Generating Subjective Responses to Opinionated Articles in Social Media: An Agenda-Driven Architecture and a Turing-Like Test

Natural language traffic in social media (blogs, microblogs, talkbacks) enjoys vast monitoring and analysis efforts. However, the question whether computer systems can generate such content in order to effectively interact with humans has been only sparsely attended to. This paper presents an architecture for generating subjective responses to opinionated articles based on users’ agenda, documents’ topics, sentiments and a knowledge graph. We present an empirical evaluation method for quantifying the humanlikeness and relevance of the generated responses. We show that responses generated using world knowledge in the input are regarded as more human-like than those that rely on topic, sentiment and agenda only, whereas the use of world knowledge does not affect perceived relevance.