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}
Pub Date : 2023-10-12DOI: 10.1007/s13278-023-01137-1
Maria Predari, Lukas Berner, Robert Kooij, Henning Meyerhenke
Abstract The total effective resistance, also called the Kirchhoff index, provides a robustness measure for a graph G . We consider two optimization problems of adding k new edges to G such that the resulting graph has minimal total effective resistance (i.e., is most robust)—one where the new edges can be anywhere in the graph and one where the new edges need to be incident to a specified focus node. The total effective resistance and effective resistances between nodes can be computed using the pseudoinverse of the graph Laplacian. The pseudoinverse may be computed explicitly via pseudoinversion, yet this takes cubic time in practice and quadratic space. We instead exploit combinatorial and algebraic connections to speed up gain computations in an established generic greedy heuristic. Moreover, we leverage existing randomized techniques to boost the performance of our approaches by introducing a sub-sampling step. Our different graph- and matrix-based approaches are indeed significantly faster than the state-of-the-art greedy algorithm, while their quality remains reasonably high and is often quite close. Our experiments show that we can now process larger graphs for which the application of the state-of-the-art greedy approach was impractical before.
{"title":"Greedy optimization of resistance-based graph robustness with global and local edge insertions","authors":"Maria Predari, Lukas Berner, Robert Kooij, Henning Meyerhenke","doi":"10.1007/s13278-023-01137-1","DOIUrl":"https://doi.org/10.1007/s13278-023-01137-1","url":null,"abstract":"Abstract The total effective resistance, also called the Kirchhoff index, provides a robustness measure for a graph G . We consider two optimization problems of adding k new edges to G such that the resulting graph has minimal total effective resistance (i.e., is most robust)—one where the new edges can be anywhere in the graph and one where the new edges need to be incident to a specified focus node. The total effective resistance and effective resistances between nodes can be computed using the pseudoinverse of the graph Laplacian. The pseudoinverse may be computed explicitly via pseudoinversion, yet this takes cubic time in practice and quadratic space. We instead exploit combinatorial and algebraic connections to speed up gain computations in an established generic greedy heuristic. Moreover, we leverage existing randomized techniques to boost the performance of our approaches by introducing a sub-sampling step. Our different graph- and matrix-based approaches are indeed significantly faster than the state-of-the-art greedy algorithm, while their quality remains reasonably high and is often quite close. Our experiments show that we can now process larger graphs for which the application of the state-of-the-art greedy approach was impractical before.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"55 9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135968987","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 : 2023-10-06DOI: 10.1007/s13278-023-01136-2
Riccardo Dondi, Pietro Hiram Guzzi, Mohammad Mehdi Hosseinzadeh, Marianna Milano
Abstract Interactions among entities are usually modeled using graphs. In many real scenarios, these relations may change over time, and different kinds exist among entities that need to be integrated. We introduce a new network model called temporal dual network, to deal with interactions which change over time and to integrate information coming from two different networks. In this new model, we consider a fundamental problem in graph mining, that is, finding the densest subgraphs. To deal with this problem, we propose an approach that, given two temporal graphs, (1) produces a dual temporal graph via alignment and (2) asks for identifying the densest subgraphs in this resulting graph. For this latter problem, we present a polynomial-time dynamic programming algorithm and a faster heuristic based on constraining the dynamic programming to consider only bounded temporal graphs and a local search procedure. We show that our method can output solutions not far from the optimal ones, even for temporal graphs having 10000 vertices and 10000 timestamps. Finally, we present a case study on a real dual temporal network.
{"title":"Dense subgraphs in temporal social networks","authors":"Riccardo Dondi, Pietro Hiram Guzzi, Mohammad Mehdi Hosseinzadeh, Marianna Milano","doi":"10.1007/s13278-023-01136-2","DOIUrl":"https://doi.org/10.1007/s13278-023-01136-2","url":null,"abstract":"Abstract Interactions among entities are usually modeled using graphs. In many real scenarios, these relations may change over time, and different kinds exist among entities that need to be integrated. We introduce a new network model called temporal dual network, to deal with interactions which change over time and to integrate information coming from two different networks. In this new model, we consider a fundamental problem in graph mining, that is, finding the densest subgraphs. To deal with this problem, we propose an approach that, given two temporal graphs, (1) produces a dual temporal graph via alignment and (2) asks for identifying the densest subgraphs in this resulting graph. For this latter problem, we present a polynomial-time dynamic programming algorithm and a faster heuristic based on constraining the dynamic programming to consider only bounded temporal graphs and a local search procedure. We show that our method can output solutions not far from the optimal ones, even for temporal graphs having 10000 vertices and 10000 timestamps. Finally, we present a case study on a real dual temporal network.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135350867","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 : 2023-10-04DOI: 10.1007/s13278-023-01138-0
Yuxin Zhang, Dafeng Xu
{"title":"Who joins which network, and why?","authors":"Yuxin Zhang, Dafeng Xu","doi":"10.1007/s13278-023-01138-0","DOIUrl":"https://doi.org/10.1007/s13278-023-01138-0","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135592175","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 : 2023-09-29DOI: 10.1007/s13278-023-01128-2
Dalia A. Rodríguez, Julia Diaz-Escobar, Arnoldo Díaz-Ramírez, Leonardo Trujillo
{"title":"Domain-adaptive pre-training on a BERT model for the automatic detection of misogynistic tweets in Spanish","authors":"Dalia A. Rodríguez, Julia Diaz-Escobar, Arnoldo Díaz-Ramírez, Leonardo Trujillo","doi":"10.1007/s13278-023-01128-2","DOIUrl":"https://doi.org/10.1007/s13278-023-01128-2","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135195130","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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