When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation.
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ABSTRACT:
SUBMITTER: Kim YB
PROVIDER: S-EPMC5428982 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundArtificial intelligence (AI) is being increasingly adopted in the health care industry for administrative tasks, patient care operations, and medical research.ObjectiveWe aimed to examine health care workers' opinions about the adoption and implementation of AI-powered technology in the health care industry.MethodsData were comments about AI posted on a web-based forum by 905 health care professionals from at least 77 countries, from May 2013 to October 2021. Structural topic modeling was used to identify the topics of discussion, and hierarchical clustering was performed to determine how these topics cluster into different groups.ResultsOverall, 12 topics were identified from the collected comments. These comments clustered into 2 groups: impact of AI on health care system and practice and AI as a tool for disease screening, diagnosis, and treatment. Topics associated with negative sentiments included concerns about AI replacing human workers, impact of AI on traditional medical diagnostic procedures (ie, patient history and physical examination), accuracy of the algorithm, and entry of IT companies into the health care industry. Concerns about the legal liability for using AI in treating patients were also discussed. Positive topics about AI included the opportunity offered by the technology for improving the accuracy of image-based diagnosis and for enhancing personalized medicine.ConclusionsThe adoption and implementation of AI applications in the health care industry are eliciting both enthusiasm and concerns about patient care quality and the future of health care professions. The successful implementation of AI-powered technologies requires the involvement of all stakeholders, including patients, health care organization workers, health insurance companies, and government regulatory agencies.
| S-EPMC9919460 | biostudies-literature
Project description:With the continuous development of information technology, more and more people have become to use online dating apps, and the trend has been exacerbated by the COVID-19 pandemic in these years. However, there is a phenomenon that most of user reviews of mainstream dating apps are negative. To study this phenomenon, we have used topic model to mine negative reviews of mainstream dating apps, and constructed a two-stage machine learning model using data dimensionality reduction and text classification to classify user reviews of dating apps. The research results show that: firstly, the reasons for the current negative reviews of dating apps are mainly concentrated in the charging mechanism, fake accounts, subscription and advertising push mechanism and matching mechanism in the apps, proposed corresponding improvement suggestions are proposed by us; secondly, using principal component analysis to reduce the dimensionality of the text vector, and then using XGBoost model to learn the low-dimensional data after oversampling, a better classification accuracy of user reviews can be obtained. We hope These findings can help dating apps operators to improve services and achieve sustainable business operations of their apps.
| S-EPMC10132638 | biostudies-literature
Project description:Bitcoin mining is not only the fundamental process to maintain Bitcoin network, but also the key linkage between the virtual cryptocurrency and the physical world. A variety of issues associated with it have been raised, such as network security, cryptoasset management and sustainability impacts. Investigating Bitcoin mining from a spatial perspective will provide new angles and empirical evidence with respect to extant literature. Here we explore the spatial distribution of Bitcoin mining through bottom-up tracking and geospatial statistics. We find that mining activity has been detected at more than 6000 geographical units across 139 countries and regions, which is in line with the distributed design of Bitcoin network. However, in terms of computing power, it has demonstrated a strong tendency of spatial concentration and association with energy production locations. We also discover that the spatial distribution of Bitcoin mining is dynamic, which fluctuates with diverse patterns, according to economic and regulatory changes.
| S-EPMC9226069 | biostudies-literature
Project description:The current dataset is obtained by text mining of ?-hydroxybutyrate (BHB) supplement products' consumer online reviews. The text data of 71 BHB products' consumer reviews were extracted with the aid of the Web Scraper Chrome extension. Then, a lexicon-based sentiment analysis approach was developed to classify the sentiment or polarity of BHB products' consumer reviews. Both word-level and sentence-level sentiment analyses were conducted to score the analyzed text snippets. In terms of word-level sentiment analysis, word clouds of selected BHB products' reviews were generated to give direct observation, and the statistics of high-frequent sentiment words were listed for comparison. In terms of sentence-level sentiment analysis, two factors such as flavor and package were taken into consideration to map the products' polarity distributions. Besides, the complex analysis provides us with the basic statistics of the analyzed BHB customer reviews data.
| S-EPMC7083782 | biostudies-literature
Project description:Emotional intelligence is a well-established indicator of performance and the ability to maintain successful social relationships. Moreover, it is potentially an important factor in social dynamics occurring on large digital platforms, e.g., opinion polarization, social conflict, and social influence. Users publicly exchange enormous amounts of text on digital platforms, which can potentially be used to extract real-life insights. Yet, currently, the prevalent approach to measuring emotional intelligence uses mainly self-report surveys and tasks-considerably limiting the feasibility of real-life large-scale studies. We analyze the online public texts of users, who also completed emotional intelligence measures, to find that characteristics of online public texts can be used to predict emotional intelligence at a level like that of commonly used psychometric indicators (e.g., SATs) to predict real-life outcomes. For example, we find that high emotional intelligence individuals consistently use more positive-affect language, less negative-affect language and use more social-oriented language than low emotional intelligence individuals. Our findings provide insight into the role of personality on digital platforms and open the possibility of studying emotional intelligence in large and diverse real-life data. To support the use of online public text as a tool to research emotional intelligence, we provide an anonymized version of the data.
| S-EPMC10130158 | biostudies-literature
Project description:The main aim of the study is to analyze BTC mining’s efficiency under current market conditions (December 2021), including soaring energy prices produced from many different sources in different geographical locations. After a thorough analysis of initial assumptions concerning the (1) price of mining machine with associated components and its effective amortization period, (2) difficulty and the hash rate of the BTC network, (3) BTC transaction fees, and (4) energy costs from various sources, we have found that currently, BTC mining is not profitable, except for some rare cases. The main reason for this phenomenon is the fast and unpredictable increase of difficulty of the BTC network over time which results in decreasing participation of already purchased mining machines in the BTC network hash rate. The research is augmented with a detailed sensitivity analysis of mining efficiency to initial parameters assumptions, which allows observing that the conditions for BTC mining to be efficient and profitable are very challenging.
| S-EPMC10062675 | biostudies-literature
Project description:Internet is a particularly dynamic way to quickly capture the perceptions of a population in real time. Complementary to traditional face-to-face communication, online social networks help patients to improve self-esteem and self-help.The aim of this study was to use text mining on material from an online forum exploring patients' concerns about treatment (antidepressants and anxiolytics).Concerns about treatment were collected from discussion titles in patients' online community related to antidepressants and anxiolytics. To examine the content of these titles automatically, we used text mining methods, such as word frequency in a document-term matrix and co-occurrence of words using a network analysis. It was thus possible to identify topics discussed on the forum.The forum included 2415 discussions on antidepressants and anxiolytics over a period of 3 years. After a preprocessing step, the text mining algorithm identified the 99 most frequently occurring words in titles, among which were escitalopram, withdrawal, antidepressant, venlafaxine, paroxetine, and effect. Patients' concerns were related to antidepressant withdrawal, the need to share experience about symptoms, effects, and questions on weight gain with some drugs.Patients' expression on the Internet is a potential additional resource in addressing patients' concerns about treatment. Patient profiles are close to that of patients treated in psychiatry.
| S-EPMC5673886 | biostudies-other
Project description:BackgroundBefore the advent of an effective vaccine, nonpharmaceutical interventions, such as mask-wearing, social distancing, and lockdowns, have been the primary measures to combat the COVID-19 pandemic. Such measures are highly effective when there is high population-wide adherence, which requires information on current risks posed by the pandemic alongside a clear exposition of the rules and guidelines in place.ObjectiveHere we analyzed online news media coverage of COVID-19. We quantified the total volume of COVID-19 articles, their sentiment polarization, and leading subtopics to act as a reference to inform future communication strategies.MethodsWe collected 26 million news articles from the front pages of 172 major online news sources in 11 countries (available online at SciRide). Using topic detection, we identified COVID-19-related content to quantify the proportion of total coverage the pandemic received in 2020. The sentiment analysis tool Vader was employed to stratify the emotional polarity of COVID-19 reporting. Further topic detection and sentiment analysis was performed on COVID-19 coverage to reveal the leading themes in pandemic reporting and their respective emotional polarizations.ResultsWe found that COVID-19 coverage accounted for approximately 25.3% of all front-page online news articles between January and October 2020. Sentiment analysis of English-language sources revealed that overall COVID-19 coverage was not exclusively negatively polarized, suggesting wide heterogeneous reporting of the pandemic. Within this heterogenous coverage, 16% of COVID-19 news articles (or 4% of all English-language articles) can be classified as highly negatively polarized, citing issues such as death, fear, or crisis.ConclusionsThe goal of COVID-19 public health communication is to increase understanding of distancing rules and to maximize the impact of governmental policy. The extent to which the quantity and quality of information from different communication channels (eg, social media, government pages, and news) influence public understanding of public health measures remains to be established. Here we conclude that a quarter of all reporting in 2020 covered COVID-19, which is indicative of information overload. In this capacity, our data and analysis form a quantitative basis for informing health communication strategies along traditional news media channels to minimize the risks of COVID-19 while vaccination is rolled out.
| S-EPMC8174556 | biostudies-literature
Project description:AimThe aim of the study was to explore the menopausal symptom experience of White midlife women.BackgroundRecent cross-cultural investigations have indicated important ethnic differences in menopausal symptoms and have challenged the universality of these symptoms. Currently available cultural knowledge on menopausal experience, however, is inadequate to guide appropriate and adequate care even for White women in the menopausal transition.MethodA cross-sectional qualitative online forum study was conducted in 2007 with 23 midlife women who self-identified as non-Hispanic Whites using convenience sampling. Seven topics related to menopausal symptom experience were used to guide the online forum for 6 months. The data were analysed using thematic analysis.ResultsThe experiences of menopause caused women to redefine themselves within their busy daily life schedules. They were optimistic about their symptoms, and tried to laugh at the experience to boost their inner strength and motivate themselves to persevere. Many thought that both generational and life-style differences were much more important than ethnic differences in menopausal symptom experiences. In seeking assistance with the symptoms of menopause, women were not satisfied with the guidance of their physicians.ConclusionNurses need to listen carefully to what women themselves say about their own experiences with menopausal symptoms and avoid imposing predetermined symptom management strategies. Further studies of women's perceptions of life-style influences are needed to guide symptom management. In addition, studies of generational changes in menopausal symptom experience are needed.
| S-EPMC2430027 | biostudies-literature
Project description:Recent years have witnessed an increased demand for mobile health (mHealth) platforms owing to the COVID-19 pandemic and preference for doorstep delivery. However, factors impacting user experiences and satisfaction levels across these platforms, using customer reviews, are still largely unexplored in academic research. The empirical framework we proposed in this paper addressed this research gap by analysing unmonitored user comments for some popular mHealth platforms. Using topic-modelling techniques, we identified the impacting factors (predictors) and categorised them into two major dimensions based on strategic adoption and motivational association. Findings from our study suggest that time and money, convenience, responsiveness, and availability emerge as significant predictors for delivering a positive user experience on m-health platforms. Next, we identified substantial moderating effects of review polarity on the predictors related to brand association and hedonic motivation, such as online booking and video consultation. Further, we also identified the top predictors for successful user experience across these platforms. Recommendations from our study will benefit business managers by offering an improved service design leading to higher user satisfaction across these m-health platforms.
| S-EPMC9715352 | biostudies-literature