Social Network Analysis and Mining最新文献

A Bayesian mixture model for Poisson network autoregression. 泊松网络自回归的贝叶斯混合模型。

IF 2.3

Social Network Analysis and Mining Pub Date : 2025-01-01 Epub Date: 2025-07-17 DOI: 10.1007/s13278-025-01485-0

Elly Hung, Anastasia Mantziou, Gesine Reinert

{"title":"A Bayesian mixture model for Poisson network autoregression.","authors":"Elly Hung, Anastasia Mantziou, Gesine Reinert","doi":"10.1007/s13278-025-01485-0","DOIUrl":"10.1007/s13278-025-01485-0","url":null,"abstract":"Multivariate count time series arise in a wide range of applications, including the number of COVID-19 cases recorded each week in different counties of the Republic of Ireland. In this example, it is natural to view the counties as nodes in a network, with edges between counties reflecting proximity. One could then model disease spread on a network through a regression model. Often Gaussian errors are assumed for such a model, but for count data this assumption may not be natural. With this motivating example in mind, we develop a model with the following features. We assume that the time series occur on the nodes of a known underlying network where the edges dictate the form of a structural vector autoregression model. In contrast to using a full vector autoregressive model, the network assumption is a means of imposing sparsity. Moreover we aim for a model that is able to accommodate heterogeneous node dynamics, and to cluster nodes that exhibit similar behaviour. To address these aims, we propose a new Bayesian Poisson network autoregression mixture model that we call a PNARM model, which combines ideas from Poisson network autoregression models, grouped network autoregression models, and non-uniform co-clustering priors.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"15 1","pages":"70"},"PeriodicalIF":2.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12271270/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144675694","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

Correction: Public sentiment toward renewable energy in Morocco: opinion mining using a rule-based approach 更正：摩洛哥公众对可再生能源的看法：利用基于规则的方法进行民意挖掘

IF 2.8

Social Network Analysis and Mining Pub Date : 2024-01-13 DOI: 10.1007/s13278-023-01191-9

M. Kasri, Anas El-Ansari, Mohamed El Fissaoui, Badreddine Cherkaoui, Marouane Birjali, A. Beni-Hssane

引用次数: 0

Do users adopt extremist beliefs from exposure to hate subreddits? 用户是否会因为接触仇恨子论坛而接受极端主义信仰？

IF 2.8

Social Network Analysis and Mining Pub Date : 2024-01-11 DOI: 10.1007/s13278-023-01184-8

Matheus Schmitz, Goran Muric, Daniel Hickey, Keith Burghardt

引用次数: 0

Comparing methods for creating a national random sample of twitter users. 比较创建全国随机twitter用户样本的方法。

IF 2.3

Social Network Analysis and Mining Pub Date : 2024-01-01 Epub Date: 2024-08-14 DOI: 10.1007/s13278-024-01327-5

Meysam Alizadeh, Darya Zare, Zeynab Samei, Mohammadamin Alizadeh, Mael Kubli, Mohammadhadi Aliahmadi, Sarvenaz Ebrahimi, Fabrizio Gilardi

{"title":"Comparing methods for creating a national random sample of twitter users.","authors":"Meysam Alizadeh, Darya Zare, Zeynab Samei, Mohammadamin Alizadeh, Mael Kubli, Mohammadhadi Aliahmadi, Sarvenaz Ebrahimi, Fabrizio Gilardi","doi":"10.1007/s13278-024-01327-5","DOIUrl":"10.1007/s13278-024-01327-5","url":null,"abstract":"Twitter data has been widely used by researchers across various social and computer science disciplines. A common aim when working with Twitter data is the construction of a random sample of users from a given country. However, while several methods have been proposed in the literature, their comparative performance is mostly unexplored. In this paper, we implement four common methods to create a random sample of Twitter users in the US: 1% Stream, Bounding Box, Location Query, and Language Query. Then, we compare these methods according to their tweet- and user-level metrics as well as their accuracy in estimating the US population. Our results show that users collected by the 1% Stream method tend to have more tweets, tweets per day, followers, and friends, a fewer number of likes, are younger accounts, and include more male users compared to the other three methods. Moreover, it achieves the minimum error in estimating the US population. However, the 1% Stream method is time-consuming, cannot be used for the past time frames, and is not suitable when user engagement is part of the study. In situation where these three drawbacks are important, our results support the Bounding Box method as the second-best method.Supplementary information: The online version contains supplementary material available at. 10.1007/s13278-024-01327-5.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"14 1","pages":"160"},"PeriodicalIF":2.3,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11861139/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143524463","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

Analyzing online public opinion on Thailand-China high-speed train and Laos-China railway mega-projects using advanced machine learning for sentiment analysis 利用先进的情感分析机器学习，分析泰国-中国高速列车和老挝-中国铁路大型项目的网络舆情

IF 2.8

Social Network Analysis and Mining Pub Date : 2023-12-18 DOI: 10.1007/s13278-023-01168-8

Manussawee Nokkaew, K. Nongpong, Tapanan Yeophantong, Pattravadee Ploykitikoon, W. Arjharn, A. Siritaratiwat, Sorawit Narkglom, W. Wongsinlatam, T. Remsungnen, A. Namvong, C. Surawanitkun

引用次数: 0

Semantic overlapping community detection with embedding multi-dimensional relationships and spatial context 嵌入多维关系和空间背景的语义重叠群落检测

IF 2.8

Social Network Analysis and Mining Pub Date : 2023-12-17 DOI: 10.1007/s13278-023-01173-x

Shulin Cheng, Shan Yang, Xiufang Cheng, Keyu Li, Yu Zheng

引用次数: 0

Survey-credible conversation and sentiment analysis 调查可信的对话和情感分析

IF 2.8

Social Network Analysis and Mining Pub Date : 2023-12-17 DOI: 10.1007/s13278-023-01176-8

Imen Fadhli, L. Hlaoua, Mohamed Nazih Omri

引用次数: 0

A novel and precise approach for similarity-based link prediction in diverse networks 基于相似性的多样化网络链接预测的新型精确方法

IF 2.8

Social Network Analysis and Mining Pub Date : 2023-12-16 DOI: 10.1007/s13278-023-01160-2

Apurva Sharma, Ajay Kumar Yadav, A. K. Rai

引用次数: 0

Community detection in social networks by spectral embedding of typed graphs 通过类型图的谱嵌入检测社交网络中的社群

IF 2.8

Social Network Analysis and Mining Pub Date : 2023-12-16 DOI: 10.1007/s13278-023-01172-y

M. Alfaqeeh, D. B. Skillicorn

引用次数: 0

Shaping climate change discourse: the nexus between political media landscape and recommendation systems in social networks 塑造气候变化话语：政治媒体景观与社交网络推荐系统之间的联系

IF 2.8

Social Network Analysis and Mining Pub Date : 2023-12-14 DOI: 10.1007/s13278-023-01175-9

Y. Sakieh

引用次数: 0