Browsing by Author "Arachie, Chidubem"

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    Data Mining Twitter to Improve Automated Vehicle Safety
    McDonald, Anthony D.; Huang, Bert; Wei, Ran; Alambeigi, Hananeh; Arachie, Chidubem; Smith, Alexander Charles; Jefferson, Jacelyn (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-02)
    Automated vehicle (AV) technologies may significantly improve driving safety, but only if they are widely adopted and used appropriately. Adoption and appropriate use are influenced by user expectations, which are increasingly being driven by social media. In the context of AVs, prior studies have observed that major news events such as crashes and technology announcements influence user responses to AVs; however, the exact impact and dynamics of this influence are not well understood. The goals of this project were to develop a novel search method to identify AV-relevant user comments on Twitter, mine these tweets to understand the influence of crashes and news events on user sentiment about AVs, and finally translate these findings into a set of guidelines for reporting about AV crashes. In service of these goals, we developed a novel semi-supervised constrained-level learning machine search approach to identify relevant tweets and demonstrated that it outperformed alternative methods. We used the relevant tweets identified to develop a topic model of AV events which illustrated that crashes, fault and safety, and technology companies were the most discussed topics following major events. While the sentiment among these topics was mostly neutral, tweets about crashes and fault and safety were negatively biased. We combined these findings with a series of interviews with Public Information Officers to develop a set of five basic guidelines for AV communication. These guidelines should aid proper public calibration and subsequent acceptance and use of AVs.
  • Tweet Analysis and Classification: Diabetes and Heartbleed Internet Virus as Use Cases
    Karajeh, Ola; Arachie, Chidubem; Powell, Edward; Hussein, Eslam (Virginia Tech, 2019-12-24)
    The 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.