Geoparsing and Geosemantics for Social Media
Stuart E. Middleton, Vadims Krivcovs · ACM Transactions on Information Systems · 2016
In recent years, there has been a growing trend to use publicly available social media sources within the field of journalism. Breaking news has tight reporting deadlines, measured in minutes not days, but content must still be checked and rumors verified. As such, journalists are looking at automated content analysis to prefilter large volumes of social media content prior to manual verification. This article describes a real-time social media analytics framework for journalists. We extend our previously published geoparsing approach to improve its scalability and efficiency. We develop and evaluate a novel approach to geosemantic feature extraction, classifying evidence in terms of situatedness, timeliness, confirmation, and validity. Our approach works for new unseen news topics. We report results from four experiments using five Twitter datasets crawled during different English-language news events. One of our datasets is the standard TREC 2012 microblog corpus. Our classification results are promising, with F1 scores varying by class from 0.64 to 0.92 for unseen event types. We lastly report results from two case studies during real-world news stories, showcasing different ways our system can assist journalists filter and cross-check content as they examine the trust and veracity of content and sources.