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Review of Socionetwork Strategies
Number of Followers: 0  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1867-3236
Published by Springer-Verlag Homepage  [2467 journals]
  • Correction to: Fluctuation in Grocery Sales by Brand: An Analysis Using
           Taylor’s Law

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      PubDate: 2022-11-27
      DOI: 10.1007/s12626-022-00132-w
       
  • Noise–Robust Sampling for Collaborative Metric Learning

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      Abstract: Abstract Abundant research has been conducted recently on recommendation systems. A recommendation system is a subfield of information retrieval and machine learning that aims to identify the value of objects and information for everyone. For example, recommendation systems on web services learn the latent preferences of users based on their behavioral history and display content that the system considers as s user favorite. Specifically, recommendation systems for web services have two main requirements: (1) use the embedding of users and items for predictions and (2) use implicit feedback data that does not require users’ active actions when learning. Recently, a method called collaborative metric learning (CML) has been developed to satisfy the first requirement. However, this method does not address noisy label issues caused by implicit feedback data in the second requirement. This study proposes a comprehensive and effective method to deal with noise in CML. The experimental results show that the proposed method significantly improves the performance of the two requirements compared with existing methods.
      PubDate: 2022-10-09
      DOI: 10.1007/s12626-022-00131-x
       
  • Fluctuations in the Number of Stores by Industry During the COVID-19
           Pandemic Based on Japanese Phone Book Entries

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      Abstract: Abstract Currently in Japan, summaries of the number of bankruptcies due to the spread of COVID-19 can only be obtained from surveys conducted by a few research firms targeting particular companies. In this study, we used Japanese telephone directory data containing detailed information on the location and industrial category of stores/facilities nationwide in an effort to infer the influence of COVID-19 on businesses in Japan. We analyzed the temporal change in the number of stores before and after the COVID-19 outbreak. Among other findings, the analysis revealed that the number of travel agencies and facilities offering karaoke and other forms of entertainment declined significantly after the outbreak in some prefectures, with the largest declines in Ibaraki, Osaka, and Hyogo prefectures, and a relatively small decline in Tochigi prefecture. Among the stores and facilities categorized as restaurants and travel-related services, the decline was particularly significant in urban areas such as Tokyo and Osaka prefectures.
      PubDate: 2022-10-09
      DOI: 10.1007/s12626-022-00120-0
       
  • Changes in Consumer Purchasing Behavior of Cultural and Creative Products
           During the COVID-19 Pandemic

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      Abstract: Abstract The COVID-19 pandemic that began in 2020 has led the changes of many business models, including rapid growth in e-commerce that had made the change of consumers’ shopping behavior from physical to online shopping. This study takes Taiwan's cultural and creative industry as an example to understand the relationship between the development of e-commerce and the design of commodity packaging during COVID-19, as well as the changes in consumer consumption patterns during the epidemic period. After exploring more than a dozen cultural and creative brand operators in the target research area using qualitative research methods, this study found that the epidemic has indeed affected the sales of some cultural and creative brands, but through the change of packaging design, consumers’ visual perception stimulates the sales of goods and re-established pay attention to the cultural and creative brands when add some new packaging designs, and bring positive effects on e-commerce sales to the brand. Also, the good packaging design brought some more attention and procurement of goods, and also brings the effect of quality improvement.
      PubDate: 2022-10-07
      DOI: 10.1007/s12626-022-00129-5
       
  • Anticipation During a Cyclic Manufacturing Process: Toward Visual Search
           Modeling of Human Factors

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      Abstract: Abstract In any model of human information-processing, it is common to represent the cycle from perception to response. In this study, we focus on what happens in the intervals between cycles of work processes in the manufacturing industry. This topic has received little attention. We also visualize the status of visual searching during cyclic processes using eye-tracking. We found that anticipation occurred in preparation for making a decision on action in the next process, and thus contributes to the time taken from perception to response. Based on this result, we discuss the modeling of visual searching by humans.
      PubDate: 2022-10-01
      DOI: 10.1007/s12626-022-00110-2
       
  • Uncovering the Strategies and Dynamics of Research Fields Using Network
           Science: Structural Evidence from a Decade of Privacy Research

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      Abstract: Objective This study aims to better understand the dynamics, organisation and collaboration strategies of a research community through social network analysis. While network is particularly adapted to handling the complexity of social interactions between groups, it is currently underused in many research fields. The topology of the social graph and collaboration strategies is rarely investigated or discussed despite its potential usefulness in understanding group dynamics. The author-strategic diagram, based on social network analysis metrics, is proposed and discussed. Methods Our analysis considers 10 years of co-authored research publications on privacy. First, we explore the dynamics of the community by analysing the incoming, remaining and departing authors, and the authors’ publishing lifetimes. Second, we focus on the dynamics using social network analysis metrics: distance, modularity, node centralities, local clustering coefficient, assortativity. Results Despite the multi-disciplinary nature of privacy research, collaborations exhibit cohesion and a small-world nature. However, the research community is hierarchical, the domain being structured around a few leaders and sub-leaders performing bridging roles between sub-communities.
      Authors collaborate with others in a similar position in the network except for the leaders, who collaborate a lot, but not with each other. Our author-strategic diagram shows the coexistence of divergent collaboration strategies within a single community related to multiple visions of social capital. Transition phases seem to be present; these could indicate future leaders of the community. Conclusion Our results highlight the disparity within the research community and the differing connection types that leaders and other researchers have. Our results show the usefulness of network science in understanding a research field’s community and its dynamics. Comparable analyses of other research communities could help to uncover similar patterns and to tie micro and macro theories together as we show for the theory of social capital.
      PubDate: 2022-10-01
      DOI: 10.1007/s12626-022-00111-1
       
  • Predicting Electric Vehicle (EV) Buyers in India: A Machine Learning
           Approach

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      Abstract: Abstract Electric mobility has been around for a long time. In recent years, with advancements in technology, electric vehicles (EVs) have shown a new potential to meet many of the challenges being faced by humanity. These challenges include increasing dependence on fossil fuels, environmental concerns, challenges posed by rapid urbanization, urban mobility, and employment. However, the adoption of electric vehicles has remained challenging despite consumers having a positive attitude toward EVs and big policy pushes by governments in many countries. Marketers from the electric vehicle (EV) industry are finding it difficult to identify genuine buyers for their products. In this context, the present study attempts to develop a machine learning model to predict whether a person would “Buy” or “Won’t Buy” an electric vehicle in India. To develop the model, an exploration of EV context was done first by conducting a text analysis of online content relating to electric vehicles. The objective was to find frequently occurring words to gain a meaningful understanding of the consumer’s interests and concerns relating to electric vehicles. The machine learning model indicates that age, gender, income, level of environmental concerns, vehicle cost, running cost, vehicle performance, driving range, and mass behavior are significant predictors of electrical vehicle purchase in India. The level of education, employment, and government subsidy are not significant predictors of EV uptake.
      PubDate: 2022-10-01
      DOI: 10.1007/s12626-022-00109-9
       
  • Network Analysis of the Gender Gap in International Remittances by
           Migrants

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      Abstract: Abstract Financial inclusion is considered a key enabler of international development goals. Despite the expansion of financial access overall, the gender inequalities in basic access have remained consistent. This research investigates the predictive power of global remittance and migration flows on the gender gap in financial inclusion. First, singular value decomposition is applied to the World Bank’s 2017 Global Findex data to identify the financial inclusion variables that most contribute to the gender gap in financial inclusion. We find that indicators pertaining to account ownership, emergency funding, and receiving payments are especially significant. Based on the identified variables, a novel Financial Inclusion Gender Gap Score is calculated for 143 economies. The score is then incorporated into a complex network analysis of global remittance and migration networks. We analyze how network features such as node attributes, community membership, and bow-tie structure can be used to make inferences about the magnitude of a financial inclusion gender gap. Our findings suggest that weaker linkages in the network, characterized by lower node strength and peripheral positions in the bow-tie structure, are determinants of a notable financial inclusion gender gap. We also highlight communities in the remittance and migration networks with a more substantial gender imbalance, and discuss the the social- and cultural-leaning factors driving community formation in the migration network that seem to predicate a greater gap.
      PubDate: 2022-10-01
      DOI: 10.1007/s12626-022-00125-9
       
  • Propagation of Shocks in Individual Firms Through Supplier–Customer
           Relationships

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      Abstract: Abstract We quantify the magnitude of shock that propagates individual firms through direct supplier–customer relationships. First, we construct machine learning models that predict a firm’s sales growth rate based on corporate attributes and sales information of the firm and its suppliers/customers. The prediction models indicate that not only macroeconomic factors, such as the year and country, but also sales fluctuation of suppliers/customers are important predictors of the firm’s sales growth rate. Second, we plot the change in the predicted sales growth rates in accordance with those of suppliers/customers using a partial dependence plot. Thus, we quantify how much a firm’s sales growth rate changes in accordance with the changes of its suppliers/customers, namely, the magnitude of shock propagation. Finally, we verify the magnitude of shock propagation by comparing it with the sales growth rate of firms that have suppliers/customers negatively impacted by Hurricane Sandy in the U.S. in 2012. The comparison indicates that there is no significant difference between them and further demonstrates that we can simulate how much the shock that occurred in the disaster-affected firms propagates to their transaction firms.
      PubDate: 2022-10-01
      DOI: 10.1007/s12626-022-00123-x
       
  • Copula-Based Synthetic Data Generation in Firm-Size Variables

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      Abstract: Abstract Using the survival Clayton copula, we propose a method for generating synthetic data on such firm-size variables as operating revenues and the number of employees. Synthetic data must satisfy two stylized facts on firm-size statistics. First, firm-size distributions have power-law tails. Second, there should be a Gibrat’s law for the ratio of two different firm-size variables. With the survival Clayton copula, we introduce random variables whose marginal distributions are uniform on the interval from 0 to 1, and transform them to obey power-law distributions. The resulting variables satisfy the two stylized facts.
      PubDate: 2022-09-28
      DOI: 10.1007/s12626-022-00128-6
       
  • Special Issue: Applications and Management Aspects of Social Networks
           Research

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      PubDate: 2022-09-23
      DOI: 10.1007/s12626-022-00130-y
       
  • Multi-dimensional Self-Exciting NBD Process and Default Portfolios

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      Abstract: Abstract In this study, we apply a multidimensional self-exciting negative binomial distribution (SE-NBD) process to default portfolios with 13 sectors. The SE-NBD process is a Poisson process with a gamma-distributed intensity function. We extend the SE-NBD process to a multidimensional process. Using the multidimensional SE-NBD process (MD-SE-NBD), we can estimate interactions between these 13 sectors as a network. By applying impact analysis, we can classify upstream and downstream sectors. The upstream sectors are real-estate and financial institution (FI) sectors. From these upstream sectors, shock spreads to the downstream sectors. This is an amplifier of the shock. This is consistent with the analysis of bubble bursts. We compare these results to the multidimensional Hawkes process (MD-Hawkes) that has a zero-variance intensity function.
      PubDate: 2022-09-21
      DOI: 10.1007/s12626-022-00122-y
       
  • Preface of Special Issue on Data Science Questing for a Better Society

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      PubDate: 2022-09-19
      DOI: 10.1007/s12626-022-00124-w
       
  • Enhancing the Predictive Performance of Credibility-Based Fake News
           Detection Using Ensemble Learning

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      Abstract: Abstract Fake news detection continues to be a major problem that affects our society today. Fake news can be classified using a variety of methods. Predicting and detecting fake news has proven to be challenging even for machine learning algorithms. This research employs Legitimacy, a unique ensemble machine learning model to accomplish the task of Credibility-Based Fake News Detection. The Legitimacy ensemble combines the learning potential of a Two-Class Boosted Decision Tree and a Two-Class Neural Network. The ensemble technique follows a pseudo-mixture-of-experts methodology. For the gating model, an instance of Two-Class Logistic Regression is implemented. This study validates Legitimacy using a standard dataset with features relating to the credibility of news publishers to predict fake news. These features are analysed using the ensemble algorithm. The results of these experiments are examined using four evaluation methodologies. The analysis of the results reveals positive performance with the use of the ensemble ML method with an accuracy of 96.9%. This ensemble’s performance is compared with the performance of the two base machine learning models of the ensemble. The performance of the ensemble surpasses that of the two base models. The performance of Legitimacy is also analysed as the size of the dataset increases to demonstrate its scalability. Hence, based on our selected dataset, the Legitimacy ensemble model has proven to be most appropriate for Credibility-Based Fake News Detection.
      PubDate: 2022-09-17
      DOI: 10.1007/s12626-022-00127-7
       
  • Impact of COVID-19 Pandemic on Spatial Separation of New and Existing
           Residents: Case Study of Tsukuba City in Greater Tokyo Area

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      Abstract: Abstract For balancing the improvement of social capital through mutual interaction among residents and measures against infectious diseases, municipalities must understand where their residents interact with each other during epidemics. By distinguishing between new and existing residents based on the average age of the houses in their residential areas, we measured the degree of separation between them at various locations and facilities in the Tsukuba City in the Greater Tokyo Area during the daytime based on smartphone location information. We also investigated separation by visitors’ residential savings and income class and their age and gender in each location. Separation was observed in almost all the public places in Tsukuba City, even before the COVID-19 outbreak. During the outbreak, many public places and facilities were visited by fewer people, and yet their separation increased. On the other hand, separation lessened in parks, increasing opportunities for residents to interact. Even after the outbreak began, lower separation environments remained in places where food courts and department stores were located compared to other places. In the post-outbreak period, separation returned to its normal level.
      PubDate: 2022-09-13
      DOI: 10.1007/s12626-022-00118-8
       
  • Analysis of Ethnic Homophily in International Trade Using Large-Scale
           Surname Data

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      Abstract: Abstract Although previous studies described the role of ethnic factors in international trade, due to the difficulty of estimating the ethnicity of trade entities, such analysis was limited to a few ethnic groups, and the differences in the strength of factors across ethnic groups were not identified. By estimating corporate ethnicity using the large-scale surname data of corporate managers, we quantitatively compare and analyze the dependence of various ethnic groups on ethnicity. Asian and Middle Eastern ethnic groups have strong ethnic homophily. An analysis of ethnic factors in commerce between same-language countries using the gravity model suggests that the effect of ethnicity is significant even when language barriers are removed.
      PubDate: 2022-09-12
      DOI: 10.1007/s12626-022-00117-9
       
  • Music Roles Affect the Selection of Consumption Means A Questionnaire
           Survey of People’s Expectations for Music and Exploratory Factor
           Analysis

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      Abstract: Abstract People listen to music for various purposes, and the roles that listeners expect from music can vary from person to person. In recent years, “subscription-based streaming services” (hereinafter referred to as “subscription”) have increased their share in the music market, thereby changing the way people listen to music. Though, the impact of this change on the role of music, is yet to be explored. This study analyzed survey data to reveal the relationship between the expected roles of music and the choice of music consumption means, including subscription. In particular, we obtained that a factor in the use of subscription is the expectation of constructing a personal identity. We also found that both purchase and live usage are affected by two factors: the expectation of fan identity construction and the expectation of artist contribution. This indicates that subscription is not an alternative to the existing means, but a new kind of consumption means in the music market. It was also found that subscription facilitates the construction of personal identity through music.
      PubDate: 2022-09-01
      DOI: 10.1007/s12626-022-00115-x
       
  • ID-POS Data Analysis Using TV Commercial Viewership Data

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      Abstract: Abstract To demonstrate the short-term advertising effects of TV commercials on shoppers, we examined the relationship between TV commercial airing and purchase timing using three types of data: TV metadata, TV viewership data, and ID-POS data. Specifically, we selected some product brands and analyzed the relationship between the gross rating point (GRP) time series and purchase timing for those brands. We selected 54 product brands in eight categories that frequently run TV commercials, including beer, carbonated beverages, instant noodles, and laundry detergents. The ID-POS data contain purchase data (over 20 million lines) for approximately 500,000 IDs shopping at approximately 70 supermarkets in the Kanto area, Japan. A no-correlation test for GRP and purchase timing using random sampling revealed a significant correlation between GRP and purchase timing for many selected brands. We further examined advertising effects from various angles by aggregating data by store, product category, product brand, and customer attributes (sex and age). The results showed some intriguing characteristics of TV commercials’ effects specific to each product brand or each customer attribute, such as the fact that older shoppers are more likely to be influenced by TV commercials, whereas shoppers at low-end stores are more likely to be influenced. In addition, by applying customer IDs to clustering, it was found that approximately 4.8% of all shoppers responded evenly to TV commercials for all brands.
      PubDate: 2022-09-01
      DOI: 10.1007/s12626-022-00116-w
       
  • Simulations of the Diffusion of Innovation by Trust–Distrust Model
           Focusing on the Network Structure

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      Abstract: Abstract The purpose of this study is to examine the role of interaction between mass media and people in the diffusion of innovation using the Trust–Distrust model, one of the theories of opinion dynamics. Therefore, in this study, we ran simulations using the Trust–Distrust model to confirm the differences in opinion distribution across different network structures. We used the five adopter categories as the agents of the Trust–Distrust model and applied the random network, scale-free network, and small-world network as the networks for simulation. As a result, we confirmed that differences in network structure lead to differences in the diffusion of innovations (distribution of opinions).
      PubDate: 2022-08-17
      DOI: 10.1007/s12626-022-00113-z
       
  • The Application of Bayesian Estimation for the Prediction of Economic
           Trends

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      Abstract: Abstract Since economic trends have a great influence on corporate activities, predicting whether the economy is in an expansion period or in retreat is important. Business condition indexes used in Japan that quantify the economy include the diffusion index (DI) and the composite index (CI). A method for predicting economic judgement is presented in this study. An economic trend is taken as an objective function and the DI and CI values are explanatory variables. The prediction model is defined as a Bayesian network. In Bayesian networks, random variables are used as nodes, and the dependency between variables is represented by a directed graph. Japan’s economic trends and DI and CI values from 1985 to 2020 are taken as experimental data. The forecast model is determined using the data from 1985 to 2017 as learning data, and the economic trend from 2018 to 2020 is predicted. The proposed algorithm is compared with a linear model for time-series data. The proposed algorithm shows better accuracy than the linear models.
      PubDate: 2022-08-14
      DOI: 10.1007/s12626-022-00114-y
       
 
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