Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017最新文献_第7页

Temporal Model of the Online Customer Review Helpfulness Prediction 在线顾客评论有用性预测的时间模型

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110156

Shih-Hung Wu, Yi-Hsiang Hsieh, Liang-Pu Chen, Ping-Che Yang, Liu Fanghuizhu

{"title":"Temporal Model of the Online Customer Review Helpfulness Prediction","authors":"Shih-Hung Wu, Yi-Hsiang Hsieh, Liang-Pu Chen, Ping-Che Yang, Liu Fanghuizhu","doi":"10.1145/3110025.3110156","DOIUrl":"https://doi.org/10.1145/3110025.3110156","url":null,"abstract":"Customer reviews provide opinions and relevant information that will affect the purchase behavior of other customers. Many studies focused on the prediction of the helpfulness of customer reviews to find the helpful reviews, which are traditionally determined by the helpful voting results. In our study, we find that the voting result of an online review is not a constant over time. Therefore, predicting the voting result based on the analysis of text is not enough; the temporal issue must be considered. We propose a system that can rank the reviews based on a set of linguistic features with a linear regression model. To evaluate our system, we collect Chinese custom reviews in eight product categories (books, digital cameras, tablet PC, backpacks, movies, men shoes, toys and cell phones) from Amazon.cn with the voting result on the helpfulness of the reviews. Since the voting result may be affected by voting time and total voting number, we define a new evaluation index and compare the regression results. The results show that the system has less prediction error when it takes the time information into the prediction model.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"60 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114116671","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: 5

Mining Twitter and Taxi Data for Predicting Taxi Pickup Hotspots 挖掘Twitter和出租车数据预测出租车接送热点

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110106

S. Mridha, Sayan Ghosh, R. Singh, Sourangshu Bhattacharya, Niloy Ganguly

引用次数: 2

Text Watermarking in Social Media 社交媒体中的文本水印

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3116203

S. Rizzo, Flavio Bertini, D. Montesi, Carlo Stomeo

引用次数: 20

Improved Stance Prediction in a User Similarity Feature Space 基于用户相似度特征空间的改进姿态预测

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110112

Kareem Darwish, Walid Magdy, Tahar Zanouda

引用次数: 39

Representation and Analysis of Twitter Activity: A Dynamic Network Perspective 推特活动的表征与分析:一个动态网络的视角

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3122118

L. Falzon, Caitlin McCurrie, John Dunn

{"title":"Representation and Analysis of Twitter Activity: A Dynamic Network Perspective","authors":"L. Falzon, Caitlin McCurrie, John Dunn","doi":"10.1145/3110025.3122118","DOIUrl":"https://doi.org/10.1145/3110025.3122118","url":null,"abstract":"Online interaction networks are highly dynamic. They provide opportunities to share information more widely and faster than ever before, but result in rather complicated trajectories of information flow that present new challenges for modeling and analysis. Collecting the right data, processing it appropriately and determining which networks to analyze are some of the challenges we face. The ease with which data can be sourced through Twitter's API has resulted in a disproportionate number of studies analyzing 'big data'. This focus has led researchers to overlook the importance of 'small data': traditional methods of data collection, such as survey and experimental studies. Clever data extraction and processing capabilities are absolutely necessary to deal with the enormous quantity of data generated by Internet-mediated interactions. However, the richness at the level of the individual may be overlooked if big data approaches are exclusively used, potentially resulting in inappropriate generalizations and conclusions. In this paper we study results from a qualitative study on Twitter users' behavior and combine them with dynamic network analysis to investigate the manifestations of Twitter relations.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123762886","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: 7

Deep Paraphrase Detection in Indian Languages 印度语言的深层释义检测

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3122119

Rupal Bhargava, Gargi Sharma, Yashvardhan Sharma

引用次数: 8

Mapping Whole DNA Sequence on Variant Maps 在变异图谱上绘制全DNA序列

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110140

Yuyuan Mao, Jeffrey Zheng, Wenjia Liu

引用次数: 6

Social Media in State Politics: Mining Policy Agendas Topics 社会媒体在国家政治:矿业政策议程议题

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110097

Lei Qi, Rihui Li, J. Wong, Wallapak Tavanapong, David A. M. Peterson

{"title":"Social Media in State Politics: Mining Policy Agendas Topics","authors":"Lei Qi, Rihui Li, J. Wong, Wallapak Tavanapong, David A. M. Peterson","doi":"10.1145/3110025.3110097","DOIUrl":"https://doi.org/10.1145/3110025.3110097","url":null,"abstract":"Twitter is a popular online microblogging service that has become widely used by politicians to communicate with their constituents. Gaining understanding of the influence of Twitter in state politics in the United States cannot be achieved without proper computational tools. We present the first attempt to automatically classify tweets of state legislatures (policy makers at the state level) into major policy agenda topics defined by Policy Agendas Project (PAP), which was initiated to group national policies. We investigated the effectiveness of three popular machine learning algorithms, Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Long Short-Term Memory Network (LSTM). We proposed a new synthetic data augmentation method to further improve classification performance. Our experimental results show that CNN provides the best F1 score of 78.3%. The new data augmentation method improves the classification perfromance by about 2%. Our tool provides a good prediction of the top three popular PAP topics in each month, which is useful for tracking popular PAP topics over time and across states and for comparing with national policy agendas.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133894956","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: 6

The Role of Different Tie Strength in Disseminating Different Topics on a Microblog 不同领带强度在微博上传播不同话题中的作用

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110130

Felicia Natali, Kathleen M. Carley, Feida Zhu, Binxuan Huang

{"title":"The Role of Different Tie Strength in Disseminating Different Topics on a Microblog","authors":"Felicia Natali, Kathleen M. Carley, Feida Zhu, Binxuan Huang","doi":"10.1145/3110025.3110130","DOIUrl":"https://doi.org/10.1145/3110025.3110130","url":null,"abstract":"The study of information flow typically does not distinguish the choices of tie strength on which the information flows. All receivers of the information are assumed to have the same potential to pass on the information. Modifying the SEIZ (susceptible, exposed, infected, skeptic) model, we discover that people choose to retweet strong or weak ties based on the topic. We made two modifications in the model. In the first modification (Model I), we assume that the contact rates of agents in different compartment and the probability of an agent transitioning from one compartment to another are different for strong ties and weak ties. In the second modification (Model II), we assume that only the probability of transitioning is different for strong ties and weak ties. We discover that people do not discriminate strong ties and weak ties when retweeting controversial topic, perhaps because this topic can both be personal and breaking news. On the other hand, people discriminate strong ties and weak ties when retweeting non-controversial topic. They prefer to retweet strong ties when the topic is donation, and kids, and weak ties when the topic is news on hurricane and music. Meanwhile, SEIZ model and its modifications are found to be inadequate to model tweets on event promotion.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127631078","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

On Fighting Fire with Fire: Strategic Destabilization of Terrorist Networks 以暴制暴:恐怖主义网络的战略不稳定

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3119404

Vahid Behzadan, Amin Nourmohammadi, M. H. Gunes, M. Yuksel

引用次数: 3