As digital technologies, the internet and social media become increasingly integrated into society, our daily lives generate unprecedented quantities of digital data. These data provide opportunities to study complex social systems in frameworks similar to those of the natural sciences with emphasis on empirical observation of patterns in large-scale data, quantitative modelling and experiments.
This ‘social data science’ can generate theory-informed predictive models and underpin the way we understand and solve social problems.
This project builds on existing data science to understand the extent to which credible public health information is outweighed by false content on social media and measure the effectiveness of public health communication responses in real‐time.
The pandemic has shown how public health depends on the availability of high‐quality medical information and clear and convincing communication on topics such as vaccines. Trusted and effective communication is a vital part of the overall public health effort to combat the virus. The spread of COVID‐19 depends in large part on the sum of individual decisions made by millions: will they wash their hands, wear face masks, self‐isolate if showing symptoms, and take a vaccine if it becomes available?
Social media is a major means of reaching these individuals. Yet we know that social media is also full of misleading rumours and false information, which can undermine public trust in official messages.
Policymakers and health practitioners urgently need to develop a capacity to identify the data deficits. They need to measure and understand the topics where there is a lack of quality information about COVID‐19, vaccines, and related health news.
This project will build on the Oxford Internet Institute’s existing expertise on misinformation to create tools and knowledge that will empower health authorities, scientists, and the public sector to communicate health information effectively and rebut disinformation when it occurs. The analysis will also help identify the data deficits, or areas where a lack of quality information fosters misinformation.
The project will work closely with health authorities to support them in communicating proactively where it matters, instead of focusing solely on reactive debunking. The project will help to build back better by contributing to a healthier, more resilient health information landscape after the pandemic.
This project will capitalize on ESRC data resources to build a more nuanced and transparent empirical understanding of the impacts of digital technologies on young people.
This project uses computational social science to understand the dissemination and reception of narratives of sustainable food consumption on Social Media.
The project will explore British Museum visitor data in order to establish a better understanding of visitor experience at the Museum.
9 June 2021
Computer scientists from Oxford University, MIT, the University of Michigan, and Meedan have joined forces to develop an algorithm to help fight misinformation being shared through private messaging apps like WhatsApp.
10 February 2021
6 March 2020
Associate Professor, Senior Research Fellow
Dr Scott A. Hale is an Associate Professor, Senior Research Fellow, and Turing Fellow. He develops and applies computer science techniques to the social sciences focusing on increasing equitable access to quality information.
Dr Scott A. Hale is an Associate Professor and Senior Research Fellow at the OII, a Fellow of the Alan Turing Institute, and a Senior Member of St Antony’s College. His work develops and applies techniques from computer science to research questions in the social sciences. His research seeks to see more equitable access to quality information and investigates the spread of information between speakers of different languages online, the roles of bilingual Internet users, collective action and mobilization, hate speech, and misinformation.
At the OII, Dr Hale has worked on projects using social data to inform transport policy, enhancing JISC’s UK web domain archive to demonstrate the value of big data in social science research, and supporting the creation of interactive visualisations for geospatial and network data by researchers. Currently, he is working on the Alternative News Networks project, which seeks to understand the health of the UK online information ecosystem, including tracking the spread of divisive and misleading content.
He has been published in major journals including PLoS One, New Media & Society, and Environment and Planning A. Dr Hale co-authored the book ‘Political Turbulence: How Social Media Shape Collective Action’ for Princeton University Press. He is Programme Co-Director of our MSc in Social Data Science.
Associate Professor, Senior Research Fellow
Bernie Hogan examines how to capture, represent and think about social networks, especially personal social networks. His work focuses on the role of design in social media, network capture techniques and theories of relationships.
DPhil Programme Director (SDS), Lecturer
Luc investigates privacy harms posed by large-scale collections of human data and deployed AI technologies, identifying gaps in how technology is regulated and risks are documented, and proposing better data sharing models for academic research.
Director of Graduate Studies, Departmental Research Lecturer
Adam Mahdi’s research focuses on digital health and application of machine learning in social sciences. He is the director of the UKRI-funded OxCOVID19 Project and a fellow at Wolfson College, University of Oxford.
