SaferCity: a system for detecting and analyzing incidents from social media

This paper presents a system to identify and characterise public safety related incidents from social media, and enrich the situational awareness that law enforcement entities have on potentially-unreported activities happening in a city. The system is based on a new spatio-temporal clustering algorithm that is able to identify and characterize relevant incidents given even a small number of social media reports. We present a web-based application exposing the features of the system, and demonstrate its usefulness in detecting, from Twitter, public safety related incidents occurred in New York City during the Occupy Wall Street protests

Predicting User Engagement in Twitter with Collaborative Ranking

Collaborative Filtering (CF) is a core component of popular web-based services such as Amazon, YouTube, Netflix, and Twitter. Most applications use CF to recommend a small set of items to the user. For instance, YouTube presents to a user a list of top-n videos she would likely watch next based on her rating and viewing history. Current methods of CF evaluation have been focused on assessing the quality of a predicted rating or the ranking performance for top-n recommended items. However, restricting the recommender system evaluation to these two aspects is rather limiting and neglects other dimensions that could better characterize a well-perceived recommendation. In this paper, instead of optimizing rating or top-n recommendation, we focus on the task of predicting which items generate the highest user engagement. In particular, we use Twitter as our testbed and cast the problem as a Collaborative Ranking task where the rich features extracted from the metadata of the tweets help to complement the transaction information limited to user ids, item ids, ratings and timestamps. We learn a scoring function that directly optimizes the user engagement in terms of nDCG@10 on the predicted ranking. Experiments conducted on an extended version of the MovieTweetings dataset, released as part of the RecSys Challenge 2014, show the effectiveness of our approach.Comment: RecSysChallenge'14 at RecSys 2014, October 10, 2014, Foster City, CA, US

SaferCity: a System for Detecting Incidents from Social Media

This paper presents a system to identify and characterise public safety related incidents from social media, and enrich the situational awareness that law enforcement entities have on potentially-unreported activities happening in a city. The system is based on a new spatio-temporal clustering algorithm that is able to identify and characterize relevant incidents given even a small number of social media reports. We present a web-based application exposing the features of the system, and demonstrate its usefulness in detecting, from Twitter, public safety related incidents occurred in New York City during the Occupy Wall Street protests

Combining Textual and Visual Information for Image Retrieval in the Medical Domain

In this article we have assembled the experience obtained from our participation in the imageCLEF evaluation task over the past two years. Exploitation on the use of linear combinations for image retrieval has been attempted by combining visual and textual sources of images. From our experiments we conclude that a mixed retrieval technique that applies both textual and visual retrieval in an interchangeably repeated manner improves the performance while overcoming the scalability limitations of visual retrieval. In particular, the mean average precision (MAP) has increased from 0.01 to 0.15 and 0.087 for 2009 and 2010 data, respectively, when content-based image retrieval (CBIR) is performed on the top 1000 results from textual retrieval based on natural language processing (NLP)

Semantic Traffic Diagnosis with STAR-CITY: Architecture and Lessons Learned from Deployment in Dublin, Bologna, Miami and Rio

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