Semi-Automated Extraction of Attributed Verification and Debunking Reports from Social Media

Content from social media sites such as Twitter, YouTube, Facebook and Instagram are becoming an important part of modern journalism. Of particular importance to real-time breaking news is amateur on the spot incident reports and eyewitness images and videos. With breaking news having tight reporting deadlines, measured in minutes not days, the need to quickly verify suspicious content is paramount [1] [3]. Journalists are increasingly looking to pre-filter and automate the simpler parts of the verification process. Current tools available to journalists can be broadly categorized as dashboard and in-depth analytic tools. Dashboard tools display filtered traffic volumes, trending hashtags and maps of content by topic, author and/or location. In-depth analysis tools use techniques such as sentiment analysis, social network graph visualization and topic tracking. These tools help journalists manage social media content but unverified rumours and fake news stories on social media are becoming both increasingly common [2] and increasingly difficult to spot. The current best practice for journalistic user generated content (UGC) verification [1] follows a hard to scale manual process involving journalists reviewing content from trusted sources with the ultimate goal of phoning up authors to verify specific images/videos and then asking permission to use that content for publication