Pub Date : 2023-09-27DOI: 10.1007/s13278-023-01134-4
Mohammad Yousuf Mehmood, Syed Junaid Haqqani, Faraz Zaidi, Céline Rozenblat
{"title":"Economic hubs and the domination of inter-regional ties in world city networks","authors":"Mohammad Yousuf Mehmood, Syed Junaid Haqqani, Faraz Zaidi, Céline Rozenblat","doi":"10.1007/s13278-023-01134-4","DOIUrl":"https://doi.org/10.1007/s13278-023-01134-4","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135536743","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 : 2023-09-25DOI: 10.1007/s13278-023-01119-3
Mohammed Kasri, Anas El-Ansari, Mohamed El Fissaoui, Badreddine Cherkaoui, Marouane Birjali, Abderrahim Beni-Hssane
{"title":"Public sentiment toward renewable energy in Morocco: opinion mining using a rule-based approach","authors":"Mohammed Kasri, Anas El-Ansari, Mohamed El Fissaoui, Badreddine Cherkaoui, Marouane Birjali, Abderrahim Beni-Hssane","doi":"10.1007/s13278-023-01119-3","DOIUrl":"https://doi.org/10.1007/s13278-023-01119-3","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135816860","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 : 2023-09-25DOI: 10.1007/s13278-023-01133-5
Ilana Porter, Bar Galam, Roni Ramon-Gonen
{"title":"Emotion detection and its influence on popularity in a social network-based on the American TV series friends","authors":"Ilana Porter, Bar Galam, Roni Ramon-Gonen","doi":"10.1007/s13278-023-01133-5","DOIUrl":"https://doi.org/10.1007/s13278-023-01133-5","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135816861","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 : 2023-09-25DOI: 10.1007/s13278-023-01122-8
Saif Aldeen Madi, Giuseppe Pirrò
Abstract Community deception is about protecting users of a community from being discovered by community detection algorithms. This paper studies community deception in directed influence network (DIN). It aims to address the limitations of the state of the art through a twofold strategy: introducing directed influence and considering the role of nodes in the deception strategy. The study focuses on using modularity as the optimization function. It offers several contributions, including an upgraded version of modularity that accommodates the concept of influence, edge-based, and node-based deception algorithms.. The study concludes with a comparison of the proposed methods with the state of the art showing that not only influence is a valuable ingredient to devising deception strategies but also that novel deception approaches centered on node operations can be successfully devised.
{"title":"Community deception in directed influence networks","authors":"Saif Aldeen Madi, Giuseppe Pirrò","doi":"10.1007/s13278-023-01122-8","DOIUrl":"https://doi.org/10.1007/s13278-023-01122-8","url":null,"abstract":"Abstract Community deception is about protecting users of a community from being discovered by community detection algorithms. This paper studies community deception in directed influence network (DIN). It aims to address the limitations of the state of the art through a twofold strategy: introducing directed influence and considering the role of nodes in the deception strategy. The study focuses on using modularity as the optimization function. It offers several contributions, including an upgraded version of modularity that accommodates the concept of influence, edge-based, and node-based deception algorithms.. The study concludes with a comparison of the proposed methods with the state of the art showing that not only influence is a valuable ingredient to devising deception strategies but also that novel deception approaches centered on node operations can be successfully devised.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135816848","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 : 2023-09-25DOI: 10.1007/s13278-023-01131-7
Samuel Munaf, Kevin Swingler, Franz Brülisauer, Anthony O’Hare, George Gunn, Aaron Reeves
Abstract Web scraping and text mining are popular computer science methods deployed by public health researchers to augment traditional epidemiological surveillance. However, within veterinary disease surveillance, such techniques are still in the early stages of development and have not yet been fully utilised. This study presents an exploration into the utility of incorporating internet-based data to better understand smallholder farming communities within the UK, by using online text extraction and the subsequent mining of this data. Web scraping of the livestock fora was conducted, with text mining and topic modelling of data in search of common themes, words, and topics found within the text, in addition to temporal analysis through anomaly detection. Results revealed that some of the key areas in pig forum discussions included identification, age management, containment, and breeding and weaning practices. In discussions about poultry farming, a preference for free-range practices was expressed, along with a focus on feeding practices and addressing red mite infestations. Temporal topic modelling revealed an increase in conversations around pig containment and care, as well as poultry equipment maintenance. Moreover, anomaly detection was discovered to be particularly effective for tracking unusual spikes in forum activity, which may suggest new concerns or trends. Internet data can be a very effective tool in aiding traditional veterinary surveillance methods, but the requirement for human validation of said data is crucial. This opens avenues of research via the incorporation of other dynamic social media data, namely Twitter, in addition to location analysis to highlight spatial patterns.
{"title":"Text mining of veterinary forums for epidemiological surveillance supplementation","authors":"Samuel Munaf, Kevin Swingler, Franz Brülisauer, Anthony O’Hare, George Gunn, Aaron Reeves","doi":"10.1007/s13278-023-01131-7","DOIUrl":"https://doi.org/10.1007/s13278-023-01131-7","url":null,"abstract":"Abstract Web scraping and text mining are popular computer science methods deployed by public health researchers to augment traditional epidemiological surveillance. However, within veterinary disease surveillance, such techniques are still in the early stages of development and have not yet been fully utilised. This study presents an exploration into the utility of incorporating internet-based data to better understand smallholder farming communities within the UK, by using online text extraction and the subsequent mining of this data. Web scraping of the livestock fora was conducted, with text mining and topic modelling of data in search of common themes, words, and topics found within the text, in addition to temporal analysis through anomaly detection. Results revealed that some of the key areas in pig forum discussions included identification, age management, containment, and breeding and weaning practices. In discussions about poultry farming, a preference for free-range practices was expressed, along with a focus on feeding practices and addressing red mite infestations. Temporal topic modelling revealed an increase in conversations around pig containment and care, as well as poultry equipment maintenance. Moreover, anomaly detection was discovered to be particularly effective for tracking unusual spikes in forum activity, which may suggest new concerns or trends. Internet data can be a very effective tool in aiding traditional veterinary surveillance methods, but the requirement for human validation of said data is crucial. This opens avenues of research via the incorporation of other dynamic social media data, namely Twitter, in addition to location analysis to highlight spatial patterns.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135816853","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 : 2023-09-23DOI: 10.1007/s13278-023-01124-6
Falah Amro, Hemant Purohit
{"title":"Integrated content-network analysis to discover influential collectives for studying social cyber-threats from online social movements","authors":"Falah Amro, Hemant Purohit","doi":"10.1007/s13278-023-01124-6","DOIUrl":"https://doi.org/10.1007/s13278-023-01124-6","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135959676","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 : 2023-09-22DOI: 10.1007/s13278-023-01125-5
Bernardo Cerqueira de Lima, Renata Maria Abrantes Baracho, Thomas Mandl, Patricia Baracho Porto
Abstract Social media platforms that disseminate scientific information to the public during the COVID-19 pandemic highlighted the importance of the topic of scientific communication. Content creators in the field, as well as researchers who study the impact of scientific information online, are interested in how people react to these information resources. This study aims to devise a framework that can sift through large social media datasets and find specific feedback to content delivery, enabling scientific content creators to gain insights into how the public perceives scientific information, and how their behavior toward science communication (e.g., through videos or texts) is related to their information-seeking behavior. To collect public reactions to scientific information, the study focused on Twitter users who are doctors, researchers, science communicators, or representatives of research institutes, and processed their replies for two years from the start of the pandemic. The study aimed in developing a solution powered by topic modeling enhanced by manual validation and other machine learning techniques, such as word embeddings, that is capable of filtering massive social media datasets in search of documents related to reactions to scientific communication. The architecture developed in this paper can be replicated for finding any documents related to niche topics in social media data.
{"title":"Reactions to science communication: discovering social network topics using word embeddings and semantic knowledge","authors":"Bernardo Cerqueira de Lima, Renata Maria Abrantes Baracho, Thomas Mandl, Patricia Baracho Porto","doi":"10.1007/s13278-023-01125-5","DOIUrl":"https://doi.org/10.1007/s13278-023-01125-5","url":null,"abstract":"Abstract Social media platforms that disseminate scientific information to the public during the COVID-19 pandemic highlighted the importance of the topic of scientific communication. Content creators in the field, as well as researchers who study the impact of scientific information online, are interested in how people react to these information resources. This study aims to devise a framework that can sift through large social media datasets and find specific feedback to content delivery, enabling scientific content creators to gain insights into how the public perceives scientific information, and how their behavior toward science communication (e.g., through videos or texts) is related to their information-seeking behavior. To collect public reactions to scientific information, the study focused on Twitter users who are doctors, researchers, science communicators, or representatives of research institutes, and processed their replies for two years from the start of the pandemic. The study aimed in developing a solution powered by topic modeling enhanced by manual validation and other machine learning techniques, such as word embeddings, that is capable of filtering massive social media datasets in search of documents related to reactions to scientific communication. The architecture developed in this paper can be replicated for finding any documents related to niche topics in social media data.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136059451","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 : 2023-09-22DOI: 10.1007/s13278-023-01118-4
Mohammed Alghobiri
{"title":"Exploring the attributes of influential users in social networks using association rule mining","authors":"Mohammed Alghobiri","doi":"10.1007/s13278-023-01118-4","DOIUrl":"https://doi.org/10.1007/s13278-023-01118-4","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136010543","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":"Advancing aspect-based sentiment analysis with a novel architecture combining deep learning models CNN and bi-RNN with the machine learning model SVM","authors":"Sarsabene Hammi, Souha Mezghani Hammami, Lamia Hadrich Belguith","doi":"10.1007/s13278-023-01126-4","DOIUrl":"https://doi.org/10.1007/s13278-023-01126-4","url":null,"abstract":"","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136130138","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 Media production and consumption behaviors are changing in response to new technologies and demands, giving birth to a new generation of social applications. Among them, crowd journalism represents a novel way of constructing democratic and trustworthy news relying on ordinary citizens arriving at breaking news locations and capturing relevant videos using their smartphones. The ARTICONF project as reported by Prodan (Euro-Par 2019: parallel processing workshops, Springer, 2019) proposes a trustworthy, resilient, and globally sustainable toolset for developing decentralized applications (DApps) to address this need. Its goal is to overcome the privacy, trust, and autonomy-related concerns associated with proprietary social media platforms overflowed by fake news. Leveraging the ARTICONF tools, we introduce a new DApp for crowd journalism called MOGPlay. MOGPlay collects and manages audiovisual content generated by citizens and provides a secure blockchain platform that rewards all stakeholders involved in professional news production. Besides live streaming, MOGPlay offers a marketplace for audiovisual content trading among citizens and free journalists with an internal token ecosystem. We discuss the functionality and implementation of the MOGPlay DApp and illustrate four pilot crowd journalism live scenarios that validate the prototype.
{"title":"ARTICONF decentralized social media platform for democratic crowd journalism","authors":"Inês Rito Lima, Vasco Filipe, Claudia Marinho, Alexandre Ulisses, Antorweep Chakravorty, Atanas Hristov, Nishant Saurabh, Zhiming Zhao, Ruyue Xin, Radu Prodan","doi":"10.1007/s13278-023-01110-y","DOIUrl":"https://doi.org/10.1007/s13278-023-01110-y","url":null,"abstract":"Abstract Media production and consumption behaviors are changing in response to new technologies and demands, giving birth to a new generation of social applications. Among them, crowd journalism represents a novel way of constructing democratic and trustworthy news relying on ordinary citizens arriving at breaking news locations and capturing relevant videos using their smartphones. The ARTICONF project as reported by Prodan (Euro-Par 2019: parallel processing workshops, Springer, 2019) proposes a trustworthy, resilient, and globally sustainable toolset for developing decentralized applications (DApps) to address this need. Its goal is to overcome the privacy, trust, and autonomy-related concerns associated with proprietary social media platforms overflowed by fake news. Leveraging the ARTICONF tools, we introduce a new DApp for crowd journalism called MOGPlay. MOGPlay collects and manages audiovisual content generated by citizens and provides a secure blockchain platform that rewards all stakeholders involved in professional news production. Besides live streaming, MOGPlay offers a marketplace for audiovisual content trading among citizens and free journalists with an internal token ecosystem. We discuss the functionality and implementation of the MOGPlay DApp and illustrate four pilot crowd journalism live scenarios that validate the prototype.","PeriodicalId":21842,"journal":{"name":"Social Network Analysis and Mining","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135394214","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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