Sponsorship: Amazon Web Services Education Grants Program Data presented in a CSCW 2014 paper titled Tweet Acts: How Constituents Lobby... Show moreSponsorship: Amazon Web Services Education Grants Program Data presented in a CSCW 2014 paper titled Tweet Acts: How Constituents Lobby Congress via Twitter. Libby Hemphill and Andrew J. Roback. 2014. Tweet acts: how constituents lobby congress via Twitter. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing (CSCW '14). ACM, New York, NY, USA, 1200-1210. DOI=10.1145/2531602.2531735http://doi.acm.org/10.1145/2531602.2531735 Show less
Marijuana use and legality has been a widely-discussed topic in the recent years. Knowing that marijuana has different effects on health, mood... Show moreMarijuana use and legality has been a widely-discussed topic in the recent years. Knowing that marijuana has different effects on health, mood and behavior after its use, it is important to understand what the underlying causes for marijuana use also are. As marijuana use is becoming more prevalent every day, it is crucial to know what the motives behind the users' tendencies are for smoking marijuana. To be able to identify the words/patterns associated with marijuana use prior to its use, we will need a real-time method to understand the problem on a deeper level with a better method than surveying users. In our study, we aim to understand the different linguistic characteristics of marijuana users based on their income. Social media's provision of data into understanding and tracking people's behavior can be very beneficial in understanding the contrast between the different social classes prior to marijuana use and understand what the underlying causes are for their marijuana use. In our experiment, we use social media to analyze the patterns and characteristics of marijuana use based on income class. By collecting data on Twitter, we then proceed to classify users based on their income. Using this method, we predict the income of each user by utilizing the user's Twitter activity and their linguistic characteristics based on the tweets associated with them. Through the experiment, we can identify patterns amongst the marijuana users in two different income classes and predict what class a user will be placed in based on their recent Twitter activity with a good accuracy. M.S. in Computer Science, May 2018 Show less
