2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS)最新文献_第3页

Conflict spectrum: An empirical study of geopolitical cyber threats from a social network perspective 冲突谱:社会网络视角下地缘政治网络威胁的实证研究

2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) Pub Date : 2021-12-06 DOI: 10.1109/SNAMS53716.2021.9732155

Narjisse Nejjari, Sara Lahlou, Oumaima Fadi, Karim Zkik, M. Oudani, H. Benbrahim

引用次数: 0

An Efficient Recommender System Based on Collaborative Filtering Recommendation and Cluster Ensemble 基于协同过滤推荐和聚类集成的高效推荐系统

2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) Pub Date : 2021-12-06 DOI: 10.1109/SNAMS53716.2021.9732118

Hafed Zarzour, Faiz Maazouzi, Mohammad Al-Zinati, Y. Jararweh, Thar Baker

{"title":"An Efficient Recommender System Based on Collaborative Filtering Recommendation and Cluster Ensemble","authors":"Hafed Zarzour, Faiz Maazouzi, Mohammad Al-Zinati, Y. Jararweh, Thar Baker","doi":"10.1109/SNAMS53716.2021.9732118","DOIUrl":"https://doi.org/10.1109/SNAMS53716.2021.9732118","url":null,"abstract":"In the last few years, cluster ensembles have emerged as powerful techniques that integrate multiple clustering methods into recommender systems. Such integration leads to improving the performance, quality and the accuracy of the generated recommendations. This paper proposes a novel recommender system based on a cluster ensemble technique for big data. The proposed system incorporates the collaborative filtering recommendation technique and the cluster ensemble to improve the system performance. Besides, it integrates the Expectation-Maximization method and the HyperGraph Partitioning Algorithm to generate new recommendations and enhance the overall accuracy. We use two real-world datasets to evaluate our system: TED Talks and MovieLens. The experimental results show that the proposed system outperforms the traditional methods that utilize single clustering techniques in terms of recommendation quality and predictive accuracy. Most importantly, the results indicate that the proposed system provides the highest precision, recall, accuracy, F1, and the lowest Root Mean Square Error regardless of the used similarity strategy.","PeriodicalId":387260,"journal":{"name":"2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130592098","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

Social Media News Credibility among Students in the Czech Republic 捷克学生在社交媒体上的新闻可信度

2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) Pub Date : 2021-12-06 DOI: 10.1109/SNAMS53716.2021.9732097

Jana Svrovátková, A. Pavlíček

{"title":"Social Media News Credibility among Students in the Czech Republic","authors":"Jana Svrovátková, A. Pavlíček","doi":"10.1109/SNAMS53716.2021.9732097","DOIUrl":"https://doi.org/10.1109/SNAMS53716.2021.9732097","url":null,"abstract":"With the development of the social network, a variety of misinformation, disinformation, and fake news has come to be often shared and spread. A clear link exists between this spread and the willingness of users to share news they receive. The reasons may differ-from actual belief in it to finding it humorous. Our article answers the following research questions: How much do students in the Czech Republic share news on social media? How much do they trust the news they read there? And does the attitude of students and older non-students differ? In the autumn of 2020, we conducted a survey at the Prague University of Economics and Business on the willingness to share news on social networks. The survey had 452 respondents, not all of which were students. Although the respondents, on the one hand, claimed they do not share messages they are not convinced are truthful, a sufficient number of likes or level of interestingness of the message often persuade them to do the opposite. We found that women are stricter about not sharing fake news than men. A further comparison between students and non-students demonstrated that students take social media as a source of news and recommend it more often, whereas non-students more often share specific messages.","PeriodicalId":387260,"journal":{"name":"2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131217216","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

“Stronger than Hate”: On the Dissemination of Hate Speech during the 2020 Vienna Terrorist Attack “强于仇恨”:关于2020年维也纳恐怖袭击期间仇恨言论的传播

2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) Pub Date : 2021-12-06 DOI: 10.1109/SNAMS53716.2021.9732081

Michaela Lindenmayr, Ema Kusen, Mark Strembeck

引用次数: 1

On Prediction of Research Excellence using Data Mining and Deep Learning Techniques 利用数据挖掘和深度学习技术预测研究成果

2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) Pub Date : 2021-12-06 DOI: 10.1109/SNAMS53716.2021.9732153

Amber Urooj, H. Khan, Saqib Iqbal, Q. Althebyan

引用次数: 1

Quasi Character-Level Transformers to Improve Neural Machine Translation on Small Datasets 小数据集上改进神经机器翻译的准字符级转换器

2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) Pub Date : 2021-12-06 DOI: 10.1109/SNAMS53716.2021.9732120

Salvador Carrión, F. Casacuberta

引用次数: 0

Applying the Approach Based on Several Social Network Analysis Metrics to Identify Influential Users of a Brand 应用基于几个社会网络分析指标的方法识别品牌的影响力用户

2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) Pub Date : 2021-12-06 DOI: 10.1109/SNAMS53716.2021.9732132

Moojan Kamalzadeh, A. Haghighat

{"title":"Applying the Approach Based on Several Social Network Analysis Metrics to Identify Influential Users of a Brand","authors":"Moojan Kamalzadeh, A. Haghighat","doi":"10.1109/SNAMS53716.2021.9732132","DOIUrl":"https://doi.org/10.1109/SNAMS53716.2021.9732132","url":null,"abstract":"Online social networks, have become an integral part of our daily lives. People widely share their views on various topics and feelings with other users on these platforms. Due to the formation of extensive relationships between users, researchers seek communities on online social networks to achieve their goals. But discovering the structure of the communities in these networks has not been enough from the e-commerce point of view, so the problem of finding influencers became apparent. In this article, a case study was conducted on the Zar Macaron brand. To do this different approaches for identifying influential users were compared, and we also created two crawlers to collect data from Instagram. In the analysis phase, we have used Gephi as a tool to identify communities and influential users. Many social network analysis metrics have been applied to the dataset to achieve reasonable results. Moreover, time analysis has been conducted to discover the hidden patterns of activity within the network to validate previous results. Our findings show that applying data analysis techniques to users' online behavior is a powerful tool for predicting user impact levels. Finally, we confirmed our results by observing objective facts.","PeriodicalId":387260,"journal":{"name":"2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115169679","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: 3

A New Fast Local Community Detection Algorithm Using the Number of Common Neighbours 一种新的基于共同邻居数的快速本地社区检测算法

2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) Pub Date : 2021-12-06 DOI: 10.1109/SNAMS53716.2021.9732134

Sahar Bakhtar, Hovhannes A. Harutyunyan

{"title":"A New Fast Local Community Detection Algorithm Using the Number of Common Neighbours","authors":"Sahar Bakhtar, Hovhannes A. Harutyunyan","doi":"10.1109/SNAMS53716.2021.9732134","DOIUrl":"https://doi.org/10.1109/SNAMS53716.2021.9732134","url":null,"abstract":"Recent years have witnessed the rapid growth of social network services. Consequently, the problems in this area have become more complex. Community detection is one of the most important problems in social networks. A good community can be defined as a group of vertices that are highly connected and loosely connected to the vertices outside the community. Regarding the fact that social networks are huge in size, having complete information of the whole network is almost impossible. As a result, the problem of local community detection has become more popular in recent years. In this paper, a new fast local community detection algorithm is proposed using a new metric, called P. The proposed algorithm includes three different steps in which relevant nodes are added in the first step and irrelevant nodes are removed in the second and third steps. Regarding the experimental results, it is shown that the proposed algorithm outperforms state-of-the-art local community detection algorithms. Also, the proposed algorithm is considerably faster than other compared algorithms.","PeriodicalId":387260,"journal":{"name":"2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS)","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128092909","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 Staffing Recommender System based on Domain-Specific Knowledge Graph 基于特定领域知识图谱的人员推荐系统

2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) Pub Date : 2021-12-06 DOI: 10.1109/SNAMS53716.2021.9732087

Yan Wang, Yacine Allouache, Christian Joubert

{"title":"A Staffing Recommender System based on Domain-Specific Knowledge Graph","authors":"Yan Wang, Yacine Allouache, Christian Joubert","doi":"10.1109/SNAMS53716.2021.9732087","DOIUrl":"https://doi.org/10.1109/SNAMS53716.2021.9732087","url":null,"abstract":"In the economics environment, Job Matching is always a challenge involving the evolution of knowledge and skills. A good matching of skills and jobs can stimulate the growth of economics. Recommender System (RecSys), as one kind of Job Matching, can help the candidates predict the future job relevant to their preferences. However, RecSys still has the problem of cold start and data sparsity. The content-based filtering in RecSys needs the adaptive data for the specific staffing tasks of Bidirectional Encoder Representations from Transformers (BERT). In this paper, we propose a job RecSys based on skills and locations using a domain-specific Knowledge Graph (KG). This system has three parts: a pipeline of Named Entity Recognition (NER) and Relation Extraction (RE) using BERT; a standardization system for pre-processing, semantic enrichment and semantic similarity measurement; a domain-specific Knowledge Graph (KG). Two different relations in the KG are computed by cosine similarity and Term Frequency-Inverse Document Frequency (TF-IDF) respectively. The raw data used in the staffing RecSys include 3000 descriptions of job offers from Indeed, 126 Curriculum Vitae (CV) in English from Kaggle and 106 CV in French from Linx of Capgemini Engineering. The staffing RecSys is integrated under an architecture of Microservices. The autonomy and effectiveness of the staffing RecSys are verified through the experiment using Discounted Cumulative Gain (DCG). Finally, we propose several potential research directions for this research.","PeriodicalId":387260,"journal":{"name":"2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133519061","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

Sentimental Analysis of Movie Reviews using Soft Voting Ensemble-based Machine Learning 基于软投票集成的机器学习的电影评论情感分析

2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS) Pub Date : 2021-12-06 DOI: 10.1109/SNAMS53716.2021.9732159

Ali Athar, Sikandar Ali, Muhammad Mohsan Sheeraz, Subrata Bhattacharjee, Hee Kim

{"title":"Sentimental Analysis of Movie Reviews using Soft Voting Ensemble-based Machine Learning","authors":"Ali Athar, Sikandar Ali, Muhammad Mohsan Sheeraz, Subrata Bhattacharjee, Hee Kim","doi":"10.1109/SNAMS53716.2021.9732159","DOIUrl":"https://doi.org/10.1109/SNAMS53716.2021.9732159","url":null,"abstract":"Sentimental analysis helps to classify a subject's sentiments (e.g., positive, negative, or neutral) automatically towards a specific topic, product, news, or any movie. Machine learning is a powerful technique of artificial intelligence (AI) to control the increasing demand for accurate sentimental analysis. The analysis of sentiment on social networks, such as Facebook or Twitter, has become a powerful source of learning about the user's opinion and it has a wide range of applications in the same field. However, the accuracy and efficiency of sentimental analysis are being impeded by different challenges faced in the field of Natural language processing (NLP). In this paper, we have proposed a state-of-the-art soft voting ensemble (SVE) approach to perform sentimental analysis of movie reviews. Five different well-known machine learning (ML) classifiers have been used for this purpose, namely Logistic Regression (LR), Naïve Bayes (NB), XGBoost (XGB), Random Forest (RF), and Multilayer Perceptron (MLP). Our proposed ensemble approach outperformed all other classifiers by giving an overall accuracy, precision, recall, and f1-score of 89.9%, 90.0%, 90.0%, and 90.0%, respectively.","PeriodicalId":387260,"journal":{"name":"2021 Eighth International Conference on Social Network Analysis, Management and Security (SNAMS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124116933","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: 4