MULTILINGUAL COVID-19
FAKE NEWS DETECTION AND INTERVENTION

Challenges

Counter infodemic

The emergence of the Coronavirus Disease 2019 (COVID-19) epidemic has produced massive information related to COVID-19. Information distribution platforms such as mass media and social media allow information to be spread widely. Unfortunately, not all of the information is accurate or trustworthy. Some of the information spreading around those platforms can be categorised as misinformation or even be fake news.

Meanwhile, a lot of fake news was produced in various languages so that it can spread more easily in particular regions and linguistic groups. The detection of multilingual COVID-19 fake news is essential for countries such as Australia and Indonesia which consist of hundreds of ethnic and linguistic groups.

However, the mainstream of existing fake news detection research mainly focuses on English based content which cannot recognise fake news spreading through multiple linguistic groups. Failing to detect and intervene the spread of multilingual COVID-19 fake news can easily cause mistrust and wrong public responses, such as panic buying, non-compliance with social distancing, and even refusal of COVID tests and vaccinations.

Previous
Next
This project aims to develop a complete set of strategies for multilingual COVID-19 fake news detection and intervention.

To collect and analyse individual online behaviour characteristics in COVID-19 news propagation and communication in Australia and Indonesia

To conduct theoretical analysis about the existing fake news detection models, and design a multilingual COVID-19 fake news detection model using advanced machine learning techniques

To apply the proposed COVID-19 fake news detection method to risk communication management to intervene in the spread of fake news, and help authoritative organisations to generate their customised COVID-19 warning policies. 

Events

Recent Activities

Friday, 28 Oct 2022

Fri., 28 October 2022 10:00 am – 4:00 pm AEDT

Days
Hours
Minutes
Seconds

Zoom Link: TBC

Lab News

Research Output

  • Hui Yin, Yutao Wu, Jianxin Li, Hilya Mudrika Arini, Xingjun Ma and Xiao Liu. COVID-19 Misinformation Detection: A Comprehensive Survey. Submitted to Health Information Science and Systems.

  • Xiao Huang, Siqin Wang, Mengxi Zhang, Tao Hu, Alexander Hohl, Bing She, Xi Gong, Jianxin Li, Xiao Liu, Oliver Gruebner, Regina Liu, Xiao Li, Zhewei Liu, Xinyue Ye, Zhenlong Li. Social media mining under the COVID-19 context: current progress, available sources, challenges, and our paths forward. Submitted to International Journal of Applied Earth Observation and Geoinformation


  • Hui Yin, Shuiqiao Yang, Jianxin Li: Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media. ADMA 2020: 610-623.

Partnerships

heizfatabq0orr2yush9

Deakin University’s School of Information Technology has world class research capacity in cybersecurity, distributed computing and artificial intelligence. 

Gadjah Mada University is one of the oldest and largest institutions of higher education in Indonesia and has well-established networks and partnerships with various industry/government institutions, which provides excellent research and commercialisation opportunities. 

Study Melbourne is a Victorian Government initiative providing support and information to this community of international students, helping them have the best possible time while studying and living in Victoria

Veski was established in 2004 to enhance Victoria’s intellectual capital through a program of fellowships, awards and international networks. Veski is at the forefront of Australia’s innovation economy.