Cotrina-Revilla, Jessica

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    Machine learning tools for detecting tweets related to vehicle crashes
    (2018) Cotrina-Revilla, Jessica; Rodríguez-Martínez, Manuel; College of Engineering; Cruzado Vélez, Ivette; Rodriguez Rivera, Nestor Josué; Department of Electrical and Computer Engineering; Figueroa Medina, Alberto
    Vehicle crashes are a global problem that occur each day. They happen due to environmental factors, the state of the road and bad maneuvers performed by drivers. Information about vehicle crashes is published on social networks by people that have been involved in a traffic accident either directly or indirectly. In these social networks, users publish that information so that they can let friends and family know about the incident or just to comment in general about it. One of the most popular social networks is Twitter, which has 330 million monthly active users around the world. The information gathered from Twitter has helped to identify people who were trapped during some natural disaster, identifying diseases, organizing protests etc. Using data collected from Twitter, the purpose of this work is to determine if a tweet coming from a real-time flow refers to a vehicular crash or not. With the benefit of obtaining a diagnostic of how many vehicle crashes occur within a given time frame based on Twitter data. This diagnostic lets researchers interested in traffic accidents, for example, determine in which places happen more vehicle crashes. To implement this, we use a set of tweets that contain keywords related to vehicle crashes. With the help of a professor and two students from Department of Civil Engineering and Surveying of the University of Puerto Rico at Mayagüez, the tweets were labeled to determine if each tweet in the data set is about a real vehicle accident or not. These classified tweets are then converted into the training data set, to produce a model for the classification of accident tweets. Once this process is completed, automatic learning tools and techniques (ML) are used, for example, logistic regression, to form a model for classifying tweets. Once this model is ready, we can use it to determine if a tweet of a real-time transmission of tweets is related to a vehicle crash. Finally, the trends per day in these tweets can be displayed in a web dashboard.