The fear of the unknown combined with the isolation generated by COVID-19 has created a fertile environment for strong disinformation, otherwise known as conspiracy theories, to flourish. Because conspiracy theories often contain a kernel of truth and feature a strong adversarial “other,” they serve as the perfect vehicle for maligned actors to use in influence campaigns. To explore the importance of conspiracies in the spread of dis-/mis-information, we propose the usage of state-of-the-art, tuned language models to classify tweets as conspiratorial or not. This model is based on the Bidirectional Encoder Representations from Transformers (BERT) model developed by Google researchers. The classification method expedites analysis by automating a process that is currently done manually (identifying tweets that promote conspiracy theories). We identified COVID-19 origin conspiracy theory tweets using this method and then used social cybersecurity methods to analyze communities, spreaders, and characteristics of the different origin-related conspiracy theory narratives. We found that tweets about conspiracy theories were supported by news sites with low fact-checking scores and amplified by bots who were more likely to link to prominent Twitter users than in non-conspiracy tweets. We also found different patterns in conspiracy vs. non-conspiracy conversations in terms of hashtag usage, identity, and country of origin. This analysis shows how we can better understand who spreads conspiracy theories and how they are spreading them.

No abstract

No abstract

The bipartisan election commission formed after the 2012 election recommended that no American should wait longer than 30 minutes to vote. However, in every presidential election year, stories surface of voters having to wait several hours. Long lines disrupt voters' schedules and hinder economic activity, but can also discourage voters from remaining in line to vote. One way to decrease the average and maximum voter wait times is to better prepare polling locations by staffing optimally and having enough voting booths available. Data was collected from a Williamsburg polling location in Virginia during the off-year November 2015 delegate election. Simulation analysis found that in order to have maximum wait times of less than 30 minutes in this Williamsburg precinct during a presidential election then at least 4-5 poll workers to check in voters and 12-15 voting booths or machines are needed. Data on the number of voters that arrive per hour and the amount of time it takes to check in and vote are often collected by the state or by certain polling places. A general, free version of this discrete-event simulation was created inJava. This resource allocation tool takes previous data as an input and estimates the number of voting booths and staff needed in order to keep approximately 99% of wait times less than 30 minutes. Simulation and statistical analysis are used to determine the number of resources necessary.

No abstract