document: Mining photo-sharing websites to study ecological phenomena 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 .
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