Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017最新文献_第4页

Predicting Friendship Strength for Privacy Preserving: A Case Study on Facebook 基于隐私保护的友谊强度预测:以Facebook为例

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3116196

Nitish Dhakal, Francesca Spezzano, Dianxiang Xu

{"title":"Predicting Friendship Strength for Privacy Preserving: A Case Study on Facebook","authors":"Nitish Dhakal, Francesca Spezzano, Dianxiang Xu","doi":"10.1145/3110025.3116196","DOIUrl":"https://doi.org/10.1145/3110025.3116196","url":null,"abstract":"Effective friend classification in Online Social Networks (OSN) has many benefits in privacy. Anything posted by a user in social networks like Facebook is distributed among all their friends. Although the user can select the manual option for their post-dissemination, it is not feasible every time. Since not all friends are the same in social networks, the visibility access for the post should be different for different strengths of friendship for privacy. Previous works in finding friendship strength in social networks have used interaction and similarity based features but none of them has considered using sentiment-based features as the driving factor to determine the strength. In this paper, we develop a supervised model to estimate the friendship strength based upon 23 different features comprising of structure based, interaction based, homophily based and sentiment based features. We evaluate our model on a real-world Facebook dataset we built that has ground truth for different types of friendship: close, good, acquaintance, and casual. Our model obtains an AUROC of 0.82 in identifying acquaintances from all the other three categories, and an AUROC of 0.85 in distinguishing between close friends and acquaintances. Our experiments suggest that features like average comment length, reaction scores for likes and love, friend tag score, Jaccard similarity and closeness variable consistently perform better in predicting friendship strength across different classifiers. In addition, combining language-based features with homophilic, structural and interaction features produces more accurate and trustworthy model to evaluate friendship strength.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"286 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":"114185415","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

MCDA: A Parameterless Algorithm for Detecting Communities in Multidimensional Networks 多维网络中群体检测的无参数算法

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110052

Oualid Boutemine, M. Bouguessa

引用次数: 2

Anomalous Reviews Owing to Referral Incentive 推荐激励下的异常评价

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110100

Noor Abu-El-Rub, Amanda J. Minnich, A. Mueen

引用次数: 6

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 2017年IEEE/ACM社会网络分析与挖掘进展国际会议论文集

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

J. Diesner, E. Ferrari, Guandong Xu

引用次数: 16

Using Social Network Analysis in Understanding The Public Discourse on Gender Violence: an Agent-Based Modelling Approach 使用社会网络分析来理解性别暴力的公共话语:一个基于主体的建模方法

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3120960

M. D. L. Paz, M. R. Estuar

{"title":"Using Social Network Analysis in Understanding The Public Discourse on Gender Violence: an Agent-Based Modelling Approach","authors":"M. D. L. Paz, M. R. Estuar","doi":"10.1145/3110025.3120960","DOIUrl":"https://doi.org/10.1145/3110025.3120960","url":null,"abstract":"There are many representations attributed to gender-based violence. Public discourse provides useful datasets that can be studied in order to study such representations. Social network modelling is a way to study that public discourse, by looking at how opinions in a discourse interact and repeat themselves on a large scale and over time. This study aims to construct a social network model using an agent-based approach to measure whether the conversation space of certain gender violence discourses are more centered on victims, perpetrators, institutions, or society. It will use network measures of centrality, immediate impact analysis, and centrality changes over time to compare the context of two cultures: Philippines and the United States. The data set from the Philippines consists of articles on the Vizconde Massacre and the data set from the United States consists of articles on the Stanford Rape Case. Results show that both datasets feature an institution-centric discourse that is consistent over time, and that society has the lowest role-centrality in both events. Perpetrators appear more central than victims, but comparatively more so in the Stanford Rape dataset compared to the Vizconde Massacre one.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"68 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":"131771628","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

InferIP: Extracting actionable information from security discussion forums 从安全论坛中提取可操作的信息

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110055

Joobin Gharibshah, Tai-Ching Li, Maria Solanas Vanrell, Andre Castro, K. Pelechrinis, E. Papalexakis, M. Faloutsos

{"title":"InferIP: Extracting actionable information from security discussion forums","authors":"Joobin Gharibshah, Tai-Ching Li, Maria Solanas Vanrell, Andre Castro, K. Pelechrinis, E. Papalexakis, M. Faloutsos","doi":"10.1145/3110025.3110055","DOIUrl":"https://doi.org/10.1145/3110025.3110055","url":null,"abstract":"How much useful information can we extract from security forums? Many security initiatives and commercial entities are harnessing the readily public information, but they seem to focus on structured sources of information. Our goal here is to extract information from hacker forums, whose information is provided in ad hoc and unstructured ways. Here, we focus on the problem of identifying malicious IPs addresses, when these are being reported in the forums. We develop a method to automate the identification of malicious IPs with the design goal of being independent of external sources. A key novelty is that we use a matrix decomposition method to extract latent features of the behavioral information of the users, which we combine with textual information from the related posts. As key design feature, our technique can be applied to different language forums since it relies on a simple NLP solution in combination with behavioral features. In particular, our solution only needs a small number of keywords in the new language plus the user's behavior captured by specific features. We also develop a tool to automate the data collection from security forums. We collect approximately 600K posts from 3 different forums. Our method exhibits high classification accuracy, while the precision of identifying malicious IP in post is greater than 88% in all three sites. Furthermore, by applying our method, we find up to 3 times more potentially malicious IPs than compared to the reference blacklist VirusTotal. As the cyber-wars are becoming more intense, having early accesses to useful information becomes more imperative to remove the hackers first-move advantage, and our work is a solid step towards this direction.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"52 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":"123903289","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: 13

Understanding and Classifying Online Amputee Users on Reddit 理解和分类在线截肢用户在Reddit上

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110037

Xing Yu, Erin L. Brady

引用次数: 3

One Size Does Not Fit All: Profiling Personalized Time-Evolving User Behaviors 一种方式不适合所有人:分析个性化的、随时间变化的用户行为

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110050

Pravallika Devineni, E. Papalexakis, Danai Koutra, A. Seza Doğruöz, M. Faloutsos

{"title":"One Size Does Not Fit All: Profiling Personalized Time-Evolving User Behaviors","authors":"Pravallika Devineni, E. Papalexakis, Danai Koutra, A. Seza Doğruöz, M. Faloutsos","doi":"10.1145/3110025.3110050","DOIUrl":"https://doi.org/10.1145/3110025.3110050","url":null,"abstract":"Given the set of social interactions of a user, how can we detect changes in interaction patterns over time? While most previous work has focused on studying network-wide properties and spotting outlier users, the dynamics of individual user interactions remain largely unexplored. This work sets out to explore those dynamics in a way that is minimally invasive to privacy, thus, avoids to rely on the textual content of user posts---except for validation. Our contributions are two-fold. First, in contrast to previous studies, we challenge the use of a fixed interval of observation. We introduce and empirically validate the \"Temporal Asymmetry Hypothesis\", which states that appropriate observation intervals should vary both among users and over time for the same user. We validate this hypothesis using eight different datasets, including email, messaging, and social networks data. Second, we propose iNET, a comprehensive analytic and visualization framework which provides personalized insights into user behavior and operates in a streaming fashion. iNET learns personalized baseline behaviors of users and uses them to identify events that signify changes in user behavior. We evaluate the effectiveness of iNET by analyzing more than half a million interactions from Facebook users. Labeling of the identified changes in user behavior showed that iNET is able to capture a wide spectrum of exogenous and endogenous events, while the baselines are less diverse in nature and capture only 66% of that spectrum. Furthermore, iNET exhibited the highest precision (95%) compared to all competing approaches.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"333 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":"122845608","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

From Social Media Analysis to Ubiquitous Event Monitoring: The case of Turkish Tweets 从社交媒体分析到无处不在的事件监控:以土耳其推特为例

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3120986

A. Erdogan, Tolga Yilmaz, Onur Can Sert, Mirun Akyüz, Tansel Özyer, R. Alhajj

引用次数: 5

Real-Time Targeted-Influence Queries over Large Graphs 大型图上的实时目标影响查询

Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 Pub Date : 2017-07-31 DOI: 10.1145/3110025.3110105

Alessandro Epasto, Ahmad Mahmoody, E. Upfal

{"title":"Real-Time Targeted-Influence Queries over Large Graphs","authors":"Alessandro Epasto, Ahmad Mahmoody, E. Upfal","doi":"10.1145/3110025.3110105","DOIUrl":"https://doi.org/10.1145/3110025.3110105","url":null,"abstract":"Social networks are important communication and information media. Individuals in a social network share information and influence each other through their social connections. Understanding social influence and information diffusion is a fundamental research endeavor and it has important applications in online social advertising and viral marketing. In this work, we introduce the Targeted-Influence problem (TIP): Given a network G = (V, ε) and a model of influence, we want to be able to estimate in real-time (e.g. a few seconds per query) the influence of a subset of users S over another subset of users T, for any possible query (S; T), S, T ⊆ V. To do so, we allow an efficient preprocessing. We provide the first scalable real-time algorithm for TIP. Our algorithm requires Õ(|V| + |ε|) space and preprocessing time, and it provides a provable approximation of the influence of S over T, for every subsets of nodes S, T ⊆ V in the query with large enough influence. The running time for answering each query (a.k.a query stage) is theoretically guaranteed to be Õ(|S| + |T|) in general undirected and for directed graphs under certain assumptions, supported by experiments. We also introduce the Snapshot model as our model of influence, which extends and includes as special case both the Independent Cascade and the Linear Threshold models. The analysis and the theoretical guarantees of our algorithms hold under the more general Snapshot model. Finally, we perform an extensive experimental analysis, demonstrating the accuracy, efficiency, and scalability of our methods.","PeriodicalId":399660,"journal":{"name":"Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017","volume":"32 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":"114726860","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