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Drug epidemics have been a major problem in the United States for decades. As of August 2022, recreational marijuana can be legally purchased in 19 states in the U.S and medical marijuana is legal in 37 states. The forward legalization of marijuana in states is a factor of increasing social media content about marijuana use among young adults. Widespread promotion of alcohol, tobacco and substances in social media and other forms of entertainment change social norms. A number of research groups study drug abuse contents on social media such as Instagram, Reddit, YouTube, Facebook and Twitter. Some researchers study drug abuse in popular songs and popular music videos. Yet none have discussed drug abuse lyrics on social media. In this study, we carry out our novel detection of drug-related lyrics on Twitter through two different approaches, the Smith-Waterman algorithm and natural language processing algorithm. We analyzed over 1.3 billion publicly available tweets from 2016 and 2017 to identify substance use lyrics. We collected 101,117 tweets that are references to substance use lyrics. The local sequence alignment algorithm or the Smith-Waterman algorithm can identify drug abuse lyrics with accuracy up to 81% where a machine learning algorithm, Long Short Term Memory (LSTM), can identify with accuracy up to 48.9%.

Genetic research projects currently can require enormous computing power to processes the vast quantities of data available. Further, DNA sequencing projects are generating data at an exponential rate greater than that of the development microprocessor technology; thus, new, faster methods and techniques of processing this data are needed. One common type of processing involves searching a sequence database for the most similar sequences. Here we present a distributed database search system that utilizes adaptive computing technologies. The search is performed using the Smith-Waterman algorithm, a common sequence comparison algorithm. To reduce the total search time, an initial search is performed using a version of the algorithm, implemented in adaptive computing hardware, which is designed to efficiently perform the initial search. A final search is performed using a complete version of the algorithm. This two-stage search, employing adaptive and distributed hardware, achieves a performance increase of several orders of magnitude over similar processor based systems.