Conference on Online Social Networks最新文献

{"title":"Partisan sharing: facebook evidence and societal consequences","authors":"Jisun An, D. Quercia, J. Crowcroft","doi":"10.1145/2660460.2660469","DOIUrl":"https://doi.org/10.1145/2660460.2660469","url":null,"abstract":"The hypothesis of selective exposure assumes that people seek out information that supports their views and eschew information that conflicts with their beliefs, and that has negative consequences on our society. Few researchers have recently found counter evidence of selective exposure in social media: users are exposed to politically diverse articles. No work has looked at what happens after exposure, particularly how individuals react to such exposure, though. Users might well be exposed to diverse articles but share only the partisan ones. To test this, we study partisan sharing on Facebook: the tendency for users to predominantly share like-minded news articles and avoid conflicting ones. We verified four main hypotheses. That is, whether partisan sharing: 1) exists at all; 2) changes across individuals (e.g., depending on their interest in politics); 3) changes over time (e.g., around elections); and 4) changes depending on perceived importance of topics. We indeed find strong evidence for partisan sharing. To test whether it has any consequence in the real world, we built a web application for BBC viewers of a popular political program, resulting in a controlled experiment involving more than 70 individuals. Based on what they share and on survey data, we find that partisan sharing has negative consequences: distorted perception of reality. However, we do also find positive aspects of partisan sharing: it is associated with people who are more knowledgeable about politics and engage more with it as they are more likely to vote in the general elections.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127278629","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":"Computing classic closeness centrality, at scale","authors":"E. Cohen, D. Delling, Thomas Pajor, Renato F. Werneck","doi":"10.1145/2660460.2660465","DOIUrl":"https://doi.org/10.1145/2660460.2660465","url":null,"abstract":"Closeness centrality, first considered by Bavelas (1948), is an importance measure of a node in a network which is based on the distances from the node to all other nodes. The classic definition, proposed by Bavelas (1950), Beauchamp (1965), and Sabidussi (1966), is (the inverse of) the average distance to all other nodes.\u0000 We propose the first highly scalable (near linear-time processing and linear space overhead) algorithm for estimating, within a small relative error, the classic closeness centralities of all nodes in the graph. Our algorithm applies to undirected graphs, as well as for centrality computed with respect to round-trip distances in directed graphs.\u0000 For directed graphs, we also propose an efficient algorithm that approximates generalizations of classic closeness centrality to outbound and inbound centralities. Although it does not provide worst-case theoretical approximation guarantees, it is designed to perform well on real networks.\u0000 We perform extensive experiments on large networks, demonstrating high scalability and accuracy.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127678435","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":"Modeling non-progressive phenomena for influence propagation","authors":"Vincent Yun Lou, Smriti Bhagat, L. Lakshmanan, Sharan Vaswani","doi":"10.1145/2660460.2660483","DOIUrl":"https://doi.org/10.1145/2660460.2660483","url":null,"abstract":"Most previous work on modeling influence propagation has focused on progressive models, i.e., once a node is influenced (active) the node stays in that state and cannot become inactive. However, this assumption is unrealistic in many settings where nodes can transition between active and inactive states. For instance, a user of a social network may stop using an app and become inactive, but again activate when instigated by a friend, or when the app adds a new feature or releases a new version. In this work, we study such non-progressive phenomena and propose an efficient model of influence propagation. Specifically, we model influence propagation as a continuous-time Markov process with 2 states: active and inactive. Such a model is both highly scalable (we evaluated on graphs with over 2 million nodes), 17-20 times faster, and more accurate for estimating the spread of influence, as compared with state-of-the-art progressive models for several applications where nodes may switch states.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129217850","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":"Fighting authorship linkability with crowdsourcing","authors":"M. A. Mishari, Ekin Oguz, G. Tsudik","doi":"10.1145/2660460.2660486","DOIUrl":"https://doi.org/10.1145/2660460.2660486","url":null,"abstract":"Massive amounts of contributed content -- including traditional literature, blogs, music, videos, reviews and tweets -- are available on the Internet today, with authors numbering in many millions. Textual information, such as product or service reviews, is an important and increasingly popular type of content that is being used as a foundation of many trendy community-based reviewing sites, such as TripAdvisor and Yelp. Some recent results have shown that, due partly to their specialized/topical nature, sets of reviews authored by the same person are readily linkable based on simple stylometric features. In practice, this means that individuals who author more than a few reviews under different accounts (whether within one site or across multiple sites) can be linked, which represents a significant loss of privacy.\u0000 In this paper, we start by showing that the problem is actually worse than previously believed. We then explore ways to mitigate authorship linkability in community-based reviewing. We first attempt to harness the global power of crowdsourcing by engaging random strangers into the process of re-writing reviews. As our empirical results (obtained from Amazon Mechanical Turk) clearly demonstrate, crowdsourcing yields impressively sensible reviews that reflect sufficiently different stylometric characteristics such that prior stylometric linkability techniques become largely ineffective. We also consider using machine translation to automatically re-write reviews. Contrary to what was previously believed, our results show that translation decreases authorship linkability as the number of intermediate languages grows. Finally, we explore the combination of crowdsourcing and machine translation and report on results.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129148618","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":"Cryptagram: photo privacy for online social media","authors":"Matt Tierney, Ian Spiro, C. Bregler, L. Subramanian","doi":"10.1145/2512938.2512939","DOIUrl":"https://doi.org/10.1145/2512938.2512939","url":null,"abstract":"While Online Social Networks (OSNs) enable users to share photos easily, they also expose users to several privacy threats from both the OSNs and external entities. The current privacy controls on OSNs are far from adequate, resulting in inappropriate flows of information when users fail to understand their privacy settings or OSNs fail to implement policies correctly. OSNs may further complicate privacy expectations when they reserve the right to analyze uploaded photos using automated face identification techniques.\u0000 In this paper, we propose the design, implementation and evaluation of Cryptagram, a system designed to enhance online photo privacy. Cryptagram enables users to convert photos into encrypted images, which the users upload to OSNs. Users directly manage access control to those photos via shared keys that are independent of OSNs or other third parties. OSNs apply standard image transformations (JPEG compression) to all uploaded images so Cryptagram provides an image encoding and encryption mechanism that is tolerant to these transformations. Cryptagram guarantees that the recipient with the right credentials can completely retrieve the original image from the transformed version of the uploaded encrypted image while the OSN cannot infer the original image. Cryptagram's browser extension integrates seamlessly with preexisting OSNs, including Facebook and Google+, and currently has over 400 active users.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"10 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":"128438002","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":"Are trending topics useful for marketing?: visibility of trending topics vs traditional advertisement","authors":"Juan Miguel Carrascosa, Roberto Gonzalez, R. C. Rumín, A. Azcorra","doi":"10.1145/2512938.2512948","DOIUrl":"https://doi.org/10.1145/2512938.2512948","url":null,"abstract":"Trending Topics seem to be a powerful tool to be used in marketing and advertisement contexts, however there is not any rigorous analysis that demonstrates this. In this paper we present a first effort in this direction. We use a dataset including more than 110K Trending Topics from 35 countries collected over a period of 3 months as basis to characterize the visibility offered by Local Trending Topics. Furthermore, by using metrics that rely on the exposure time of Trending Topics and the penetration of Twitter, we compare the visibility provided by Trending Topics and traditional advertisement channels such as newspapers' ads or radio-stations' commercials for several countries. Our study confirms that Trending Topics offer a comparable visibility to the aforementioned traditional advertisement channels in those countries where we have conducted our comparison study. Then, we conclude that Trending Topics can be useful in marketing and advertisement contexts at least in the analyzed countries.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"8 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":"126352851","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":"COSN'13 keynote speaker (Jaron Lanier biography)","authors":"J. Lanier","doi":"10.1145/2512938.2526242","DOIUrl":"https://doi.org/10.1145/2512938.2526242","url":null,"abstract":"A Renaissance Man for the 21st century, Jaron Lanier is a computer scientist, composer, artist, and author who writes on numerous topics, including high-technology business, the social impact of technology, the philosophy of consciousness and information, Internet politics, and the future of humanism. In 2010, Lanier was named one of the 100 most influential people in the world by Time Magazine. He has also been named one of top one hundred public intellectuals in the world by Prospect and Foreign Policy magazines, and one of history's 300 or so greatest inventors in the Encyclopedia Britannica. In 2009 Jaron Lanier received a Lifetime Career Award from the IEEE, the preeminent international engineering society. A pioneer in virtual reality (a term he coined), Lanier founded VPL Research, the first company to sell VR products, and led teams creating VR applications for medicine, design, and numerous other fields. He is currently a computer scientist at Microsoft Research. In January 2010, Knopf published Lanier's book You Are Not a Gadget, A Manifesto, which became a New York Times, Los Angeles Times, and Boston Globe bestseller. You Are Not a Gadget was chosen as one of the best books of the year by Time Magazine and The New York Times, among others.\u0000 Lanier's writing appears in Discover, The Wall Street Journal, Forbes, Harpers Magazine, Atlantic, Wired Magazine (where he was a founding contributing editor), and Scientific American. He has appeared on TV shows such as PBS NewsHour, Nightline and Charlie Rose, and has been profiled on the front pages of The Wall Street Journal and The New York Times multiple times. Jaron Lanier is also a musician and artist. He has been active in the world of new \"classical\" music since the late '70s, and writes chamber and orchestral works. He is a pianist and a specialist in unusual and historical musical instruments, and maintains one of the largest and most varied collections of actively played instruments in the world. Recent works include a symphony with full choral settings about William Shakespeare's contemporary and friend Amelia Lanier, commissioned for the Bach Festival Society of Winter Park. He has performed with a wide range of musicians, including Philip Glass, Yoko Ono, Ornette Coleman, George Clinton, and Steve Reich. He composes and performs frequently on film soundtracks.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"19 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":"121225182","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 interests from tweet times","authors":"D. Ramasamy, S. Venkateswaran, Upamanyu Madhow","doi":"10.1145/2512938.2512960","DOIUrl":"https://doi.org/10.1145/2512938.2512960","url":null,"abstract":"We propose and demonstrate the feasibility of a probabilistic framework for mining user interests from their tweet times alone, by exploiting the known timing of external events associated with these interests. This approach allows for making inferences on the interests of a large number of users for which text-based mining may become cumbersome, and also sidesteps the difficult problem of semantic/contextual analysis required for such text-based inferences. The statistic that we propose for gauging the user's interest level is the probability that he/she tweets more frequently at certain times when this topic is in the ``public eye'' than at other times. We report on promising experimental results using Twitter data on detecting whether or not a user is a fan of a given baseball team, leveraging the known timing of games played by the team. Since people often interact with others who share similar interests, we extend our probabilistic framework to use the interest level estimates for other users with whom a person interacts (by referring to them in his/her tweets). We demonstrate that it is possible to significantly improve the detection probability (for a given false alarm rate) by such information pooling on the social graph.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"13 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":"132248591","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":"Comparing and combining sentiment analysis methods","authors":"Pollyanna Gonçalves, Matheus Araújo, Fabrício Benevenuto, M. Cha","doi":"10.1145/2512938.2512951","DOIUrl":"https://doi.org/10.1145/2512938.2512951","url":null,"abstract":"Several messages express opinions about events, products, and services, political views or even their author's emotional state and mood. Sentiment analysis has been used in several applications including analysis of the repercussions of events in social networks, analysis of opinions about products and services, and simply to better understand aspects of social communication in Online Social Networks (OSNs). There are multiple methods for measuring sentiments, including lexical-based approaches and supervised machine learning methods. Despite the wide use and popularity of some methods, it is unclear which method is better for identifying the polarity (i.e., positive or negative) of a message as the current literature does not provide a method of comparison among existing methods. Such a comparison is crucial for understanding the potential limitations, advantages, and disadvantages of popular methods in analyzing the content of OSNs messages. Our study aims at filling this gap by presenting comparisons of eight popular sentiment analysis methods in terms of coverage (i.e., the fraction of messages whose sentiment is identified) and agreement (i.e., the fraction of identified sentiments that are in tune with ground truth). We develop a new method that combines existing approaches, providing the best coverage results and competitive agreement. We also present a free Web service called iFeel, which provides an open API for accessing and comparing results across different sentiment methods for a given text.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"6 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":"131275816","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":"On the precision of social and information networks","authors":"R. Zadeh, Ashish Goel, Kamesh Munagala, Aneesh Sharma","doi":"10.1145/2512938.2512955","DOIUrl":"https://doi.org/10.1145/2512938.2512955","url":null,"abstract":"The diffusion of information on online social and information networks has been a popular topic of study in recent years, but attention has typically focused on speed of dissemination and recall (i.e. the fraction of users getting a piece of information). In this paper, we study the complementary notion of the precision of information diffusion. Our model of information dissemination is \"broadcast-based'', i.e., one where every message (original or forwarded) from a user goes to a fixed set of recipients, often called the user's ``friends'' or ``followers'', as in Facebook and Twitter. The precision of the diffusion process is then defined as the fraction of received messages that a user finds interesting.\u0000 On first glance, it seems that broadcast-based information diffusion is a \"blunt\" targeting mechanism, and must necessarily suffer from low precision. Somewhat surprisingly, we present preliminary experimental and analytical evidence to the contrary: it is possible to simultaneously have high precision (i.e. is bounded below by a constant), high recall, and low diameter!\u0000 We start by presenting a set of conditions on the structure of user interests, and analytically show the necessity of each of these conditions for obtaining high precision. We also present preliminary experimental evidence from Twitter verifying that these conditions are satisfied. We then prove that the Kronecker-graph based generative model of Leskovec et al. satisfies these conditions given an appropriate and natural definition of user interests. Further, we show that this model also has high precision, high recall, and low diameter. We finally present preliminary experimental evidence showing Twitter has high precision, validating our conclusion. This is perhaps a first step towards a formal understanding of the immense popularity of online social networks as an information dissemination mechanism.","PeriodicalId":304931,"journal":{"name":"Conference on Online Social Networks","volume":"39 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":"114231120","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}