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The United States’ War on Drugs declared in 1971 by President Richard Nixon and revamped by President Reagan in the 1980s has been an objectively failed initiative with origins based in racism and oppression. After exploring the repercussions of this endeavor for societies and individuals around the world, global researchers

The United States’ War on Drugs declared in 1971 by President Richard Nixon and revamped by President Reagan in the 1980s has been an objectively failed initiative with origins based in racism and oppression. After exploring the repercussions of this endeavor for societies and individuals around the world, global researchers and policymakers have declared that the policies and institutions created to fight the battle have left devastation in their wake. Despite high economic and social costs, missed opportunities in public health and criminal justice sectors, and increasing limits on our personal freedoms, all the measures taken to eradicate drug abuse and trafficking have been unsuccessful. Not only that, but militarized police tactics, mass incarceration, and harsh penalties that stifle opportunities for rehabilitation, growth, and change disproportionately harm poor and minority communities. <br/>Because reform in U.S. drug policy is badly needed, the goals of America’s longest war need to be reevaluated, implications of the initiative reexamined, and alternative strategies reconsidered. Solutions must be propagated from a diverse spectrum of contributors and holistic understanding through scientific research, empirical evidence, innovation, public health, social wellbeing, and measurable outcomes. But before we can know where we should be headed, we need to appreciate how we got to where we are. This preliminary expository investigation will explore and outline the history of drug use and prohibition in the United States before the War on Drugs was officially declared. Through an examination of the different patterns of substance use, evolving civil tolerance of users, racially-charged anti-drug misinformation/propaganda campaigns, and increasingly restrictive drug control policies, a foundation for developing solutions and strengths-based strategies for drug reform will emerge.

ContributorsSherman, Brooke (Author) / Jimenez-Arista, Laura (Thesis director) / Mitchell, Ojmarrh (Committee member) / College of Integrative Sciences and Arts (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Propaganda bots are malicious bots on Twitter that spread divisive opinions and support political accounts. This project is based on detecting propaganda bots on Twitter using machine learning. Once I began to observe patterns within propaganda followers on Twitter, I determined that I could train algorithms to detect

Propaganda bots are malicious bots on Twitter that spread divisive opinions and support political accounts. This project is based on detecting propaganda bots on Twitter using machine learning. Once I began to observe patterns within propaganda followers on Twitter, I determined that I could train algorithms to detect these bots. The paper focuses on my development and process of training classifiers and using them to create a user-facing server that performs prediction functions automatically. The learning goals of this project were detailed, the focus of which was to learn some form of machine learning architecture. I needed to learn some aspect of large data handling, as well as being able to maintain these datasets for training use. I also needed to develop a server that would execute these functionalities on command. I wanted to be able to design a full-stack system that allowed me to create every aspect of a user-facing server that can execute predictions using the classifiers that I design.
Throughout this project, I decided on a number of learning goals to consider it a success. I needed to learn how to use the supporting libraries that would help me to design this system. I also learned how to use the Twitter API, as well as create the infrastructure behind it that would allow me to collect large amounts of data for machine learning. I needed to become familiar with common machine learning libraries in Python in order to create the necessary algorithms and pipelines to make predictions based on Twitter data.
This paper details the steps and decisions needed to determine how to collect this data and apply it to machine learning algorithms. I determined how to create labelled data using pre-existing Botometer ratings, and the levels of confidence I needed to label data for training. I use the scikit-learn library to create these algorithms to best detect these bots. I used a number of pre-processing routines to refine the classifiers’ precision, including natural language processing and data analysis techniques. I eventually move to remotely-hosted versions of the system on Amazon web instances to collect larger amounts of data and train more advanced classifiers. This leads to the details of my final implementation of a user-facing server, hosted on AWS and interfacing over Gmail’s IMAP server.
The current and future development of this system is laid out. This includes more advanced classifiers, better data analysis, conversions to third party Twitter data collection systems, and user features. I detail what it is I have learned from this exercise, and what it is I hope to continue working on.
ContributorsPeterson, Austin (Author) / Yang, Yezhou (Thesis director) / Sadasivam, Aadhavan (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05