2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献

{"title":"Proximity, Communities, and Attributes in Social Network Visualisation","authors":"H. Purchase, Nathan Stirling, D. Archambault","doi":"10.1109/ASONAM49781.2020.9381332","DOIUrl":"https://doi.org/10.1109/ASONAM49781.2020.9381332","url":null,"abstract":"The identification of groups in social networks drawn as graphs is an important task for social scientists who wish to know how a population divides with respect to relationships or attributes. Community detection algorithms identify communities (groups) in social networks by finding clusters in the graph: that is, sets of people (nodes) where the relationships (edges) between them are more numerous than their relationships with other nodes. This approach to determining communities is naturally based on the underlying structure of the network, rather than on attributes associated with nodes. In this paper, we report on an experiment that (a) compares the effectiveness of several force-directed graph layout algorithms for visually identifying communities, and (b) investigates their usefulness when group membership is based not on structure, but on attributes associated with the people in the network. We find algorithms that clearly separate communities with large distances to be most effective, while using colour to represent community membership is more successful than reliance on structural layout.","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117149472","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}

{"title":"Spatial-temporal patterns of aggressive behaviors. A case study Bogotá, Colombia","authors":"Jorge Victorino, J. Rudas, A. Reyes, Cristian Pulido, L. Chaparro, L. A. Narváez, Darwin Martínez, Francisco Gómez","doi":"10.1109/ASONAM49781.2020.9381311","DOIUrl":"https://doi.org/10.1109/ASONAM49781.2020.9381311","url":null,"abstract":"Understanding of crime patterns is paramount important for citizen's security planning. In particular, the comprehension of the Spatio-temporal dynamics related to aggressive behaviors is fundamental for deploying policial resources and devising mitigation actions. Currently, a significant number of approaches to find patterns, characterize dynamics, and predict crime have been proposed in state of the art. However, the operation of these approaches is strongly adapted to the specific conditions of a city. In this paper, we propose a novel approach to finding spatio-temporal crime patterns in the city of Bogotá (Colombia), particularly aggressive behaviors reported to the emergency line. We characterize aggressive behaviors through rhythm and tempo based on the theory of routine activity. Finally, we show that the dynamics of aggressive behaviors in the city are shared by several spatial units in which specific strategies can be applied to mitigate it.","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121918496","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}

{"title":"Recent Trends in Emotion Analysis: A Big Data Analysis Perspective","authors":"Tansel Özyer, Ak Duygu Selin, R. Alhajj","doi":"10.1109/ASONAM49781.2020.9381441","DOIUrl":"https://doi.org/10.1109/ASONAM49781.2020.9381441","url":null,"abstract":"Human action recognition has recently started to find its way into applications in different applications. Accordingly, human action recognition methods are becoming increasingly important in our daily life. They are used for different purposes such as automation, security, surveillance, health, smart home systems, and customer behaviour prediction, among others. Though have more systems with methods provides a rich pool of choices, it is important to well understand the performance of these systems and their success rates in recognizing the right activities in order to decide on the most appropriate system for the current application domain. This survey tackles this issue by analyzing and commenting on the available human action recognition systems and methods.","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129760022","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}

{"title":"A Pre-training Approach for Stance Classification in Online Forums","authors":"Jean Marie Tshimula, B. Chikhaoui, Shengrui Wang","doi":"10.1109/ASONAM49781.2020.9381467","DOIUrl":"https://doi.org/10.1109/ASONAM49781.2020.9381467","url":null,"abstract":"Stance detection is the task of automatically determining whether the author of a piece of text is in favor of, against, or neutral towards a target such as a topic, entity, or claim. In this paper, we propose a method based on RoBERTa to classify stances by capturing the context of the discussion through the examination of pairs of stances and relational structures of debates specific to each topic within the defined window of each forum participant's interventions. Furthermore, we examine the degree of disagreement and neutrality in various debate topics to measure divergence of opinion in the course of the debate and estimate the emotional state manifested in different debate topics. We conduct extensive experiments using two publicly available datasets and demonstrate that our method considers more stance classes, provides better results and yields statistical improvements over existing techniques. Our quantitative analysis of model performance yields F-1 scores of over 0.745. Interestingly, we obtained the highest F-1 score, 0.814, on a stance class which was not taken into consideration in prior work. We report that none of the metrics utilized to measure divergence of opinion yield values exceeding 50 % and the correlations between the same topics over 10-fold cross-validation are statistically significant for the majority of them (p < 0.005). Several future research avenues are proposed.","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125537415","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}

{"title":"Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2020","authors":"","doi":"10.1109/asonam49781.2020.9381375","DOIUrl":"https://doi.org/10.1109/asonam49781.2020.9381375","url":null,"abstract":"","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126753388","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}

{"title":"On Sentiment of Online Fake News","authors":"Razieh Nokhbeh Zaeem, Chengjing Li, K. S. Barber","doi":"10.1109/ASONAM49781.2020.9381323","DOIUrl":"https://doi.org/10.1109/ASONAM49781.2020.9381323","url":null,"abstract":"The presence of disinformation and fake news on the Internet and especially social media has become a major concern. Prime examples of such fake news surged in the 2016 U.S. presidential election cycle and the COVID-19 pandemic. We quantify sentiment differences between true and fake news on social media using a diverse body of datasets from the literature that contains about 100K previously labeled true and fake news. We also experiment with a variety of sentiment analysis tools. We model the association between sentiment and veracity as conditional probability and also leverage statistical hypothesis testing to uncover the relationship between sentiment and veracity. With a significance level of 99.999%, we observe a statistically significant relationship between negative sentiment and fake news and between positive sentiment and true news. The degree of association, as measured by Goodman and Kruskal's gamma, ranges between. 037 to. 475. Finally, we make our data and code publicly available to support reproducibility. Our results assist in the development of automatic fake news detectors.","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127717535","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}

{"title":"Analysis of COVID-19 Mitigation Measures on a Small Liberal Arts College Network","authors":"Robin M. Givens","doi":"10.1109/ASONAM49781.2020.9381382","DOIUrl":"https://doi.org/10.1109/ASONAM49781.2020.9381382","url":null,"abstract":"Small, liberal arts colleges are known to have close campus communities with strong relationships between professors and students. In this paper we consider the person-to-group and related person-to-person network at one of these institutions using student and faculty data from Fall 2019 courses, athletics, ensembles, housing, and student organizations. This data is used as a baseline to model the Fall 2020 semester with the college’s COVID-19 mitigation strategies: cancel or virtualize some groups, split the semester into two independent sessions, and separate larger courses into hybrid meetings. Network analysis shows that students and faculty had at most 4 degrees of separation in Fall 2019, student organizations can have a large impact on campus connectedness, all semester modifications implemented in Fall 2020 can reduce connectedness, and the largest reduction was seen by splitting the semester into two sessions.","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127816868","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}

{"title":"The 11th International Workshop on Mining and Analyzing Social Networks for Decision Support (MSNDS 2020) MSNDS 2020 Organizing Committee","authors":"","doi":"10.1109/asonam49781.2020.9381297","DOIUrl":"https://doi.org/10.1109/asonam49781.2020.9381297","url":null,"abstract":"","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132315029","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}

{"title":"Mechanisms of Behavioral Contagion: An Approximate Bayesian Approach","authors":"C. Luhmann, Brian Yang","doi":"10.1109/ASONAM49781.2020.9381449","DOIUrl":"https://doi.org/10.1109/ASONAM49781.2020.9381449","url":null,"abstract":"Researchers have proposed that contagion processes govern how information and behavior itself spreads through social networks. Empirical evidence for such contagion often makes unjustified, but implicit assumptions about the mechanisms underlying contagion. Here, we present an approximate Bayesian method that uses empirical data to draw inferences about the underlying mechanisms. We provide initial validation of our approach in three simulation experiments, each investigating how a real-world factor (e.g., noise) impacts inferential accuracy.","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130442726","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}

{"title":"Investigating Side Effects of Existing Drugs Used in Covid-19 Treatment","authors":"Sleiman Alhajj, S. Gencer","doi":"10.1109/ASONAM49781.2020.9381474","DOIUrl":"https://doi.org/10.1109/ASONAM49781.2020.9381474","url":null,"abstract":"Following the rapid spread and evolution of the novel Corona virus starting in December 2019, the lack of a vaccine or a medication that proved to be effective for Covid-19 was addressed as a major concern by the World Health Organization (WHO), the Center for Disease Control and Prevention (CDC), and the U.S. Food and Drug Administration (FDA) [1]. Accordingly, physicians from countries like China and Korea rushed to provide some potential treatment for Covid-19 from their experience in treating patients of the novel Coronavirus - they used antiviral medications like lopinavir, ritonavir, chloroquine, hydroxychloroquine, ribavirin, interferon, remdesivir, sofosbuvir, nitazoxanide, favipiravir, ivermectin, etc. [1]–[3]. These drugs showed improvement in conditions of Covid-19 patients when used individually, or sometimes using a combination of multiple of them. This does not mean that any combinations of these drugs could be beneficial. Some combinations can be lethal and may lead to increasing health risks or mortality. The drugs are being used in vitro (i.e., on cells in a laboratory for experiments) and vivo (i.e., on humans or animals as clinical trials). In vitro analysis, the chemical structure of the drug and the disease are analyzed to generate a hypothesis on the performance of the drug, then the hypothesis is tested in vivo to measure the actual performance of the drug on a living creature. Although these drugs showed promising results with proper dosage, overdose and incorrect combination with other drugs sometimes proved to be lethal. The effectiveness and side-effects of some of these drugs as reported by recent researchers and trials are described in this paper. We address some related research questions concerning the side effects of the covered drugs and their interaction with other drugs based on some well tested results extracted from approved web sites of drug-drug interactions. The findings are interesting and confirmed favipiravir as the most effective and safe compared to the others, and this coincides with and supports the announcement by Turkish Ministry of Health where favipiravir has been used in treating COVID-19 patients since the early days.","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131719969","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}