Pub Date : 2023-09-14DOI: 10.1007/s13278-023-01127-3
Arnaldo Santoro, Alessandro Galeazzi, Teresa Scantamburlo, Andrea Baronchelli, Walter Quattrociocchi, Fabiana Zollo
Abstract The issue of vaccine hesitancy has posed a significant challenge during the Covid-19 pandemic, as it increases the risk of undermining public health interventions aimed at mitigating the spread of the virus. While the swift development of vaccines represents a remarkable scientific achievement, it has also contributed to skepticism and apprehension among some populations. Against this backdrop, the suspension of the AstraZeneca vaccine by the European Medicines Agency further exacerbated an already contentious debate around vaccine safety. This paper examines the Twitter discourse surrounding Covid-19 vaccines, focusing on the temporal and geographical dimensions of the discussion. Using over a year’s worth of data, we study the public debate in five countries (Germany, France, UK, Italy, and the USA), revealing differences in the interaction structure and in the production volume of questionable and reliable sources. Topic modeling highlights variations in the perspectives of reliable and questionable sources, but some similarities across nations. Also, we quantify the effect of vaccine announcement and suspension, finding that only the former had a significant impact in all countries. Finally, we analyze the evolution of the communities in the interaction network, revealing a relatively stable scenario with a few considerable shifts between communities with different levels of reliability. Our results suggest that major external events can be associated with changes in the online debate in terms of content production and interaction patterns. However, despite the AZ suspension, we do not observe any noticeable changes in the production and consumption of misinformation related to Covid-19 vaccines.
{"title":"Analyzing the changing landscape of the Covid-19 vaccine debate on Twitter","authors":"Arnaldo Santoro, Alessandro Galeazzi, Teresa Scantamburlo, Andrea Baronchelli, Walter Quattrociocchi, Fabiana Zollo","doi":"10.1007/s13278-023-01127-3","DOIUrl":"https://doi.org/10.1007/s13278-023-01127-3","url":null,"abstract":"Abstract The issue of vaccine hesitancy has posed a significant challenge during the Covid-19 pandemic, as it increases the risk of undermining public health interventions aimed at mitigating the spread of the virus. While the swift development of vaccines represents a remarkable scientific achievement, it has also contributed to skepticism and apprehension among some populations. Against this backdrop, the suspension of the AstraZeneca vaccine by the European Medicines Agency further exacerbated an already contentious debate around vaccine safety. This paper examines the Twitter discourse surrounding Covid-19 vaccines, focusing on the temporal and geographical dimensions of the discussion. Using over a year’s worth of data, we study the public debate in five countries (Germany, France, UK, Italy, and the USA), revealing differences in the interaction structure and in the production volume of questionable and reliable sources. Topic modeling highlights variations in the perspectives of reliable and questionable sources, but some similarities across nations. Also, we quantify the effect of vaccine announcement and suspension, finding that only the former had a significant impact in all countries. Finally, we analyze the evolution of the communities in the interaction network, revealing a relatively stable scenario with a few considerable shifts between communities with different levels of reliability. Our results suggest that major external events can be associated with changes in the online debate in terms of content production and interaction patterns. However, despite the AZ suspension, we do not observe any noticeable changes in the production and consumption of misinformation related to Covid-19 vaccines.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134911652","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 : 2023-09-14DOI: 10.1007/s13278-023-01121-9
Nunziato Cassavia, Luca Caviglione, Massimo Guarascio, Angelica Liguori, Giuseppe Manco, Marco Zuppelli
Abstract An increasing volume of malicious software exploits information hiding techniques to cloak additional attack stages or bypass frameworks enforcing security. This trend has intensified with the growing diffusion of mobile ecosystems, and many threat actors now conceal scripts or configuration data within high-resolution icons. Even if machine learning has proven to be effective in detecting various hidden payloads, modern mobile scenarios pose further challenges in terms of scalability and privacy. In fact, applications can be retrieved from multiple stores or directly from the Web or social media. Therefore, this paper introduces an approach based on federated learning to reveal information hidden in high-resolution icons bundled with mobile applications. Specifically, multiple nodes are used to mitigate the impact of different privacy regulations, the lack of comprehensive datasets, or the computational burden arising from distributed stores and unofficial repositories. Results collected through simulations indicate that our approach achieves performances similar to those of centralized blueprints. Moreover, federated learning demonstrated its effectiveness in coping with simple “obfuscation” schemes like Base64 encoding and zip compression used by attackers to avoid detection.
{"title":"A federated approach for detecting data hidden in icons of mobile applications delivered via web and multiple stores","authors":"Nunziato Cassavia, Luca Caviglione, Massimo Guarascio, Angelica Liguori, Giuseppe Manco, Marco Zuppelli","doi":"10.1007/s13278-023-01121-9","DOIUrl":"https://doi.org/10.1007/s13278-023-01121-9","url":null,"abstract":"Abstract An increasing volume of malicious software exploits information hiding techniques to cloak additional attack stages or bypass frameworks enforcing security. This trend has intensified with the growing diffusion of mobile ecosystems, and many threat actors now conceal scripts or configuration data within high-resolution icons. Even if machine learning has proven to be effective in detecting various hidden payloads, modern mobile scenarios pose further challenges in terms of scalability and privacy. In fact, applications can be retrieved from multiple stores or directly from the Web or social media. Therefore, this paper introduces an approach based on federated learning to reveal information hidden in high-resolution icons bundled with mobile applications. Specifically, multiple nodes are used to mitigate the impact of different privacy regulations, the lack of comprehensive datasets, or the computational burden arising from distributed stores and unofficial repositories. Results collected through simulations indicate that our approach achieves performances similar to those of centralized blueprints. Moreover, federated learning demonstrated its effectiveness in coping with simple “obfuscation” schemes like Base64 encoding and zip compression used by attackers to avoid detection.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134912644","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 : 2023-09-11DOI: 10.1007/s13278-023-01117-5
Kiran Fahd, Shah J. Miah
Abstract The academic intervention to predict at-risk higher education (HE) students requires effective data model development. Such data modelling projects in the HE context may have common issues related to (a) adopting small-scale modelling that gives limited options for early intervention and (b) using imbalanced data that hinders capturing effective details of poorly performing students. We address the issues going beyond the distribution-based algorithm, using a multilayer perceptron classifier which shows better on confusion metric, recall, and precision measures for identifying at-risk students. Our proposed deep learning-based model, which uses data augmentation techniques to supplement the data instances and balance the dataset, aims to improve the prediction accuracy of whether the student will fail or not based on their interaction with the learning management systems to prevent struggling students from evasion.
{"title":"Effectiveness of data augmentation to predict students at risk using deep learning algorithms","authors":"Kiran Fahd, Shah J. Miah","doi":"10.1007/s13278-023-01117-5","DOIUrl":"https://doi.org/10.1007/s13278-023-01117-5","url":null,"abstract":"Abstract The academic intervention to predict at-risk higher education (HE) students requires effective data model development. Such data modelling projects in the HE context may have common issues related to (a) adopting small-scale modelling that gives limited options for early intervention and (b) using imbalanced data that hinders capturing effective details of poorly performing students. We address the issues going beyond the distribution-based algorithm, using a multilayer perceptron classifier which shows better on confusion metric, recall, and precision measures for identifying at-risk students. Our proposed deep learning-based model, which uses data augmentation techniques to supplement the data instances and balance the dataset, aims to improve the prediction accuracy of whether the student will fail or not based on their interaction with the learning management systems to prevent struggling students from evasion.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135981789","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 : 2023-09-04DOI: 10.1007/s13278-023-01115-7
Mohammadmahdi Zafarmand, Yashar Talebirad, Eric Austin, Christine Largeron, Osmar R Zaiane
{"title":"Fast local community discovery relying on the strength of links","authors":"Mohammadmahdi Zafarmand, Yashar Talebirad, Eric Austin, Christine Largeron, Osmar R Zaiane","doi":"10.1007/s13278-023-01115-7","DOIUrl":"https://doi.org/10.1007/s13278-023-01115-7","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":" ","pages":"1-21"},"PeriodicalIF":2.8,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49592107","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 : 2023-09-02DOI: 10.1007/s13278-023-01116-6
J. A. Núñez-Mora, Román A. Mendoza-Urdiales
{"title":"Social sentiment and impact in US equity market: an automated approach","authors":"J. A. Núñez-Mora, Román A. Mendoza-Urdiales","doi":"10.1007/s13278-023-01116-6","DOIUrl":"https://doi.org/10.1007/s13278-023-01116-6","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":" ","pages":"1-11"},"PeriodicalIF":2.8,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46997532","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 : 2023-08-31DOI: 10.1007/s13278-023-01114-8
D. Shanthi, S. Prabha, N. Indumathi, S. Naganandhini, S. T. Shenbagavalli, M. Jayanthi
{"title":"A satin optimized dynamic learning model (SODLM) for sentiment analysis using opinion mining","authors":"D. Shanthi, S. Prabha, N. Indumathi, S. Naganandhini, S. T. Shenbagavalli, M. Jayanthi","doi":"10.1007/s13278-023-01114-8","DOIUrl":"https://doi.org/10.1007/s13278-023-01114-8","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":" ","pages":"1-14"},"PeriodicalIF":2.8,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49084955","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 : 2023-08-27DOI: 10.1007/s13278-023-01103-x
Sarah Masud, T. Charaborty
{"title":"Political mud slandering and power dynamics during Indian assembly elections","authors":"Sarah Masud, T. Charaborty","doi":"10.1007/s13278-023-01103-x","DOIUrl":"https://doi.org/10.1007/s13278-023-01103-x","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":" ","pages":"1-8"},"PeriodicalIF":2.8,"publicationDate":"2023-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47729243","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 : 2023-08-27DOI: 10.1007/s13278-023-01107-7
Jyotsna Anthal, Bhavna Sharma, J. Manhas
{"title":"Hybrid optimization-based deep learning classifier for sentiment classification using review data","authors":"Jyotsna Anthal, Bhavna Sharma, J. Manhas","doi":"10.1007/s13278-023-01107-7","DOIUrl":"https://doi.org/10.1007/s13278-023-01107-7","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":" ","pages":"1-17"},"PeriodicalIF":2.8,"publicationDate":"2023-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43403268","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 : 2023-08-25DOI: 10.1007/s13278-023-01111-x
P. Vinod, S. Sheeja
{"title":"Sentiment prediction model in social media data using beluga dodger optimization-based ensemble classifier","authors":"P. Vinod, S. Sheeja","doi":"10.1007/s13278-023-01111-x","DOIUrl":"https://doi.org/10.1007/s13278-023-01111-x","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":" ","pages":"1-16"},"PeriodicalIF":2.8,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49595866","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 : 2023-08-22DOI: 10.1007/s13278-023-01113-9
Vitória Lemos, Luciano Ignaczak
{"title":"An analysis of the public consequences of cybersecurity incidents in Brazil","authors":"Vitória Lemos, Luciano Ignaczak","doi":"10.1007/s13278-023-01113-9","DOIUrl":"https://doi.org/10.1007/s13278-023-01113-9","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":" ","pages":"1-14"},"PeriodicalIF":2.8,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43918127","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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