D. Helic, Christian Körner, M. Granitzer, M. Strohmaier, C. Trattner
Although many social tagging systems share a common tripartite graph structure, the collaborative processes that are generating these structures can differ significantly. For example, while resources on Delicious are usually tagged by all users who bookmark the web page cnn.com, photos on Flickr are usually tagged just by a single user who uploads the photo. In the literature, this distinction has been described as a distinction between broad vs. narrow folksonomies. This paper sets out to explore navigational differences between broad and narrow folksonomies in social hypertextual systems. We study both kinds of folksonomies on a dataset provided by Mendeley - a collaborative platform where users can annotate and organize scientific articles with tags. Our experiments suggest that broad folksonomies are more useful for navigation, and that the collaborative processes that are generating folksonomies matter qualitatively. Our findings are relevant for system designers and engineers aiming to improve the navigability of social tagging systems.
{"title":"Navigational efficiency of broad vs. narrow folksonomies","authors":"D. Helic, Christian Körner, M. Granitzer, M. Strohmaier, C. Trattner","doi":"10.1145/2309996.2310008","DOIUrl":"https://doi.org/10.1145/2309996.2310008","url":null,"abstract":"Although many social tagging systems share a common tripartite graph structure, the collaborative processes that are generating these structures can differ significantly. For example, while resources on Delicious are usually tagged by all users who bookmark the web page cnn.com, photos on Flickr are usually tagged just by a single user who uploads the photo. In the literature, this distinction has been described as a distinction between broad vs. narrow folksonomies. This paper sets out to explore navigational differences between broad and narrow folksonomies in social hypertextual systems. We study both kinds of folksonomies on a dataset provided by Mendeley - a collaborative platform where users can annotate and organize scientific articles with tags. Our experiments suggest that broad folksonomies are more useful for navigation, and that the collaborative processes that are generating folksonomies matter qualitatively. Our findings are relevant for system designers and engineers aiming to improve the navigability of social tagging systems.","PeriodicalId":91270,"journal":{"name":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","volume":"22 1","pages":"63-72"},"PeriodicalIF":0.0,"publicationDate":"2012-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85167874","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}
Karin Schöfegger, Christian Körner, Philipp Singer, M. Granitzer
In social tagging systems the tagging activities of users leave a huge amount of implicit information about them. The users choose tags for the resources they annotate based on their interests, background knowledge, personal opinion and other criteria. Whilst existing research in mining social tagging data mostly focused on gaining a deeper understanding of the user's interests and the emerging structures in those systems, little work has yet been done to use the rich implicit information in tagging activities to unveil to what degree users' tags convey information about their background. The automatic inference of user background information can be used to complete user profiles which in turn supports various recommendation mechanisms. This work illustrates the application of supervised learning mechanisms to analyze a large online corpus of tagged academic literature for extraction of user characteristics from tagging behavior. As a representative example of background characteristics we mine the user's research discipline. Our results show that tags convey rich information that can help designers of those systems to better understand and support their prolific users - users that tag actively - beyond their interests.
{"title":"Learning user characteristics from social tagging behavior","authors":"Karin Schöfegger, Christian Körner, Philipp Singer, M. Granitzer","doi":"10.1145/2309996.2310031","DOIUrl":"https://doi.org/10.1145/2309996.2310031","url":null,"abstract":"In social tagging systems the tagging activities of users leave a huge amount of implicit information about them. The users choose tags for the resources they annotate based on their interests, background knowledge, personal opinion and other criteria. Whilst existing research in mining social tagging data mostly focused on gaining a deeper understanding of the user's interests and the emerging structures in those systems, little work has yet been done to use the rich implicit information in tagging activities to unveil to what degree users' tags convey information about their background. The automatic inference of user background information can be used to complete user profiles which in turn supports various recommendation mechanisms. This work illustrates the application of supervised learning mechanisms to analyze a large online corpus of tagged academic literature for extraction of user characteristics from tagging behavior. As a representative example of background characteristics we mine the user's research discipline. Our results show that tags convey rich information that can help designers of those systems to better understand and support their prolific users - users that tag actively - beyond their interests.","PeriodicalId":91270,"journal":{"name":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","volume":"37 1","pages":"207-212"},"PeriodicalIF":0.0,"publicationDate":"2012-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85281397","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}
Online social networks (OSNs) have become one of the most effective channels for marketing and advertising. Since users are often influenced by their friends, "word-of-mouth" exchanges so-called viral marketing in social networks can be used to increases product adoption or widely spread content over the network. The common perception of viral marketing about being cheap, easy, and massively effective makes it an ideal replacement of traditional advertising. However, recent studies have revealed that the propagation often fades quickly within only few hops from the sources, counteracting the assumption on the self-perpetuating of influence considered in literature. With only limited influence propagation, is massively reaching customers via viral marketing still affordable? How to economically spend more resources to increase the spreading speed? We investigate the cost-effective massive viral marketing problem, taking into the consideration the limited influence propagation. Both analytical analysis based on power-law network theory and numerical analysis demonstrate that the viral marketing might involve costly seeding. To minimize the seeding cost, we provide mathematical programming to find optimal seeding for medium-size networks and propose VirAds, an efficient algorithm, to tackle the problem on large-scale networks. VirAds guarantees a relative error bound of O(1) from the optimal solutions in power-law networks and outperforms the greedy heuristics which realizes on the degree centrality. Moreover, we also show that, in general, approximating the optimal seeding within a ratio better than O(log n) is unlikely possible.
{"title":"Cheap, easy, and massively effective viral marketing in social networks: truth or fiction?","authors":"Thang N. Dinh, Dung T. Nguyen, M. Thai","doi":"10.1145/2309996.2310024","DOIUrl":"https://doi.org/10.1145/2309996.2310024","url":null,"abstract":"Online social networks (OSNs) have become one of the most effective channels for marketing and advertising. Since users are often influenced by their friends, \"word-of-mouth\" exchanges so-called viral marketing in social networks can be used to increases product adoption or widely spread content over the network. The common perception of viral marketing about being cheap, easy, and massively effective makes it an ideal replacement of traditional advertising. However, recent studies have revealed that the propagation often fades quickly within only few hops from the sources, counteracting the assumption on the self-perpetuating of influence considered in literature. With only limited influence propagation, is massively reaching customers via viral marketing still affordable? How to economically spend more resources to increase the spreading speed?\u0000 We investigate the cost-effective massive viral marketing problem, taking into the consideration the limited influence propagation. Both analytical analysis based on power-law network theory and numerical analysis demonstrate that the viral marketing might involve costly seeding. To minimize the seeding cost, we provide mathematical programming to find optimal seeding for medium-size networks and propose VirAds, an efficient algorithm, to tackle the problem on large-scale networks. VirAds guarantees a relative error bound of O(1) from the optimal solutions in power-law networks and outperforms the greedy heuristics which realizes on the degree centrality. Moreover, we also show that, in general, approximating the optimal seeding within a ratio better than O(log n) is unlikely possible.","PeriodicalId":91270,"journal":{"name":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","volume":"12 1","pages":"165-174"},"PeriodicalIF":0.0,"publicationDate":"2012-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76995849","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}
In this paper, we propose a method to identify high-quality Wikipedia articles by mutually evaluating editors and texts. A major approach for assessing articles using edit history is a text survival ratio based approach. However, the problem is that many high-quality articles are identified as low quality, because many vandals delete high-quality texts, then the survival ratios of high-quality texts are decreased by vandals. Our approach's strongest point is its resistance to vandalism. Using our method, if we calculate text quality values using editor quality values, vandals do not affect any quality values of the other editors, then the accuracy of text quality values should improve. However, the problem is that editor quality values are calculated by text quality values, and text quality values are calculated by editor quality values. To solve this problem, we mutually calculate editor and text quality values until they converge. Using this method, we can calculate a quality value of a text that takes into consideration that of its editors.
{"title":"QualityRank: assessing quality of wikipedia articles by mutually evaluating editors and texts","authors":"Yumiko Suzuki, Masatoshi Yoshikawa","doi":"10.1145/2309996.2310047","DOIUrl":"https://doi.org/10.1145/2309996.2310047","url":null,"abstract":"In this paper, we propose a method to identify high-quality Wikipedia articles by mutually evaluating editors and texts. A major approach for assessing articles using edit history is a text survival ratio based approach. However, the problem is that many high-quality articles are identified as low quality, because many vandals delete high-quality texts, then the survival ratios of high-quality texts are decreased by vandals. Our approach's strongest point is its resistance to vandalism. Using our method, if we calculate text quality values using editor quality values, vandals do not affect any quality values of the other editors, then the accuracy of text quality values should improve. However, the problem is that editor quality values are calculated by text quality values, and text quality values are calculated by editor quality values. To solve this problem, we mutually calculate editor and text quality values until they converge. Using this method, we can calculate a quality value of a text that takes into consideration that of its editors.","PeriodicalId":91270,"journal":{"name":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","volume":"22 2","pages":"307-308"},"PeriodicalIF":0.0,"publicationDate":"2012-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72623665","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}
In this work we developed a surveillance architecture to detect diseases-related postings in social networks using Twitter as an example for a high-traffic social network. Our real-time architecture uses Twitter streaming API to crawl Twitter messages as they are posted. Data mining techniques have been used to index, extract and classify postings. Finally, we evaluate the performance of the classifier with a dataset of public health postings and also evaluate the run-time performance of whole system with respect to latency and throughput.
{"title":"A real-time architecture for detection of diseases using social networks: design, implementation and evaluation","authors":"Mustafa Sofean, Matthew Smith","doi":"10.1145/2309996.2310048","DOIUrl":"https://doi.org/10.1145/2309996.2310048","url":null,"abstract":"In this work we developed a surveillance architecture to detect diseases-related postings in social networks using Twitter as an example for a high-traffic social network. Our real-time architecture uses Twitter streaming API to crawl Twitter messages as they are posted. Data mining techniques have been used to index, extract and classify postings. Finally, we evaluate the performance of the classifier with a dataset of public health postings and also evaluate the run-time performance of whole system with respect to latency and throughput.","PeriodicalId":91270,"journal":{"name":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","volume":"14 1","pages":"309-310"},"PeriodicalIF":0.0,"publicationDate":"2012-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79281655","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}
Prima Chairunnanda, Simon Forsyth, Khuzaima S. Daudjee
Online social networks have become important vehicles for connecting people for work and leisure. As these networks grow, data that are stored over these networks also grow, and management of these data becomes a challenge. Graph data models are a natural fit for representing online social networks but need to support distribution to allow the associated graph databases to scale while offering acceptable performance. We provide scalability by considering methods for partitioning graph databases and implement one within the Neo4j architecture based on distributing the vertices of the graph. We evaluate its performance in several simple scenarios and demonstrate that it is possible to partition a graph database without incurring significant overhead other than that required by network delays. We identify and discuss several methods to reduce the observed network delays in our prototype.
{"title":"Graph data partition models for online social networks","authors":"Prima Chairunnanda, Simon Forsyth, Khuzaima S. Daudjee","doi":"10.1145/2309996.2310026","DOIUrl":"https://doi.org/10.1145/2309996.2310026","url":null,"abstract":"Online social networks have become important vehicles for connecting people for work and leisure. As these networks grow, data that are stored over these networks also grow, and management of these data becomes a challenge. Graph data models are a natural fit for representing online social networks but need to support distribution to allow the associated graph databases to scale while offering acceptable performance. We provide scalability by considering methods for partitioning graph databases and implement one within the Neo4j architecture based on distributing the vertices of the graph. We evaluate its performance in several simple scenarios and demonstrate that it is possible to partition a graph database without incurring significant overhead other than that required by network delays. We identify and discuss several methods to reduce the observed network delays in our prototype.","PeriodicalId":91270,"journal":{"name":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","volume":"68 1","pages":"175-180"},"PeriodicalIF":0.0,"publicationDate":"2012-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74562275","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}
It is well known that online social networking sites (OSNs) such as Facebook pose risks to their users' privacy. OSNs store vast amounts of users' private data and activities and therefore subject the user to the risk of undesired disclosure. The regular non tech-savvy Facebook user either has little awareness of his privacy needs or is not willing or capable to invest much extra effort into securing his online activities. In this paper, we present a non-disruptive and easy to-use service that helps to protect users' most private information, namely their private messages and chats against the OSN provider itself and external adversaries. Our novel Confidentiality as a Service paradigm was designed with usability and non-obtrusiveness in mind and requires little to no additional knowledge on the part of the users. The simplicity of the service is achieved through a novel trust splitting approach integrated into the Confidentiality as a Service paradigm. To show the feasibility of our approach we present a fully-working prototype for Facebook and an initial usability study. All of the participating subjects completed the study successfully without any problems or errors and only required three minutes on average for the entire installation and setup procedure.
{"title":"TrustSplit: usable confidentiality for social network messaging","authors":"S. Fahl, M. Harbach, T. Muders, Matthew Smith","doi":"10.1145/2309996.2310022","DOIUrl":"https://doi.org/10.1145/2309996.2310022","url":null,"abstract":"It is well known that online social networking sites (OSNs) such as Facebook pose risks to their users' privacy. OSNs store vast amounts of users' private data and activities and therefore subject the user to the risk of undesired disclosure. The regular non tech-savvy Facebook user either has little awareness of his privacy needs or is not willing or capable to invest much extra effort into securing his online activities.\u0000 In this paper, we present a non-disruptive and easy to-use service that helps to protect users' most private information, namely their private messages and chats against the OSN provider itself and external adversaries. Our novel Confidentiality as a Service paradigm was designed with usability and non-obtrusiveness in mind and requires little to no additional knowledge on the part of the users. The simplicity of the service is achieved through a novel trust splitting approach integrated into the Confidentiality as a Service paradigm. To show the feasibility of our approach we present a fully-working prototype for Facebook and an initial usability study. All of the participating subjects completed the study successfully without any problems or errors and only required three minutes on average for the entire installation and setup procedure.","PeriodicalId":91270,"journal":{"name":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","volume":"110 1","pages":"145-154"},"PeriodicalIF":0.0,"publicationDate":"2012-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87982202","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}
Ian R. O’Keeffe, A. O'Connor, P. Cass, S. Lawless, V. Wade
Despite the recent interest in extending Adaptive Hypermedia beyond the closed corpus domain and into the open corpus world of the web, many current approaches are limited by their reliance on closed metadata model repositories. The need to produce large quantities of high quality metadata is an expensive task which results in silos of high quality metadata. These silos are often underutilized due to the proprietary nature of the content described by the metadata and the perceived value of the metadata itself. Meanwhile, the Linked Open Data movement is promoting a pragmatic approach to exposing, sharing and connecting pieces of machine-readable data and knowledge on the WWW using an agreed set of best practices. In this paper we identify the potential issues that arise from building personalization systems based on Linked Open Data.
{"title":"Linked open corpus models, leveraging the semantic web for adaptive hypermedia","authors":"Ian R. O’Keeffe, A. O'Connor, P. Cass, S. Lawless, V. Wade","doi":"10.1145/2309996.2310054","DOIUrl":"https://doi.org/10.1145/2309996.2310054","url":null,"abstract":"Despite the recent interest in extending Adaptive Hypermedia beyond the closed corpus domain and into the open corpus world of the web, many current approaches are limited by their reliance on closed metadata model repositories. The need to produce large quantities of high quality metadata is an expensive task which results in silos of high quality metadata. These silos are often underutilized due to the proprietary nature of the content described by the metadata and the perceived value of the metadata itself. Meanwhile, the Linked Open Data movement is promoting a pragmatic approach to exposing, sharing and connecting pieces of machine-readable data and knowledge on the WWW using an agreed set of best practices. In this paper we identify the potential issues that arise from building personalization systems based on Linked Open Data.","PeriodicalId":91270,"journal":{"name":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","volume":"25 1","pages":"321-322"},"PeriodicalIF":0.0,"publicationDate":"2012-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89350481","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}
Folksonomies like Delicious and LastFm are modeled as tripartite (user-resource-tag) hypergraphs for studying their network properties. Detecting communities of similar nodes from such networks is a challenging problem. Most existing algorithms for community detection in folksonomies assign unique communities to nodes, whereas in reality, users have multiple topical interests and the same resource is often tagged with semantically different tags. The few attempts to detect overlapping communities work on projections of the hypergraph, which results in significant loss of information contained in the original tripartite structure. We propose the first algorithm to detect overlapping communities in folksonomies using the complete hypergraph structure. Our algorithm converts a hypergraph into its corresponding line-graph, using measures of hyperedge similarity, whereby any community detection algorithm on unipartite graphs can be used to produce overlapping communities in the folksonomy. Through extensive experiments on synthetic as well as real folksonomy data, we demonstrate that the proposed algorithm can detect better community structures as compared to existing state-of-the-art algorithms for folksonomies.
{"title":"Detecting overlapping communities in folksonomies","authors":"Abhijnan Chakraborty, Saptarshi Ghosh, Niloy Ganguly","doi":"10.1145/2309996.2310032","DOIUrl":"https://doi.org/10.1145/2309996.2310032","url":null,"abstract":"Folksonomies like Delicious and LastFm are modeled as tripartite (user-resource-tag) hypergraphs for studying their network properties. Detecting communities of similar nodes from such networks is a challenging problem. Most existing algorithms for community detection in folksonomies assign unique communities to nodes, whereas in reality, users have multiple topical interests and the same resource is often tagged with semantically different tags. The few attempts to detect overlapping communities work on projections of the hypergraph, which results in significant loss of information contained in the original tripartite structure. We propose the first algorithm to detect overlapping communities in folksonomies using the complete hypergraph structure. Our algorithm converts a hypergraph into its corresponding line-graph, using measures of hyperedge similarity, whereby any community detection algorithm on unipartite graphs can be used to produce overlapping communities in the folksonomy. Through extensive experiments on synthetic as well as real folksonomy data, we demonstrate that the proposed algorithm can detect better community structures as compared to existing state-of-the-art algorithms for folksonomies.","PeriodicalId":91270,"journal":{"name":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","volume":"137 1","pages":"213-218"},"PeriodicalIF":0.0,"publicationDate":"2012-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77225772","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}
Predicting the positive or negative attitude of individuals towards each other in a social environment has long been of interest, with applications in many domains. We investigate this problem in the context of the collaborative editing of articles in Wikipedia, showing that there is enough information in the edit history of the articles that can be utilized for predicting the attitude of co-editors. We train a model using a distant supervision approach, by labeling interactions between editors as positive or negative depending on how these editors vote for each other in Wikipedia admin elections. We use the model to predict the attitude among other editors, who have neither run nor voted in an election. We validate our model by assessing its accuracy in the tasks of predicting the results of the actual elections, and identifying controversial articles. Our analysis reveals that the interactions in co-editing articles can accurately predict votes, although there are differences between positive and negative votes. For instance, the accuracy when predicting negative votes substantially increases by considering longer traces of the edit history. As for predicting controversial articles, we show that exploiting positive and negative interactions during the production of an article provides substantial improvements on previous attempts at detecting controversial articles in Wikipedia.
{"title":"Leveraging editor collaboration patterns in wikipedia","authors":"Hoda Sepehri Rad, Aibek Makazhanov, Davood Rafiei, Denilson Barbosa","doi":"10.1145/2309996.2310001","DOIUrl":"https://doi.org/10.1145/2309996.2310001","url":null,"abstract":"Predicting the positive or negative attitude of individuals towards each other in a social environment has long been of interest, with applications in many domains. We investigate this problem in the context of the collaborative editing of articles in Wikipedia, showing that there is enough information in the edit history of the articles that can be utilized for predicting the attitude of co-editors. We train a model using a distant supervision approach, by labeling interactions between editors as positive or negative depending on how these editors vote for each other in Wikipedia admin elections. We use the model to predict the attitude among other editors, who have neither run nor voted in an election. We validate our model by assessing its accuracy in the tasks of predicting the results of the actual elections, and identifying controversial articles. Our analysis reveals that the interactions in co-editing articles can accurately predict votes, although there are differences between positive and negative votes. For instance, the accuracy when predicting negative votes substantially increases by considering longer traces of the edit history. As for predicting controversial articles, we show that exploiting positive and negative interactions during the production of an article provides substantial improvements on previous attempts at detecting controversial articles in Wikipedia.","PeriodicalId":91270,"journal":{"name":"HT ... : the proceedings of the ... ACM Conference on Hypertext and Social Media. ACM Conference on Hypertext and Social Media","volume":"61 1","pages":"13-22"},"PeriodicalIF":0.0,"publicationDate":"2012-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82048205","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：