a traditional platform (Reddit). Using topic modeling and sentiment analysis–based methods in machine learning, we analyzed these posts across the following 3 dimensions: (1) emotion and language style using the IBM Watson Tone Analyzer service, (2) topic similarity and difference from contrastive topic modeling, and (3) the extent to which posts resemble clickbait. We also conducted a survey using 276 Amazon Mechanical Turk (MTurk) users and asked them to score the quality of Steemit and Reddit posts. Results Using the Watson Tone Analyzer in a sample of 2000 posts from Steemit and Reddit, we found that more than double the number of Steemit posts had a confident language style compared with Reddit posts (77 vs 30). Moreover, 50% more Steemit posts had analytical content and 33% less Steemit posts had a tentative language style compared with Reddit posts (619 vs 430 and 416 vs 627, respectively). Furthermore, more than double the number of Steemit posts were considered joyful compared with Reddit posts (435 vs 200), whereas negative posts (eg, sadness, fear, and anger) were 33% less on Steemit than on Reddit (384 vs 569). Contrastive topic discovery showed that only 20% (2/10) of topics were common, and Steemit had more unique topics than Reddit (5 vs 3). Qualitatively, Steemit topics were more informational, while Reddit topics involved discussions, which may explain some of the quantitative differences. Manual labeling marked more Steemit headlines as clickbait than Reddit headlines (66 vs 26), and machine learning model labeling consistently identified a higher percentage of Steemit headlines as clickbait than Reddit headlines. In the survey, MTurk users said that at least 57% of Steemit posts had better quality than Reddit posts, and they were at least 52% more likely to like and comment on Steemit posts than Reddit posts. Conclusions It is becoming increasingly important to ensure high-quality health content on social media; therefore, incentive-based social media could be important in the design of next-generation social platforms for health information.

BACKGROUND Seeking online health information (OHI) has become a habit of investigating one's health condition. As of July 2022, the global social media, one of the main OHI resources, user base has reached 59% of the world's total population. While social media could provide high-quality health information, low-quality pieces also can be found on these platforms, e.g., online misinformation. Distinguishing high from low-quality health information is a major problem on social media platforms and remains an issue that has not been sufficiently addressed in healthcare communities. OBJECTIVE The quality of health-related information posted on social media has been a significant concern. Providing incentives for good content could be one approach to improve this. This study empirically investigates the effect of incentive mechanisms in social media on the quality of health-related content. METHODS Based on a large sample of health-related social media posts from each of an incentive-based (Steemit) and traditional (Reddit), platform, we explore differences across three dimensions: (a) emotion and tone, (b) topic similarity and difference, and (c) the extent to which these resemble clickbait. RESULTS The incentive mechanism in play likely motivated health-related posts that are more informational. We also find differences in emotion, tone, and the extent to which posts are created as potential ‘clickbait' content. CONCLUSIONS It is increasingly important to ensure high-quality health content on social media, particularly as more users gravitate toward using these platforms for health-related information. Monetary incentives - used in other domains successfully - could play a role here, and the exploratory comparison of Steemit and Reddit shows systematic differences in health-related content that offer important insights to improve the quality of content. Therefore, Incentive-based social media could have the potential to provide more informational health-related content, which may also be more diverse. CLINICALTRIAL This survey was conducted under protocols approved by the University of South Florida Institutional Review Board (IRB) (STUDY\#003306: "Investing the drivers of currency in blockchain social platforms")