Hi all, The next Research Showcase will be live-streamed on Wednesday, March 18, at 9:30 AM PDT/16:30 UTC. We’ll have a presentation on topic modeling by Jordan Boyd-Graber. A question-and-answer session will follow. YouTube stream: https://www.youtube.com/watch?v=fiD9QTHNVVM As usual, you can join the conversation on IRC at #wikimedia-research. You can also watch our past research showcases here: https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase This month's presentation: Big Data Analysis with Topic Models: Evaluation, Interaction, and Multilingual Extensions By: Jordan Boyd-Graber, University of Maryland A common information need is to understand large, unstructured datasets: millions of e-mails during e-discovery, a decade worth of science correspondence, or a day's tweets. In the last decade, topic models have become a common tool for navigating such datasets even across languages. This talk investigates the foundational research that allows successful tools for these data exploration tasks: how to know when you have an effective model of the dataset; how to correct bad models; how to measure topic model effectiveness; and how to detect framing and spin using these techniques. After introducing topic models, I argue why traditional measures of topic model quality---borrowed from machine learning---are inconsistent with how topic models are actually used. In response, I describe interactive topic modeling, a technique that enables users to impart their insights and preferences to models in a principled, interactive way. I will then address measuring topic model effectiveness in real-world tasks. Overview of topic models: https://mimno.infosci.cornell.edu/papers/2017_fntir_tm_applications.pdf Topic model evaluation: http://umiacs.umd.edu/~jbg//docs/nips2009-rtl.pdf Interactive topic modeling: http://umiacs.umd.edu/~jbg//docs/2014_mlj_itm.pdf Topic Models for Categorization: http://users.umiacs.umd.edu/~jbg//docs/2016_acl_doclabel.pdf -- Janna Layton (she, her) Administrative Assistant - Product & Technology Wikimedia Foundation <https://wikimediafoundation.org/>