Neutralisation techniques, e.g.denial of responsibility and denial of victim, are used in the narrative of climate change scepticism to justify lack of action or to promote an alternative view.We collect manual annotations of neutralised techniques used in these texts, and explore semi-supervised models to automatically classify them.

Topic coherence is increasingly being used to evaluate topic models and filter topics for enduser applications.Topic coherence measures how well topic words relate to each other, but offers little insight into the utility of the topics in describing the documents.In this paper, we explore the topic intrusion task -the task of guessing an outlier topic given a document and a set of topics -and propose a method to automate it.We improve upon the state-of-the-art substantially, demonstrating its viability as an alternative method for topic model evaluation.

Topic models jointly learn topics and document-level topic distribution.Extrinsic evaluation of topic models tends to focus exclusively on topic-level evaluation, e.g. by assessing the coherence of topics.We demonstrate that there can be large discrepancies between topic-and documentlevel model quality, and that basing model evaluation on topic-level analysis can be highly misleading.We propose a method for automatically predicting topic model quality based on analysis of documentlevel topic allocations, and provide empirical evidence for its robustness.

Topics generated by topic models are typically represented as list of terms. To reduce the cognitive overhead of interpreting these topics for end-users, we propose labelling a topic with a succinct phrase that summarises its theme or idea. Using Wikipedia document titles as label candidates, we compute neural embeddings for documents and words to select the most relevant labels for topics. Comparing to a state-of-the-art topic labelling system, our methodology is simpler, more efficient and finds better topic labels.