Contextual Priming for Object Detection
International Journal of Computer Vision
Discriminative parameter learning for Bayesian networks
Proceedings of the 25th international conference on Machine learning
Influence and correlation in social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Proceedings of the 2009 International Workshop on Location Based Social Networks
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Social pixels: genesis and evaluation
Proceedings of the international conference on Multimedia
The wisdom of social multimedia: using flickr for prediction and forecast
Proceedings of the international conference on Multimedia
Computer Vision: Algorithms and Applications
Computer Vision: Algorithms and Applications
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Modeling people and places with internet photo collections
Communications of the ACM
Modeling People and Places with Internet Photo Collections
Queue - Networks
Latent geographic feature extraction from social media
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Chelsea won, and you bought a t-shirt: characterizing the interplay between Twitter and e-commerce
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
A probablistic model for spatio-temporal signal extraction from social media
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Reliable spatio-temporal signal extraction and exploration from human activity records
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
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The popularity of social media websites like Flickr and Twitter has created enormous collections of user-generated content online. Latent in these content collections are observations of the world: each photo is a visual snapshot of what the world looked like at a particular point in time and space, for example, while each tweet is a textual expression of the state of a person and his or her environment. Aggregating these observations across millions of social sharing users could lead to new techniques for large-scale monitoring of the state of the world and how it is changing over time. In this paper we step towards that goal, showing that by analyzing the tags and image features of geo-tagged, time-stamped photos we can measure and quantify the occurrence of ecological phenomena including ground snow cover, snow fall and vegetation density. We compare several techniques for dealing with the large degree of noise in the dataset, and show how machine learning can be used to reduce errors caused by misleading tags and ambiguous visual content. We evaluate the accuracy of these techniques by comparing to ground truth data collected both by surface stations and by Earth-observing satellites. Besides the immediate application to ecology, our study gives insight into how to accurately crowd-source other types of information from large, noisy social sharing datasets.