Daniel Dajun Zeng

Agent-Based Social Simulation and Modeling in Social Computing

Agent-based social simulation (ABSS) as a main computational approach to social simulation has attracted increasing attention in the field of social computing. With the development of computer and information technologies, many new ABSS approaches have been proposed with wide application.. In this paper, we aim at reviewing research and applications of agent-based social simulation and modeling in recent years from a social computing perspective. We identify the underlying social theories for ABSS, its simulation and modeling techniques, and computational frameworks from both individual agent and multi-agent system perspective. We finally address some future research issues in agent-based social simulation and modeling.

Portrayal of electronic cigarettes on YouTube

BackgroundAs the most popular video sharing website in the world, YouTube has the potential to reach and influence a huge audience. This study aims to gain a systematic understanding of what e-cigarette messages people are being exposed to on YouTube by assessing the quantity, portrayal and reach of e-cigarette videos.MethodsResearchers identified the top 20 search results on YouTube by relevance and view count for the following search terms: “electronic cigarettes”, “e-cigarettes”, “ecigarettes”, “ecigs”, “smoking electronic cigarettes”, “smoking e-cigarettes”, “smoking ecigarettes”, “smoking ecigs”. A sample of 196 unique videos was coded for overall portrayal and genre. Main topics covered in e-cigarette videos were recorded and video statistics and viewer demographic information were documented.ResultsAmong the 196 unique videos, 94% (n = 185) were “pro” to e-cigarettes and 4% (n = 8) were neutral, while there were only 2% (n = 3) that were “anti” to e-cigarettes. The top 3 most prevalent genres of videos were advertisement, user sharing and product review. 84.3% of “pro” videos contained Web links for e-cigarette purchase. 71.4% of “pro” videos claimed that e-cigarettes were healthier than conventional cigarettes. Audience was primarily from the United States, the United Kingdom and Canada and “pro” e-cigarette videos were watched more frequently and rated much more favorably than “anti” ones.ConclusionsThe vast majority of information on YouTube about e-cigarettes promoted their use and depicted the use of e-cigarettes as socially acceptable. It is critical to develop appropriate health campaigns to inform e-cigarette consumers of potential harms associated with e-cigarette use.

Epidemiological analysis, detection, and comparison of space-time patterns of Beijing hand-foot-mouth disease (2008-2012)

Background Hand, foot, and mouth disease (HFMD) mostly affects the health of infants and preschool children. Many studies of HFMD in different regions have been published. However, the epidemiological characteristics and space-time patterns of individual-level HFMD cases in a major city such as Beijing are unknown. The objective of this study was to investigate epidemiological features and identify high relative risk space-time HFMD clusters at a fine spatial scale. Methods Detailed information on age, occupation, pathogen and gender was used to analyze the epidemiological features of HFMD epidemics. Data on individual-level HFMD cases were examined using Local Indicators of Spatial Association (LISA) analysis to identify the spatial autocorrelation of HFMD incidence. Spatial filtering combined with scan statistics methods were used to detect HFMD clusters. Results A total of 157,707 HFMD cases (60.25% were male, 39.75% were female) reported in Beijing from 2008 to 2012 included 1465 severe cases and 33 fatal cases. The annual average incidence rate was 164.3 per 100,000 (ranged from 104.2 in 2008 to 231.5 in 2010). Male incidence was higher than female incidence for the 0 to 14-year age group, and 93.88% were nursery children or lived at home. Areas at a higher relative risk were mainly located in the urban-rural transition zones (the percentage of the population at risk ranged from 33.89% in 2011 to 39.58% in 2012) showing High-High positive spatial association for HFMD incidence. The most likely space-time cluster was located in the mid-east part of the Fangshan district, southwest of Beijing. Conclusions The spatial-time patterns of Beijing HFMD (2008–2012) showed relatively steady. The population at risk were mainly distributed in the urban-rural transition zones. Epidemiological features of Beijing HFMD were generally consistent with the previous research. The findings generated computational insights useful for disease surveillance, risk assessment and early warning.

Social balance in signed networks

The theory of social balance, also called structural balance, is first proposed by Heider in 1940s, which is utilized to describe the potential social dynamics process. This theory is of great importance in sociology, computer science, psychology and other disciplines where social systems can be represented as signed networks. The social balance problem is hard but very interesting. It has attracted many researchers from various fields working on it over the past few years. Many significant theories and approaches have been developed and now exhibit tremendous potential for future applications. A comprehensive review of these existing studies can provide us significant insights into understanding the dynamic patterns of social systems. Yet to our investigation, existing studies have not done this, especially from a dynamical perspective. In this paper, we make an attempt towards conducting a brief survey of these scientific activities on social balance. Our efforts aim to review what has been done so far in this evolving area. We firstly introduce the fundamental concepts and significant properties of social balance. Then we summarize the existing balance measures and present detecting/partitioning algorithms, as well as important empirical investigations in both physical world and cyberspace. We next mainly focus on describing and comparing the fundamental mechanisms of the dynamics models. Several existing problems not yet satisfactorily solved in this area are also discussed.

Exploring How the Tobacco Industry Presents and Promotes Itself in Social Media

Background The commercial potential of social media is utilized by tobacco manufacturers and vendors for tobacco promotion online. However, the prevalence and promotional strategies of pro-tobacco content in social media are still not widely understood. Objective The goal of this study was to reveal what is presented by the tobacco industry, and how it promotes itself, on social media sites. Methods The top 70 popular cigarette brands are divided into two groups according to their retail prices: group H (brands with high retail prices) and group L (brands with low retail prices). Three comprehensive searches were conducted on Facebook, Wikipedia, and YouTube respectively using the top 70 popular cigarette brands as keywords. We identified tobacco-related content including history and culture, product features, health warnings, home page of cigarette brands, and Web-based tobacco shops. Furthermore, we examined the promotional strategies utilized in social media. Results According to the data collected from March 3, 2014 to March 10, 2014, 43 of the 70 representative cigarette brands had created 238 Facebook fan pages, 46 cigarette brands were identified in Wikipedia, and there were over 120,000 pro-tobacco videos on YouTube, associated with 61 cigarette brands. The main content presented on the three social media websites differs significantly. Wikipedia focuses on history and culture (67%, 32/48; P<.001). Facebook mainly covers history and culture (37%, 16/43; P<.001) and major products (35%, 15/43), while YouTube focuses on the features of major tobacco products (79%, 48/61; P=.04) and information about Web-based shops (49%, 30/61; P=.004). Concerning the content presented by groups H and L, there is no significant difference between the two groups. With regard to the promotional strategies used, sales promotions exist extensively in social media. Sales promotion is more prevalent on YouTube than on the other two sites (64%, 39/61 vs 35%, 15/43; P=.004). Generally, the sale promotions of higher-cost brands in social media are more prevalent than those of lower-cost brands (55%, 16/29 vs 7%, 1/14; P<.001 for Facebook; 78%, 28/36 vs 44%, 11/25; P=.005 for YouTube). Conclusions The prevalence of cigarette brands in social media allows more pro-tobacco information to be accessed by online users. This dilemma indicates that corresponding regulations should be established to prevent tobacco promotion in social media.

A Random Walk Model for Item Recommendation in Social Tagging Systems

Social tagging, as a novel approach to information organization and discovery, has been widely adopted in many Web 2.0 applications. Tags contributed by users to annotate a variety of Web resources or items provide a new type of information that can be exploited by recommender systems. Nevertheless, the sparsity of the ternary interaction data among users, items, and tags limits the performance of tag-based recommendation algorithms. In this article, we propose to deal with the sparsity problem in social tagging by applying random walks on ternary interaction graphs to explore transitive associations between users and items. The transitive associations in this article refer to the path of the link between any two nodes whose length is greater than one. Taking advantage of these transitive associations can allow more accurate measurement of the relevance between two entities (e.g., user-item, user-user, and item-item). A PageRank-like algorithm has been developed to explore these transitive associations by spreading users’ preferences on an item similarity graph and spreading items’ influences on a user similarity graph. Empirical evaluation on three real-world datasets demonstrates that our approach can effectively alleviate the sparsity problem and improve the quality of item recommendation.

System for Infectious Disease Information Sharing and Analysis: Design and Evaluation

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 BioPortals 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.

Why Does Collaborative Filtering Work? Transaction-Based Recommendation Model Validation and Selection by Analyzing Bipartite Random Graphs

A large number of collaborative filtering algorithms have been proposed in the literature as the foundation of automated recommender systems. However, the underlying justification for these algorithms is lacking, and their relative performances are typically domain and data dependent. In this paper, we aim to develop initial understanding of the recommendation model/algorithm validation and selection issues based on the graph topological modeling methodology. By representing the input data in the form of consumer--product interactions as a bipartite graph, the consumer--product graph, we develop bipartite graph topological measures to capture patterns that exist in the input data relevant to the transaction-based recommendation task. We observe the deviations of these topological measures of real-world consumer--product graphs from the expected values for simulated random bipartite graphs. These deviations help explain why certain collaborative filtering algorithms work for particular recommendation data sets. They can also serve as the basis for a comprehensive model selection framework that “recommends” appropriate collaborative filtering algorithms given characteristics of the data set under study. We validate our approach using three real-world recommendation data sets and demonstrate the effectiveness of the proposed bipartite graph topological measures in selection and validation of commonly used heuristic-based recommendation algorithms, the user-based, item-based, and graph-based algorithms.

Information Seeking Regarding Tobacco and Lung Cancer: Effects of Seasonality

This paper conducted one of the first comprehensive international Internet analyses of seasonal patterns in information seeking concerning tobacco and lung cancer. Search query data for the terms “tobacco” and “lung cancer” from January 2004 to January 2014 was collected from Google Trends. The relevant countries included the USA, Canada, the UK, Australia, and China. Two statistical approaches including periodogram and cross-correlation were applied to analyze seasonal patterns in the collected search trends and their associations. For these countries except China, four out of six cross-correlations of seasonal components of the search trends regarding tobacco were above 0.600. For these English-speaking countries, similar patterns existed in the data concerning lung cancer, and all cross-correlations between seasonal components of the search trends regarding tobacco and that regarding lung cancer were also above 0.700. Seasonal patterns widely exist in information seeking concerning tobacco and lung cancer on an international scale. The findings provide a piece of novel Internet-based evidence for the seasonality and health effects of tobacco use.

Tracking Dabbing Using Search Query Surveillance: A Case Study in the United States.

Background Dabbing is an emerging method of marijuana ingestion. However, little is known about dabbing owing to limited surveillance data on dabbing. Objective The aim of the study was to analyze Google search data to assess the scope and breadth of information seeking on dabbing. Methods Google Trends data about dabbing and related topics (eg, electronic nicotine delivery system [ENDS], also known as e-cigarettes) in the United States between January 2004 and December 2015 were collected by using relevant search terms such as “dab rig.” The correlation between dabbing (including topics: dab and hash oil) and ENDS (including topics: vaping and e-cigarette) searches, the regional distribution of dabbing searches, and the impact of cannabis legalization policies on geographical location in 2015 were analyzed. Results Searches regarding dabbing increased in the United States over time, with 1,526,280 estimated searches during 2015. Searches for dab and vaping have very similar temporal patterns, where the Pearson correlation coefficient (PCC) is .992 (P<.001). Similar phenomena were also obtained in searches for hash oil and e-cigarette, in which the corresponding PCC is .931 (P<.001). Dabbing information was searched more in some western states than other regions. The average dabbing searches were significantly higher in the states with medical and recreational marijuana legalization than in the states with only medical marijuana legalization (P=.02) or the states without medical and recreational marijuana legalization (P=.01). Conclusions Public interest in dabbing is increasing in the United States. There are close associations between dabbing and ENDS searches. The findings suggest greater popularity of dabs in the states that legalized medical and recreational marijuana use. This study proposes a novel and timely way of cannabis surveillance, and these findings can help enhance the understanding of the popularity of dabbing and provide insights for future research and informed policy making on dabbing.