Workshop on Location-based Social Networks最新文献_第2页

Comparing the spatial characteristics of corresponding cyber and physical communities: a case study 网络社区与实体社区空间特征的对比研究

Workshop on Location-based Social Networks Pub Date : 2013-11-05 DOI: 10.1145/2536689.2536805

Xu Lu, A. Croitoru, Jacek R. Radzikowski, A. Crooks, A. Stefanidis

引用次数: 3

Temporal decomposition and semantic enrichment of mobility flows 流动流的时间分解与语义丰富

Workshop on Location-based Social Networks Pub Date : 2013-09-01 DOI: 10.1145/2536689.2536806

C. Coffey, A. Pozdnoukhov

引用次数: 37

Traffic observatory: a system to detect and locate traffic events and conditions using Twitter 交通观测站:一个使用Twitter检测和定位交通事件和状况的系统

Workshop on Location-based Social Networks Pub Date : 2012-11-06 DOI: 10.1145/2442796.2442800

S. Ribeiro, C. Davis, Diogo Rennó Rocha de Oliveira, Wagner Meira Jr, Tatiana S. Gonçalves, G. Pappa

引用次数: 63

The anatomy of Sindbad: a location-aware social networking system 辛巴达剖析:一个位置感知的社交网络系统

Workshop on Location-based Social Networks Pub Date : 2012-11-06 DOI: 10.1145/2442796.2442798

Mohamed Sarwat, Jie Bao, A. Eldawy, Justin J. Levandoski, A. Magdy, M. Mokbel

引用次数: 2

Multimodal geo-tagging in social media websites using hierarchical spatial segmentation 基于分层空间分割的社交媒体网站多模态地理标记

Workshop on Location-based Social Networks Pub Date : 2012-11-06 DOI: 10.1145/2442796.2442805

P. Kelm, S. Schmiedeke, T. Sikora

{"title":"Multimodal geo-tagging in social media websites using hierarchical spatial segmentation","authors":"P. Kelm, S. Schmiedeke, T. Sikora","doi":"10.1145/2442796.2442805","DOIUrl":"https://doi.org/10.1145/2442796.2442805","url":null,"abstract":"These days the sharing of photographs and videos is very popular in social networks. Many of these social media websites such as Flickr, Facebook and Youtube allows the user to manually label their uploaded videos with geo-information using a interface for dragging them into the map. However, the manually labelling for a large set of social media is still borring and error-prone. For this reason we present a hierarchical, multi-modal approach for estimating the GPS information. Our approach makes use of external resources like gazetteers to extract toponyms in the metadata and of visual and textual features to identify similar content. First, the national borders detection recognizes the country and its dimension to speed up the estimation and to eliminate geographical ambiguity. Next, we use a database of more than 3.2 million Flickr images to group them together into geographical regions and to build a hierarchical model. A fusion of visual and textual methods for different granularities is used to classify the videos' location into possible regions. The Flickr videos are tagged with the geo-information of the most similar training image within the regions that is previously filtered by the probabilistic model for each test video. In comparison with existing GPS estimation and image retrieval approaches at the Placing Task 2011 we will show the effectiveness and high accuracy relative to the state-of-the art solutions.","PeriodicalId":107369,"journal":{"name":"Workshop on Location-based Social Networks","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122747920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Can off-the-shelf object detectors be used to extract geographic information from geo-referenced social multimedia? 现成的对象检测器能否用于从地理参考的社交多媒体中提取地理信息?

Workshop on Location-based Social Networks Pub Date : 2012-11-06 DOI: 10.1145/2442796.2442801

Daniel Leung, S. Newsam

引用次数: 6

SNAIR: a framework for personalised recommendations based on social network analysis SNAIR:基于社会网络分析的个性化推荐框架

Workshop on Location-based Social Networks Pub Date : 2012-11-06 DOI: 10.1145/2442796.2442809

Saajan Shridhar, Mithleshwar Lakhanpuria, Abhishek Charak, Ankur Gupta, Swapan Shridhar

引用次数: 11

TweoLocator: a non-intrusive geographical locator system for Twitter TweoLocator: Twitter的非侵入式地理定位系统

Workshop on Location-based Social Networks Pub Date : 2012-11-06 DOI: 10.1145/2442796.2442804

Rodolfo Gonzalez, Gerardo Figueroa, Yi-Shin Chen

引用次数: 23

Public checkins versus private queries: measuring and evaluating spatial preference 公共签到与私人查询:测量和评估空间偏好

Workshop on Location-based Social Networks Pub Date : 2012-11-06 DOI: 10.1145/2442796.2442806

James Caverlee, Zhiyuan Cheng, Wai Gen Yee, Roger Liew, Yuan Liang

{"title":"Public checkins versus private queries: measuring and evaluating spatial preference","authors":"James Caverlee, Zhiyuan Cheng, Wai Gen Yee, Roger Liew, Yuan Liang","doi":"10.1145/2442796.2442806","DOIUrl":"https://doi.org/10.1145/2442796.2442806","url":null,"abstract":"Understanding the spatial preference of mobile and web users is of great significance to creating and improving location-based recommendation systems, travel planners, search engines, and other emerging mobile applications. However, traditional sources of spatial preference -- which reflect the patterns of geo-spatial interest of large numbers of users -- have typically been expensive to collect, proprietary, and unavailable for widespread use. In this paper, we investigate the viability of new publicly-available geospatial information to capture spatial preference. Concretely, we compare a set of 35 million publicly shared check-ins voluntarily generated by users of a popular location sharing service with a set of over 400 million private query logs recorded by a commercial hotel search engine. Although generated by users with fundamentally different intentions, we find common conclusions may be drawn from both data sources -- (i) that the relative geo-spatial \"footprint\" of different locations is surprisingly consistent across both; (ii) that methods to identify significant locations results in similar conclusions; and (iii) that similar performance may be achieved for automatically identifying groups of related locations. These results indicate the viability of publicly shared location information to complement (and replace, in some cases), privately held location information.","PeriodicalId":107369,"journal":{"name":"Workshop on Location-based Social Networks","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133239961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploratory analysis on heterogeneous tag-point patterns for ranking and extracting hot-spot related tags 基于异构标记点模式的热点相关标签排序与提取的探索性分析

Workshop on Location-based Social Networks Pub Date : 2012-11-06 DOI: 10.1145/2442796.2442802

M. Ruocco, H. Ramampiaro

{"title":"Exploratory analysis on heterogeneous tag-point patterns for ranking and extracting hot-spot related tags","authors":"M. Ruocco, H. Ramampiaro","doi":"10.1145/2442796.2442802","DOIUrl":"https://doi.org/10.1145/2442796.2442802","url":null,"abstract":"The availability of a huge amount of geotagged resources on the web can be exploited to extract new useful information. We propose a set of estimators that are able to evaluate the degree of clustering of the spatial distribution of terms used to tag such geotagged resources. We introduce the concept of tag point pattern to derive indexes from the exploratory analysis by taking advantage of the second order Ripley's K-function, previously used in epidemiology, geo-statistics and ecology. The derived model estimates the degree of aggregation of the geotagged resources, taking into account the heterogeneity of the spatial distribution of the underlying population. Further, thanks to subsampling techniques, our approach is able to handle large datasets. Without losing of generality, we perform our experiments on a dataset derived Flickr pictures, as a use case. This dataset consists of tags that were extracted from a set of 1.2 million of pictures. We evaluate our proposed indexes with respect to their ability to extract tags related to geographical landmarks and hotspots. Our experiments show that we get good results using our estimators.","PeriodicalId":107369,"journal":{"name":"Workshop on Location-based Social Networks","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123130360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8