2022 8th International Conference on Web Research (ICWR)最新文献_第2页

A Comparison of Centrality Measures for Community Evolution Discovery 群落进化发现的中心性测度比较

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786228

Javad RafieeFard, B. Teimourpour, Mohammadreza Shaghouzi, Maryam Jami

{"title":"A Comparison of Centrality Measures for Community Evolution Discovery","authors":"Javad RafieeFard, B. Teimourpour, Mohammadreza Shaghouzi, Maryam Jami","doi":"10.1109/ICWR54782.2022.9786228","DOIUrl":"https://doi.org/10.1109/ICWR54782.2022.9786228","url":null,"abstract":"Many of real-world social networks, show structural changes over time, so they can be modeled as dynamic graphs. However, most methods in social network analysis, including community detection, are focused on performing on static networks. Therefore, methods of studying community evolution still have room for improvement. In this article, we investigated one of the methods introduced in independent community detection and matching approach. It is an approach for tracking dynamic community evolution, but it has the advantage of using methods that have been studied in detail for static networks. Previous studies have examined and compared some of the centralities that can be used in this method. In this study, we examined its performance by using other centralities called betweenness centrality and closeness centrality, and compared them with the usage of social position. Our analysis was performed on a subgraph of the word co-occurrence network, which is a type of bibliometric network, and the results of the algorithm were evaluated by experts. The results show that betweenness centrality represents more transparent and useful events and using it in community evolution discovery is recommended for small networks.","PeriodicalId":355187,"journal":{"name":"2022 8th International Conference on Web Research (ICWR)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125718077","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

Spatial, Temporal, and Semantic Crime Analysis Using Information Extraction From Online News 利用在线新闻信息提取的空间、时间和语义犯罪分析

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786256

Y. Norouzi

{"title":"Spatial, Temporal, and Semantic Crime Analysis Using Information Extraction From Online News","authors":"Y. Norouzi","doi":"10.1109/ICWR54782.2022.9786256","DOIUrl":"https://doi.org/10.1109/ICWR54782.2022.9786256","url":null,"abstract":"Crime is a behavioral disorder with various scales that are intimately linked to a variety of circumstances such as spatial, temporal, sociological, and ecological aspects. The massive amounts of crime-related data, which is being published and grows with each passing day, in the form of online news reports have prompted researchers to pursue studies in the field of violence and criminal investigations. In this work, we developed a semantic approach to extract spatiotemporal and crime-related information from news reports to detect crime spatial distribution. The proposed method, in particular, aims to extract geographical and temporal information to detect regions with a high number of criminal cases, as well as to represent semantic knowledge of criminal incidents by annotating spatiotemporal information from their web domains. This approach incorporates the use of Natural Language Processing (NLP) techniques and a crime domain ontology into the information extraction process to automatically retrieve spatial, temporal, and other relevant information about criminal behavior from news reports. Our proposal consists of a comprehensive solution built on a fully functional architecture that has been tested in a use case scenario for the crime news reported in London, United Kingdom.","PeriodicalId":355187,"journal":{"name":"2022 8th International Conference on Web Research (ICWR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130948685","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

TedGram: Twitter Event Detection using Graphbased Methods 使用基于图的方法进行Twitter事件检测

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786233

Zahra Akhgari, M. Malekimajd, H. Rahmani

{"title":"TedGram: Twitter Event Detection using Graphbased Methods","authors":"Zahra Akhgari, M. Malekimajd, H. Rahmani","doi":"10.1109/ICWR54782.2022.9786233","DOIUrl":"https://doi.org/10.1109/ICWR54782.2022.9786233","url":null,"abstract":"The proliferation of social networks has made researchers turn to the analysis of these networks. Event detection is one of the important topics in the analysis of social networks, especially Twitter. In this paper, we propose an online graph-based approach, called TedGram, for event detection in Twitter using word embedding techniques and graph partitioning algorithms. In the TedGram model, for each incoming tweet, candidate tweets are gathered from preceding tweets using co-occurrence in entities keywords, and correspondingly the similarity between tweets are computed using the Word Mover’s Distance (WMD) algorithm and pretrained word2vec model. In this regard, the TTI (Tweet Tweet Interaction) graph is computed and updated using an online greedy community detection method based on the Barabási-Albert generative model. Furthermore, we utilize Latent Dirichlet Allocation (LDA) and WMD to combine duplicate communities for detecting and merging duplicate events. Our proposed method is applied to a sample of the Event2012 dataset and is evaluated regarding Precision, Recall, and Fscore. The experimental results show that TedGram performs well against the existing methods.","PeriodicalId":355187,"journal":{"name":"2022 8th International Conference on Web Research (ICWR)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122419987","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

Semi-Supervised Medical Insurance Fraud Detection by Predicting Indirect Reductions Rate using Machine Learning Generalization Capability 利用机器学习泛化能力预测间接减少率的半监督医疗保险欺诈检测

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786251

Parvin Esmaeili Ataabadi, Behzad Soleimani Neysiani, Mohammad Zahiri Nogorani, Nazanin Mehraby

{"title":"Semi-Supervised Medical Insurance Fraud Detection by Predicting Indirect Reductions Rate using Machine Learning Generalization Capability","authors":"Parvin Esmaeili Ataabadi, Behzad Soleimani Neysiani, Mohammad Zahiri Nogorani, Nazanin Mehraby","doi":"10.1109/ICWR54782.2022.9786251","DOIUrl":"https://doi.org/10.1109/ICWR54782.2022.9786251","url":null,"abstract":"There is 10% fraud in medical insurance based on published statistics in Insurance Research Institute of Islamic Republic of Iran in 1399 –solar system eq. 2020 in the Gregorian calendar-which cost about 28 thousand billion RIALs –the official currency of Iran eq. to about 320 million dollars-. This study proposes a machine learning-based technique to predict the claim cost based on other patients’ history and predict fraud or abnormal costs in claims that significantly differ from other claims. Besides, a new data sampling approach is proposed to lead the machine learning algorithms that focus on exceptional cases. A real-world private dataset is used to evaluate 700,000 claims of the RASA web portal, used for supplementary insurance by famous companies like Day. The proposed data sampling approach reduced absolute error in exceptional cases from 35 to 23 errors for deduction rate. The evaluation results show about 0.5% of abnormal cases in the dataset with a higher than 20% absolute error. The abnormal rates can be adjusted to a lower or higher range.","PeriodicalId":355187,"journal":{"name":"2022 8th International Conference on Web Research (ICWR)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117020726","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

Community Detection in Social Networks Considering the Depth of Relationships 考虑关系深度的社交网络社区检测

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786230

Sevda Fottovat, Habib Izadkhah, Javad Hajipour

引用次数: 2

Video Sharing Network’s Popularity Detection (Aparat) 视频分享网络人气检测(Aparat)

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786255

Seyedeh Nika Mirzabagherbarzi, Mohammad Hossein Salehinezhad, Fateme Rezaeian Kuchesfehan

引用次数: 0

Sem-TED: Semantic Twitter Event Detection and Adapting with News Stories 语义Twitter事件检测和适应新闻故事

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786234

Zahra Akhgari, M. Malekimajd, H. Rahmani

{"title":"Sem-TED: Semantic Twitter Event Detection and Adapting with News Stories","authors":"Zahra Akhgari, M. Malekimajd, H. Rahmani","doi":"10.1109/ICWR54782.2022.9786234","DOIUrl":"https://doi.org/10.1109/ICWR54782.2022.9786234","url":null,"abstract":"Public acceptance of social networks has made the analysis of these networks essential. Event detection in these networks including Twitter is one of the most momentous subjects in the field of natural language processing and text mining. In this paper, we investigated how to link popular social media topics and news stories using transformer models and neural networks. Accordingly, this study consists of two parts: First, detecting popular topics and, second, linking them to the news. Event detection techniques have been applied to detect popular topics, while an event detection method comprises text preprocessing, text embedding using Sentence Transformer, dimension reduction using the UMAP algorithm, and grouping them using the HDBSCAN algorithm. To examine relevance or non-relevance between the news and topics, a single-layer perceptron neural network is applied, in which the output of the model indicates relevance or nonrelevance. We have implemented the mentioned parts and have investigated them on a small sampling of two known datasets. The evaluation outcomes reveal that the first part leads to an average improvement of 8% compared to the entity-based methods. Moreover, the results of the second part demonstrate that the used neural network in this study has a better performance comparing several other methods.","PeriodicalId":355187,"journal":{"name":"2022 8th International Conference on Web Research (ICWR)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131723974","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

A Novel Consensus Protocol in Blockchain Network based on Proof of Activity Protocol and Game Theory 基于活动证明协议和博弈论的新型区块链网络共识协议

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786224

Zahra Boreiri, Alireza Norouzi Azad

引用次数: 5

Abnormal Behavior Detection in Health Insurance Assessment Process 健康保险评估过程中的异常行为检测

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786232

Nazanin Mehraby, Behzad Soleimani Neysiani, Mohammad Zahiri Nogorani, Parvin Esmaeili Ataabadi

{"title":"Abnormal Behavior Detection in Health Insurance Assessment Process","authors":"Nazanin Mehraby, Behzad Soleimani Neysiani, Mohammad Zahiri Nogorani, Parvin Esmaeili Ataabadi","doi":"10.1109/ICWR54782.2022.9786232","DOIUrl":"https://doi.org/10.1109/ICWR54782.2022.9786232","url":null,"abstract":"Every insurance company plays various roles and so implements many processes. One of the essential processes is the assessment which occurs due to investigating bills and claims and may lead to cost deduction based on fraud or limitation rules. This process may go forward in one or more steps by one or more assessors; some last some minutes, and others last more. This study tries to investigate actions, durations, assessors, and everything related to this process to help the insurer companies know useless steps, verify assessor’s behaviors, and make better decisions for the assessment process. This paper aims to demonstrate the methods and efforts of each mining rule and its strengths and weaknesses. The clustering and association rules mining are proposed for this objective. The gathered dataset has about 110,000 records from the Rasa web portal and is selected from an Iranian insurer company’s one-year indirect assessment process. The results show that some steps are useless, some assessors do not assess at an appropriate duration, and some bills consider several times with one assessor, which is suspicious behavior.","PeriodicalId":355187,"journal":{"name":"2022 8th International Conference on Web Research (ICWR)","volume":"224 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116310115","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

Data Driven Artificial Neural Network LSTM Hybrid Predictive Model Applied for International Stock Index Prediction 数据驱动人工神经网络LSTM混合预测模型在国际股指预测中的应用

2022 8th International Conference on Web Research (ICWR) Pub Date : 2022-05-11 DOI: 10.1109/ICWR54782.2022.9786223

Ashkan Safari

引用次数: 3