Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017最新文献_第8页

From Retweet to Believability: Utilizing Trust to Identify Rumor Spreaders on Twitter 从转发到可信度:利用信任识别Twitter上的谣言传播者

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110121

Bhavtosh Rath, Wei Gao, Jing Ma, J. Srivastava

{"title":"From Retweet to Believability: Utilizing Trust to Identify Rumor Spreaders on Twitter","authors":"Bhavtosh Rath, Wei Gao, Jing Ma, J. Srivastava","doi":"10.1145/3110025.3110121","DOIUrl":"https://doi.org/10.1145/3110025.3110121","url":null,"abstract":"Ubiquitous use of social media such as microblogging platforms brings about ample opportunities for the false information to diffuse online. It is very important not just to determine the veracity of information but also the authenticity of the users who spread the information, especially in time-critical situations like real-world emergencies, where urgent measures have to be taken for stopping the spread of fake information. In this work, we propose a novel machine learning based approach for automatic identification of the users spreading rumorous information by leveraging the concept of believability, i.e., the extent to which the propagated information is likely to be perceived as truthful, based on the trust measures of users in Twitter's retweet network. We hypothesize that the believability between two users is proportional to the trustingness of the retweeter and the trustworthiness of the tweeter, which are two complementary measures of user trust and can be inferred from retweeting behaviors using a variant of HITS algorithm. With the retweet network edge-weighted by believability scores, we use network representation learning to generate user embeddings, which are then leveraged to classify users into as rumor spreaders or not. Based on experiments on a very large real-world rumor dataset collected from Twitter, we demonstrate that our method can effectively identify rumor spreaders and outperform four strong baselines with large margin.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"35 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":"115469958","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: 59

On Quantifying Predictability in Online Social Media Cascades Using Entropy 利用熵量化在线社交媒体级联中的可预测性

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110071

Naimisha Kolli, N. Balakrishnan, K. Ramakrishnan

{"title":"On Quantifying Predictability in Online Social Media Cascades Using Entropy","authors":"Naimisha Kolli, N. Balakrishnan, K. Ramakrishnan","doi":"10.1145/3110025.3110071","DOIUrl":"https://doi.org/10.1145/3110025.3110071","url":null,"abstract":"Predicting cascade volumes in social media communication is an important topic in furthering the use of social media for viral marketing, impact of political campaigns and in home-land security. Several techniques have been reported in the literature to estimate the cascade volumes. These algorithms use a variety of information such as Content, Structural and Temporal features, depending on their availability. Due to the spread of information infused into the algorithms the prediction accuracy has been shown in the literature to be different for different algorithms. Entropy based measures that are tailored for the differing situations of information availability have been successfully applied in the prediction scenarios in many fields including network traffic, human mobility and radio spectrum state dynamics as well as in atmospheric science. In this paper we adopt a multitude of entropy based measures for quantifying the predictability of cascade volumes in online social media communications. The limit derived from the entropy measures discussed in this paper has also been used to explain the difference in accuracies of some of the algorithms for cascade volume predictions reported in the literature. For the purpose of illustration and to demonstrate the utility of the entropy based predictability limits we have used two data sets, the MemeTracker dataset and Twitter Hashtags dataset. The results obtained in this paper demonstrate clearly the utility of entropy based measures for quantifying the predictability in online social media cascades. We have also shown that temporal relevancy is a dominant contributing factor in cascade predictability and how additional features such as the knowledge of a small number of large media sites and blogs can have significant influence on the prediction performance.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"37 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":"114177897","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

A Dynamic Influence Keyword Model for Identifying Implicit User Interests on Social Networks 基于动态影响关键字的社交网络隐性用户兴趣识别模型

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3120987

Elvis Saravia, Shaomei Wu, Yi-Shin Chen

引用次数: 2

Optimizing Network Discovery with Clever Walks 优化网络发现与智能行走

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3120961

Ralucca Gera, Nicholas R. Juliano, Karl R. B. Schmitt

引用次数: 2

Social networks and healthcare coordination: Lessons learned from an Australian cancer care survey 社会网络和医疗保健协调:从澳大利亚癌症护理调查中吸取的教训

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3120994

I. Durcinoska, K. S. Chung, Jane M. Young, M. Solomon

引用次数: 2

Content Driven Profile Matching across Online Social Networks 跨在线社交网络的内容驱动配置文件匹配

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110095

R. Roedler, Dennis Kergl, G. Rodosek

{"title":"Content Driven Profile Matching across Online Social Networks","authors":"R. Roedler, Dennis Kergl, G. Rodosek","doi":"10.1145/3110025.3110095","DOIUrl":"https://doi.org/10.1145/3110025.3110095","url":null,"abstract":"Many publications deal with profile matching across online social networks and the approaches become increasingly complex. Almost all of them rely on common profile attributes like names and hobbies or structural attributes like relations to other user profiles. These approaches require high effort concerning computation, because each profile of one network has to be compared to all profiles of the other network. Complex approaches are not well suited to handle large datasets. Therefore, we present an approach to significantly reduce complexity by exploiting special properties of dataset IDs. We provide a proof of concept by an implementation of the use case of matching user profiles accross Twitter and Instagram. Additionally to the complexity problem of existing approaches, many profiles with similar attributes often lead to a restrictive trade-off between precision and recall of the matching strategy. Furthermore, profile attributes and relationships are not trustworthy, as these are due to arbitrary change by profile owners. In contrast to most existing approaches that rely on user definable attributes, we rather focus on timing properties of user publications across social media platforms. There are already profile matching approaches based on timing patterns. However, these do not aim to reduce complexity, what is a necessary requirement to be applicable to real-world online social networks. As we will show, the approach can be easily transferred to other networks.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"18 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":"123756791","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

Multiplex Media Attention and Disregard Network among 129 Countries 129个国家的多元媒体关注与忽视网络

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-16 DOI: 10.1145/3110025.3120984

Haewoon Kwak, Jisun An

引用次数: 4

Simultaneous Inference of User Representations and Trust 用户表示与信任的同时推理

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-06-03 DOI: 10.1145/3110025.3110093

Shashank Gupta, Pulkit Parikh, Manish Gupta, Vasudeva Varma

引用次数: 0

Big Data and Graph Theoretic Models: Simulating the Impact of Collateralization on a Financial System 大数据与图论模型:模拟抵押对金融系统的影响

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-05-29 DOI: 10.1145/3110025.3120989

Sharyn O'Halloran, N. Nowaczyk, Donal Gallagher

引用次数: 6

Diving Deep into Clickbaits: Who Use Them to What Extents in Which Topics with What Effects? 深入研究点击诱饵:谁在哪些主题中使用到什么程度，有什么效果?

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-03-28 DOI: 10.1145/3110025.3110054

Md Main Uddin Rony, Naeemul Hassan, M. Yousuf

引用次数: 97