Social Network Analysis and Mining最新文献_第4页

Explaining population variation after the 2016 Central Italy earthquake using Call Data Records and Twitter 利用通话数据记录和推特解释2016年意大利中部地震后的人口变化

Social Network Analysis and Mining Pub Date : 2023-10-25 DOI: 10.1007/s13278-023-01139-z

Natalia Selini Hadjidimitriou, Marco Lippi, Marco Mamei

引用次数: 0

Identifying discernible indications of psychological well-being using ML: explainable AI in reddit social media interactions 在reddit社交媒体互动中使用ML识别可识别的心理健康迹象:可解释的AI

Social Network Analysis and Mining Pub Date : 2023-10-25 DOI: 10.1007/s13278-023-01145-1

Pahalage Dona Thushari, Nitisha Aggarwal, Vajratiya Vajrobol, Geetika Jain Saxena, Sanjeev Singh, Amit Pundir

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Investigating the cyberbullying risk in digital media: protecting victims in school teenagers 调查数字媒体中的网络欺凌风险:保护在校青少年受害者

Social Network Analysis and Mining Pub Date : 2023-10-24 DOI: 10.1007/s13278-023-01152-2

Ibrahim Obaidat, Aseel Al-zou’bi, Ala Mughaid, Laith Abualigah

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Contribution to the Moroccan Darija sentiment analysis in social networks 对社交网络中摩洛哥Darija情绪分析的贡献

Social Network Analysis and Mining Pub Date : 2023-10-20 DOI: 10.1007/s13278-023-01129-1

Sara El Ouahabi, Safâa El Ouahabi, El Wardani Dadi

引用次数: 0

The rise of user profiling in social media: review, challenges and future direction 社交媒体中用户特征分析的兴起:回顾、挑战和未来方向

Social Network Analysis and Mining Pub Date : 2023-10-19 DOI: 10.1007/s13278-023-01146-0

Justin Gilbert, Suraya Hamid, Ibrahim Abaker Targio Hashem, Norjihan Abdul Ghani, Fatokun Faith Boluwatife

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HatEmoTweet: low-level emotion classifications and spatiotemporal trends of hate and offensive COVID-19 tweets 仇恨推文(HatEmoTweet):仇恨和攻击性推文的低级情感分类和时空趋势

Social Network Analysis and Mining Pub Date : 2023-10-18 DOI: 10.1007/s13278-023-01132-6

Ademola Adesokan, Sanjay Madria, Long Nguyen

引用次数: 0

An investigation in detection and mitigation of smishing using machine learning techniques 使用机器学习技术检测和减轻欺骗的调查

Social Network Analysis and Mining Pub Date : 2023-10-17 DOI: 10.1007/s13278-023-01142-4

Mohd Shoaib, Mohammad Sarosh Umar

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The effect of the Katz parameter on node ranking, with a medical application Katz参数对节点排序的影响，并以医学应用为例

Social Network Analysis and Mining Pub Date : 2023-10-16 DOI: 10.1007/s13278-023-01135-3

Hunter Rehm, Mona Matar, Puck Rombach, Lauren McIntyre

{"title":"The effect of the Katz parameter on node ranking, with a medical application","authors":"Hunter Rehm, Mona Matar, Puck Rombach, Lauren McIntyre","doi":"10.1007/s13278-023-01135-3","DOIUrl":"https://doi.org/10.1007/s13278-023-01135-3","url":null,"abstract":"Abstract The Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT), developed by NASA, is an event-based risk modeling tool that assesses human health and medical risk during space exploration missions. The Susceptibility Inference Network (SIN), a sub-element of MEDPRAT, is a prototype model informed with data that represents the probabilities of medical conditions progressing from one to another and the expected quality time lost associated with the disease progression for each condition. The work presented in this paper aims to determine which conditions in the SIN have the greatest effect on MEDPRAT-predicted medical risk. Here, we propose to measure this expected quality time lost using a weighted version of Katz centrality and investigate the effect of the $$alpha$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mi>α</mml:mi> </mml:math> -parameter on the lengths of walks that significantly affect the ranking of nodes. To do this, we introduce a tool to compare different centrality measures in their node rankings. This general tool is of independent interest, as it considers that a relative ranking of two nodes by a centrality measure is unreliable if their scores are within a margin of error. In particular, we find an upper bound on the lengths of the walks that determine the node ranking up to this margin of error. If an application imposes a realistic bound on possible walk lengths, this set of tools may help determine a suitable value for $$alpha$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mi>α</mml:mi> </mml:math> .","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136114572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On the usage of epidemiological models for information diffusion over twitter 关于twitter上信息传播的流行病学模型的使用

Social Network Analysis and Mining Pub Date : 2023-10-16 DOI: 10.1007/s13278-023-01130-8

Nirmal Kumar Sivaraman, Shivansh Baijal, Sakthi Balan Muthiah

引用次数: 0

How COVID-19 affects user interaction with online streaming service providers on twitter COVID-19如何影响用户在twitter上与在线流媒体服务提供商的互动

Social Network Analysis and Mining Pub Date : 2023-10-16 DOI: 10.1007/s13278-023-01143-3

Marco Arazzi, Daniele Murer, Serena Nicolazzo, Antonino Nocera

{"title":"How COVID-19 affects user interaction with online streaming service providers on twitter","authors":"Marco Arazzi, Daniele Murer, Serena Nicolazzo, Antonino Nocera","doi":"10.1007/s13278-023-01143-3","DOIUrl":"https://doi.org/10.1007/s13278-023-01143-3","url":null,"abstract":"Abstract The worldwide diffusion of COVID-19, declared pandemic in March 2020, has led to significant changes in people’s lifestyles and behavior, especially when it comes to the consumption of media and entertainment. Indeed, during this period, online streaming platforms have become the preferred providers of recreational content, whereas Online Social Networks proved to be the favorite place to find social connections while adhering to distancing measures. In the meantime, from the online Streaming Service Providers’ point of view, Online Social Networks have gained more and more importance both as valuable data sources for business intelligence and as connected and co-viewing platforms. This study starts from these considerations to explore the impact of COVID-19 on user interaction with Streaming Service Providers in Online Social Networks. In particular, our investigation focuses on the Twitter platform; by comparing several large datasets referring to different periods (i.e., before, during, and after COVID-19 emergence), we investigate interesting patterns and dynamics leveraging both Natural Language Processing and sentiment analysis techniques. Our data science campaign, and the main findings derived, adopts a peculiar perspective focusing on the different categories of users and Streaming Service Providers. The main objective of the analysis is to uncover the dynamics underlying the evolution of the interaction between people and businesses during the COVID-19 outbreak.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136114430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0