Twitter the Rioter : Analyzing roles through a protest on social media. What was your part during the 2014 Ferguson riots?

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master_Blegean_Julien_2015.pdf (5.42 MB)

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Perustieteiden korkeakoulu | Master's thesis
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Date

2015-06-10

Department

Major/Subject

Data mining and Machine Learning

Mcode

-

Degree programme

Tietotekniikan koulutusohjelma

Language

en

Pages

68+0

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Abstract

We introduce a framework for analyzing roles of users during a riot. The dataset contains messages from Twitter during the 2014 US Ferguson protests. First, after some preprocessing one the data, we extract topics from a riot by comparing two techniques : k-means and Latent Dirichlet Allocation. Secondly, we focus on the content of the tweets. We train and test a Naive Bayes classifier to predict if a tweet is either supportive or informative about the riot. We also study the medias shared by users in order to improve our classifier and in the end we define and compute user polarity score. Then, we perform graph analysis on the hashtags and users and we visualize these networks through examples. We also define and compute user influence score with degree centrality and Page Rank. Finally, we define the role of a user during a riot (or several) as being the feature vector of its polarity and influence scores for each topic. We perform dimension reduction with PCA and t-SNE for visualization purpose and neighbors research. Through experiments, we show how users with the same job (for example journalists or activists) are grouped together.

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Supervisor

Gionis, Aristides

Thesis advisor

Mathioudakis, Michael

Keywords

social network analysis, graph analysis, natural language processing, twitter

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https://urn.fi/URN:NBN:fi:aalto-201506303586

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[dipl] Perustieteiden korkeakoulu / SCI