Lu, JinghuiJinghuiLuHenchion, MaeveMaeveHenchionMacNamee, BrianBrianMacNamee2024-02-092024-02-092019 the A2019-12-061613-0073http://hdl.handle.net/10197/25415The 27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2019), Galway, Ireland, 5-6 December 2019Corpus comparison techniques are often used to compare different types of online media, for example social media posts and news articles. Most corpus comparison algorithms operate at a word-level and results are shown as lists of individual discriminating words which makes identifying larger underlying differences between corpora challenging. Most corpus comparison techniques also work on pairs of corpora and do need easily extend to multiple corpora. To counter these issues, we introduce Multi-corpus Topic-based Corpus Comparison (MTCC) a corpus comparison approach that works at a topic level and that can compare multiple corpora at once. Experiments on multiple real-world datasets are carried demonstrate the effectiveness of MTCC and compare the usefulness of different statistical discrimination metrics - the χ2 and Jensen-Shannon Divergence metrics are shown to work well. Finally we demonstrate the usefulness of reporting corpus comparison results via topics rather than individual words. Overall we show that the topic-level MTCC approach can capture the difference between multiple corpora, and show the results in a more meaningful and interpretable way than approaches that operate at a word-level.enCorpus comparisonTopic modellingJensen-Shannon divergenceConference Publication2021-01-24201605312/RC/2289 P22016053https://creativecommons.org/licenses/by-nc-nd/3.0/ie/