In the 2020 election cycle, election officials in the United States were subjected to personal attacks, hate speech, and in some instances physical threats. Many of these attacks were launched on social media, which may have amplified their toxicity and reach. This project uses machine learning and artificial intelligence methods to sift through social media posts to identify hate speech and online attacks directed at election and other public officials. The project’s novelties are the use of machine learning and artificial intelligence methods to find attacks and hate speech directed at public officials in near real-time, and in the study of the networks where these attacks and toxic speech originate. The project’s broader significance and importance are the development of methodologies that detect toxicity in social media conversations quickly and accurately, when these attacks are directed at public officials administering elections.
The project contributes to computer science and social science. Identifying online attacks directed at specific public officials in streaming social media data is complex. The first contribution of the project is building tools to determine the domain of streaming social media data to collect and analyze, as there are thousands of state, county, and local election officials. The second contribution of this project is the use of artificial intelligence methods that adaptively seek hate speech and online attacks, in particular in situations where the adversaries try to avoid detection by shifting online identities and the language they use. Substantively, the project contributes to the growing research on social media misinformation and harassment activities, in particular the identification of organized efforts to attack and delegitimize the work of election officials. Finally, the project’s software and code will be available for other researchers to use.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
