Tom De Nies presents on developing methods to automatically assess the social media impact of news content. He describes an initial proof of concept that searches social media like Twitter for posts related to an input news article based on keywords. It returns metrics like the number of followers and retweets to gauge influence. He proposes next steps like expanding social media sources and refining related content identification through techniques like named entity recognition. The goal is to help journalists identify popular stories and evaluate how well automatic impact assessments compare to human judgments.