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Title: Searching For Revolution Google Trends and Egypt’s Arab Spring
Authors: Fulmer, Ryan
Advisors: Shapiro, Jacob
Department: Woodrow Wilson School
Class Year: 2016
Abstract: For 10,712 days, or nearly thirty years, Egyptian President Hosni Mubarak’s led the Middle East’s most populous nation. During that time, he survived six assassination attempts, numerous offenses by opposition parties, and an insurgent Islamist movement. He controlled Egypt’s powerful security apparatus, remained a staunch US ally, and wielded the might of a draconian Emergency Law through which he could suspend constitutional rights at will. Yet, on February 11, 2011, an unarmed and unorganized wave of Egyptians toppled Mubarak in the largest series of protests in Egypt to date. Unlike past protests, this modern movement existed in large part online. I see that as a valuable window for analysis. Can we derive the protestors’ motives be examining Egyptians’ Internet activity --- the words they googled and the topics they searched? I focus my study on the political frustrations, the economic concerns, and the social media sites at the heart of the Revolution. Do these frustrations appear online before protests—and can that be used to predict future unrest? In order to understand the digital reverberations of an entire country, I turn to big data. Google Trends and the Global Database of Events, Language, and Tone serve as my lens into the world of 1s and 0s. The first provides records of Egyptians’ Google searches, and the other quantifies when Egyptians took to the street. I use the resultant time-series data to test economic, political, and media theories behind protests from 2009 to 2016, then use my findings to build a vector autoregressive model to forecast protests one week out. In this thesis, I determine four things. First, economic concerns do not appear to play a role online during protests. Second, searches for political variables surge after unrest. Third, social media site searches, especially those for Twitter, increase during weeks of protest. Fourth, a vector autoregression model using both GDELT and Google Trends data holds promise in forecasting protests. The conclusion from these findings proposes a series of policy recommendations and areas of future research in order to explore Egypt’s Arab Spring more deeply, and utilize the big data tools of the modern era.
Extent: 81 pages
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Woodrow Wilson School, 1929-2016

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