Journal of Computational Social Science最新文献

Identifying the factors influencing the development of bilateral investment treaties with health safeguards: a Machine Learning-based link prediction approach.

IF 2

Journal of Computational Social Science Pub Date : 2025-01-01 Epub Date: 2024-12-05 DOI: 10.1007/s42001-024-00341-z

Haohui Lu, Anne Marie Thow, Dori Patay, Takwa Tissaoui, Nicholas Frank, Holly Rippin, Tien Dat Hoang, Fabio Gomes, Wolfgang Alschner, Shahadat Uddin

{"title":"Identifying the factors influencing the development of bilateral investment treaties with health safeguards: a Machine Learning-based link prediction approach.","authors":"Haohui Lu, Anne Marie Thow, Dori Patay, Takwa Tissaoui, Nicholas Frank, Holly Rippin, Tien Dat Hoang, Fabio Gomes, Wolfgang Alschner, Shahadat Uddin","doi":"10.1007/s42001-024-00341-z","DOIUrl":"10.1007/s42001-024-00341-z","url":null,"abstract":"A network analysis approach, complemented by machine learning (ML) techniques, is applied to analyse the factors influencing Bilateral Investment Treaties (BITs) at the country level. Using the Electronic Database of Investment Treaties, BITs with health safeguards from 167 countries were charted, resulting in 534 connections with countries as nodes and their BITs as edges. Network analysis found that, on average, a country established BITs with six other nations. Additionally, we used node embedding techniques to generate features from the network, such as the Jaccard coefficient, resource allocation, and Adamic Adar for downstream link prediction. This study employed five tree-based ML models to predict future BIT formations with health inclusion. The eXtreme Gradient Boosting model proved to be superior, achieving a 64.02% accuracy rate. Notably, the Common Neighbor centrality feature and the Capital Account Balance Ratio emerged as influential factors in creating new BITs with health inclusions. Beyond economic considerations, our study highlighted a vital intersection: the nexus between BITs, economic growth, and public health policies. In essence, this research underscores the importance of safeguarding public health in BITs and showcases the potential of ML in understanding the intricacies of international treaties.","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":"8 1","pages":"8"},"PeriodicalIF":2.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11621195/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142801605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Telegram channels covering Russia’s invasion of Ukraine: a comparative analysis of large multilingual corpora 报道俄罗斯入侵乌克兰的 Telegram 频道：大型多语言语料库比较分析

IF 3.2

Journal of Computational Social Science Pub Date : 2024-01-03 DOI: 10.1007/s42001-023-00240-9

Anton Oleinik

引用次数: 0

A modelling study to explore the effects of regional socio-economics on the spreading of epidemics. 通过建模研究探讨地区社会经济对流行病传播的影响。

IF 2

Journal of Computational Social Science Pub Date : 2024-01-01 Epub Date: 2024-08-14 DOI: 10.1007/s42001-024-00322-2

Jan E Snellman, Rafael A Barrio, Kimmo K Kaski, Maarit J Korpi-Lagg

{"title":"A modelling study to explore the effects of regional socio-economics on the spreading of epidemics.","authors":"Jan E Snellman, Rafael A Barrio, Kimmo K Kaski, Maarit J Korpi-Lagg","doi":"10.1007/s42001-024-00322-2","DOIUrl":"https://doi.org/10.1007/s42001-024-00322-2","url":null,"abstract":"Epidemics, apart from affecting the health of populations, can have large impacts on their social and economic behavior and subsequently feed back to and influence the spreading of the disease. This calls for systematic investigation which factors affect significantly and either beneficially or adversely the disease spreading and regional socio-economics. Based on our recently developed hybrid agent-based socio-economy and epidemic spreading model we perform extensive exploration of its six-dimensional parameter space of the socio-economic part of the model, namely, the attitudes towards the spread of the pandemic, health and the economic situation for both, the population and government agents who impose regulations. We search for significant patterns from the resulting simulated data using basic classification tools, such as self-organizing maps and principal component analysis, and we monitor different quantities of the model output, such as infection rates, the propagation speed of the epidemic, economic activity, government regulations, and the compliance of population on government restrictions. Out of these, the ones describing the epidemic spreading were resulting in the most distinctive clustering of the data, and they were selected as the basis of the remaining analysis. We relate the found clusters to three distinct types of disease spreading: wave-like, chaotic, and transitional spreading patterns. The most important value parameter contributing to phase changes and the speed of the epidemic was found to be the compliance of the population agents towards the government regulations. We conclude that in compliant populations, the infection rates are significantly lower and the infection spreading is slower, while the population agents' health and economical attitudes show a weaker effect.","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":"7 3","pages":"2535-2562"},"PeriodicalIF":2.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11541270/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142628922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fast meta-analytic approximations for relational event models: applications to data streams and multilevel data. 关系事件模型的快速元分析近似：数据流和多层次数据的应用。

IF 2

Journal of Computational Social Science Pub Date : 2024-01-01 Epub Date: 2024-06-08 DOI: 10.1007/s42001-024-00290-7

Fabio Vieira, Roger Leenders, Joris Mulder

{"title":"Fast meta-analytic approximations for relational event models: applications to data streams and multilevel data.","authors":"Fabio Vieira, Roger Leenders, Joris Mulder","doi":"10.1007/s42001-024-00290-7","DOIUrl":"10.1007/s42001-024-00290-7","url":null,"abstract":"Large relational-event history data stemming from large networks are becoming increasingly available due to recent technological developments (e.g. digital communication, online databases, etc). This opens many new doors to learn about complex interaction behavior between actors in temporal social networks. The relational event model has become the gold standard for relational event history analysis. Currently, however, the main bottleneck to fit relational events models is of computational nature in the form of memory storage limitations and computational complexity. Relational event models are therefore mainly used for relatively small data sets while larger, more interesting datasets, including multilevel data structures and relational event data streams, cannot be analyzed on standard desktop computers. This paper addresses this problem by developing approximation algorithms based on meta-analysis methods that can fit relational event models significantly faster while avoiding the computational issues. In particular, meta-analytic approximations are proposed for analyzing streams of relational event data, multilevel relational event data and potentially combinations thereof. The accuracy and the statistical properties of the methods are assessed using numerical simulations. Furthermore, real-world data are used to illustrate the potential of the methodology to study social interaction behavior in an organizational network and interaction behavior among political actors. The algorithms are implemented in the publicly available R package 'remx'.","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":"7 2","pages":"1823-1859"},"PeriodicalIF":2.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11452451/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142381790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Small-area population forecasting in a segregated city using density-functional fluctuation theory. 利用密度函数波动理论预测隔离城市的小区域人口。

IF 2

Journal of Computational Social Science Pub Date : 2024-01-01 Epub Date: 2024-08-28 DOI: 10.1007/s42001-024-00305-3

Yuchao Chen, Yunus A Kinkhabwala, Boris Barron, Matthew Hall, Tomás A Arias, Itai Cohen

{"title":"Small-area population forecasting in a segregated city using density-functional fluctuation theory.","authors":"Yuchao Chen, Yunus A Kinkhabwala, Boris Barron, Matthew Hall, Tomás A Arias, Itai Cohen","doi":"10.1007/s42001-024-00305-3","DOIUrl":"https://doi.org/10.1007/s42001-024-00305-3","url":null,"abstract":"Policy decisions concerning housing, transportation, and resource allocation would all benefit from accurate small-area population forecasts. However, despite the success of regional-scale migration models, developing neighborhood-scale forecasts remains a challenge due to the complex nature of residential choice. Here, we introduce an innovative approach to this challenge by extending density-functional fluctuation theory (DFFT), a proven approach for modeling group spatial behavior in biological systems, to predict small-area population shifts over time. The DFFT method uses observed fluctuations in small-area populations to disentangle and extract effective social and spatial drivers of segregation, and then uses this information to forecast intra-regional migration. To demonstrate the efficacy of our approach in a controlled setting, we consider a simulated city constructed from a Schelling-type model. Our findings indicate that even without direct access to the underlying agent preferences, DFFT accurately predicts how broader demographic changes at the city scale percolate to small-area populations. In particular, our results demonstrate the ability of DFFT to incorporate the impacts of segregation into small-area population forecasting using interactions inferred solely from steady-state population count data.","PeriodicalId":29946,"journal":{"name":"Journal of Computational Social Science","volume":"7 3","pages":"2255-2275"},"PeriodicalIF":2.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11541354/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142629126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Impact of income inequality on health and education in Africa: the long-run role of public spending with short-run dynamics 收入不平等对非洲卫生和教育的影响：公共支出的长期作用与短期动态变化

IF 3.2

Journal of Computational Social Science Pub Date : 2023-12-16 DOI: 10.1007/s42001-023-00237-4

Tonmoy Chatterjee, Ghirmai Tesfamariam Teame, Sharmi Sen

引用次数: 0

An empirical study of sentiment analysis utilizing machine learning and deep learning algorithms 利用机器学习和深度学习算法进行情感分析的实证研究

IF 3.2

Journal of Computational Social Science Pub Date : 2023-12-09 DOI: 10.1007/s42001-023-00236-5

Betul Erkantarci, Gokhan Bakal

引用次数: 0

The risk co-de model: detecting psychosocial processes of risk perception in natural language through machine learning 风险共感模型：通过机器学习检测自然语言中风险认知的社会心理过程

IF 3.2

Journal of Computational Social Science Pub Date : 2023-11-30 DOI: 10.1007/s42001-023-00235-6

Valentina Rizzoli

引用次数: 0

Exploring statistical approaches for predicting student dropout in education: a systematic review and meta-analysis 探索预测学生辍学的统计方法：系统回顾与荟萃分析

IF 3.2

Journal of Computational Social Science Pub Date : 2023-11-29 DOI: 10.1007/s42001-023-00231-w

Raghul Gandhi Venkatesan, Dhivya Karmegam, Bagavandas Mappillairaju

引用次数: 0

Automated measures of sentiment via transformer- and lexicon-based sentiment analysis (TLSA) 通过基于转换器和词典的情感分析（TLSA）自动测量情感

IF 3.2

Journal of Computational Social Science Pub Date : 2023-11-21 DOI: 10.1007/s42001-023-00233-8

Xinyan Zhao, Chau-Wai Wong

引用次数: 0