CS4624: Multimedia, Hypertext, and Information Access

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https://hdl.handle.net/10919/18655
This collection contains the final projects of the students in in the course Computer Science 4624: Multimedia, Hypertext, and Information Access, at Virginia Tech. This course, taught by Professor Ed Fox, is part of the Human-Computer Interaction track, the Knowledge, Information, and Data track, and the Media/Creative Computing track. The curriculum introduces the architectures, concepts, data, hardware, methods, models, software, standards, structures, technologies, and issues involved with: networked multimedia (e.g., image, audio, video) information, access and systems; hypertext and hypermedia; electronic publishing; virtual reality. Coverage includes text processing, search, retrieval, browsing, time-based performance, synchronization, quality of service, video conferencing and authoring.

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Browsing CS4624: Multimedia, Hypertext, and Information Access by Author "Agren, Erik"

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    Twitter Equity Firm Value
    Smith, Jacob; Wiskur, Christian; Guinn, Nathaniel; Agren, Erik; Rane, Rohan (Virginia Tech, 2018-05-09)
    We analyzed how a company's response on social media (Twitter) can affect their stock market value following a data breach. Given a list of all data breaches since 2006 we collected their stock value for 150 days before the data breach and 120 after. Using a Fama French Model we came up with an abnormality value that demonstrated how the stock would have performed if no data breach occurred. While doing this we simultaneously collected tweets from the companies and customers about the data breach. We wanted to compare the stock performance to things such as the number of replies from a company, customer tweet sentiment, and links tweeted by the company. The way we did all of this work was by building Python scripts for all of the functionalities. When scraping the tweets the user would just need to supply a CSV file with the company's Twitter handle and company name. The other Python scripts used, do things like compute the abnormality difference from the client's Fama French Model, scrub the stock data to only have the date range needed, compute tweet sentiment, and grab client profiles. Our conclusion was that companies need to make few but comprehensive announcement tweets to decrease reply tweets. This could keep the sentiment of client tweets positive. Lastly, companies need to focus on replying to customer tweets to also keep sentiment positive.