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

Link Clustering for Extracting Collaborative Patterns in a Scientific Co-Authored Network 基于链接聚类的科学合著网络协同模式提取

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110146

Erick Stattner, M. Collard

引用次数: 0

Analyzing Disproportionate Reaction via Comparative Multilingual Targeted Sentiment in Twitter 通过比较多语言目标情绪分析Twitter中的不成比例反应

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110066

K. S. Smith, R. McCreadie, C. Macdonald, I. Ounis

{"title":"Analyzing Disproportionate Reaction via Comparative Multilingual Targeted Sentiment in Twitter","authors":"K. S. Smith, R. McCreadie, C. Macdonald, I. Ounis","doi":"10.1145/3110025.3110066","DOIUrl":"https://doi.org/10.1145/3110025.3110066","url":null,"abstract":"Global events such as terrorist attacks are commented upon in social media, such as Twitter, in different languages and from different parts of the world. Most prior studies have focused on monolingual sentiment analysis, and therefore excluded an extensive proportion of the Twitter userbase. In this paper, we perform a multilingual comparative sentiment analysis study on the terrorist attack in Paris, during November 2015. In particular, we look at targeted sentiment, investigating opinions on specific entities, not simply the general sentiment of each tweet. Given the potentially inflammatory and polarizing effect that these types of tweets may have on attitudes, we examine the sentiments expressed about different targets and explore whether disproportionate reaction was expressed about such targets across different languages. Specifically, we assess whether the sentiment for French speaking Twitter users during the Paris attack differs from English-speaking ones. We identify disproportionately negative attitudes in the English dataset over the French one towards some entities and, via a crowdsourcing experiment, illustrate that this also extends to forming an annotator bias.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"46 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":"123299565","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: 3

TrollSpot: Detecting misbehavior in commenting platforms TrollSpot:在评论平台上检测不当行为

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110057

Tai-Ching Li, Joobin Gharibshah, E. Papalexakis, M. Faloutsos

引用次数: 16

Transfer Learning for Multi-language Twitter Election Classification 多语言推特选举分类的迁移学习

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110059

Xiao Yang, R. McCreadie, C. Macdonald, I. Ounis

{"title":"Transfer Learning for Multi-language Twitter Election Classification","authors":"Xiao Yang, R. McCreadie, C. Macdonald, I. Ounis","doi":"10.1145/3110025.3110059","DOIUrl":"https://doi.org/10.1145/3110025.3110059","url":null,"abstract":"Both politicians and citizens are increasingly embracing social media as a means to disseminate information and comment on various topics, particularly during significant political events, such as elections. Such commentary during elections is also of interest to social scientists and pollsters. To facilitate the study of social media during elections, there is a need to automatically identify posts that are topically related to those elections. However, current studies have focused on elections within English-speaking regions, and hence the resultant election content classifiers are only applicable for elections in countries where the predominant language is English. On the other hand, as social media is becoming more prevalent worldwide, there is an increasing need for election classifiers that can be generalised across different languages, without building a training dataset for each election. In this paper, based upon transfer learning, we study the development of effective and reusable election classifiers for use on social media across multiple languages. We combine transfer learning with different classifiers such as Support Vector Machines (SVM) and state-of-the-art Convolutional Neural Networks (CNN), which make use of word embedding representations for each social media post. We generalise the learned classifier models for cross-language classification by using a linear translation approach to map the word embedding vectors from one language into another. Experiments conducted over two election datasets in different languages show that without using any training data from the target language, linear translations outperform a classical transfer learning approach, namely Transfer Component Analysis (TCA), by 80% in recall and 25% in F1 measure.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"8 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":"122429589","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: 9

Mining the Networks of Telecommunication Fraud Groups using Social Network Analysis 利用社会网络分析挖掘电信诈骗集团的网络

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3119396

Yi-Chun Chang, Kuan-Ting Lai, S. Chou, Ming-Syan Chen

引用次数: 9

Efficient Mining of 'Following' Patterns from Very Big but Sparse Social Networks 从非常大但稀疏的社交网络中有效挖掘“跟随”模式

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110089

C. Leung, Fan Jiang

{"title":"Efficient Mining of 'Following' Patterns from Very Big but Sparse Social Networks","authors":"C. Leung, Fan Jiang","doi":"10.1145/3110025.3110089","DOIUrl":"https://doi.org/10.1145/3110025.3110089","url":null,"abstract":"Advances in technology in the current era of big data has led to the high-velocity generation of high volumes of a wide variety of valuable data of different veracity. As rich sources of big data, social networks consist of users (or social entities) who are often linked by some interdependency such as 'following' relationships. Given these big social networks keep growing, there are situations in which an individual user (or business) wants to find those frequently followed groups of social entities so that he can follow the same groups. Discovery of these frequently followed groups can be challenging because the social networks are usually very big (with lots of users/social entities) but can be sparse (with most users only know some but not all users/social entities in a social network). In this paper, we present a few social network mining algorithms that use compressed models in mining these very big but sparse social networks for discovering groups of frequently followed social entities. Evaluation results show the practicality of our algorithms in efficient mining of 'following' patterns from very big but sparse social networks.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"47 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":"126963565","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: 12

Estimating users' mode transition functions and activity levels from social media 估计用户模式转换功能和社交媒体活动水平

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3116195

Hamilton E. Link, Jeremy D. Wendt, R. Field, Jocelyn Marthe

引用次数: 1

Identity vs. Attribute Disclosure Risks for Users with Multiple Social Profiles 具有多个社交档案的用户的身份与属性披露风险

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110046

Athanasios Andreou, Oana Goga, P. Loiseau

{"title":"Identity vs. Attribute Disclosure Risks for Users with Multiple Social Profiles","authors":"Athanasios Andreou, Oana Goga, P. Loiseau","doi":"10.1145/3110025.3110046","DOIUrl":"https://doi.org/10.1145/3110025.3110046","url":null,"abstract":"Individuals sharing data on today's social computing systems face privacy losses due to information disclosure that go much beyond the data they directly share. Indeed, it was shown that it is possible to infer additional information about a user from data shared by other users--- this type of information disclosure is called attribute disclosure. Such studies, however, were limited to a single social computing system. In reality, users have identities across several social computing systems and reveal different aspects of their lives in each. This enlarges considerably the scope of information disclosure, but also complicates its analysis. Indeed, when considering multiple social computing systems, information disclosure can be of two types: attribute disclosure or identity disclosure--- which relates to the risk of pinpointing, for a given identity in a social computing system, the identity of the same individual in another social computing system. This raises the key question: how do these two privacy risks relate to each other? In this paper, we perform the first combined study of attribute and identity disclosure risks across multiple social computing systems. We first propose a framework to quantify these risks. Our empirical evaluation on a real-world dataset from Facebook and Twitter then shows that, in some regime, there is a tradeoff between the two information disclosure risks, that is, users with a lower identity disclosure risk suffer a higher attribute disclosure risk. We investigate in depth the different parameters that impact this tradeoff.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"7 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":"125572534","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: 24

Stationary Randomness of Quantum Cryptographic Sequences on Variant Maps 变异映射上量子密码序列的平稳随机性

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110151

Jeffrey Z. J. Zheng, Chris Zheng

{"title":"Stationary Randomness of Quantum Cryptographic Sequences on Variant Maps","authors":"Jeffrey Z. J. Zheng, Chris Zheng","doi":"10.1145/3110025.3110151","DOIUrl":"https://doi.org/10.1145/3110025.3110151","url":null,"abstract":"Natural and artificial sequences of big data streams have various stationary and non-stationary properties. A Quantum Key Distribution (QKD) system has a quantum random number generator to protect data streams in quantum communication environments. From a cryptanalysis viewpoint, it is necessary to use statistical probability, stochastic processes and time series to evaluate quality of stationary randomness in quantum cryptographic sequrences. In this paper, a testing model is proposed to use statistical probability to illustrate multiple visual distributions on three maps for a selected random sequence. Under a shift operation to transfer the sequence into a shifted sequence, multiple segments are divided on the shifted sequence as three measuring sets to form three maps. For a given map, its maximal value is extracted from the distribution and three maximal values for the testing. Three 2D maps represent stationary random properties for the sequence under different shift operations. Conditions of station/stationary random sequences are investigated. Testing data sets are from two quantum cryptographic resources: Australian National University (ANU) and University of Science and Technology of China (USTC), two quantum cryptographic sequences are selected. Multiple results are created on three maps, and measurements of stationary randomness are illustrated. Using the testing system, measurements of stationary randomness are compared. There are only 0.07 ~ 0.27% variation differences identified among ANU and USTC samples for testing stationary randomness. Both samples show excellent stationary properties.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"65 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":"134111580","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

DBSTexC: Density-Based Spatio-Textual Clustering on Twitter 基于密度的Twitter空间文本聚类

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110096

Minh D. Nguyen, Won-Yong Shin

{"title":"DBSTexC: Density-Based Spatio-Textual Clustering on Twitter","authors":"Minh D. Nguyen, Won-Yong Shin","doi":"10.1145/3110025.3110096","DOIUrl":"https://doi.org/10.1145/3110025.3110096","url":null,"abstract":"Density-based spatial clustering of applications with noise (DBSCAN) is the most commonly used density-based clustering algorithm, where it can discover multiple clusters with arbitrary shapes. DBSCAN works properly when the input data type is homogeneous, but the DBSCAN's approach may not be sufficient when the input dataset has textual heterogeneity (e.g., when we intend to find clusters from geo-tagged posts on social media relevant to a certain point-of-interest (POI)), thus leading to poor performance. In this paper, we present DBSTexC, a new density-based clustering algorithm using spatio--textual information on Twitter. We first define POI-relevant and POI-irrelevant tweets as the records that contain and do not contain a POI name or its coherent variations, respectively. By taking into account the fractions of POI-relevant and POI-irrelevant tweets, our DBSTexC algorithm shows a much higher clustering quality than the DBSCAN case in terms of the F1 score and its variants. DBSTexC can be thought of as a generalized version of DBSCAN due to the findings that it performs identically as DBSCAN when the inputs are homogeneous and far outperforms DBSCAN when the heterogeneous input data type is given.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"8 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":"128884939","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: 12