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Abstract Background In recent years, the emerging electronic cigarette (e-cigarette) marketplace has developed prosperously all over the world. By analyzing online e-liquid reviews, we seek to identify the features attracting users. Methods We collected e-liquid reviews from one of the largest online e-liquid review websites and extracted the e-liquid features by keywords. Then we used sentiment analysis to classify the features into two polarities: positive and negative. The positive sentiment ratio of a feature reflects the e-cigarette users’ preference on this feature. Results The popularity and preference of e-liquid features are not correlated. Nuts and cream are the favorite flavor categories, while fruit and cream are the most popular categories. The top mixed flavors are preferable to single flavors. Fruit and cream categories are most frequently mixed with other flavors. E-cigarette users are satisfied with cloud production, but not satisfied with the ingredients and throat hit. Conclusions We identified the flavors that e-cigarette users were satisfied with, and we found the users liked e-cigarette cloud production. Therefore, flavors and cloud production are potential factors attracting new users.

Script event prediction aims to infer subsequent events given an incomplete script. It requires a deep understanding of events, and can provide support for a variety of tasks. Existing models rarely consider the relational knowledge between events, they regard scripts as sequences or graphs, which cannot capture the relational information between events and the semantic information of script sequences jointly. To address this issue, we propose a new script form, relational event chain, that combines event chains and relational graphs. We also introduce a new model, relational-transformer, to learn embeddings based on this new script form. In particular, we first extract the relationship between events from an event knowledge graph to formalize scripts as relational event chains, then use the relational-transformer to calculate the likelihood of different candidate events, where the model learns event embeddings that encode both semantic and relational knowledge by combining transformers and graph neural networks (GNNs). Experimental results on both one-step inference and multistep inference tasks show that our model can outperform existing baselines, indicating the validity of encoding relational knowledge into event embeddings. The influence of using different model structures and different types of relational knowledge is analyzed as well.

As part of ongoing efforts to contain the coronavirus disease (COVID-19) pandemic, understanding the role of asymptomatic patients in the transmission system is essential for infection control. However, the optimal approach to risk assessment and management of asymptomatic cases remains unclear. This study proposed a Susceptible, Exposed, Infectious, No symptoms, Hospitalized and reported, Recovered, Death (SEINRHD) epidemic propagation model. The model was constructed based on epidemiological characteristics of COVID-19 in China and accounting for the heterogeneity of social contact networks. The early community outbreaks in Wuhan were reconstructed and fitted with the actual data. We used this model to assess epidemic control measures for asymptomatic cases in three dimensions. The impact of asymptomatic cases on epidemic propagation was examined based on the effective reproduction number, abnormally high transmission events, and type and structure of transmission. Management of asymptomatic cases can help flatten the infection curve. Tracing 75% of the asymptomatic cases corresponds to a 32.5% overall reduction in new cases (compared with tracing no asymptomatic cases). Regardless of population-wide measures, household transmission is higher than other types of transmission, accounting for an estimated 50% of all cases. The magnitude of tracing of asymptomatic cases is more important than the timing; when all symptomatic patients were traced, tested, and isolated in a timely manner, the overall epidemic was not sensitive to the time of implementing the measures to trace asymptomatic patients. Disease control and prevention within families should be emphasized during an epidemic. Note to Practitioners -This article addresses the urgent need to assess the risk of another COVID-19 outbreak caused by asymptomatic cases and to find the optimal, most practical approach to asymptomatic case management. Previous studies mostly focused on the clinical and statistical characteristics of asymptomatic cases; few have evaluated the impact of asymptomatic case measures using mathematical modeling at the community scale. This study proposed a Susceptible, Exposed, Infectious, No symptoms, Hospitalized and reported, Recovered, Death (SEINRHD) propagation model based on local community structures and social contact networks, according to the development characteristics and trend of COVID-19 in a Chinese community. The conclusion provides theoretical support for emergency work of relevant departments in different periods of an epidemic. In the early stages of the epidemic, timely detection and isolation of symptomatic patients should be a priority. Where there are surplus resources for epidemic prevention, the authorities should consider increasing the proportion of asymptomatic patients being traced. Epidemic prevention measures among family members should be a primary focus of attention. This combination of strategies can help reduce the rate of viral transmission and result in extinguishing the epidemic.

Information spread on social media has been extensively studied through both model-driven theoretical research and data-driven case studies. Recent empirical studies have analyzed the differences and complexity of information dissemination, but theoretical explanations of its characteristics from a modeling perspective are underresearched. To capture the complex patterns of the information dissemination mechanism, we propose a resistant linear threshold (RLT) dissemination model based on psychological theories and empirical findings. In this article, we validate the RLT model on three types of networks and then quantify and compare the dissemination characteristics of the simulation results with those from the empirical results. In addition, we examine the factors affecting dissemination. Finally, we perform two case studies of the 2019 novel Corona Virus Disease (COVID-19)-related information dissemination. The dissemination characteristics derived by the simulations are consistent with the empirical research. These results demonstrate that the RLT model is able to capture the patterns of information dissemination on social media and thus provide model-driven insights into the interpretation of public opinion, rumor control, and marketing strategies on social media.

Hand-foot-and-month disease (HFMD), especially the enterovirus A71 (EV-A71) subtype, is a major health problem in Beijing, China. Previous studies mainly used regressive models to forecast the prevalence of HFMD, ignoring its intrinsic age groups. This study aims to predict HFMD of EV-A71 subtype in three age groups (0-3, 3-6 and > 6 years old) from 2011 to 2018 using residual-convolutional-recurrent neural network (CNNRNN-Res), convolutional-recurrent neural network (CNNRNN) and recurrent neural network (RNN). They were compared with auto-regressio, global auto-regression and vector auto-regression on both short-term and long-term prediction. Results showed that CNNRNN-Res and RNN had higher accuracies on point forecast tasks, as well as robust performances in long-term prediction. Three deep learning models also had better skills in peak intensity forecast, and CNNRNN-Res achieved the best results in the peak month forecast. We also found that three age groups had consistent outbreak trends and similar patterns of prediction errors. These results highlight the superior performance of deep learning models in HFMD prediction and can assist the decision-makers to refine the HFMD control measures according to age groups.

Objective: To identify who were social media active e-cigarette users, to compare the use patterns from both survey and social media data for data triangulation, and to jointly use both datasets to conduct a comprehensive analysis on e-cigarette future use intentions., Materials and Methods: We jointly used an e-cigarette use online survey (n = 5132) and a social media dataset. We conducted analysis from 3 different perspectives. We analyzed online forum participation patterns using survey data. We compared e-cigarette use patterns, including brand and flavor types, ratings, and purchase approaches, between the 2 datasets. We used logistic regression to study intentions to use e-cigarettes using both datasets., Results: Male and younger e-cigarette users were the most likely to participate in e-cigarette-related discussion forums. Forum active survey participants were hardcore vapers. The e-cigarette use patterns were similar in the online survey data and the social media data. Intention to use e-cigarettes was positively related to e-liquid ratings and flavor ratings. Social media provided a valuable source of information on users' ratings of e-cigarette refill liquids., Discussion: For hardcore vapers, social media data were consistent with online survey data, which suggests that social media may be useful to study e-cigarette use behaviors and can serve as a useful complement to online survey research. We proposed an innovative framework for social media data triangulation in public health studies., Conclusion: We illustrated how social media data, combined with online survey data, can serve as a new and rich information source for public health research.

Cryptocurrency is a well-developed blockchain technology application that is currently a heated topic throughout the world. The public availability of transaction histories offers an opportunity to analyze and compare different cryptocurrencies. In this paper, we present a dynamic network analysis of three representative blockchain-based cryptocurrencies: Bitcoin, Ethereum, and Namecoin. By analyzing the accumulated network growth, we find that, unlike most other networks, these cryptocurrency networks do not always densify over time, and they are changing all the time with relatively low node and edge repetition ratios. Therefore, we then construct separate networks on a monthly basis, trace the changes of typical network characteristics (including degree distribution, degree assortativity, clustering coefficient, and the largest connected component) over time, and compare the three. We find that the degree distribution of these monthly transaction networks cannot be well fitted by the famous power-law distribution, at the same time, different currency still has different network properties, e.g., both Bitcoin and Ethereum networks are heavy-tailed with disassortative mixing, however, only the former can be treated as a small world. These network properties reflect the evolutionary characteristics and competitive power of these three cryptocurrencies and provide a foundation for future research., Competing Interests: The authors have declared that no competing interests exist.

Emotion entrainment, which is generally defined as the synchronous convergence of human emotions, performs many important social functions. However, what the specific mechanisms of emotion entrainment are beyond in-person interactions, and how human emotions evolve under different entrainment patterns in large-scale social communities, are still unknown. In this paper, we aim to examine the massive emotion entrainment patterns and understand the underlying mechanisms in the context of social media. As modeling emotion dynamics on a large scale is often challenging, we elaborate a pragmatic framework to characterize and quantify the entrainment phenomenon. By applying this framework on the datasets from two large-scale social media platforms, we find that the emotions of online users entrain through social networks. We further uncover that online users often form their relations via dual entrainment, while maintain it through single entrainment. Remarkably, the emotions of online users are more convergent in nonreciprocal entrainment. Building on these findings, we develop an entrainment augmented model for emotion prediction. Experimental results suggest that entrainment patterns inform emotion proximity in dyads, and encoding their associations promotes emotion prediction. This work can further help us to understand the underlying dynamic process of large-scale online interactions and make more reasonable decisions regarding emergency situations, epidemic diseases, and political campaigns in cyberspace.

The prevalence of social media has greatly catalyzed the dissemination and proliferation of online memes (e.g., ideas, topics, melodies, tags, etc.). However, this information abundance is exceeding the capability of online users to consume it. Ranking memes based on their popularities could promote online advertisement and content distribution. Despite such importance, few existing work can solve this problem well. They are either daunted by unpractical assumptions or incapability of characterizing dynamic information. As such, in this paper, we elaborate a model-free scheme to rank online memes in the context of social media. This scheme is capable to characterize the nonlinear interactions of online users, which mark the process of meme diffusion. Empirical studies on two large-scale, real-world datasets (one in English and one in Chinese) demonstrate the effectiveness and robustness of the proposed scheme. In addition, due to its fine-grained modeling of user dynamics, this ranking scheme can also be utilized to explain meme popularity through the lens of social influence.

Knowledge is considered actionable if users can take direct actions based on such knowledge to their advantage. Among the most important and distinctive actionable knowledge are actionable behavioral rules that can directly and explicitly suggest specific actions to take to influence (restrain or encourage) the behavior in the users' best interest. However, in mining such rules, it often occurs that different rules may suggest the same actions with different expected utilities, which we call conflicting rules. To resolve the conflicts, a previous valid method was proposed. However, inconsistency of the measure for rule evaluating may hinder its performance. To overcome this problem, we develop a new method that utilizes rule ranking procedure as the basis for selecting the rule with the highest utility prediction accuracy. More specifically, we propose an integrative measure, which combines the measures of the support and antecedent length, to evaluate the utility prediction accuracies of conflicting rules. We also introduce a tunable weight parameter to allow the flexibility of integration. We conduct several experiments to test our proposed approach and evaluate the sensitivity of the weight parameter. Empirical results indicate that our approach outperforms those from previous research.

Background: Crowd-powered search is a new form of search and problem solving scheme that involves collaboration among a potentially large number of voluntary Web users. Human flesh search (HFS), a particular form of crowd-powered search originated in China, has seen tremendous growth since its inception in 2001. HFS presents a valuable test-bed for scientists to validate existing and new theories in social computing, sociology, behavioral sciences, and so forth., Methodology: In this research, we construct an aggregated HFS group, consisting of the participants and their relationships in a comprehensive set of identified HFS episodes. We study the topological properties and the evolution of the aggregated network and different sub-groups in the network. We also identify the key HFS participants according to a variety of measures., Conclusions: We found that, as compared with other online social networks, HFS participant network shares the power-law degree distribution and small-world property, but with a looser and more distributed organizational structure, leading to the diversity, decentralization, and independence of HFS participants. In addition, the HFS group has been becoming increasingly decentralized. The comparisons of different HFS sub-groups reveal that HFS participants collaborated more often when they conducted the searches in local platforms or the searches requiring a certain level of professional knowledge background. On the contrary, HFS participants did not collaborate much when they performed the search task in national platforms or the searches with general topics that did not require specific information and learning. We also observed that the key HFS information contributors, carriers, and transmitters came from different groups of HFS participants.

Social networks often serve as a critical medium for information dissemination, diffusion of epidemics, and spread of behavior, by shared activities or similarities between individuals. Recently, we have witnessed an explosion of interest in studying social influence and spread dynamics in social networks. To date, relatively little material has been provided on a comprehensive review in this field. This brief survey addresses this issue. We present the current significant empirical studies on real social systems, including network construction methods, measures of network, and newly empirical results. We then provide a concise description of some related social models from both macro- and micro-level perspectives. Due to the difficulties in combining real data and simulation data for verifying and validating real social systems, we further emphasize the current research results of computational experiments. We hope this paper can provide researchers significant insights into better understanding the characteristics of personal influence and spread patterns in large-scale social systems., (© Higher Education Press and Springer-Verlag Berlin Heidelberg 2012.)

Background: Many researchers have evaluated the performance of outbreak detection algorithms with recommended parameter values. However, the influence of parameter values on algorithm performance is often ignored., Methods: Based on reported case counts of bacillary dysentery from 2005 to 2007 in Beijing, semi-synthetic datasets containing outbreak signals were simulated to evaluate the performance of five outbreak detection algorithms. Parameters' values were optimized prior to the evaluation., Results: Differences in performances were observed as parameter values changed. Of the five algorithms, space-time permutation scan statistics had a specificity of 99.9% and a detection time of less than half a day. The exponential weighted moving average exhibited the shortest detection time of 0.1 day, while the modified C1, C2 and C3 exhibited a detection time of close to one day., Conclusion: The performance of these algorithms has a correlation to their parameter values, which may affect the performance evaluation.

Considerable attention has been given lately to the need for global systems for animal disease surveillance that support real-time assessment of changing temporal-spatial risks. Until recently, however, prospects for development of such systems have been limited by the lack of informatics tools and an overarching collaboration framework to enable real-time data capturing, sharing, analysis, and related decision-making. In this paper, we present some of the tools of the FMD BioPortal System (www.fmd.ucdavis.edu/bioportal), which is a web-based system that facilitates near real-time information sharing, visualization, and advanced space-time cluster analysis for foot-and-mouth disease (FMD). Using this system, FMD information that is collected and maintained at various data acquisition and management sites around the world can be submitted to a data repository using various mutually agreed upon Extensible Markup Language (XML) formats, including Health Level Seven (HL7). FMD BioPortal makes available a set of advanced space-time cluster analysis techniques, including scan statistic-based methods and machine learning-based clustering methods. These techniques are aimed at identifying local clusters of disease cases in relation to the background risk. Data and analysis results can be displayed using a novel visualization environment, which supports multiple views including GIS, timeline, and periodical patterns. All FMD BioPortal functionalities are accessible through the Web and data confidentiality can be secured through user access control and computer network security techniques such as Secure Sockets Layer (SSL). FMD BioPortal is currently operational with limited data routinely collected by the Office International des Epizooties, the GenBank, the FMD World Reference Laboratory in Pirbright, and by the FMD Laboratory at the University of California in Davis. Here we describe technical attributes and capabilities of FMD BioPortal and illustrate its functionality by analyzing and displaying information from a simulated FMD epidemic in California.

Purpose: Syndromic surveillance is aimed at early detection of disease outbreaks. An important data source for syndromic surveillance is free-text chief complaints (CCs), which may be recorded in different languages. For automated syndromic surveillance, CCs must be classified into predefined syndromic categories to facilitate subsequent data aggregation and analysis. Despite the fact that syndromic surveillance is largely an international effort, existing CC classification systems do not provide adequate support for processing CCs recorded in non-English languages. This paper reports a multilingual CC classification effort, focusing on CCs recorded in Chinese., Methods: We propose a novel Chinese CC classification system leveraging a Chinese-English translation module and an existing English CC classification approach. A set of 470 Chinese key phrases was extracted from about one million Chinese CC records using statistical methods. Based on the extracted key phrases, the system translates Chinese text into English and classifies the translated CCs to syndromic categories using an existing English CC classification system., Results: Compared to alternative approaches using a bilingual dictionary and a general-purpose machine translation system, our approach performs significantly better in terms of positive predictive value (PPV or precision), sensitivity (recall), specificity, and F measure (the harmonic mean of PPV and sensitivity), based on a computational experiment using real-world CC records., Conclusions: Our design provides satisfactory performance in classifying Chinese CCs into syndromic categories for public health surveillance. The overall design of our system also points out a potentially fruitful direction for multilingual CC systems that need to handle languages beyond English and Chinese.

Emergency department free-text chief complaints (CCs) are a major data source for syndromic surveillance. CCs need to be classified into syndromic categories for subsequent automatic analysis. However, the lack of a standard vocabulary and high-quality encodings of CCs hinder effective classification. This paper presents a new ontology-enhanced automatic CC classification approach. Exploiting semantic relations in a medical ontology, this approach is motivated to address the CC vocabulary variation problem in general and to meet the specific need for a classification approach capable of handling multiple sets of syndromic categories. We report an experimental study comparing our approach with two popular CC classification methods using a real-world dataset. This study indicates that our ontology-enhanced approach performs significantly better than the benchmark methods in terms of sensitivity, F measure, and F2 measure.

Motivated by the importance of infectious disease informatics (IDI) and the challenges to IDI system development and data sharing, we design and implement BioPortal, a Web-based IDI system that integrates cross-jurisdictional data to support information sharing, analysis, and visualization in public health. In this paper, we discuss general challenges in IDI, describe BioPortal's architecture and functionalities, and highlight encouraging evaluation results obtained from a controlled experiment that focused on analysis accuracy, task performance efficiency, user information satisfaction, system usability, usefulness, and ease of use.