Pub Date : 2023-10-25DOI: 10.1007/s13278-023-01139-z
Natalia Selini Hadjidimitriou, Marco Lippi, Marco Mamei
{"title":"Explaining population variation after the 2016 Central Italy earthquake using Call Data Records and Twitter","authors":"Natalia Selini Hadjidimitriou, Marco Lippi, Marco Mamei","doi":"10.1007/s13278-023-01139-z","DOIUrl":"https://doi.org/10.1007/s13278-023-01139-z","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135113449","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}
Pub Date : 2023-10-25DOI: 10.1007/s13278-023-01145-1
Pahalage Dona Thushari, Nitisha Aggarwal, Vajratiya Vajrobol, Geetika Jain Saxena, Sanjeev Singh, Amit Pundir
{"title":"Identifying discernible indications of psychological well-being using ML: explainable AI in reddit social media interactions","authors":"Pahalage Dona Thushari, Nitisha Aggarwal, Vajratiya Vajrobol, Geetika Jain Saxena, Sanjeev Singh, Amit Pundir","doi":"10.1007/s13278-023-01145-1","DOIUrl":"https://doi.org/10.1007/s13278-023-01145-1","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"15 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135218241","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}
Pub Date : 2023-10-24DOI: 10.1007/s13278-023-01152-2
Ibrahim Obaidat, Aseel Al-zou’bi, Ala Mughaid, Laith Abualigah
{"title":"Investigating the cyberbullying risk in digital media: protecting victims in school teenagers","authors":"Ibrahim Obaidat, Aseel Al-zou’bi, Ala Mughaid, Laith Abualigah","doi":"10.1007/s13278-023-01152-2","DOIUrl":"https://doi.org/10.1007/s13278-023-01152-2","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"65 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135266640","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}
Pub Date : 2023-10-20DOI: 10.1007/s13278-023-01129-1
Sara El Ouahabi, Safâa El Ouahabi, El Wardani Dadi
{"title":"Contribution to the Moroccan Darija sentiment analysis in social networks","authors":"Sara El Ouahabi, Safâa El Ouahabi, El Wardani Dadi","doi":"10.1007/s13278-023-01129-1","DOIUrl":"https://doi.org/10.1007/s13278-023-01129-1","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"5 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135567053","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}
Pub Date : 2023-10-19DOI: 10.1007/s13278-023-01146-0
Justin Gilbert, Suraya Hamid, Ibrahim Abaker Targio Hashem, Norjihan Abdul Ghani, Fatokun Faith Boluwatife
{"title":"The rise of user profiling in social media: review, challenges and future direction","authors":"Justin Gilbert, Suraya Hamid, Ibrahim Abaker Targio Hashem, Norjihan Abdul Ghani, Fatokun Faith Boluwatife","doi":"10.1007/s13278-023-01146-0","DOIUrl":"https://doi.org/10.1007/s13278-023-01146-0","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"147 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135731749","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}
Pub Date : 2023-10-18DOI: 10.1007/s13278-023-01132-6
Ademola Adesokan, Sanjay Madria, Long Nguyen
{"title":"HatEmoTweet: low-level emotion classifications and spatiotemporal trends of hate and offensive COVID-19 tweets","authors":"Ademola Adesokan, Sanjay Madria, Long Nguyen","doi":"10.1007/s13278-023-01132-6","DOIUrl":"https://doi.org/10.1007/s13278-023-01132-6","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135884769","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}
Pub Date : 2023-10-17DOI: 10.1007/s13278-023-01142-4
Mohd Shoaib, Mohammad Sarosh Umar
{"title":"An investigation in detection and mitigation of smishing using machine learning techniques","authors":"Mohd Shoaib, Mohammad Sarosh Umar","doi":"10.1007/s13278-023-01142-4","DOIUrl":"https://doi.org/10.1007/s13278-023-01142-4","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135992770","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}
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 $$alpha$$ α -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 $$alpha$$ α .
{"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}
{"title":"On the usage of epidemiological models for information diffusion over twitter","authors":"Nirmal Kumar Sivaraman, Shivansh Baijal, Sakthi Balan Muthiah","doi":"10.1007/s13278-023-01130-8","DOIUrl":"https://doi.org/10.1007/s13278-023-01130-8","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"56 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":"136114434","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}
Pub Date : 2023-10-16DOI: 10.1007/s13278-023-01143-3
Marco Arazzi, Daniele Murer, Serena Nicolazzo, Antonino Nocera
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.
{"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}
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