Machine Learning Algorithms for the Analysis of Social Media and Detection of Malicious User Generated Content

One of the de ning characteristics of the modern Internet is its massive connectedness,
with information and human connection simply a few clicks away. Social
media and online retailers have revolutionized how we communicate and purchase
goods or services. User generated content on the web, through social media, plays
a large role in modern society; Twitter has been in the forefront of political discourse,
with politicians choosing it as their platform for disseminating information,
while websites like Amazon and Yelp allow users to share their opinions on products
via online reviews. The information available through these platforms can provide
insight into a host of relevant topics through the process of machine learning. Speci -
cally, this process involves text mining for sentiment analysis, which is an application
domain of machine learning involving the extraction of emotion from text.
Unfortunately, there are still those with malicious intent and with the changes
to how we communicate and conduct business, comes changes to their malicious practices.
Social bots and fake reviews plague the web, providing incorrect information
and swaying the opinion of unaware readers. The detection of these false users or
posts from reading the text is di cult, if not impossible, for humans. Fortunately, text mining provides us with methods for the detection of harmful user generated
content.
This dissertation expands the current research in sentiment analysis, fake online
review detection and election prediction. We examine cross-domain sentiment
analysis using tweets and reviews. Novel techniques combining ensemble and feature
selection methods are proposed for the domain of online spam review detection. We
investigate the ability for the Twitter platform to predict the United States 2016 presidential
election. In addition, we determine how social bots in
uence this prediction.