Karajeh, OlaArachie, ChidubemPowell, EdwardHussein, Eslam2020-01-112020-01-112019-12-24http://hdl.handle.net/10919/96396The proliferation of data on social media has driven the need for researchers to develop algorithms to filter and process this data into meaningful information. In this project, we consider the task of classifying tweets relative to some topic or event and labeling them as informational or non-informational, using the features in the tweets. We focus on two collections from different domains: a diabetes dataset in the health domain and a heartbleed dataset in the security domain. We show the performance of our method in classifying tweets in the different collections. We employ two approaches to generate features for our models: 1) a graph based feature representation and 2) a vector space model, e.g., with TF-IDF weighting or a word embedding. The representations generated are fed into different machine learning algorithms (Logistic Regression, Naïve Bayes, and Decision Tree) to perform the classification task. We evaluate these approaches using metrics (accuracy, precision, recall, and F1-score) on a held out test dataset. Our results show that we can generalize our approach with tweets across different domains.en-USCreative Commons CC0 1.0 Universal Public Domain DedicationTwitterMachine learningTerm-Document Matrix (TDM)Graph Based ModelWord EmbeddingDataset