Social Network Analysis and Mining最新文献_第6页

Text mining of veterinary forums for epidemiological surveillance supplementation 兽医论坛的文本挖掘用于流行病学监测补充

Social Network Analysis and Mining Pub Date : 2023-09-25 DOI: 10.1007/s13278-023-01131-7

Samuel Munaf, Kevin Swingler, Franz Brülisauer, Anthony O’Hare, George Gunn, Aaron Reeves

{"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}

引用次数: 0

Integrated content-network analysis to discover influential collectives for studying social cyber-threats from online social movements 整合内容-网络分析，发现有影响力的集体，用于研究来自在线社会运动的社会网络威胁

Social Network Analysis and Mining Pub Date : 2023-09-23 DOI: 10.1007/s13278-023-01124-6

Falah Amro, Hemant Purohit

引用次数: 0

Reactions to science communication: discovering social network topics using word embeddings and semantic knowledge 对科学传播的反应:利用词嵌入和语义知识发现社会网络主题

Social Network Analysis and Mining Pub Date : 2023-09-22 DOI: 10.1007/s13278-023-01125-5

Bernardo Cerqueira de Lima, Renata Maria Abrantes Baracho, Thomas Mandl, Patricia Baracho Porto

{"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}

引用次数: 0

Exploring the attributes of influential users in social networks using association rule mining 利用关联规则挖掘挖掘社交网络中有影响力用户的属性

Social Network Analysis and Mining Pub Date : 2023-09-22 DOI: 10.1007/s13278-023-01118-4

Mohammed Alghobiri

引用次数: 0

Advancing aspect-based sentiment analysis with a novel architecture combining deep learning models CNN and bi-RNN with the machine learning model SVM 结合深度学习模型CNN和bi-RNN与机器学习模型SVM的新架构，推进基于方面的情感分析

Social Network Analysis and Mining Pub Date : 2023-09-21 DOI: 10.1007/s13278-023-01126-4

Sarsabene Hammi, Souha Mezghani Hammami, Lamia Hadrich Belguith

引用次数: 0

ARTICONF decentralized social media platform for democratic crowd journalism ARTICONF民主大众新闻的去中心化社交媒体平台

Social Network Analysis and Mining Pub Date : 2023-09-15 DOI: 10.1007/s13278-023-01110-y

Inês Rito Lima, Vasco Filipe, Claudia Marinho, Alexandre Ulisses, Antorweep Chakravorty, Atanas Hristov, Nishant Saurabh, Zhiming Zhao, Ruyue Xin, Radu Prodan

{"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}

引用次数: 1

Analyzing the changing landscape of the Covid-19 vaccine debate on Twitter 分析推特上关于Covid-19疫苗辩论的变化格局

Social Network Analysis and Mining Pub Date : 2023-09-14 DOI: 10.1007/s13278-023-01127-3

Arnaldo Santoro, Alessandro Galeazzi, Teresa Scantamburlo, Andrea Baronchelli, Walter Quattrociocchi, Fabiana Zollo

{"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}

引用次数: 2

A federated approach for detecting data hidden in icons of mobile applications delivered via web and multiple stores 一种联合方法，用于检测通过web和多个商店交付的移动应用程序图标中隐藏的数据

Social Network Analysis and Mining Pub Date : 2023-09-14 DOI: 10.1007/s13278-023-01121-9

Nunziato Cassavia, Luca Caviglione, Massimo Guarascio, Angelica Liguori, Giuseppe Manco, Marco Zuppelli

{"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}

引用次数: 0

Effectiveness of data augmentation to predict students at risk using deep learning algorithms 使用深度学习算法预测有风险学生的数据增强有效性

Social Network Analysis and Mining Pub Date : 2023-09-11 DOI: 10.1007/s13278-023-01117-5

Kiran Fahd, Shah J. Miah

引用次数: 1

Fast local community discovery relying on the strength of links 依靠链接的强度快速发现本地社区

IF 2.8

Social Network Analysis and Mining Pub Date : 2023-09-04 DOI: 10.1007/s13278-023-01115-7

Mohammadmahdi Zafarmand, Yashar Talebirad, Eric Austin, Christine Largeron, Osmar R Zaiane

引用次数: 2