Geoparsing and geosemantics for social media: spatio-temporal grounding of content propagating rumours to support trust and veracity analysis during breaking news

摘要

In recent years there has been a growing trend to use publically 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 rumours verified. As such journalists are looking at automated content analysis to pre-filter large volumes of social media content prior to manual verification. This paper 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 4 experiments using 5 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