Social influence on selection behaviour: distinguishing local- and global-driven preferential attachment

Social influence drives human selection behaviours when numerous objects competing for limited attentions, which leads to the 'rich get richer' dynamics where popular objects tend to get more attentions. However, evidences have been found that, both the global information of the whole system and the local information among one's friends have significant influence over the one's selection. Consequently, a key question raises that, it is the local information or the global information more determinative for one's selection? Here we compare the local-based influence and global-based influence. We show that, the selection behaviour is mainly driven by the local popularity of the objects while the global popularity plays a supplementary role driving the behaviour only when there is little local information for the user to refer to. Thereby, we propose a network model to describe the mechanism of user-object interaction evolution with social influence, where the users perform either local-driven or global-driven preferential attachments to the objects, i.e., the probability of an objects to be selected by a target user is proportional to either its local popularity or global popularity. The simulation suggests that, about 75% of the attachments should be driven by the local popularity to reproduce the empirical observations. It means that, at least in the studied context where users chose businesses on Yelp, there is a probability of 75% for a user to make a selection according to the local popularity. The proposed model and the numerical findings may shed some light on the study of social influence and evolving social systems

Do reviews from friends and the crowd affect online consumer posting behaviour differently?

User-generated reviews are valuable resources for consumers to gain information of products which has significant impact on their following decision-making. With the development of social network service, consumers are exposed to reviews coming from both friends and the crowds (non-friends). However, the impact of friends’ and crowds’ reviews on consumer posting behaviour has not been well differentiated. Using the online review information as well as the underlying social network from Yelp, this paper develops a multilevel mixed effect probit model to study the impact of consumer characteristics and reviews of different sources, i.e. friends or crowds, on the possibility of consumer further engaging in posting behaviour. Despite the common perception that the volume, valance and variance of reviews significantly impact the possibility of following posting behaviour, we show that such influence majorly comes from the friend reviews. The volume of friend reviews has much stronger impact on the target user’s posting behaviour than that of the crowds. The valance and variance of the crowd reviews show no significant influence when ignoring the friend reviews, but negative influence when considering it. The friend reviews and crowd reviews are further divided as positive and negative ones, and only the positive friend reviews and negative crowd review are found significantly enhancing the posting possibility

The effect of product distance on the eWOM in recommendation network

The online Product Recommendation Networks (PRNs), connecting similar products with hyperlinks, have been widely implemented in user-generated content websites and ecommerce systems. With the PRNs as the virtual shelves, this paper explores the impact of the distance between products on the formation of product electronic Word-of-Mouth (eWOM). Employing an empirical book recommendation network of Amazon, the study one explores the effect of a focal product’s neighborhood (nearby others) on its eWOM, and study two explores the eWOM similarity between product pairs that are at one, two and three clicks away from each other. The results reveal the significant role played by the product distance on the association of their eWOM. On one hand, a focal product’s eWOM is largely influenced by that of its neighborhood. On the other hand, the good connectivity between two products, which is defined as the number of paths connecting them, is closely associated with the eWOM similarity between them. The findings suggest that the products should be considered as interactive collectives rather than separated individuals particularly in the eWOM studies

Medical service unity: an effective approach for medical care in rural areas in China

Medical care in rural China has long suffered because of a concentration of medical resources in major hospitals in cities. The patients in rural areas thus do not have affordable access to quality medical services. To tackle such issues, a tiered medical scheme (TMS) was promoted by the Chinese State Council in 2015. It divides hospitals into three tiers and encourages collaborations among different tiers within a region in order to provide better accessibility to medical care for patients in rural areas. The implementation of the TMS policy has not been successful, because the previous funding model, which allocated funding to each hospital according to the number of patients treated, did not facilitate close collaborations between different hospitals. In this report, the medical service unity (MSU) approach, which has been piloted in Funan county, is reported. The MSU organises the tiered hospitals as a unity in terms of medical capabilities and financial abilities. With the radical reform of financial decentralisation, three flows are thereby enabled: the funding flow binds together the hospitals into a unity, the patient flow shares the load across the providers and eases barriers to access, and the resource flow ensures accessibility and affordability for patients. The MSU approach has been shown by the pilot project in Funan to be effective for the realisation of the TMS policy, benefiting hospitals, doctors and patients. The successful experience of the Funan MSU could be introduced to other regions across China and other countries. In particular, future finance reform policies for the health system would largely benefit the health reforms and especially the decentralisation of medical resources to rural areas

Balancing the popularity bias of object similarities for personalised recommendation

Network-based similarity measures have found wide applications in recommendation algorithms and made signicant contributions for uncovering users' potential interests. However, existing measures are generally biased in terms of popularity, that the popular objects tend to have more common neighbours with others and thus are considered more similar to others. Such popularity bias of similarity quantification will result in the biased recommendations, with either poor accuracy or poor diversity. Based on the bipartite network modelling of the user-object interactions, this paper firstly calculates the expected number of common neighbours of two objects with given popularities in random networks. A Balanced Common Neighbour similarity index is accordingly developed by removing the random-driven common neighbours, estimated as the expected number, from the total number. Recommendation experiments in three data sets show that balancing the popularity bias in a certain degree can significantly improve the recommendations' accuracy and diversity simultaneously

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Predicting the future increment of review helpfulness: an empirical study based on a two-wave data set

Purpose Identifying and predicting the most helpful reviews has been a focal interest in the fields including information management, e-commerce and marketing, etc. Though many factors are found correlated to the helpfulness of reviews, they may suffer endogeneity problems, as normally the data is observed in the same time window. This paper aims to tackle such a problem by examining the predictive power of different factors on the future increment of review helpfulness. Design/methodology/approach Adopting a longitudinal data of 443 K empirical business reviews from Yelp.com collected at two different time points, six groups of predictors are extracted from the first snapshot of data to predict the helpfulness increment of old and recent reviews, respectively, between the two snapshots. Findings It is found that these factors in general are with moderate accuracy predicting the helpfulness increment. A different group of features shows quite different predictive power. The reviewer disclosure information is the most significant factor, while the review readability does not significantly improve the accuracy of prediction. Originality/value Instead of the total number of helpful votes observed in the same time window with the explanatory variables, this paper focuses on the future increment of helpful votes observed in the following time window. With such a two-wave data set, the endogeneity problem can be avoided and the explanatory factors for review helpfulness can, thus, be further tested in the prediction scenario

Sea-Land Clutter Classification Based on Graph Spectrum Features

In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset into two parts. The first data sequence from sea and land is used to train the model to compute the parameters of the classifier, and the second data sequence from sea and land under the same conditions is used to test the performance of the algorithm. In order to find the essential structure of the data, a new data representation method based on the graph spectrum is utilized. The method reveals the nondominant correlation implied in the data, and it is quite different from the traditional method. Furthermore, this representation is combined with the support vector machine (SVM) artificial intelligence algorithm to solve the problem of sea and land clutter classification. We compare the proposed graph feature set with nine exciting valid features that have been used to classify sea clutter from the radar in other works, especially when the average amplitude levels of the two types of clutter are very close. The experimental results prove that the proposed extraction can represent the characteristics of the raw data efficiently in this application

(Colour online) Results of the evolutionary model.

<p>With respect to the empirical data, we set the initial state of the simulation same with the data applied in this study, i.e. <i>M</i> = 61, 184, <i>N</i> = 366, 715 and we use the empirical social structure as the pre-defined network among users. Furthermore, each simulation continues for 1,569,264 steps (same with the empirical data). <b>a,</b> Distributions of the simulated global popularity. The simulations with different parameters <i>μ</i> can all reproduce the power-law global popularity distribution with slope same to the empirical observation. <b>b,</b> Distributions of the real-time local popularity with different parameters <i>μ</i>. Each distribution exhibits a linear pattern in the log-log plot. <b>c,</b> The slope <i>γ</i> of the linear pattern for local popularity distributions with different parameter <i>μ</i>. For each parameter <i>μ</i>, the result is calculated based on 100 independent simulations. For each simulation, the fitting is based on a linear regression after taking logarithm for the simulated local popularity <i>LP</i>(<i>c</i>) and the frequency (p.d.f.) of it <i>p</i>(<i>LP</i>(<i>c</i>)) (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0175761#pone.0175761.s001" target="_blank">S1 File</a>). The inset in the subplot (c) shows the coefficient of determination <i>R</i>² of corresponding fittings. The <i>R</i>² of the fittings are generally larger than 0.98 which indicates that the fittings can be considered good for all the experiments with different parameters <i>μ</i>. The red dashed line is the slope <i>γ</i> of the empirical local popularity distribution shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0175761#pone.0175761.g001" target="_blank">Fig 1(d)</a>, i.e. <i>γ</i>_<i>em</i> = −2.7. The green boxes are those which agree with the empirical result.</p

Social influence on selection behaviour: Distinguishing local- and global-driven preferential attachment - Fig 1

<p>(Color online) <b>a,</b> An example of user-business bipartite network with social structure to illustrate the Yelp data set and the research scenario. On the user layer, each user may establish friendships with others and those friends are the target user’s local neighbourhood. On the other hand, the whole user layer is the global environment for each user. The interactions between the user and business layer represented by the bipartite links, are the comment behaviours. Although it is impossible to know exactly each user’s real-world consumption for those businesses, we assume the online comment behaviour could largely reflect what those users have selected (consumed). <b>b,</b> The distribution of the businesses’ final global popularity, i.e. popularity at the end <i>t</i> = <i>T</i> of the Yelp data set, <i>GP</i>(<i>T</i>). As what have been observed from most networks, the global popularity distribution displays a power-law form with slope of −1.7. <b>c,</b> Local popularity of selection behaviours <i>LP</i>(<i>c</i>) versus the expected local popularity <i>LP</i>^<i>exp</i>. The red dashed line shows the condition that <i>LP</i>(<i>c</i>) = <i>LP</i>^<i>exp</i>. While the local popularity of the random experiments and global-driven preferential attachment (GPA) model are very similar to the expected value, the empirical local popularity is significantly higher which suggests that the users tend to select locally popular businesses. <b>d,</b> The distribution of real-time local popularity <i>LP</i>(<i>c</i>). For the empirical data, the local popularity follows the power-law distribution with slope of −2.7. On the other hand, the local popularity of the GPA model being very similar to the random experiment, cannot reproduce the empirical observation.</p