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Disinformation Defence Initiative: Delivering tools and analysis to fight the growing threat of disinformation for Australia.

Project Member(s): Rizoiu, M., Berry, A.

Funding or Partner Organisation: Department of Home Affairs (Department of Homeaffairs)
Department of Home Affairs (Department of Homeaffairs)

Start year: 2022

Summary: The rise of disinformation presents a growing and material risk to the operation of Australia’s democracy. A response that is specific to the Australian information environment and co-designed with Australian government decision-makers will enable an agile, practical and evidence-based response to emerging threats in this space. In 2017, organised social media manipulation campaigns took place in 28 countries. In 2019, organised social media manipulation campaigns took place in 70 countries. Russia has historically spent around $1.2b on pro-Kremlin media activities to drive disinformation. China has historically deployed between 300,000 and 2,000,000 people on information warfare and psychological operations. “Millions have already been exposed to false information about the [coronavirus]... it is undeniable that the damage [of misinformation] is real.“ (The Guardian) The UTS Behavioural Data Science Team within the UTS Data Science Institute draws on extensive experience in the analysis of, and response to, viral disinformation content. Focus areas include the spread and nature of disinformation in the Australian information environment, the presentation and visualisation of disinformation flows, and appropriate mitigation measures in both policy and defence contexts.


Calderon, P & Rizoiu, M-A 2024, 'What Drives Online Popularity: Author, Content or Sharers? Estimating Spread Dynamics with Bayesian Mixture Hawkes'.

Kong, Q, Calderon, P, Ram, R, Boichak, O & Rizoiu, M-A 1970, 'Interval-censored Transformer Hawkes: Detecting Information Operations using the Reaction of Social Systems', Proceedings of the ACM Web Conference 2023 (WWW '23), May 1--5, 2023, Austin, TX, USA.
View/Download from: Publisher's site

Ram, R & Rizoiu, M-A 1970, 'Data-driven ideology detection: a case study of far-right extremist', Defence Human Sciences Symposium, Defence Human Sciences Symposium, Sydney, Australia.

Kong, Q, Booth, E, Bailo, F, Johns, A & Rizoiu, M-A 1970, 'Slipping to the Extreme: A Mixed Method to Explain How Extreme Opinions Infiltrate Online Discussions', Proceedings of the International AAAI Conference on Web and Social Media, pp. 524-535.

FOR Codes: Data engineering and data science, Data mining and knowledge discovery, Expanding knowledge in the information and computing sciences, Expanding knowledge in human society