In this talk we show that LOD annotations are a powerful tool for semantic enrichment of Social Media microposts, allowing for reasoning with information that is not transmitted directly through the Social Media channels, but available in rich knowledge bases. Given the very short text of tweets, such enrichment provides with the necessary context, which is crucial for understanding opinions, trends, veracity in Social Media. LOD enrichment allows for the computer algorithms to ‘understand’ tweets in a way that a human would, by referring to common knowledge external to the micropost.
We choose a showcase that is highly relevant for the international political scene at the moment of the deliverable, namely a post-Brexit analysis. Post-Brexit discussions on Twitter provide with insights on the mixed feelings, attitude, propaganda, interests that follow the referendum and precede the political actions that need to be taken. We show that one can automatically mine the general opinion of the main UK administrative regions. We also identified the main actors of the political scene, with side comments on their age - an aspect that has been so many times brought to the public attention and even used for manipulating the opinion of the voters. Reasoning about age or birth year is only possible via LOD annotations.
