We have Elena Simperl, a Professor of Computer Science at King’s College London, talking to us in January. See details below. A Zoom link will be sent via the mailing list, or please email the Data Viz Organisers group firstname.lastname@example.org
Wednesday 13th January 2021, 13:00-14:00 (online event)
Abstract: We live in a world full of data, in which charts are routinely used to communicate complex insights more effectively than spreadsheets or reports. Twitter is no exception – tens of thousands of data visualisations on virtually any topic are shared every day. We aim to understand how data, rendered visually as charts or infographics, “travels” on social media. To do so we propose a neural network architecture that is trained to distinguish among different types of charts, for instance line graphs or scatter plots, and predict how much they will be shared. This poses significant challenges because of the varying format and quality of the charts that are posted, and the limitations in existing training data. To start with, our proposed system outperforms related work in chart type classification on the ReVision corpus, a benchmark from the literature. Furthermore, we use crowdsourcing to build a new corpus, more suitable to our aims, consisting of chart images shared by data journalists on Twitter. We evaluate the system on the second corpus with respect to both chart identification and virality prediction, with promising results.
Our system and findings could be used in different scenarios, from generating automatic text captions and recommending chart improvements in data visualisation tools to informing marketing strategies for brands that use data visuals to gauge customer engagement. In addition, our approach, including both the neural architecture and the method to create labelled data, could form the basis for the development of visual question answering solutions tailored to data visualisations, with applications in fact checking and misinformation online.
Biography: Elena Simperl is professor of computer science at King’s College London, a Fellow of the British Computer Society and former Turing fellow. According to AMiner, she is in the top 100 most influential scholars in knowledge engineering of the last decade, as well as in the Women in AI 2000 ranking. Before joining King’s College early 2020, she held positions at the University of Southampton, as well as in Germany and Austria. She has contributed to more than 20 research projects, often as principal investigator or project lead. Currently, she is the PI of two grants: H2020 ACTION, where she develops human-AI methods to make participatory science thrive, and EPSRC Data Stories, where she works on frameworks and tools to make data more engaging for everyone. She authored more than 200 peer-reviewed publications in knowledge engineering, semantic technologies, open and linked data, social computing, crowdsourcing and data-driven innovation. Over the years she served as programme and general chair to several conferences, including the European and International Semantic Web Conference, the European Data Forum and the AAAI Conference on Human Computation and Crowdsourcing.