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

An Implicit Crowdsourcing Approach to Rumor Identification in Online Social Networks 基于隐式众包方法的在线社交网络谣言识别

2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2020-12-07 DOI: 10.1109/ASONAM49781.2020.9381339

Abiola Osho, Caden Waters, G. Amariucai

{"title":"An Implicit Crowdsourcing Approach to Rumor Identification in Online Social Networks","authors":"Abiola Osho, Caden Waters, G. Amariucai","doi":"10.1109/ASONAM49781.2020.9381339","DOIUrl":"https://doi.org/10.1109/ASONAM49781.2020.9381339","url":null,"abstract":"With the increasing use of online social networks as a source of news and information, the propensity for a rumor to disseminate widely and quickly poses a great concern, especially in disaster situations where users do not have enough time to fact-check posts before making the informed decision to react to a post that appears to be credible. At the same time, we know that misinformation is easily detectable by a certain few, very skeptical, or very informed users. In this study, we demonstrate how blending artificial intelligence and human skills can create a new paradigm for credibility prediction. The crowdsourcing part of the detection mechanism is implemented implicitly, by simply observing the natural interaction between users encountering the messages. Specifically, we explore the spread of information on Twitter at the microscopic (user-to-user propagation) level and propose a model that predicts if a message is True or False by observing the latent attributes of the message, along with those of the users interacting with it, and their reactions to the message. We demonstrate the application of this model to the detection of misinformation and rank the relevant message and user features that are most critical in influencing the spread of rumor over the network. Our experiments using real-world data show that the proposed model achieves over 90% accuracy in predicting the credibility of posts on Twitter, a significant boost over state-of-the-art models.","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"161 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":"121024929","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

High-Quality Prediction of Tourist Movements using Temporal Trajectories in Graphs 利用图中的时空轨迹对游客流动进行高质量预测

2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2020-12-07 DOI: 10.1109/ASONAM49781.2020.9381450

Shima Moghtasedi, Cristina Ioana Muntean, F. M. Nardini, R. Grossi, Andrea Marino

引用次数: 4

Welcome from the ASONAM 2020 Program Chairs 欢迎各位ASONAM 2020项目主席

2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2020-12-07 DOI: 10.1109/asonam49781.2020.9381350

引用次数: 0

Database Knowledge Enrichment Utilizing Trending Topics from Twitter 利用Twitter趋势主题丰富数据库知识

2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2020-12-07 DOI: 10.1109/ASONAM49781.2020.9381421

C. Vassilakis, Dimitra Maniataki, George Lepouras, Angeliki Antoniou, D. Spiliotopoulos, V. Poulopoulos, Manolis Wallace, Dionisis Margaris

{"title":"Database Knowledge Enrichment Utilizing Trending Topics from Twitter","authors":"C. Vassilakis, Dimitra Maniataki, George Lepouras, Angeliki Antoniou, D. Spiliotopoulos, V. Poulopoulos, Manolis Wallace, Dionisis Margaris","doi":"10.1109/ASONAM49781.2020.9381421","DOIUrl":"https://doi.org/10.1109/ASONAM49781.2020.9381421","url":null,"abstract":"Every day, many people use at least one social network (or social media) account. This development has been boosted by the rapid growth of technology, making both smartphones and mobile data much more accessible and inexpensive. Therefore, the number of social networks users is growing rapidly, accounting more than 1 billion active users worldwide. The ease of use, as well as the ability to communicate without spatial and temporal restrictions underpinned the rapid increase of the popularity of social networks, as well as their wide acceptance by the general public. This popularity influences people's opinion on many issues, shapes consumer habits and behaviour, mood, etc. The work of many scientists across multiple disciplines has focused on studying social media from various perspectives, including marketing, journalism and sociology. This paper investigates how trending information from social media can be used to match topics of interest from cultural database indices. Matches identified in this process are then presented to cultural venue curators, who can then review matches, mark them as useful or reject them, and exploit them for various tasks, and most notably for the promotion of the venue and its content. More specifically, we have developed an application, which collects the 10 most popular twitter trends and then matches their content with the contents of a given cultural database. Using the results of this match, items from the database that may be related to current issues may be recommended to the user. As a result, these matches, after being inspected and approved by the administrator, can be used to attract the interest of the target audience, highlighting the correlation of current issues with the database's items.","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"39 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":"126861558","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

Semantics Embedded Sequential Recommendation for E-Commerce Products (SEMSRec) 电子商务产品语义嵌入式顺序推荐(SEMSRec)

2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2020-12-07 DOI: 10.1109/ASONAM49781.2020.9381352

Mahreen Nasir, C. Ezeife

{"title":"Semantics Embedded Sequential Recommendation for E-Commerce Products (SEMSRec)","authors":"Mahreen Nasir, C. Ezeife","doi":"10.1109/ASONAM49781.2020.9381352","DOIUrl":"https://doi.org/10.1109/ASONAM49781.2020.9381352","url":null,"abstract":"In Collaborative Filtering methods, tailored recommendations cannot be obtained when the user-item matrix is sparse (i.e., has low user-item interactions such as item ratings or purchases). Conventional recommendation systems (ChoiRec12, HPCRec18, HSPRec19) utilizing mining techniques such as clustering, frequent and sequential pattern mining along with click and purchase similarity measures for item recommendation cannot perform well when the user-item interactions are less, as the number of items keep increasing rapidly. Additionally, they have not explored the integration of semantic information of products extracted from customers' purchase histories into the item matrix and the pattern mining process. To address this problem, this paper proposes (SEMSRec) which integrates semantic information of E-commerce products extracted from purchase histories into all phases of recommendation process (pre-processing, pattern mining and recommendation). This is achieved by i) learning semantic similarities between items from customers' purchase histories using Prod2vec model, ii) leveraging this information to mine semantically rich sequential purchase patterns and, iii) enriching the item matrix with semantic and sequential product purchase information before applying item based collaborative filtering. Thus, SEMSRec can provide Top-K personalized recommendations based on semantic similarities between items without the need for users' ratings on items. Experimental results on publically available E-commerce data set show that SEMSRec provides more relevant recommendations over other existing methods.","PeriodicalId":196317,"journal":{"name":"2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"42 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":"122531291","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

Opening and Closing Dynamics of Competing Shop Groups over Spatial Networks 空间网络中竞争商店群的开放与关闭动态

2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2020-12-07 DOI: 10.1109/ASONAM49781.2020.9381388

Takayasu Fushimi, Kazumi Saito, K. Ohara, M. Kimura, H. Motoda

{"title":"Opening and Closing Dynamics of Competing Shop Groups over Spatial Networks","authors":"Takayasu Fushimi, Kazumi Saito, K. Ohara, M. Kimura, H. Motoda","doi":"10.1109/ASONAM49781.2020.9381388","DOIUrl":"https://doi.org/10.1109/ASONAM49781.2020.9381388","url":null,"abstract":"We address the problem of opening and closing shops in group competitive environment, i.e., shops in the same group work cooperatively and those in different groups competitively, and analyze how the market share and location changes over time. We formulate a stochastic utility of each shop as a function of shop distance and attractiveness from which a market share is computed by weighting consumers buying power. We further place a constraint on the traveling time, which is crucial to reduce the computation time, and use a marginal gain of the market share as a measure to rank the candidate location. Using the real dataset of three convenience stores in four cities in Japan, we confirm that, despite the simplification we made in the model, rankings of the existing shops are shown to be high which implies that our model is reasonable. Further, comparison with the baseline gravity model shows that our model gives much more realistic results. Analyses of the dynamics of opening and closing shops indicate that the reasonable time-bound for walking is about 10 min., the market share of each group, thus total share, eventually increases although small, and the difference of the share within each group gradually becomes smaller, revealing that the spatial distribution of the shops in each group becomes more uniform.","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":"122059749","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

IEEE/ACM ASONAM 2020 ASONAM 2020 Program Committee

2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2020-12-07 DOI: 10.1109/asonam49781.2020.9381376

引用次数: 0

Network Analysis of Bilateral Trade Data Under Asymmetry 不对称条件下双边贸易数据的网络分析

2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2020-12-07 DOI: 10.1109/ASONAM49781.2020.9381408

N. Meshcheryakova

引用次数: 2

Ideology Detection in the Indian Mass Media 印度大众传媒中的意识形态侦查

2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2020-12-07 DOI: 10.1109/ASONAM49781.2020.9381344

Ankur Sharma, N. Kaur, Anirban Sen, Aaditeshwar Seth

引用次数: 4

Using Communication Networks to Predict Team Performance in Massively Multiplayer Online Games 利用通讯网络预测大型多人在线游戏中的团队表现

2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2020-12-07 DOI: 10.1109/ASONAM49781.2020.9381481

S. Müller, Raji Ghawi, J. Pfeffer

引用次数: 7