Mapping Problematic Content: Mixing qualitative approach & machine learning

TheSummer Institutes in Computational Social Science(SICSS) is an international program created to train the next generation of social science researchers and incubate cutting-edge research across disciplinary boundaries. Since its inception at Princeton in 2017, the program has delivered training to more than 700 early-career researchers from 124 academic fields worldwide. In 2022, the University of Sydney became the first Australian university to host the Summer Institute at theSydney Social Sciences and Humanities Advanced Research Centre(SSSHARC). The Sydney Social Sciences and Humanities Advanced Research Centre is a collaborative research space designed to advance innovative research partnerships and methodologies across the humanities and social sciences.

Sydney CSS Lab

Learn to map problematic online content by mixing qualitative approaches with state-of-the-art machine learning in behavioural data science.

SICSS-Festival

SICSS-Festival: a series of research workshops and events organised by SICSS-Sydney and hosted at the Sydney Social Sciences and Humanities Advanced Research Centre (SSSHARC).
Learn more about SICSS-Sydney: https://sicss.io/2022/sydney/ and @SICSSSydney

This presentation details our recent advances in detecting problematic opinions in online discussions (conspiracy theories, hate speech, mis- and dis-information). We have built a system that leverages deep qualitative analysis and advanced artificial intelligence in a human-in-the-loop machine learning approach. Our methodology – published in the prestigious computational social science international academic outlet ICWSM – allows the building of very large and representative samples of the online discussion and the opinion ecosystem. Our research pioneers quantifiable measures of how extreme opinions infiltrate online groups by being promoted together with largely accepted conspiracy theories. The presentation will detail several techniques. First, how we built Natural Language Processing (NLP) algorithms that use Deep Neural Networks architectures to automatically problematic speech. Second, how we developed and deployed the human-in-the-loop algorithm that blends human expertise and machine learning to annotate at scale large volumes of data. Finally, we will take our audience through a series of results detailing our theory on how extreme content infiltrates public online discourse.For queries contact sicss.admin@sydney.edu.auMarian-Andrei Rizoiu Dr Marian-Andrei Rizoiu is a Senior Lecturer in Behavioral Data Science at the University of Technology Sydney. He is interested in stochastic behavioural modelling of human actions online, at the intersection of applied statistics, artificial intelligence and social data science. He leads the Behavioral Data Science lab, which studies human attention dynamics in the online environment, the emergence of influence and opinion polarisation. Marian-Andrei’s research has been funded by Facebook Research and Defence Science and Technology (DST). His work has appeared in the PNAS, PLOS ONE, PLOS Computations Biology, WWW, NeurIPS, IJCAI, and CIKM.

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Mapping Problematic Content: Mixing qualitative approach & machine learning

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