creator Kadam, Mayuri advisor Culotta, Aron Vaccination being one of the most important decisions for public health, has become a debatable topic with the rise in anti-vaccination sentiments in recent years. Knowing that vaccines have eradicated many endemic diseases, the rise in antivaccination sentiments jeopardizes the human health by altering the vaccine decisions. Rapidly changing information sources with the increased reach of online social media provide users with a huge amount of information and misinformation. Users exposed to these media perceive the provided information and hold an attitude towards it. Being an open platform of discussions and opinion expressions, online social media provides a great source for understanding people’s behavior. We use supervised learning for understanding the flow of vaccine sentiments and analyzing the user attitudes through online social media. In this thesis, we determine the events and incidences responsible for amplifying pro-vaccination and anti-vaccination sentiments. We investigate user behaviors and important topics of interest for these users. We develop a model for predicting a new user’s attitude utilizing that user’s recent Twitter activity. Submitted by Erma Thomas (thomase@iit.edu) on 2017-11-02T21:39:43Z No. of bitstreams: 1 etdadmin_upload_496593.zip: 585769 bytes, checksum: 9a019c0a4634a297a74c738eb16d7b38 (MD5) Made available in DSpace on 2017-11-02T21:39:43Z (GMT). No. of bitstreams: 1 etdadmin_upload_496593.zip: 585769 bytes, checksum: 9a019c0a4634a297a74c738eb16d7b38 (MD5) Previous issue date: 2017-05 M.S. in Computer Science, May 2017 2017 2017-05 http://hdl.handle.net/10560/4139 en Attitude Prediction Event Detection Online Social Media Sentiment Analysis Supervised Learning Vaccination Thesis born digital application/pdf In Copyright http://rightsstatements.org/page/InC/1.0/ Restricted Access CS / Computer Science Illinois Institute of Technology Affiliated department