Conference on Online Social Networks最新文献

{"title":"It's the way you check-in: identifying users in location-based social networks","authors":"L. Rossi, Mirco Musolesi","doi":"10.1145/2660460.2660485","DOIUrl":"https://doi.org/10.1145/2660460.2660485","url":null,"abstract":"In recent years, the rapid spread of smartphones has led to the increasing popularity of Location-Based Social Networks (LBSNs). Although a number of research studies and articles in the press have shown the dangers of exposing personal location data, the inherent nature of LBSNs encourages users to publish information about their current location (i.e., their check-ins). The same is true for the majority of the most popular social networking websites, which offer the possibility of associating the current location of users to their posts and photos. Moreover, some LBSNs, such as Foursquare, let users tag their friends in their check-ins, thus potentially releasing location information of individuals that have no control over the published data. This raises additional privacy concerns for the management of location information in LBSNs.\u0000 In this paper we propose and evaluate a series of techniques for the identification of users from their check-in data. More specifically, we first present two strategies according to which users are characterized by the spatio-temporal trajectory emerging from their check-ins over time and the frequency of visit to specific locations, respectively. In addition to these approaches, we also propose a hybrid strategy that is able to exploit both types of information. It is worth noting that these techniques can be applied to a more general class of problems where locations and social links of individuals are available in a given dataset. We evaluate our techniques by means of three real-world LBSNs datasets, demonstrating that a very limited amount of data points is sufficient to identify a user with a high degree of accuracy. For instance, we show that in some datasets we are able to classify more than 80% of the users correctly.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130302431","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":"WTF, GPU! computing twitter's who-to-follow on the GPU","authors":"Afton Geil, Yangzihao Wang, John Douglas Owens","doi":"10.1145/2660460.2660481","DOIUrl":"https://doi.org/10.1145/2660460.2660481","url":null,"abstract":"In this paper, we investigate the potential of GPUs for performing link structure analysis of social graphs. Specifically, we implement Twitter's WTF (\"Who to Follow\") recommendation system on a single GPU. Our implementation shows promising results on moderate-sized social graphs. It can return the top-K relevant users for a single user in 172 ms when running on a subset of the 2009 Twitter follow graph with 16 million users and 85 million social relations. For our largest dataset, which contains 75% of the users (30 million) and 50% of the social relations (680 million) of the complete follow graph, this calculation takes 1.0 s. We also propose possible solutions to apply our system to follow graphs of larger sizes that do not fit into the on-board memory of a single GPU.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131140043","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":"The socio-monetary incentives of online social network malware campaigns","authors":"Ting-Kai Huang, Bruno Ribeiro, H. Madhyastha, M. Faloutsos","doi":"10.1145/2660460.2660478","DOIUrl":"https://doi.org/10.1145/2660460.2660478","url":null,"abstract":"Online social networks (OSNs) offer a rich medium of malware propagation. Unlike other forms of malware, OSN malware campaigns direct users to malicious websites that hijack their accounts, posting malicious messages on their behalf with the intent of luring their friends to the malicious website, thus triggering word-of-mouth infections that cascade through the network compromising thousands of accounts. But how are OSN users lured to click on the malicious links? In this work, we monitor 3.5 million Facebook accounts and explore the role of pure monetary, social, and combined socio-monetary psychological incentives in OSN malware campaigns. Among other findings we see that the majority of the malware campaigns rely on pure social incentives. However, we also observe that malware campaigns using socio-monetary incentives infect more accounts and last longer than campaigns with pure monetary or social incentives. The latter suggests the efficiency of an epidemic tactic surprisingly similar to the mechanism used by biological pathogens to cope with diverse gene pools.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"443 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133365830","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":"Price trade-offs in social media advertising","authors":"Milad Eftekhar, Saravanan Thirumuruganathan, Gautam Das, Nick Koudas","doi":"10.1145/2660460.2660462","DOIUrl":"https://doi.org/10.1145/2660460.2660462","url":null,"abstract":"The prevalence of social media has sparked novel advertising models, vastly different from the traditional keyword based bidding model adopted by search engines. One such model is topic based advertising, popular with micro-blogging sites. Instead of bidding on keywords, the approach is based on bidding on topics, with the winning bid allowed to disseminate messages to users interested in the specific topic.\u0000 Naturally topics have varying costs depending on multiple factors (e.g., how popular or prevalent they are). Similarly users in a micro-blogging site have diverse interests. Assuming one wishes to disseminate a message to a set V of users interested in a specific topic, a question arises whether it is possible to disseminate the same message by bidding on a set of topics that collectively reach the same users in V albeit at a cheaper cost.\u0000 In this paper, we show how an alternative set of topics R with a lower cost can be identified to target (most) users in V. Two approximation algorithms are presented to address the problem with strong bounds. Theoretical analysis and extensive quantitative and qualitative experiments over real-world data sets at realistic scale containing millions of users and topics demonstrate the effectiveness of our approach.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"257 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121177097","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":"Mining democracy","authors":"Vincent Etter, J. Herzen, M. Grossglauser, Patrick Thiran","doi":"10.1145/2660460.2660476","DOIUrl":"https://doi.org/10.1145/2660460.2660476","url":null,"abstract":"Switzerland has a long tradition of direct democracy, which makes it an ideal laboratory for research on real-world politics. Similar to recent \"open government\" initiatives launched worldwide, the Swiss government regularly releases datasets related to state affairs and politics. In this paper, we propose an exploratory, data-driven study of the political landscape of Switzerland, in which we use opinions expressed by candidates and citizens on a web platform during the recent Swiss parliamentary elections, together with fine-grained vote results and parliament votes.\u0000 Following this purely data-driven approach, we show that it is possible to uncover interesting patterns that would otherwise require both tedious manual analysis and domain knowledge. In particular, we show that traditional cultural and/or ideological idiosyncrasies can be highlighted and quantified by looking at vote results and pre-election opinions. We propose a technique for comparing the candidates' opinions expressed before the elections with their actual votes cast in the parliament after the elections. This technique spots politicians that do not vote consistently with the opinions that they expressed during the campaign. We also observe that it is possible to predict surprisingly precisely the outcome of nationwide votes, by looking at the outcome in a single, carefully selected municipality. Our work applies to any country where similar data is available; it points to some of the avenues created by user-generated data emerging from open government initiatives, which enable new data-mining approaches to political and social sciences.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"630 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133165451","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":"Simultaneous detection of communities and roles from large networks","authors":"Yiye Ruan, S. Parthasarathy","doi":"10.1145/2660460.2660482","DOIUrl":"https://doi.org/10.1145/2660460.2660482","url":null,"abstract":"Community detection and structural role detection are two distinct but closely-related perspectives in network analytics. In this paper, we propose RC-Joint, a novel algorithm to simultaneously identify community and structural role assignments in a network. Rather than being agnostic to one assignment while inferring the other, RC-Joint employs a principled approach to guide the detection process in a nonparametric fashion and ensures that the two sets of assignments are sufficiently different from each other. Roles and communities generated by RC-Joint are both soft assignments, reflecting the fact that many real-world networks have overlapping community structures and role memberships. By comparing with state-of-the-art methods in community detection and structural role detection, we demonstrate that RC-Joint harvests the best of two worlds and outperforms existing approaches, while still being competitive in efficiency. We also investigate the effect of different initialization schemes, and find that using the results of RC-Joint on a sparse network as the seed often leads to faster convergence and higher quality.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114692322","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":"Analysis of the semi-synchronous approach to large-scale parallel community finding","authors":"Erika Duriakova, N. Hurley, Deepak Ajwani, A. Sala","doi":"10.1145/2660460.2660474","DOIUrl":"https://doi.org/10.1145/2660460.2660474","url":null,"abstract":"Community-finding in graphs is the process of identifying highly cohesive vertex subsets. Recently the vertex-centric approach has been found effective for scalable graph processing and is implemented in systems such as GraphLab and Pregel. In the vertex-centric approach, the analysis is decomposed into a set of local computations at each vertex of the graph, with results propagated to neighbours along the vertex's edges. Many community finding algorithms are amenable to this approach as they are based on the optimisation of an objective through a process of iterative local update (ILU), in which vertices are successively moved to the community of one of their neighbours in order to achieve the highest local gain in the quality of the objective. The sequential processing of such iterative algorithms generally benefits from an asynchronous approach, where a vertex update uses the most recent state as generated by the previous update of vertices in its neighbourhood. When vertices are distributed over a parallel machine, the asynchronous approach can encounter race conditions that impact on its performance and destroy the consistency of the results. Alternatively, a semi-synchronous approach ensures that only non-conflicting vertices are updated simultaneously. In this paper we study the semi-synchronous approach to ILU algorithms for community finding on social networks. Because of the heavy-tailed vertex distribution, the order in which vertex updates are applied in asynchronous ILU can greatly impact both convergence time and quality of the found communities. We study the impact of ordering on the distributed label propagation and modularity maximisation algorithms implemented on a shared-memory multicore architecture. We demonstrate that the semi-synchronous ILU approach is competitive in time and quality with the asynchronous approach, while allowing the analyst to maintain consistent control over update ordering. Thus, our implementation results in a more robust and predictable performance and provides control over the order in which the node labels are updated, which is crucial to obtaining the correct trade-off between running time and quality of communities on many graph classes.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117286838","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":"Measurement and analysis of OSN ad auctions","authors":"Yabing Liu, Chloe Kliman-Silver, Robert M. Bell, B. Krishnamurthy, A. Mislove","doi":"10.1145/2660460.2660475","DOIUrl":"https://doi.org/10.1145/2660460.2660475","url":null,"abstract":"Advertising is ubiquitous on the Web; numerous ad networks serve billions of ads daily via keyword or search term auctions. Recently, online social networks (OSNs) such as Facebook have created site-specific ad services that differ from traditional ad networks by letting advertisers bid on users rather than keywords. With Facebook's annual ad revenue exceeding $4 billion, OSN-based ad services are emerging to be a significant fraction of the online ad market. In contrast to other online ad markets (e.g., Google's ad market), there has been little academic study of OSN ad services, and OSNs have released very little data about their advertising markets; as a result, researchers currently lack the tools to measure and understand these markets.\u0000 In this paper, our goal is to bring visibility to OSN ad markets, focusing on Facebook. We demonstrate that the (undocumented) feature that suggests bids to advertisers is most likely calculated via sampling recent winning bids. We then show how this feature can be used to explore the relative value of different user demographics and the overall stability of the advertising market. Through the exploration of suggested bid data for different demographics, we find dramatic differences in prices paid across different user interests and locations. Finally, we show that the ad market shows long-term variability, suggesting that OSN ad services have yet to mature.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"520 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132284527","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":"Popularity dynamics of foursquare micro-reviews","authors":"Marisa A. Vasconcelos, J. Almeida, Marcos André Gonçalves, Daniel Souza, Guilherme C. M. Gomes","doi":"10.1145/2660460.2660484","DOIUrl":"https://doi.org/10.1145/2660460.2660484","url":null,"abstract":"Foursquare, the currently most popular location-based social network, allows users not only to share the places (venues) they visit but also post micro-reviews (tips) about their previous experiences at specific venues as well as \"like\" previously posted tips. The number of \"likes\" a tip receives ultimately reflects its popularity among users, providing valuable feedback to venue owners and other users.\u0000 In this paper, we provide an extensive analysis of the popularity dynamics of Foursquare tips using a large dataset containing over 10 million tips and 9 million likes posted by over 13,5 million users. Our results show that, unlike other types of online content such as news and photos, Foursquare tips experience very slow popularity evolution, attracting user likes through longer periods of time. Moreover, we find that the social network of the user who posted the tip plays an important role on the tip popularity throughout its lifetime, but particularly at earlier periods after posting time. We also find that most tips experience their daily popularity peaks within the first month in the system, although most of their likes are received after the peak. Moreover, compared to other types of online content (e.g., videos), we observe a weaker presence of the rich-get-richer effect in our data, demonstrating a lower correlation between the early and late popularities. Finally, we evaluate the stability of the tip popularity ranking over time, assessing to which extent the current popularity ranking of a set of tips can be used to predict their popularity ranking at a future time. To that end, we compare a prediction approach based solely on the current popularity ranking against a method that exploits a linear regression model using a multidimensional set of predictors as input. Our results show that use of the richer set of features can indeed improve the prediction accuracy, provided that enough data is available to train the regression model.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128660582","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":"Invite your friends and get rewards: dynamics of incentivized friend invitation in kakaotalk mobile games","authors":"Jiwan Jeong, S. Moon","doi":"10.1145/2660460.2660468","DOIUrl":"https://doi.org/10.1145/2660460.2660468","url":null,"abstract":"Incentivized friend invitation is an efficient and effective user growth mechanism, more so when combined with social platforms, such as online social networks (OSNs) or mobile instant messengers (MIMs). KakaoGame, a two-year-old mobile game platform based on a dominant MIM called KakaoTalk, brought 5.2 billion sign-ups over 520 games with quota-based reward schemes. How does the reward scheme help the spread of services?\u0000 In this paper, we analyze the friend invitation logs from 4 mobile games on KakaoGame, consisting of 330 million invitations from 8.4 million users to 36 million users. Our analysis aims at answering the following three key questions. (a) How do quota-based reward schemes stimulate invitation behavior? (b) How many invitations trigger the invitee to sign up for the game or become an annoyance to make the invitee turn a blind eye? (c) How fast are the invitations sent out and how does the diffusion slow down? Based on the analysis, we provide practical insights for viral marketing.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114566267","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}