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

{"title":"Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media.","authors":"Amir Hossein Yazdavar, Hussein S Al-Olimat, Monireh Ebrahimi, Goonmeet Bajaj, Tanvi Banerjee, Krishnaprasad Thirunarayan, Jyotishman Pathak, Amit Sheth","doi":"10.1145/3110025.3123028","DOIUrl":"10.1145/3110025.3123028","url":null,"abstract":"<p><p>With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter. Unlike traditional observational cohort studies conducted through questionnaires and self-reported surveys, we explore the reliable detection of clinical depression from tweets obtained unobtrusively. Based on the analysis of tweets crawled from users with self-reported depressive symptoms in their Twitter profiles, we demonstrate the potential for detecting clinical depression symptoms which emulate the PHQ-9 questionnaire clinicians use today. Our study uses a semi-supervised statistical model to evaluate how the duration of these symptoms and their expression on Twitter (in terms of word usage patterns and topical preferences) align with the medical findings reported via the PHQ-9. Our proactive and automatic screening tool is able to identify clinical depressive symptoms with an accuracy of 68% and precision of 72%.</p>","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"2017 ","pages":"1191-1198"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5914530/pdf/nihms911348.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36055250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Finding Street Gang Members on Twitter.","authors":"Lakshika Balasuriya,&nbsp;Sanjaya Wijeratne,&nbsp;Derek Doran,&nbsp;Amit Sheth","doi":"10.1109/ASONAM.2016.7752311","DOIUrl":"https://doi.org/10.1109/ASONAM.2016.7752311","url":null,"abstract":"<p><p>Most street gang members use Twitter to intimidate others, to present outrageous images and statements to the world, and to share recent illegal activities. Their tweets may thus be useful to law enforcement agencies to discover clues about recent crimes or to anticipate ones that may occur. Finding these posts, however, requires a method to discover gang member Twitter profiles. This is a challenging task since gang members represent a very small population of the 320 million Twitter users. This paper studies the problem of automatically finding gang members on Twitter. It outlines a process to curate one of the largest sets of verifiable gang member profiles that have ever been studied. A review of these profiles establishes differences in the language, images, YouTube links, and emojis gang members use compared to the rest of the Twitter population. Features from this review are used to train a series of supervised classifiers. Our classifier achieves a promising <i>F</i>₁ score with a low false positive rate.</p>","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"206 ","pages":"685-692"},"PeriodicalIF":0.0,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ASONAM.2016.7752311","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35175990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Which Tweets Will Be Headlines? A Hierarchical Bayesian Model for Bridging Social Media and Traditional Media","authors":"Dan Zhang, Yan Liu, Luo Si","doi":"10.1145/2659480.2659497","DOIUrl":"https://doi.org/10.1145/2659480.2659497","url":null,"abstract":"Microblogging platforms such as Twitter provide a convenient channel for people to express their feelings, report news, and communicate with friends. Most existing work on social media analysis has been focused on predicting users' behaviors, analyzing the corresponding social networks, tracking the popular topics, etc. However, there is limited research effort on uncovering the relationships between social media (e.g. Twitter) and traditional media (e.g., Washington Post and New York Times), which has a big impact in our daily lives and our society. This paper targets on a novel and important research problem as which and whose tweets are favored by the traditional media. The basic intuition is that whether a tweet could be picked up or not by traditional media depends not only on whether its content matches traditional media's interests towards this specific user but also the writer's personal influence, reflected by factors such as the number of followers. Based on this intuition, this paper proposes a Twitter Pick-Up Relational (TPUR) model to simultaneously integrate these factors. In particular, the dependence between the traditional media's interests towards a user and the content of each tweet, and the influence of each user are integrated in a hierarchical bayesian model. An extensive set of experiments are conducted on two datasets from two popular microblogging platforms, i.e., Twitter and Sina Weibo (Chinese version Twitter), to demonstrate the advantages of our algorithm against baseline methods on the proposed problem.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"1 1","pages":"5:1-5:9"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81452355","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}

{"title":"Utilizing Purchase Intervals in Latent Clusters for Product Recommendation","authors":"Gang Zhao, M. Lee, W. Hsu","doi":"10.1145/2659480.2659496","DOIUrl":"https://doi.org/10.1145/2659480.2659496","url":null,"abstract":"Collaborative filtering have become increasingly important with the development of Web 2.0. Online shopping service providers aim to provide users with quality list of recommended items that will enhance user satisfaction and loyalty. Matrix factorization approaches have become the dominant method as they can reduce the dimension of the data set and alleviate the sparsity problem. However, matrix factorization approaches are limited because they depict each user as one preference vector. In practice, we observe that users may have different preferences when purchasing different subsets of items, and the periods between purchases also vary from one user to another. In this work, we propose a probabilistic approach to learn latent clusters in the large user-item matrix, and incorporate temporal information into the recommendation process. Experimental results on a real world dataset demonstrate that our approach significantly improves the conversion rate, precision and recall of state-of-the-art methods.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"14 1","pages":"4:1-4:9"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87081524","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}

{"title":"Contextual Feature Analysis to Improve Link Prediction for Location Based Social Networks","authors":"A. Bayrak, Faruk Polat","doi":"10.1145/2659480.2659499","DOIUrl":"https://doi.org/10.1145/2659480.2659499","url":null,"abstract":"In recent years, people started to communicate, interact, maintain relationship and share data (image, video, note, location, etc.) with their acquaintances through varying online social network sites. Online social networks with location and time sharing/interaction among people are called Location Based Social Networks (LBSNs). Link prediction in social networks aims at predicting future possible links for representing the real life relations better. In this work, we studied the link prediction problem and proposed new contextual features that improve the link prediction performance for LBSNs.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"1 1","pages":"7:1-7:5"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82209886","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}

{"title":"Assortativity in Chung Lu Random Graph Models","authors":"Stephen Mussmann, John Moore, Joseph J. Pfeiffer, Jennifer Neville","doi":"10.1145/2659480.2659495","DOIUrl":"https://doi.org/10.1145/2659480.2659495","url":null,"abstract":"Due to the widespread interest in networks as a representation to investigate the properties of complex systems, there has been a great deal of interest in generative models of graph structure that can capture the properties of networks observed in the real world. Recent models have focused primarily on accurate characterization of sparse networks with skewed degree distributions, short path lengths, and local clustering. While assortativity---degree correlation among linked nodes---is used as a measure to both describe and evaluate connectivity patterns in networks, there has been little effort to explicitly incorporate patterns of assortativity into model representations. This is because many graph models are edge-based (modeling whether a link should be placed between a pair of nodes i and j) and assortativity is a second-order characteristic that depends on the global properties of the graph (i.e., the final degree of i and j). As such, it is difficult to incorporate direct optimization of assortativity into edge-based generative models.\u0000 One exception is the BTER method [5], which generates graphs with positive assortativity (e.g., high degree nodes link to each other). However, BTER does not directly estimate assortativity and also is not applicable for networks with negative assortativity (e.g, high degree nodes link primarily to low degree nodes). In this work, we present a novel approach to directly model observed assortativity (both positive and negative) via accept-reject sampling. Our key observation is to use a coarse approximation of the observed joint degree distribution and modify the likelihood that two nodes i, j should link based on the output properties of the original model. We implement our approach as an augmentation of Chung-Lu models and refer to it as Binning Chung Lu (BCL). We apply our method to six network datasets and show that it captures assortativity significantly more accurately than other methods while maintaining other graph properties of the original CL models. Also, our BCL approach is efficient (linear in the number of observed edges), thus it scales easily to large networks.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"85 1","pages":"3:1-3:8"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85517845","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}

{"title":"Predicting Company Quitting From Online Social Enterprise Activity","authors":"Gaurang Gavai, K. Sricharan, Juan Liu, Oliver Brdiczka, J. Hanley","doi":"10.1145/2659480.2659503","DOIUrl":"https://doi.org/10.1145/2659480.2659503","url":null,"abstract":"Modeling and predicting attrition in organizations has real-world business significance. In this paper, we take a novel approach of analyzing a corporate social network (Yammer) to predict if people are likely to quit their company. Via a data-driven approach, we compute a rich set of features derived from graph structure, content, and work practice characteristics derived from Yammer. Our experiment shows that the proposed data-driven approach can be used to predict employee quitting with a fair accuracy of approximately 68% and a moderately high recall rate of 62%. Given the difficulty of the quitting prediction problem, these accuracy and recall rates are fairly encouraging.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"4 1","pages":"11:1-11:5"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75181516","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}

{"title":"Inferring User Social Class in Online Social Networks","authors":"Renato Miranda, Guilherme R. Borges, J. Almeida, G. Pappa","doi":"10.1145/2659480.2659502","DOIUrl":"https://doi.org/10.1145/2659480.2659502","url":null,"abstract":"Although information posted in online social networks has proven to be accurate enough to monitor and predict real world phenomena, not a lot is known about users spreading this information. Previous studies have explored user public data to infer personal attributes such as gender, age and location, but one aspect is yet to be explored: social class. Assuming an objective definition of social class, based on income and wealth, we propose a new method to automatically generate a user social class dataset, taking advantage of Foursquare user interactions and Twitter messages. The basic idea to build our social class dataset is: the wealthier the place, the richer the users who usually visit it. We build our dataset by describing users using the contents of their tweets, and a machine learning algorithm is employed to automatically generate social class classification models. Our experimental results show that, considering social class divisions into two, three and four segments, the predictive accuracies of our models varied from 57% to 73%.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"142 1","pages":"10:1-10:5"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77376602","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}

{"title":"Fast In-Memory Triangle Listing for Large Real-World Graphs","authors":"M. Sevenich, Sungpack Hong, Adam Welc, Hassan Chafi","doi":"10.1145/2659480.2659494","DOIUrl":"https://doi.org/10.1145/2659480.2659494","url":null,"abstract":"Triangle listing, or identifying all the triangles in an undirected graph, is a very important graph problem that serves as a building block of many other graph algorithms. The compute-intensive nature of the problem, however, necessitates an efficient method to solve this problem, especially for large real-world graphs. In this paper we propose a fast and precise in-memory solution for the triangle listing problem. Our solution includes fast common neighborhoods finding methods that consider power law degree distribution of real-word graphs. We prove how theoretic lower bound can be achieved by sorting the nodes in the graph by their degree and applying pruning. We explain how our techniques can be applied automatically by an optimizing DSL compiler. Our experiments show that hundreds of billions of triangles in a five billion edge graph can be enumerated in about a minute with a single server-class machine.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"112 1","pages":"2:1-2:9"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74759078","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}

{"title":"Understanding Cross-site Linking in Online Social Networks","authors":"Yang Chen, Chenfan Zhuang, Q. Cao, P. Hui","doi":"10.1145/2659480.2659498","DOIUrl":"https://doi.org/10.1145/2659480.2659498","url":null,"abstract":"Online social networks (OSNs) have attracted billions of users, and play an important role in people's daily life. A user often has accounts on multiple OSN sites. In this paper, we study the emerging \"cross-site linking\" function, which is supported by many OSNs. Our study is based on Foursquare, a representative location-based social networking (LBSN) service. We conduct a data-driven analysis by using crawled public profiles of almost all (if not all) Foursquare users. Our analysis has shown that the cross-site linking function is widely adopted by Foursquare users, and the users who have enabled this function are more active than other users. We have also found that users who are more concerned with online privacy have a lower probability to enable the cross-site linking function. Besides analyzing crawled Foursquare user profiles, we further explore cross-site linking between Foursquare and other OSN sites, i.e., Facebook and Twitter. The study on \"Foursquare-Facebook\" linking indicates that users have a high probability to provide consistent information to different OSNs. Meanwhile, \"Foursquare-Twitter\" linking is used to demonstrate the usefulness of aggregating user-generated content across multiple OSN sites. We present a gender-based analysis of Twitter, which is made accurate by leveraging cross-site links between Foursquare and Twitter.","PeriodicalId":74521,"journal":{"name":"Proceedings of the ... IEEE/ACM International Conference on Advances in Social Network Analysis and Mining. International Conference on Advances in Social Network Analysis and Mining","volume":"202 1","pages":"6:1-6:9"},"PeriodicalIF":0.0,"publicationDate":"2014-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77001220","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}