Conference on Online Social Networks最新文献_第5页

Dynamics of personal social relationships in online social networks: a study on twitter 在线社交网络中个人社会关系的动态:基于twitter的研究

Conference on Online Social Networks Pub Date : 2013-10-07 DOI: 10.1145/2512938.2512949

V. Arnaboldi, M. Conti, A. Passarella, Robin I. M. Dunbar

{"title":"Dynamics of personal social relationships in online social networks: a study on twitter","authors":"V. Arnaboldi, M. Conti, A. Passarella, Robin I. M. Dunbar","doi":"10.1145/2512938.2512949","DOIUrl":"https://doi.org/10.1145/2512938.2512949","url":null,"abstract":"The growing popularity of Online Social Networks (OSN) is generating a large amount of communication records that can be easily accessed and analysed to study human social behaviour. This represents a unique opportunity to understand properties of social networks that were impossible to assess in the past. Although analyses on OSN conducted hitherto revealed some important global properties of the networks, there is still a lack of understanding of the mechanisms underpinning these properties, their relation to human behaviour, and their dynamic evolution over time. These aspects are clearly important to understand and characterise OSN and to identify the evolutionary strategy that favoured the diffusion of the use of online communications in our society.\u0000 In this paper we analyse a data set of Twitter communication records, studying the dynamic processes that govern the maintenance of online social relationships. The results reveal that people in Twitter have highly dynamic social networks, with a large percentage of weak ties and high turnover. This suggests that this behaviour can be the product of an evolutionary strategy aimed at coping with the extremely challenging conditions imposed by our society, where dynamism seems to be the key to success.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126705966","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: 60

Launch hard or go home!: predicting the success of kickstarter campaigns 要么用力发射，要么回家!预测kickstarter活动的成功

Conference on Online Social Networks Pub Date : 2013-10-07 DOI: 10.1145/2512938.2512957

Vincent Etter, M. Grossglauser, Patrick Thiran

引用次数: 170

Landmark-based user location inference in social media 社交媒体中基于地标的用户位置推断

Conference on Online Social Networks Pub Date : 2013-10-07 DOI: 10.1145/2512938.2512941

Yuto Yamaguchi, T. Amagasa, H. Kitagawa

引用次数: 30

We know how you live: exploring the spectrum of urban lifestyles 我们知道你的生活方式:探索城市生活方式的光谱

Conference on Online Social Networks Pub Date : 2013-10-07 DOI: 10.1145/2512938.2512945

Nicholas Jing Yuan, Fuzheng Zhang, Defu Lian, Kai Zheng, Siyu Yu, Xing Xie

{"title":"We know how you live: exploring the spectrum of urban lifestyles","authors":"Nicholas Jing Yuan, Fuzheng Zhang, Defu Lian, Kai Zheng, Siyu Yu, Xing Xie","doi":"10.1145/2512938.2512945","DOIUrl":"https://doi.org/10.1145/2512938.2512945","url":null,"abstract":"An incisive understanding of human lifestyles is not only essential to many scientific disciplines, but also has a profound business impact for targeted marketing. In this paper, we present LifeSpec, a computational framework for exploring and hierarchically categorizing urban lifestyles. Specifically, we have developed an algorithm to connect multiple social network accounts of millions of individuals and collect their publicly available heterogeneous behavioral data as well as social links. In addition, a nonparametric Bayesian approach is developed to model the lifestyle spectrum of a group of individuals. To demonstrate the effectiveness of LifeSpec, we conducted extensive experiments and case studies, with a large dataset we collected covering 1 million individuals from 493 cities. Our results suggest that LifeSpec offers a powerful paradigm for 1) revealing an individual's lifestyle from multiple dimensions, and 2) uncovering lifestyle commonalities and variations of a group with various demographic attributes, such as vocation, education, gender, sexual orientation, and place of residence. The proposed method provides emerging implications for personalized recommendation and targeted advertising.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125425513","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: 86

Fit or unfit: analysis and prediction of 'closed questions' on stack overflow 适合或不适合:对堆栈溢出的“封闭问题”进行分析和预测

Conference on Online Social Networks Pub Date : 2013-07-27 DOI: 10.1145/2512938.2512954

D. Correa, A. Sureka

{"title":"Fit or unfit: analysis and prediction of 'closed questions' on stack overflow","authors":"D. Correa, A. Sureka","doi":"10.1145/2512938.2512954","DOIUrl":"https://doi.org/10.1145/2512938.2512954","url":null,"abstract":"Stack Overflow is widely regarded as the most popular Community driven Question Answering (CQA) website for programmers. Questions posted on Stack Overflow which are not related to programming topics, are marked as `closed' by experienced users and community moderators. A question can be `closed' for five reasons -- duplicate, off-topic, subjective, not a real question and too localized. In this work, we present the first study of `closed' questions on Stack Overflow. We download 4 years of publicly available data which contains 3.4 Million questions. We first analyze and characterize the complete set of 0.1 Million `closed' questions. Next, we use a machine learning framework and build a predictive model to identify a `closed' question at the time of question creation.\u0000 One of our key findings is that despite being marked as `closed', subjective questions contain high information value and are very popular with the users. We observe an increasing trend in the percentage of closed questions over time and find that this increase is positively correlated to the number of newly registered users. In addition, we also see a decrease in community participation to mark a `closed' question which has led to an increase in moderation job time. We also find that questions closed with the Duplicate and Off Topic labels are relatively more prone to reputation gaming. Our analysis suggests broader implications for content quality maintenance on CQA websites. For the `closed' question prediction task, we make use of multiple genres of feature sets based on - user profile, community process, textual style and question content. We use a state-of-art machine learning classifier based on an ensemble framework and achieve an overall accuracy of 70.3%. Analysis of the feature space reveals that `closed' questions are relatively less informative and descriptive than non-`closed' questions. To the best of our knowledge, this is the first experimental study to analyze and predict `closed' questions on Stack Overflow.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"316 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132139535","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: 76

Social resilience in online communities: the autopsy of friendster 网络社区的社会弹性:friendster的解剖

Conference on Online Social Networks Pub Date : 2013-02-25 DOI: 10.1145/2512938.2512946

David García, Pavlin Mavrodiev, F. Schweitzer

{"title":"Social resilience in online communities: the autopsy of friendster","authors":"David García, Pavlin Mavrodiev, F. Schweitzer","doi":"10.1145/2512938.2512946","DOIUrl":"https://doi.org/10.1145/2512938.2512946","url":null,"abstract":"We empirically analyze five online communities: Friendster, Livejournal, Facebook, Orkut, and Myspace, to study how social networks decline. We define social resilience as the ability of a community to withstand changes. We do not argue about the cause of such changes, but concentrate on their impact. Changes may cause users to leave, which may trigger further leaves of others who lost connection to their friends. This may lead to cascades of users leaving. A social network is said to be resilient if the size of such cascades can be limited. To quantify resilience, we use the k-core analysis, to identify subsets of the network in which all users have at least k friends. These connections generate benefits (b) for each user, which have to outweigh the costs (c) of being a member of the network. If this difference is not positive, users leave. After all cascades, the remaining network is the k-core of the original network determined by the cost-to-benefit (c/b) ratio. By analysing the cumulative distribution of k-cores we are able to calculate the number of users remaining in each community. This allows us to infer the impact of the c/b ratio on the resilience of these online communities. We find that the different online communities have different k-core distributions. Consequently, similar changes in the c/b ratio have a different impact on the amount of active users. Further, our resilience analysis shows that the topology of a social network alone cannot explain its success of failure. As a case study, we focus on the evolution of Friendster. We identify time periods when new users entering the network observed an insufficient c/b ratio. This measure can be seen as a precursor of the later collapse of the community. Our analysis can be applied to estimate the impact of changes in the user interface, which may temporarily increase the c/b ratio, thus posing a threat for the community to shrink, or even to collapse.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122513475","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: 150