Words Analysis of Online Chinese News Headlines about Trending Events: A Complex Network Perspective

Because the volume of information available online is growing at breakneck speed, keeping up with meaning and information communicated by the media and netizens is a new challenge both for scholars and for companies who must address public relations crises. Most current theories and tools are directed at identifying one website or one piece of online news and do not attempt to develop a rapid understanding of all websites and all news covering one topic. This paper represents an effort to integrate statistics, word segmentation, complex networks and visualization to analyze headlines’ keywords and words relationships in online Chinese news using two samples: the 2011 Bohai Bay oil spill and the 2010 Gulf of Mexico oil spill. We gathered all the news headlines concerning the two trending events in the search results from Baidu, the most popular Chinese search engine. We used Simple Chinese Word Segmentation to segment all the headlines into words and then took words as nodes and considered adjacent relations as edges to construct word networks both using the whole sample and at the monthly level. Finally, we develop an integrated mechanism to analyze the features of words’ networks based on news headlines that can account for all the keywords in the news about a particular event and therefore track the evolution of news deeply and rapidly.     

Public Opinions about Overdiagnosis: A National Community Survey

BackgroundDespite evidence about the "modern epidemic" of overdiagnosis, and expanding disease definitions that medicalize more people, data are lacking on public views about these issues. Our objective was to measure public perceptions about overdiagnosis and views about financial ties of panels setting disease definitions.MethodsWe conducted a 15 minute Computer Assisted Telephone Interview with a randomly selected community sample of 500 Australians in January 2014. We iteratively developed and piloted a questionnaire, with a convenience sample (n=20), then with participants recruited by a research company (n=20). Questions included whether respondents had been informed about overdiagnosis; opinions on informing people; and views about financial ties among panels writing disease definitions.FindingsOur sample was generally representative, but included a higher proportion of females and seniors, typical of similar surveys. American Association for Public Opinion Research response rate was 20% and cooperation rate was 44%. Only 10% (95% CI 8%–13%) of people reported ever being told about overdiagnosis by a doctor. 18% (95% CI 11%–28%) of men who reported having prostate cancer screening, and 10% (95% CI 6%–15%) of women who reported having mammography said they were told about overdiagnosis. 93% (95% CI 90%–95%) agreed along with screening benefits, people should be informed about overdiagnosis. On panels setting disease definitions, 78% (95% CI 74%–82%) felt ties to pharmaceutical companies inappropriate, and 91% (95% CI 82%–100%) believed panels should have a minority or no members with ties. Limitations included questionnaire novelty and complexity.ConclusionsA small minority of Australians surveyed, including those reporting being screened for prostate or breast cancer, reported being informed of overdiagnosis; most believed people should be informed; and a majority felt it inappropriate that doctors with ties to pharmaceutical companies write disease definitions. Results suggest strategies to better inform people about overdiagnosis, and review disease definition processes, have significant public sympathy.     

Climate Change Sentiment on Twitter: An Unsolicited Public Opinion Poll

The consequences of anthropogenic climate change are extensively debated through scientific papers, newspaper articles, and blogs. Newspaper articles may lack accuracy, while the severity of findings in scientific papers may be too opaque for the public to understand. Social media, however, is a forum where individuals of diverse backgrounds can share their thoughts and opinions. As consumption shifts from old media to new, Twitter has become a valuable resource for analyzing current events and headline news. In this research, we analyze tweets containing the word “climate” collected between September 2008 and July 2014. Through use of a previously developed sentiment measurement tool called the Hedonometer, we determine how collective sentiment varies in response to climate change news, events, and natural disasters. We find that natural disasters, climate bills, and oil-drilling can contribute to a decrease in happiness while climate rallies, a book release, and a green ideas contest can contribute to an increase in happiness. Words uncovered by our analysis suggest that responses to climate change news are predominately from climate change activists rather than climate change deniers, indicating that Twitter is a valuable resource for the spread of climate change awareness.     

基于复杂网络的脑疾病功能网络研究进展

脑功能连接能够反映脑区间的相互联系状况,目前已成为脑功能研究的主要方法。复杂网络方法源于图论分析,可以对功能连接所构网络进行量化分析,提供多种量化指标。本文介绍了当前复杂网络的多种基本概念,及其在几种典型脑部疾病上的应用情况。     

Investigation on Law and Economics Based on Complex Network and Time Series Analysis

The research focuses on the cooperative relationship and the strategy tendency among three mutually interactive parties in financing: small enterprises, commercial banks and micro-credit companies. Complex network theory and time series analysis were applied to figure out the quantitative evidence. Moreover, this paper built up a fundamental model describing the particular interaction among them through evolutionary game. Combining the results of data analysis and current situation, it is justifiable to put forward reasonable legislative recommendations for regulations on lending activities among small enterprises, commercial banks and micro-credit companies. The approach in this research provides a framework for constructing mathematical models and applying econometrics and evolutionary game in the issue of corporation financing.     

Cumulative Effect in Information Diffusion: Empirical Study on a Microblogging Network

Cumulative effect in social contagion underlies many studies on the spread of innovation, behavior, and influence. However, few large-scale empirical studies are conducted to validate the existence of cumulative effect in information diffusion on social networks. In this paper, using the population-scale dataset from the largest Chinese microblogging website, we conduct a comprehensive study on the cumulative effect in information diffusion. We base our study on the diffusion network of message, where nodes are the involved users and links characterize forwarding relationship among them. We find that multiple exposures to the same message indeed increase the possibility of forwarding it. However, additional exposures cannot further improve the chance of forwarding when the number of exposures crosses its peak at two. This finding questions the cumulative effect hypothesis in information diffusion. Furthermore, to clarify the forwarding preference among users, we investigate both structural motif in the diffusion network and temporal pattern in information diffusion process. Findings provide some insights for understanding the variation of message popularity and explain the characteristics of diffusion network.     

Norwegian Physicians' Knowledge of and Opinions about Evidence-Based Medicine: Cross-Sectional Study

Objective

To answer five research questions: Do Norwegian physicians know about the three important aspects of EBM? Do they use EBM methods in their clinical practice? What are their attitudes towards EBM? Has EBM in their opinion changed medical practice during the last 10 years? Do they use EBM based information sources?

Design

Cross sectional survey in 2006.

Setting

Norway.

Participants

966 doctors who responded to a questionnaire (70% response rate).

Results

In total 87% of the physicians mentioned the use of randomised clinical trials as a key aspect of EBM, while 53% of them mentioned use of clinical expertise and only 19% patients'' values. 40% of the respondents reported that their practice had always been evidence-based. Many respondents experienced difficulties in using EBM principles in their clinical practice because of lack of time and difficulties in searching EBM based literature. 80% agreed that EBM helps physicians towards better practice and 52% that it improves patients'' health. As reasons for changes in medical practice 86% of respondents mentioned medical progress, but only 39% EBM.

Conclusions

The results of the study indicate that Norwegian physicians have a limited knowledge of the key aspects of EBM but a positive attitude towards the concept. They had limited experience in the practice of EBM and were rather indifferent to the impact of EBM on medical practice. For solving a patient problem, physicians would rather consult a colleague than searching evidence based resources such as the Cochrane Library.     

人代谢网络巨强连通体的复杂网络分析

采用复杂网络理论分析了人代谢网络的巨强连通体。通过对高质量人代谢网络数据的整理,获取了该网络巨强连通体中的所有代谢反应。用代谢物图表示这些反应形成了一个网络,它包含了256个节点和648条连线。选用模拟退火算法对该网络进行社团结构分析,发现得到的模块具备重要的生物学功能。通过多种不同的中心化方法,分析了该网络的关键节点,并讨论了它们的生物学和理疗意义。     

Uncovering and Testing the Fuzzy Clusters Based on Lumped Markov Chain in Complex Network

Identifying clusters, namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and function of a network. By means of a lumped Markov chain model of a random walker, we propose two novel ways of inferring the lumped markov transition matrix. Furthermore, some useful results are proposed based on the analysis of the properties of the lumped Markov process. To find the best partition of complex networks, a novel framework including two algorithms for network partition based on the optimal lumped Markovian dynamics is derived to solve this problem. The algorithms are constructed to minimize the objective function under this framework. It is demonstrated by the simulation experiments that our algorithms can efficiently determine the probabilities with which a node belongs to different clusters during the learning process and naturally supports the fuzzy partition. Moreover, they are successfully applied to real-world network, including the social interactions between members of a karate club.     

Virtual World Currency Value Fluctuation Prediction System Based on User Sentiment Analysis

In this paper, we present a method for predicting the value of virtual currencies used in virtual gaming environments that support multiple users, such as massively multiplayer online role-playing games (MMORPGs). Predicting virtual currency values in a virtual gaming environment has rarely been explored; it is difficult to apply real-world methods for predicting fluctuating currency values or shares to the virtual gaming world on account of differences in domains between the two worlds. To address this issue, we herein predict virtual currency value fluctuations by collecting user opinion data from a virtual community and analyzing user sentiments or emotions from the opinion data. The proposed method is straightforward and applicable to predicting virtual currencies as well as to gaming environments, including MMORPGs. We test the proposed method using large-scale MMORPGs and demonstrate that virtual currencies can be effectively and efficiently predicted with it.     

Knowledge Transfer on Complex Social Interventions in Public Health: A Scoping Study

Objectives

Scientific knowledge can help develop interventions that improve public health. The objectives of this review are (1) to describe the status of research on knowledge transfer strategies in the field of complex social interventions in public health and (2) to identify priorities for future research in this field.

Method

A scoping study is an exploratory study. After searching databases of bibliographic references and specialized periodicals, we summarized the relevant studies using a predetermined assessment framework. In-depth analysis focused on the following items: types of knowledge transfer strategies, fields of public health, types of publics, types of utilization, and types of research specifications.

Results

From the 1,374 references identified, we selected 26 studies. The strategies targeted mostly administrators of organizations and practitioners. The articles generally dealt with instrumental utilization and most often used qualitative methods. In general, the bias risk for the studies is high.

Conclusion

Researchers need to consider the methodological challenges in this field of research in order to improve assessment of more complex knowledge transfer strategies (when they exist), not just diffusion/dissemination strategies and conceptual and persuasive utilization.     

Coherence Potentials: Loss-Less,All-or-None Network Events in the Cortex

Transient associations among neurons are thought to underlie memory and behavior. However, little is known about how such associations occur or how they can be identified. Here we recorded ongoing local field potential (LFP) activity at multiple sites within the cortex of awake monkeys and organotypic cultures of cortex. We show that when the composite activity of a local neuronal group exceeds a threshold, its activity pattern, as reflected in the LFP, occurs without distortion at other cortex sites via fast synaptic transmission. These large-amplitude LFPs, which we call coherence potentials, extend up to hundreds of milliseconds and mark periods of loss-less spread of temporal and amplitude information much like action potentials at the single-cell level. However, coherence potentials have an additional degree of freedom in the diversity of their waveforms, which provides a high-dimensional parameter for encoding information and allows identification of particular associations. Such nonlinear behavior is analogous to the spread of ideas and behaviors in social networks.     

Flood Risk Analysis Based on Information Diffusion Theory

Due to the fact that the flood data series of small drainage basins is relatively short, available data are often not sufficient for flood risk analysis. This presents the problem of risk analysis using very small data samples. One method that can be applied is to regard the available small samples as fuzzy information and optimize them using information diffusion technology to yield analytical results with greater reliability. In this article a risk analysis method based on information diffusion theory is applied to create a new flood risk analysis model. Application of the model is illustrated taking the Jinhuajiang and Qujiang drainage basins as examples. This is a new attempt at applying information diffusion theory in flood risk analysis. Computations based on this analytical flood risk model can yield an estimated flood damage value that is relatively accurate. This study indicates that the aforementioned model exhibits fairly stable analytical results, even when using a small set of sample data. The results also indicate that information diffusion technology is highly capable of extracting useful information and therefore improves system recognition accuracy. This method can be easily applied and the analytical results produced are easy to understand. Results are accurate enough to act as a guide in disaster situations.     

CleavPredict: A Platform for Reasoning about Matrix Metalloproteinases Proteolytic Events

CleavPredict (http://cleavpredict.sanfordburnham.org) is a Web server for substrate cleavage prediction for matrix metalloproteinases (MMPs). It is intended as a computational platform aiding the scientific community in reasoning about proteolytic events. CleavPredict offers in silico prediction of cleavage sites specific for 11 human MMPs. The prediction method employs the MMP specific position weight matrices (PWMs) derived from statistical analysis of high-throughput phage display experimental results. To augment the substrate cleavage prediction process, CleavPredict provides information about the structural features of potential cleavage sites that influence proteolysis. These include: secondary structure, disordered regions, transmembrane domains, and solvent accessibility. The server also provides information about subcellular location, co-localization, and co-expression of proteinase and potential substrates, along with experimentally determined positions of single nucleotide polymorphism (SNP), and posttranslational modification (PTM) sites in substrates. All this information will provide the user with perspectives in reasoning about proteolytic events. CleavPredict is freely accessible, and there is no login required.     

Diffusion Magnetic Resonance Imaging: What Water Tells Us about Biological Tissues

Since its introduction in the mid-1980s, diffusion magnetic resonance imaging (MRI), which measures the random motion of water molecules in tissues, revealing their microarchitecture, has become a pillar of modern neuroimaging. Its main clinical domain has been the diagnosis of acute brain stroke and neurogical disorders, but it is also used in the body for the detection and management of cancer lesions. It can also produce stunning maps of white matter tracks in the brain, with the potential to aid in the understanding of some psychiatric disorders. However, in order to exploit fully the potential of this method, a deeper understanding of the mechanisms that govern the diffusion of water in tissues is needed.In the mid-1980s, we showed that water diffusion in the human brain could be imaged by using magnetic resonance imaging (MRI) [1]. Since then, diffusion MRI has enjoyed a dramatic growth, with about 24,000 articles referenced in PubMed in 2014. MRI is a medical imaging technique consisting of magnetizing body atom nuclei, generally hydrogen nuclei of water molecules, using a very strong magnetic field (typically 30,000 to 60,000 times the earth’s natural magnetic field). The resulting very tiny magnetization can be manipulated in time by sending radiofrequency wave pulses at a resonant frequency. In turn, magnetized nuclei re-emit radiofrequency waves, creating a signal that is received through a coil (a kind of antenna), giving information on the nuclei magnetization properties. Magnetic field “gradient” pulses are used in addition to induce small variations of the magnetic field (and the associated radiowaves’ resonant frequency) in space, so as to spatially encode the magnetization information and create images. Magnetization varies a lot between tissues and various disease conditions, making MRI a very versatile imaging modality. However, the resolution of MRI images used for clinical practice often remains limited, typically around 1 mm (microscopic MRI is possible, but with dedicated preclinical MRI systems using ultra-high magnetic fields; see below). The concept of diffusion MRI emerged as a way to probe tissue structure at a microscopic (invisible) scale, although images are acquired at a millimetric scale: during their random, diffusion-driven displacements in the tissue, the water molecules probe the tissue structure at a microscopic scale, interacting with cell membranes, thus providing unique information on the functional architecture of tissues. Diffusion MRI has become a pillar of modern clinical imaging, used mainly to investigate neurological disorders such as acute brain ischemia, although it is now also a standard imaging method for other organs too, especially for the management of cancer patients. Indeed, diffusion MRI that does not require any tracer injection is rapidly becoming a modality of choice to detect and characterize malignant lesions. Moreover, in the brain, diffusion anisotropy in white matter can be exploited to produce stunning three-dimensional maps of brain connections, revealing faulty connections in some psychiatric disorders. More recently, diffusion MRI has been applied to monitor the dynamic changes occurring in the neural tissue structure during activation, a new approach to investigate functional neuroimaging and the mechanisms underlying neuronal activation.It is amazing that all these applications of diffusion MRI have emerged or developed while so little is known about water diffusion mechanisms in biological tissues. The relative importance of many factors governing water in tissues and their effects on the observed MRI signal are still not fully understood and are sometimes a subject of controversy.We will discuss the main applications and the outstanding issues remaining in the field in more detail below.