2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献_第7页

Social Network Analysis of Popular YouTube Videos via Vertical Quantitative Mining 基于垂直定量挖掘的YouTube热门视频社交网络分析

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068640

Adam G. M. Pazdor, C. Leung, Thomas J. Czubryt, Junyi Lu, Denys Popov, Sanskar Raval

引用次数: 5

ComMit: Blind Community-based Early Mitigation Strategy against Viral Spread 提交:针对病毒传播的盲目社区早期缓解战略

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068568

Pegah Hozhabrierdi, S. Soundarajan

{"title":"ComMit: Blind Community-based Early Mitigation Strategy against Viral Spread","authors":"Pegah Hozhabrierdi, S. Soundarajan","doi":"10.1109/ASONAM55673.2022.10068568","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068568","url":null,"abstract":"In the early stages of a pandemic, epidemiological knowledge of the disease is limited and no vaccination is available. This poses the problem of determining an Early Mitigation Strategy. Previous studies have tackled this problem through finding globally influential nodes that contribute the most to the spread. These methods are often not practical due to their assumptions that (1) accessing the full contact social network is possible; (2) there is an unlimited budget for the mitigation strategy; (3) healthy individuals can be isolated for indefinite amount of time, which in practice can have serious mental health and economic consequences. In this work, we study the problem of developing an early mitigation strategy from a community perspective and propose a dynamic Community-based Mitigation strategy, ComMit. The distinguishing features of ComMit are: (1) It is agnostic to the dynamics of the spread; (2) does not require prior knowledge of contact network; (3) it works within a limited budget; and (4) it enforces bursts of short-term restriction on small communities instead of long-term isolation of healthy individuals. ComMit relies on updated data from test-trace reports and its strategy evolves over time. We have tested ComMit on several real-world social networks. The results of our experiments show that, within a small budget, ComMit can reduce the peak of infection by 73% and shorten the duration of infection by 90%, even for spreads that would reach a steady state of non-zero infections otherwise (e.g., SIS contagion model).","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"12 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":"130062461","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

Learning to Predict Transitions within the Homelessness System from Network Trajectories 学习从网络轨迹预测无家可归系统内的过渡

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068708

Khandker Sadia Rahman, C. Chelmis

引用次数: 1

Characteristics Analysis of Moving Conversations to Detect Events on Twitter 移动对话特征分析以检测Twitter上的事件

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068690

Hansi Senaratne, Dominic Lehle, T. Schreck

{"title":"Characteristics Analysis of Moving Conversations to Detect Events on Twitter","authors":"Hansi Senaratne, Dominic Lehle, T. Schreck","doi":"10.1109/ASONAM55673.2022.10068690","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068690","url":null,"abstract":"A conversation is an exchange of thoughts, news, or ideas about a particular topic between two or more people. On Twit-ter, hashtags allow its users to collate all conversations pertaining to a particular topic. The progressions that occur in such conversations through the geographic space, the time, or the thematic contexts, create trajectories of conversations on Twitter, and they can give us valuable insights into interesting events that take place around us. In this paper we develop an approach based on data analysis and visualisation, to (1) construct such conversation trajectories for chosen popular hashtags, (2) analyse the various geospatial- and content-characteristics of the conversation trajectories (e.g., distance variance, speed of propagation, topic diversity, or credibility) to determine co-located events, and (3) rank and sort the resulting conversation trajectories according to a user-defined interestingess-measure, to narrow down the search space for interesting conversation trajectories. Our approach is among the first to introduce the us-age of movement of conversations across geographic space and time for the exploratory detection and analysis of events, whereas most existing works use keyword-based text analysis to detect events on Twitter. All the three stages of the approach (construct, analyse, rank & sort) are presented in a visual-interactive interface that allows us to explore Twitter text data without extensive prior knowledge, and benefit from the pure exploratory capabilities of the tool. The usefulness of our approach is demonstrated as a proof-of-concept to detect sports-related events, where we were able to identify the outcome of a contest for Major League Baseball sportsmen on Twitter.","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":"129683084","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

Understanding the Impact of Awards on Award Winners and the Community on Reddit 了解奖项对获奖者和Reddit社区的影响

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068596

Avinash Tulasi, Mainack Mondal, Arun Balaji Buduru, P. Kumaraguru

{"title":"Understanding the Impact of Awards on Award Winners and the Community on Reddit","authors":"Avinash Tulasi, Mainack Mondal, Arun Balaji Buduru, P. Kumaraguru","doi":"10.1109/ASONAM55673.2022.10068596","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068596","url":null,"abstract":"Non-financial incentives in the form of awards often act as a driver of positive reinforcement and elevation of social status in the offline world. The elevated social status results in people becoming more active, aligning to a change in the communities' expectations. However, the impact in terms of longevity of social influence and community acceptance of leaders of these incentives in the form of awards are not well-understood in the online world. Our work aims to shed light on the impact of these awards on the awardee and the community. We focus on three large subreddits with a snapshot of 219K posts and 5.8 million comments contributed by 88K Reddit users who received 14,146 awards. Our work establishes that the behaviour of awardees change statistically significantly for a short time after getting an award; however, the change is ephemeral since the awardees return to their pre-award behaviour within days. Additionally, via a user survey, we identified a long-lasting impact of awards-we found that the community's stance softened towards awardees.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"74 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":"129942244","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

Dynamic Healthcare Embeddings for Improving Patient Care 用于改善患者护理的动态医疗保健嵌入

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068627

Hankyu Jang, Sulyun Lee, D. M. H. Hasan, P. Polgreen, S. Pemmaraju, Bijaya Adhikari Department of Computer Science, U. Iowa, Interdisciplinary Graduate Program in Informatics, Department of Preventive Medicine

{"title":"Dynamic Healthcare Embeddings for Improving Patient Care","authors":"Hankyu Jang, Sulyun Lee, D. M. H. Hasan, P. Polgreen, S. Pemmaraju, Bijaya Adhikari Department of Computer Science, U. Iowa, Interdisciplinary Graduate Program in Informatics, Department of Preventive Medicine","doi":"10.1109/ASONAM55673.2022.10068627","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068627","url":null,"abstract":"As hospitals move towards automating and integrating their computing systems, more fine-grained hospital operations data are becoming available. These data include hospital architectural drawings, logs of interactions between patients and healthcare professionals, prescription data, procedures data, and data on patient admission, discharge, and transfers. This has opened up many fascinating avenues for healthcare-related prediction tasks for improving patient care. However, in order to leverage off-the-shelf machine learning software for these tasks, one needs to learn structured representations of entities involved from heterogeneous, dynamic data streams. Here, we propose DECENT, an auto-encoding heterogeneous co-evolving dynamic neural network, for learning heterogeneous dynamic embeddings of patients, doctors, rooms, and medications from diverse data streams. These embeddings capture similarities among doctors, rooms, patients, and medications based on static attributes and dynamic interactions. DECENT enables several applications in healthcare prediction, such as predicting mortality risk and case severity of patients, adverse events (e.g., transfer back into an intensive care unit), and future healthcare-associated infections. The results of using the learned patient embeddings in predictive modeling show that DECENT has a gain of up to 48.1% on the mortality risk prediction task, 12.6% on the case severity prediction task, 6.4% on the medical intensive care unit transfer task, and 3.8% on the Clostridioides difficile (C.diff) Infection (CDI) prediction task over the state-of-the-art baselines. In addition, case studies on the learned doctor, medication, and room embeddings show that our approach learns meaningful and interpretable embeddings.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"55 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":"130953943","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

Extremism on Social Media: Lynching of Priyantha Kumara Diyawadana 社交媒体上的极端主义:对Priyantha Kumara Diyawadana处以私刑

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068622

Musa Muhammad, Muhammad Usama, M. Uppal

引用次数: 0

PriMe: A Novel Privacy Measuring Framework for Online Social Networks 一种新的在线社交网络隐私测量框架

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068701

Ahmad Hassanpour, Bian Yang

引用次数: 0

Segmentation and Classification of Dermoscopic Skin Cancer on Green Channel 皮肤镜下皮肤癌的绿色通道分割与分类

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068614

Hind Abouche, Anwar Jimi, Nabila Zrira, Ibtissam Benmiloud

{"title":"Segmentation and Classification of Dermoscopic Skin Cancer on Green Channel","authors":"Hind Abouche, Anwar Jimi, Nabila Zrira, Ibtissam Benmiloud","doi":"10.1109/ASONAM55673.2022.10068614","DOIUrl":"https://doi.org/10.1109/ASONAM55673.2022.10068614","url":null,"abstract":"Melanoma the most dangerous type of skin cancer, has been on the rise in recent years. Hands-on identification of melanoma in its early stages with the unaided eye is error-prone and necessitates extensive expertise and experience. Due to the scarcity of skilled dermatologists, a computerized and automated technique is required to effectively identify melanoma. The following approach attempts to accomplish this task by creating a new approach capable of segmenting, then classifying melanoma. The procedure begins with the preparation of dermoscopic images to remove hairs using the Dull Razor algorithm, followed by image segmentation, in which we computed the Hausdorff Distance, Dice, and Jaccard coefficients to determine which channel of the RGB space was best to utilize to separate the skin lesion from the background. The segmented images using the green channel are then utilized to calculate the Gray Level Co-occurrence Matrices (GLCM) and to extract the color characteristics of the region of interest. Our approach is able to achieve a Dice coefficient and an accuracy of 95% on the PH2 dermoscopic images.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"30 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":"132130505","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

Gender dynamics of German journalists on Twitter 推特上德国记者的性别动态

2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2022-11-10 DOI: 10.1109/ASONAM55673.2022.10068698

Benedict Witzenberger, Jürgen Pfeffer

引用次数: 1