2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)最新文献_第9页

Text Generation with Diversified Source Literature Review 多元来源文本生成文献综述

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3343510

A. Müngen, Emre Dogan, Mehmet Kaya

{"title":"Text Generation with Diversified Source Literature Review","authors":"A. Müngen, Emre Dogan, Mehmet Kaya","doi":"10.1145/3341161.3343510","DOIUrl":"https://doi.org/10.1145/3341161.3343510","url":null,"abstract":"Almost all academic studies include a literature review section. This section is of significance in terms of presenting the value of the suggested method of the researcher and making comparisons. Due to the increasing number of academic papers and the emergence of various directories and indices, the time spent for finding the related previous studies is an important period for the researcher, which consumes a significant amount of time. By means of the suggested method, researchers can access various types of featured publications related to the keyword from different years from a single address. The system also helps to reveal an exemplary and guiding literature review among the found publications by conducting a text generation. The system uses the TF-IDF method for keyword-based publication search and “Template-Based Text Generation” method for the text generation algorithm. In the study, the largest open-access journal platform, TÜBİTAK Dergipark and SOBIAD Citation Index were used as the data set. As a result of the conducted tests, a method that supports the literature review process, even helping to the writing of literature review, was suggested. Along with the fact that there has not been an equivalent of the suggested study, the comparisons for success, “Text Generation” and “Literature Review” were independently calculated and presented.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126307790","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

Finding Your Social Space: Empirical Study of Social Exploration in Multiplayer Online Games 寻找你的社交空间:多人在线游戏中社交探索的实证研究

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3345333

Arpita Chandra, Z. Borbora, P. Kumaraguru, J. Srivastava

{"title":"Finding Your Social Space: Empirical Study of Social Exploration in Multiplayer Online Games","authors":"Arpita Chandra, Z. Borbora, P. Kumaraguru, J. Srivastava","doi":"10.1145/3341161.3345333","DOIUrl":"https://doi.org/10.1145/3341161.3345333","url":null,"abstract":"Social dynamics are based on human needs for trust, support, resource sharing, irrespective of whether they operate in real life or in a virtual setting. Massively multiplayer online role-playing games (MMORPGS) serve as enablers of leisurely social activity and are important tools for social interactions. Past research has shown that socially dense gaming environments like MMORPGs can be used to study important social phenomena, which may operate in real life, too. We describe the process of social exploration to entail the following components 1) finding the balance between personal and social time 2) making choice between a large number of weak ties or few strong social ties. 3) finding a social group. In general, these are the major determinants of an individual's social life. This paper looks into the phenomenon of social exploration in an activity based online social environment. We study this process through the lens of the following research questions, 1) What are the different social behavior types? 2) Is there a change in a player's social behavior over time? 3) Are certain social behaviors more stable than the others? 4) Can longitudinal research of player behavior help shed light on the social dynamics and processes in the network? We use an unsupervised machine learning approach to come up with 4 different social behavior types - Lone Wolf, Pack Wolf of Small Pack, Pack Wolf of a Large Pack and Social Butterfly. The types represent the degree of socialization of players in the game. Our research reveals that social behaviors change with time. While lone wolf and pack wolf of small pack are more stable social behaviors, pack wolf of large pack and social butterflies are more transient. We also observe that players progressively move from large groups with weak social ties to settle in small groups with stronger ties.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130457310","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

On the Causal Relation between Users' Real-World Activities and their Affective Processes 论用户真实世界活动与情感过程之间的因果关系

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3342918

Seyed Amin Mirlohi Falavarjani, E. Bagheri, Ssu Yu Zoe Chou, J. Jovanović, A. Ghorbani

引用次数: 0

A non-negative matrix factorization approach to update communities in temporal networks using node features 一种基于节点特征的非负矩阵分解方法来更新时态网络中的社区

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3343677

Renny Márquez, R. Weber, A. Carvalho

引用次数: 2

Identifying Infrastructure Damage during Earthquake using Deep Active Learning 利用深度主动学习识别地震中基础设施的损坏

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3342955

S. Priya, Saharsh Singh, Sourav Kumar Dandapat, Kripabandhu Ghosh, Joydeep Chandra

{"title":"Identifying Infrastructure Damage during Earthquake using Deep Active Learning","authors":"S. Priya, Saharsh Singh, Sourav Kumar Dandapat, Kripabandhu Ghosh, Joydeep Chandra","doi":"10.1145/3341161.3342955","DOIUrl":"https://doi.org/10.1145/3341161.3342955","url":null,"abstract":"Twitter provides important information for emergency responders in the rescue process during disasters. However, tweets containing relevant information are sparse and are usually hidden in a vast set of noisy contents. This leads to inherent challenges in generating suitable training data that are required for neural network models. In this paper, we study the problem of retrieving the infrastructure damage information from tweets generated from different location during crisis using the model actively trained on past but similar events. We combine RNN and GRU based model coupled with active learning that gets trained on most uncertain samples and captures the latent features of different data distribution. It reduces the uses of around 90% less training data, thereby significantly reducing the manual annotation efforts. We use the model pre-trained using active learning based approach to retrieve the infrastructure damage tweets originated from different regions. We obtain a minimum of 18% gain on F1-measure and considerably on other metrics over recent state-of-the-art IR techniques.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115227336","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: 8

Feature Driven Learning Framework for Cybersecurity Event Detection 网络安全事件检测的特征驱动学习框架

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3342871

Taoran Ji, Xuchao Zhang, Nathan Self, Kaiqun Fu, Chang-Tien Lu, Naren Ramakrishnan

{"title":"Feature Driven Learning Framework for Cybersecurity Event Detection","authors":"Taoran Ji, Xuchao Zhang, Nathan Self, Kaiqun Fu, Chang-Tien Lu, Naren Ramakrishnan","doi":"10.1145/3341161.3342871","DOIUrl":"https://doi.org/10.1145/3341161.3342871","url":null,"abstract":"Cybersecurity event detection is a crucial problem for mitigating effects on various aspects of society. Social media has become a notable source of indicators for detection of diverse events. Though previous social media based strategies for cyber-security event detection focus on mining certain event-related words, the dynamic and evolving nature of online discourse limits the performance of these approaches. Further, because these are typically unsupervised or weakly supervised learning strategies, they do not perform well in an environment of biased samples, noisy context, and informal language which is routine for online, user-generated content. This paper takes a supervised learning approach by proposing a novel multi-task learning based model. Our model can handle diverse structures in feature space by learning models for different types of potential high-profile targets simultaneously. For parameter optimization, we develop an efficient algorithm based on the alternating direction method of multipliers. Through extensive experiments on a real world Twitter dataset, we demonstrate that our approach consistently outperforms existing methods at encoding and identifying cyber-security incidents.","PeriodicalId":403360,"journal":{"name":"2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116497653","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: 4

Estimating Tie Strength in Follower Networks to Measure Brand Perceptions 估计追随者网络中的联系强度以衡量品牌认知

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3343675

T. Nguyen, Li Zhang, A. Culotta

引用次数: 2

Deep Learning for Automated Sentiment Analysis of Social Media 深度学习用于社交媒体的自动情感分析

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3344821

L. Cheng, Song-Lin Tsai

引用次数: 34

Examining MOOC superposter behavior using social network analysis 使用社会网络分析来检验MOOC超级海报的行为

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3345310

M. Hegde, I. McCulloh, J. Piorkowski

引用次数: 0

A Comparison of Methods for Link Sign Prediction with Signed Network Embeddings 链接符号预测方法与带签名网络嵌入的比较

2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) Pub Date : 2019-08-01 DOI: 10.1145/3341161.3345335

Sandra Mitrovic, Laurent Lecoutere, Jochen De Weerdt

引用次数: 1