BibSonomyburst/user/beate/socialBibSonomy RSS feed for /user/beate/social2012-02-15T22:16:00+01:00http://www.bibsonomy.org/bibtex/2ddd2a643e1575e025e94c5605452a570/beatebeate2010-09-20T14:28:28+02:00tagging spam social <span class="authorEditorList"><a href="/author/Liu">Kaipeng Liu</a>, <a href="/author/Fang">Binxing Fang</a>, and <a href="/author/Zhang">Yu Zhang</a> </span><em>FSKD&#039;09: Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery, </em><em>page 427--431. </em><em>Piscataway, NJ, USA, </em><em>IEEE Press, </em>(<em>2009</em>)Mon Sep 20 14:28:28 CEST 2010Piscataway, NJ, USAFSKD'09: Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery427--431Detecting tag spam in social tagging systems with collaborative knowledge2009tagging spam social Social tagging systems allow collaborative users to annotate shared resources with tags. Since they rely on user-contributed content, social tagging systems are vulnerable to spam annotations, which are generated by malicious users to mislead or confuse legitimate users. Thus, mechanisms for spam detection need to be developed to combat the flexible strategies of spammers for the success of social tagging systems. Since annotations are lack of relevant feature, the classical method of training classifier to detect spam is hard to implement. However, with their collaborative nature, knowledge on the tagging scheme do exists in the way numerous participants annotating resources with tags. In this paper, we propose a simple but remarkably effective approach for detecting tag spam in social tagging systems with collaborative knowledge. We harness the wisdom of crowds to discover the knowledge on what should be high quality annotations for resources. This knowledge is then used to tell spam posts from the legitimate ones. A distinct feature of our approach is that, it can be easily extended for user level spam detection and can do well in both levels. The proposed approach is evaluated on data set collected from real-world system. Experimental results show a convincing performance of proposed approach.Detecting tag spam in social tagging systems with collaborative knowledgehttp://www.bibsonomy.org/bibtex/2b2091d2cd69c750b57d93d066a8a55c6/beatebeate2010-09-20T14:24:18+02:00social spam voting <span class="authorEditorList"><a href="/author/Bian">Jiang Bian</a>, <a href="/author/Liu">Yandong Liu</a>, <a href="/author/Agichtein">Eugene Agichtein</a>, and <a href="/author/Zha">Hongyuan Zha</a> </span><em>AIRWeb &#039;08: Proceedings of the 4th international workshop on Adversarial information retrieval on the web, </em><em>page 53--60. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Mon Sep 20 14:24:18 CEST 2010New York, NY, USAAIRWeb '08: Proceedings of the 4th international workshop on Adversarial information retrieval on the web53--60A few bad votes too many?: towards robust ranking in social media2008social spam voting Online social media draws heavily on active reader participation, such as voting or rating of news stories, articles, or responses to a question. This user feedback is invaluable for ranking, filtering, and retrieving high quality content - tasks that are crucial with the explosive amount of social content on the web. Unfortunately, as social media moves into the mainstream and gains in popularity, the quality of the user feedback degrades. Some of this is due to noise, but, increasingly, a small fraction of malicious users are trying to "game the system" by selectively promoting or demoting content for profit, or fun. Hence, an effective ranking of social media content must be robust to noise in the user interactions, and in particular to vote spam. We describe a machine learning based ranking framework for social media that integrates user interactions and content relevance, and demonstrate its effec- tiveness for answer retrieval in a popular community question answering portal. We consider several vote spam attacks, and introduce a method of training our ranker to increase its robustness to some common forms of vote spam attacks. The results of our large-scale experimental evaluation show that our ranker is signifcicantly more robust to vote spam compared to a state-of-the-art baseline as well as the ranker not explicitly trained to handle malicious interactions.http://www.bibsonomy.org/bibtex/2041a40db631707cb15bd17e36a77bd87/beatebeate2010-02-24T12:56:49+01:00bookmarking folksonomy ir retrieval search social tagging web <span class="authorEditorList"><a href="/author/Morrison">P. Jason Morrison</a> </span><em>Information Processing &amp; Management</em> <em>44(4):1562 - 1579</em> (<em>2008</em>)Wed Feb 24 12:56:49 CET 2010Information Processing & Management41562 - 1579Tagging and searching: Search retrieval effectiveness of folksonomies on the World Wide Web442008bookmarking folksonomy ir retrieval search social tagging web Many Web sites have begun allowing users to submit items to a collection and tag them with keywords. The folksonomies built from these tags are an interesting topic that has seen little empirical research. This study compared the search information retrieval (IR) performance of folksonomies from social bookmarking Web sites against search engines and subject directories. Thirty-four participants created 103 queries for various information needs. Results from each IR system were collected and participants judged relevance. Folksonomy search results overlapped with those from the other systems, and documents found by both search engines and folksonomies were significantly more likely to be judged relevant than those returned by any single IR system type. The search engines in the study had the highest precision and recall, but the folksonomies fared surprisingly well. Del.icio.us was statistically indistinguishable from the directories in many cases. Overall the directories were more precise than the folksonomies but they had similar recall scores. Better query handling may enhance folksonomy IR performance further. The folksonomies studied were promising, and may be able to improve Web search performance.ScienceDirect - Information Processing & Management : Tagging and searching: Search retrieval effectiveness of folksonomies on the World Wide Webhttp://www.bibsonomy.org/bibtex/2ed36dd983b57a930d59f9e8996abd1ba/beatebeate2009-12-23T06:51:49+01:00reflection-problem social social-networks <span class="authorEditorList"><a href="/author/Manski">Charles F. Manski</a> </span><em>The Review of Economic Studies</em> <em>60(3):531--542</em> (<em>1993</em>)Wed Dec 23 06:51:49 CET 2009The Review of Economic Studies3531--542Identification of Endogenous Social Effects: The Reflection Problem601993reflection-problem social social-networks This paper examines the reflection problem that arises when a researcher observing the distribution of behaviour in a population tries to infer whether the average behaviour in some group influences the behaviour of the individuals that comprise the group. It is found that inference is not possible unless the researcher has prior information specifying the composition of reference groups. If this information is available, the prospects for inference depend critically on the population relationship between the variables defining reference groups and those directly affecting outcomes. Inference is difficult to impossible if these variables are functionally dependent or are statistically independent. The prospects are better if the variables defining reference groups and those directly affecting outcomes are moderately related in the population.JSTOR: The Review of Economic Studies, Vol. 60, No. 3 (Jul., 1993), pp. 531-542http://www.bibsonomy.org/bibtex/23c59e12518021b255e624fc88fcb6195/beatebeate2009-10-16T11:30:53+02:00info2.0 privacy social social-networks tagging web <span class="authorEditorList"><a href="/author/Schrammel">Johann Schrammel</a>, <a href="/author/Köffel">Christina Köffel</a>, and <a href="/author/Tscheligi">Manfred Tscheligi</a> </span><em>C\&amp;\#38;T &#039;09: Proceedings of the fourth international conference on Communities and technologies, </em><em>page 275--284. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2009</em>)Fri Oct 16 11:30:53 CEST 2009New York, NY, USAC\&\#38;T '09: Proceedings of the fourth international conference on Communities and technologies275--284How much do you tell?: information disclosure behaviour indifferent types of online communities2009info2.0 privacy social social-networks tagging web Online communities of different types have become an important part of the internet life of many people within the last couple of years. Both research and business have shown interest in studying the possibilities and risks of these relatively new phenomena. Very controversial aspects of these communities are their implications and effects on privacy issues, as research has shown that users generally provide information rather freely on such communities. However, no systematic comparison of differences in information disclosure behavior considering different types of communities is available. Furthermore only few is known about the information disclosure behavior related to demographic variables, usage contexts and usage patterns. To better understand these aspects of online communities we conducted an online survey that questioned users of various popular online communities about their information disclosure behavior and usage patterns of these sites. More than 850 users responded to our questionnaire. In this paper we present the main results of the analysis and provide linear regression models that allow understanding the involved factors in detail.How much do you tell?http://www.bibsonomy.org/bibtex/21d806b573191a6ebc47d44f6c9cf859f/beatebeate2009-02-05T08:39:57+01:00facebook search social social-networking study <span class="authorEditorList"><a href="/author/Lampe">Cliff Lampe</a>, <a href="/author/Ellison">Nicole Ellison</a>, and <a href="/author/Steinfield">Charles Steinfield</a> </span><em>CSCW &#039;06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work, </em><em>page 167--170. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2006</em>)Thu Feb 05 08:39:57 CET 2009New York, NY, USACSCW '06: Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work167--170A face(book) in the crowd: social Searching vs. social browsing2006facebook search social social-networking study Large numbers of college students have become avid Facebook users in a short period of time. In this paper, we explore whether these students are using Facebook to find new people in their offline communities or to learn more about people they initially meet offline. Our data suggest that users are largely employing Facebook to learn more about people they meet offline, and are less likely to use the site to initiate new connections.Study: Facebook is not used for social networking but to grab news about friendshttp://www.bibsonomy.org/bibtex/276d81124951ae39060a8bc98f4883435/beatebeate2009-01-14T14:02:36+01:002008 analysis engine information l3s logsonomy myown networks retrieval search social wp5 <span class="authorEditorList"><a href="/author/Krause">Beate Krause</a>, <a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>HT &#039;08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia, </em><em>page 157--166. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Wed Jan 14 14:02:36 CET 2009New York, NY, USAHT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia157--166Logsonomy - Social Information Retrieval with Logdata20082008 analysis engine information l3s logsonomy myown networks retrieval search social wp5 Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today’s search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.http://www.bibsonomy.org/bibtex/2ad5f4664d1f5b967a0793b4a26a0edbb/beatebeate2009-01-14T13:50:05+01:002008 bookmarking myown social <span class="authorEditorList"><a href="/author/Krause">Beate Krause</a>, <a href="/author/Hotho">Andreas Hotho</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>30th European Conference on IR Research, ECIR 2008, </em><em>volume 4956 of Lecture Notes in Computer Science, </em><em>page 101-113. </em><em>Glasgow, UK, </em><em>Springer, </em>(<em>April 2008</em>)Wed Jan 14 13:50:05 CET 2009Glasgow, UK30th European Conference on IR Research, ECIR 2008April101-113Lecture Notes in Computer ScienceA Comparison of Social Bookmarking with Traditional Search495620082008 bookmarking myown social http://www.bibsonomy.org/bibtex/2ce3bc25c126d94cb8f5874b2581ca3c8/beatebeate2008-12-14T18:17:04+01:00data-mining graph-analysis networks social <span class="authorEditorList"><a href="/author/McGlohon">Mary McGlohon</a>, <a href="/author/Akoglu">Leman Akoglu</a>, and <a href="/author/Faloutsos">Christos Faloutsos</a> </span><em>KDD &#039;08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, </em><em>page 524--532. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2008</em>)Sun Dec 14 18:17:04 CET 2008New York, NY, USAKDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining524--532Weighted graphs and disconnected components: patterns and a generator2008data-mining graph-analysis networks social Weighted graphs and disconnected componentshttp://www.bibsonomy.org/bibtex/2b3a5e9851647ca0a7dfb62f041872504/beatebeate2008-06-03T21:19:09+02:002007 bookmarking folksonomy myown social system <span class="authorEditorList"><a href="/author/Jäschke">Robert Jäschke</a>, <a href="/author/Grahl">Miranda Grahl</a>, <a href="/author/Hotho">Andreas Hotho</a>, <a href="/author/Krause">Beate Krause</a>, <a href="/author/Schmitz">Christoph Schmitz</a>, and <a href="/author/Stumme">Gerd Stumme</a> </span><em>Workshop on Social and Collaborative Construction of Structured Knowledge CKC 2007 at WWW 2007, </em><em>Banff, Canada, </em>(<em>2007</em>)Tue Jun 03 21:19:09 CEST 2008Banff, CanadaWorkshop on Social and Collaborative Construction of Structured Knowledge (CKC 2007) at WWW 2007Organizing Publications and Bookmarks in BibSonomy20072007 bookmarking folksonomy myown social system http://www.bibsonomy.org/bibtex/2ffc17b74fd9b1e88d8b4533595d0c123/beatebeate2007-12-04T15:34:03+01:00analysis logbib networks social <span class="authorEditorList"><a href="/author/Shi">Xiaodong Shi</a> </span><em>School of Information, University of Michigan, </em>(<em>2007</em>)Tue Dec 04 15:34:03 CET 2007School of Information, University of MichiganSocial network analysis of web search engine query logs2007analysis logbib networks social http://www.bibsonomy.org/bibtex/235942842dc6d7bd6a95dbc623dcfe830/beatebeate2007-06-29T16:24:38+02:00explorative-study list media social <span class="authorEditorList"><a href="/author/Halvey">Martin J. Halvey</a>, and <a href="/author/Keane">Mark T. Keane</a> </span><em>WWW &#039;07: Proceedings of the 16th international conference on World Wide Web, </em><em>page 1273--1274. </em><em>New York, NY, USA, </em><em>ACM Press, </em>(<em>2007</em>)Fri Jun 29 16:24:38 CEST 2007New York, NY, USAWWW '07: Proceedings of the 16th international conference on World Wide Web1273--1274Exploring social dynamics in online media sharing2007explorative-study list media social Exploring social dynamics in online media sharinghttp://www.bibsonomy.org/bibtex/288603ee0903b30dc642aebdaa6a22f93/beatebeate2007-06-28T16:46:42+02:00community list navigation search social <span class="authorEditorList"><a href="/author/Freyne">Jill Freyne</a>, <a href="/author/Farzan">Rosta Farzan</a>, <a href="/author/Brusilovsky">Peter Brusilovsky</a>, <a href="/author/Smyth">Barry Smyth</a>, and <a href="/author/Coyle">Maurice Coyle</a> </span><em>IUI &#039;07: Proceedings of the 12th international conference on Intelligent user interfaces, </em><em>page 52--61. </em><em>New York, NY, USA, </em><em>ACM Press, </em>(<em>2007</em>)Thu Jun 28 16:46:42 CEST 2007New York, NY, USAIUI '07: Proceedings of the 12th international conference on Intelligent user interfaces52--61Collecting community wisdom: integrating social search \& social navigation2007community list navigation search social The goal of this paper is to detail the integration of two "social Web" technologies - social search and social navigation - and to highlight the benefits of such integration on two levels. Firstly, both technologies harvest and harness "community wisdom" and in an integrated system each of the search and navigation components can benefit from the additional community wisdom gathered by the other when assisting users to locate relevant information. Secondly, by integrating search and browsing we facilitate the development of a unique interface that effectively blends search and browsing functionality as part of a seamless social information access service. This service allows users to effectively combine their search and browsing behaviors. In this paper we will argue that this integration provides significantly more than the simple sum of the parts.Collecting community wisdomhttp://www.bibsonomy.org/bibtex/29c3d83cf38edf3acede2f496638e0eaa/beatebeate2007-06-28T14:44:44+02:00art collaboration folksonomy social tagging <span class="authorEditorList"><a href="/author/Trant">J. Trant</a>, and <a href="/author/Wyman">B. Wyman</a> </span><em>Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland, May</em> (<em>2006</em>)Thu Jun 28 14:44:44 CEST 2007Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland, May{Investigating social tagging and folksonomy in art museums with steve. museum}2006art collaboration folksonomy social tagging http://www.bibsonomy.org/bibtex/2225256375e738dcf448829a176d64bc7/beatebeate2007-06-28T14:38:46+02:00annotation classification folksonomy list logbib social <span class="authorEditorList"><a href="/author/Mathes">A. Mathes</a> </span><em>Computer Mediated Communication, LIS590CMC Doctoral Seminar, Graduate School of Library and Information Science, University of Illinois Urbana-Champaign, December</em> (<em>2004</em>)Thu Jun 28 14:38:46 CEST 2007Computer Mediated Communication, LIS590CMC (Doctoral Seminar), Graduate School of Library and Information Science, University of Illinois Urbana-Champaign, December{Folksonomies-Cooperative Classification and Communication Through Shared Metadata}2004annotation classification folksonomy list logbib social