Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Analytics for Local Events and News最新文献

DeLLe 的

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Analytics for Local Events and News Pub Date : 2019-11-05 DOI: 10.1145/3356473.3365188

Hong Wei, Hao Zhou, Jagan Sankaranarayanan, Sudipta Sengupta, H. Samet

{"title":"DeLLe","authors":"Hong Wei, Hao Zhou, Jagan Sankaranarayanan, Sudipta Sengupta, H. Samet","doi":"10.1145/3356473.3365188","DOIUrl":"https://doi.org/10.1145/3356473.3365188","url":null,"abstract":"Geotagged tweet streams contain invaluable information about the real-world local events like sports games, protests and traffic accidents. Timely detecting and extracting such events may have various applications but yet unsolved challenges. In this paper, we present DeLLe, a methodology for automatically Detecting Latest Local Events from geotagged tweets. With the help of novel spatio temporal tweet count prediction models, DeLLe first finds unusual locations which have aggregated unexpected number of tweets in the latest time period and thereby imply potential local events. Next, DeLLe calculates, for each such unusual location, a ranking score to identify the ones most likely having ongoing local events by addressing the temporal burstiness, spatial burstiness and topical coherence. Furthermore, DeLLe infers an event candidate's spatio temporal range by tracking its event-focus point, which essentially reflects the most recent representative occurrence site. Finally, DeLLe chooses the most influential tweets to summarize local events and thereby presents succinct but yet representative descriptions. We evaluate DeLLe on the city of Seattle, WA as well as a larger city of New York. The results show that the proposed method generally outperforms competitive baseline approaches.","PeriodicalId":322596,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Analytics for Local Events and News","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115768317","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

A Blockchain-based Solution to Fake Check-ins in Location-Based Social Networks 在基于位置的社交网络中，基于区块链的假签到解决方案

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Analytics for Local Events and News Pub Date : 2019-11-05 DOI: 10.1145/3356473.3365191

S. Migliorini, Mauro Gambini, A. Belussi

引用次数: 4

Detecting (Unusual) Events in Urban Areas using Bike-Sharing Data 利用共享单车数据检测城市(异常)事件

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Analytics for Local Events and News Pub Date : 2019-11-05 DOI: 10.1145/3356473.3365190

Alex Lam, Matthew Schofield, S. Ho

{"title":"Detecting (Unusual) Events in Urban Areas using Bike-Sharing Data","authors":"Alex Lam, Matthew Schofield, S. Ho","doi":"10.1145/3356473.3365190","DOIUrl":"https://doi.org/10.1145/3356473.3365190","url":null,"abstract":"Social media, traffic sensors, GPS trajectories, and location-based social network data provide diverse Spatio temporal information sources that help to detect and analysis Spatio temporal events. Nowadays, bike sharing systems are active all over the world in major cities, and collecting a large amount of data regarding trips taken by users and status of the stations. Through analysis of the data aggregated by bike sharing systems, one can gain an understanding of crowd/commuter movements and behaviors. However, no one has used only the bike sharing data for generic event detection. In this paper, we propose a clustering-based detection method to identify Spatio temporal events that deviate from normal or regular everyday life using publicly available bike sharing data. In particular, we apply spectral clustering on bike station and bike flow data as evolving graphs and monitor changes of the bike share network (edge/node values) over time. Our proposed method decides whether a cluster is expected or anomalous (unusual). When a cluster is anomalous, there is an unusual event occurring at that time instance. Preliminary results on 6-months of data from Philadelphia and Washington DC are used to show the feasibility of our proposed method. In particular, our preliminary results show that some signatures of local (and less prominent) events (e.g., university events/activities in an urban area) can show up when bike sharing data is utilized for generic event detection.","PeriodicalId":322596,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Analytics for Local Events and News","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126605391","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: 3

DeepSpot DeepSpot

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Analytics for Local Events and News Pub Date : 2019-11-05 DOI: 10.1145/3356473.3365187

Avik Nayak, Haiquan Chen, Xiaojun Ruan, J. Ouyang

{"title":"DeepSpot","authors":"Avik Nayak, Haiquan Chen, Xiaojun Ruan, J. Ouyang","doi":"10.1145/3356473.3365187","DOIUrl":"https://doi.org/10.1145/3356473.3365187","url":null,"abstract":"Recently opinion spam has been widespread on online review websites and has received significant research attention. Existing approaches to detecting online opinion spam can be categorized into three groups: (1) review behavior-based approaches, which use metadata associated with user review behavior and product profile, (2) language-based approaches, which focus on the characteristics of the language that the opinion spammers use, and (3) graph-based approaches, where various user-review-product networks are constructed for node connectivity and similarity analysis. Unfortunately, all the aforementioned approaches have their limitations. In this paper, we introduce a holistic system, DeepSpot, for fake review detection. DeepSpot recognizes the true and fake reviews based on both the real human-posted reviews and the synthetic machine-generated reviews leveraging sentiment classification. Specifically, DeepSpot augments the original reviews with synthetic reviews using the encoder-decoder neural networks trained by the positive and negative reviews, respectively. Extensive experiments on real-world data showed that DeepSpot outperformed the state-of-the-art approaches in terms of various effectiveness metrics for recognizing true and fake reviews.","PeriodicalId":322596,"journal":{"name":"Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Analytics for Local Events and News","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122255087","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: 1

Scalable Community Detection over Geo-Social Network 基于地理社交网络的可扩展社区检测

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Analytics for Local Events and News Pub Date : 2019-06-13 DOI: 10.1145/3356473.3365189

Xiuwen Zheng, Qiyu Liu, Amarnath Gupta

引用次数: 0

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Analytics for Local Events and News 第三届ACM SIGSPATIAL本地事件和新闻分析国际研讨会论文集

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Analytics for Local Events and News Pub Date : 1900-01-01 DOI: 10.1145/3356473

引用次数: 0