The internet is an important source of information for many people, and today’s social media platforms continue to shape how people access and act on health information.
Social media platforms serve as channels for top-down communication from health officials to the public, for peer sharing of health information across users, and for finding communities with shared health goals and challenges.
Nonetheless, along with these useful interactions come false information that has the potential to cause people to take harmful measures, reject factual updates from authorities, and upend the work of local health institutions.
Events such as the 2014 Ebola epidemic and today’s COVID-19 pandemic bring to light the need for social media platforms to facilitate access to accurate and reliable information.
In this project, we aim to use a mixed-method approach to study the use of social media as an information channel during the ongoing COVID-19 pandemic in Nigeria – what accounts shared news that was later found to be false, how did false news spread within the network before they were corrected, why do people share certain kinds of information, and what strategies can we learn to navigate the spread of health misinformation online within developing and under-developing countries.
The overall objective of this project is to study the dynamics of the spread of factual and false information in online social networks in Nigeria during a pandemic.
Our study will combine approaches in social network feature engineering and analysis, machine learning (ML), and natural language processing (NLP) with qualitative insights from social network users.
These findings will help online platforms, journalists, the general public, and health institutions in Nigeria identify ways that health misinformation is spread online and rethink what strategies can be employed to mitigate the danger it poses.
Our results will include code (in Python and/or R), social media data analyses, anonymized survey data, visualizations, a blog post, and a research publication.
We will release new code or point to existing open-source resources that will be used for our analyses. These will be hosted on Github to allow independent reruns. We will refer to the privacy policies of specific online communities regarding sharing identifiable data. We will release blog posts and visualizations with simple readable information for a wider audience.
We also aim to publish our findings at conference venues interested in the interaction between technology and society, and how both factors influence themselves e.g. CHI Human Factors in Computing Systems), CSCW (Computer Supported Cooperative Work), The Web Conference, WSDM (Web Search and Data Mining), etc.
Long term vision
We hope that this research will support on-the-ground healthcare work by helping to inform how workers interact with the public, and how to address the publics’ constantly changing perception of what is true or not.
We hope to contribute to the joint effort of journalists and government officials to stop the spread of the virus in Nigeria and other developing countries. Our approach will be useful for studying other forms of misinformation in future health crises and/or political events (i.e. elections).
User perceptions of these events are very much shaped by social media, however, is currently understudied in many African countries. Additionally, research on social media echo chambers and political polarization are widely studied in America but not in the African context.
Previous research in this space is focused on diseases like ebola1 but had not specifically focused on Africa2, or HIV but did not include a qualitative analysis3.