2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献_第5页

Comparing Deep and Machine Learning Models for Sentiment and Emotion Classification from Vaccine #sideffects 比较基于疫苗副作用的情绪和情绪分类的深度和机器学习模型

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068605

Aditya Dubey, S. Gokhale

{"title":"Comparing Deep and Machine Learning Models for Sentiment and Emotion Classification from Vaccine #sideffects","authors":"Aditya Dubey, S. Gokhale","doi":"10.1109/ASONAM55673.2022.10068605","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068605","url":null,"abstract":"The accelerated development of Covid-19 vaccines offered tremendous promise and hope, yet stirred significant trepidation and fear. These conflicting emotions motivated many to turn to social media to share their experiences and side effects during the process of getting vaccinated. This paper analyzes sentiment and emotions from tweets collected using the hashtag #sideffects during the early roll out of the Covid-19 vaccine. Each tweet was labeled according to its sentiment polarity (positive vs. negative), and was assigned one of four emotion labels (joy, gratitude, apprehension, and sadness). Exploratory analysis of the tweets through word cloud visualizations revealed that the negativity of emotions intensified with the severity of side effects. Word and numerical features extracted from the text of the tweets and metadata were used to train conventional machine learning and deep learning models. These models resulted in an accuracy of 81% for binary sentiment classification, and 71 % for multi-label emotion identification. The proposed framework, which yielded competitive performance, may be employed to gain insights into people's thoughts and feelings from vaccine-related conversations. These insights can be helpful in devising communication and education strategies to mitigate vaccine hesitancy.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122712828","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

Using Dominating Sets to Block Contagions in Social Networks 利用支配集阻止社交网络中的传染

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068610

Robert Chen Bao, Matthew Hancock, C. Kuhlman, Sujith Ravi

{"title":"Using Dominating Sets to Block Contagions in Social Networks","authors":"Robert Chen Bao, Matthew Hancock, C. Kuhlman, Sujith Ravi","doi":"10.1109/ASONAM55673.2022.10068610","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068610","url":null,"abstract":"There are myriad real-life examples of contagion processes on human social networks, e.g., spread of viruses, information, and social unrest. Also, there are many methods to control or block contagion spread. In this work, we introduce a novel method of blocking contagions that uses nodes from dominating sets (DSs). To our knowledge, this is the first use of DS nodes to block contagions. Finding minimum dominating sets of graphs is an NP-Complete problem, so we generalize a well-known heuristic, enabling us to customize its execution. Our method produces a prioritized list of dominating nodes, which is, in turn, a prioritized list of blocking nodes. Thus, for a given network, we compute this list of blocking nodes and we use it to block contagions for all blocking node budgets, contagion seed sets, and parameter values of the contagion model. We report on computational experiments of the blocking efficacy of our approach using two mined networks. We also demonstrate the effectiveness of our approach by comparing blocking results with those from the high degree heuristic, which is a common standard in blocking studies.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125219556","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

Quarantine in Motion: A Graph Learning Framework to Reduce Disease Transmission Without Lockdown 运动中的隔离:在没有封锁的情况下减少疾病传播的图学习框架

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068686

Sofia Hurtado, R. Marculescu, Justin Drake

引用次数: 0

Evaluation of Joint Modeling Techniques for Node Embedding and Community Detection on Graphs 图上节点嵌入和社区检测联合建模技术的评价

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068594

Simon Hiel, Lore Nicolaers, Carlos Ortega Vázquez, Sandra Mitrovic, B. Baesens, Jochen De Weerdt

{"title":"Evaluation of Joint Modeling Techniques for Node Embedding and Community Detection on Graphs","authors":"Simon Hiel, Lore Nicolaers, Carlos Ortega Vázquez, Sandra Mitrovic, B. Baesens, Jochen De Weerdt","doi":"10.1109/ASONAM55673.2022.10068594","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068594","url":null,"abstract":"Novel joint techniques capture both the microscopic context and the mesoscopic structure of networks by leveraging two previously separated fields of research: node representation learning (NRL) and community detection (CD). However, several limitations exist in the literature. First, a comprehensive comparison between these joint NRL-CD techniques is non-existent. Second, baseline techniques, datasets, evaluation metrics, and classification algorithms differ significantly between each method. Thirdly, the literature lacks a synchronized experimental approach, thus rendering comparison between these methods strenuous. To overcome these limitations, we present a uni-fied experimental setup mutually comparing six joint NRL-CD techniques and comparing them with corresponding NRL/CD baselines in three different settings: non-overlapping and over-lapping CD and node classification. Our results show that joint methods underperform on the node classification task but achieve relatively solid results for overlapping community detection. Our research contribution is two-fold: first, we show specific weaknesses of selected joint techniques in different tasks and data sets; and second, we suggest a more thorough experimental setup to benchmark joint techniques with simpler NRL and CD techniques.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134129846","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

Whole-File Chunk-Based Deduplication Using Reinforcement Learning for Cloud Storage 基于强化学习的云存储全文件块重复数据删除

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068661

Xincheng Yuan, M. Moh, Teng-Sheng Moh

引用次数: 0

Stylometric and Semantic Analysis of Demographically Diverse Non-native English Review Data 人口统计学差异的非母语英语评论数据的文体和语义分析

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068612

Salim Sazzed

{"title":"Stylometric and Semantic Analysis of Demographically Diverse Non-native English Review Data","authors":"Salim Sazzed","doi":"10.1109/ASONAM55673.2022.10068612","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068612","url":null,"abstract":"The demographic knowledge facilitates a fine-grained interpretation of the user-generated review text and enables better decision-making. In this study, we aim to com-prehend how various attributes of non-native English text vary across demographically distinct groups. We introduce a non-native English corpus of around 1150 reviews representing four demographically diverse country-specific groups: Finland, Kenya, Bangladesh, and China. The reviews differ in various contexts, including geography, native language family, race and culture, and English proficiency levels of the reviewers. We then perform stylometric and semantic analysis on these distinct sets of reviews to unveil how the linguistic characteristics differ across the demography. The investigation reveals that stylometric features are mostly similar across the reviews of various groups; nevertheless, dissimilarities are observed in attributes, such as review length, presence of articles, or prepositions. We employ classical machine learning (ML) algorithms and transformer-based fine-tuned language models for categorizing the reviews into distinct demographic groups. We observe that semantic features yield slightly better efficacy than syntactic features for distinguishing the demography-specific reviews.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116303507","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

Multigraph transformation for community detection applied to financial services 面向社区检测的多图变换在金融服务中的应用

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068607

S. E. Ayeb, B. Hemery, Fabrice Jeanne, Christophe Charrier, Estelle Cherrier

{"title":"Multigraph transformation for community detection applied to financial services","authors":"S. E. Ayeb, B. Hemery, Fabrice Jeanne, Christophe Charrier, Estelle Cherrier","doi":"10.1109/ASONAM55673.2022.10068607","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068607","url":null,"abstract":"Networks have provided a representation for a wide range of real systems, including communication networks, money transfer networks and biological systems. Communities repre-sent fundamental structures for understanding the organization of real-world networks. Uncovering coherent groups in these networks is the goal of community detection. A community is a mesoscopic structure with nodes heavily connected in their groups by comparison to the nodes in other groups. Commu-nities might also overlap as they may share one or multiple nodes. This paper lays the foundation for an application on transactional multigraphs (networks of financial transactions in which nodes can be linked with multiple edges), through the discovery of communities. Due to their complexity, our goal is to find the most effective way of simplifying multigraphs to weighted graphs, while preserving properties of the network. We tested five weights' calculation function and community detection algorithms were applied. A comparison of the outputs based on extrinsic and intrinsic evaluation metrics is then held.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114634655","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

Impact of Work from Home During the Pandemic in Saudi Arabia 疫情期间在家工作对沙特阿拉伯的影响

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068624

Omar Hammad, Shivakant Mishra

引用次数: 0

A Hybrid Artificial Bee Colony Algorithm with Simulated Annealing for Enhanced Community Detection in Social Networks 基于模拟退火的混合人工蜂群算法增强社交网络中的社区检测

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068713

Narimene Dakiche, K. Benatchba, F. B. Tayeb, Y. Slimani, Mehdi Anis Brahmi

引用次数: 2

DMNP: A Deep Learning Approach for Missing Node Prediction in Partially Observed Graphs DMNP:一种局部观察图缺失节点预测的深度学习方法

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068642

Faezeh Faez, Ali Akhoondian Amiri, M. Baghshah, H. Rabiee

引用次数: 0