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The spread of rumors on social media has become a significant concern, as platforms like X (formerly known as Twitter) enable rapid dissemination of unverified information. These rumors can shape public perception and behavior, making it crucial to understand the dynamics of their spread. The main objective of this study is to explore how users interact with rumor-related tweets and identify key factors that predict tweet engagement. By analyzing tweets from seven different rumor events, this research aims to uncover patterns in user engagement and provide insights into the spread of misinformation. The study utilized a dataset of tweets from events concerning COVID-19, Antifa, the 2020 Presidential Election, and the war in Ukraine. Machine learning models were employed to analyze engagement metrics such as retweets, quotes, likes, and replies. Key factors like user information and tweet sentiment were identified as significant predictors of engagement.The findings reveal that tweets from users with a large number of followers and those perceived to have positive or negative sentiment receive higher engagement. User information emerged as the most influential factor, followed by tweet sentiment. These insights can guide efforts to control and manage the spread of rumors on social media, helping to develop strategies to mitigate the negative effects of misinformation and enhance the reliability of information shared online.

ABSTRACT Analyses of the execution and learning times have been completed for the defined custo!Iler requirements document (CRD) of Rhone-Poulenc Rorer Pharmaceuticals' (RPR) Dendrite Sales Representative ETMS Series 6.1 Interface. The analyses are based on system storyboards and design documents obtained from RPR in September of 1994. The GOMS predictive modeling approach and the Natural GOMS Language were utilized to provide estimates of the time required for a novice, average individual, and expert to learn to use the system and to execute given tasks. In order to estimate the learning and execution times for this system, the task goals that would be accomplished by typical users of the system needed to be identified. The goals chosen reflect top -level goals the RPR pharmaceutical representatives utilize most frequently. These goals were determined via reviews of employee manuals, interviews with upper division and district managers, and two weeks of field research and task analyses with three different RPR representatives from three different districts. The goals identified would also be utilized to evaluate future generations ofRPR interfaces and to predict user performance based on estimated execution and learning times.

Energy efficiency of all forms is a major area of research; particularly in housing. Engineers, working along side of builders and architects, are designing homes to be more energy efficient than ever. Recent trends have shifted the energy savings focus to smaller, entry level housing. Recent advances in computers have enabled powerful simulations to be used to predict energy savings in homes. The Florida Solar Energy Center (FSEC) is developing software, Energy Gauge USA, that talces advantage of today's powerful computer systems to run detailed hourly simulations. Measured hourly data from three entry level homes in Apopka, FL, was compared to the simulated hourly output data of Energy Gauge USA to assess the validity of the simulation. These three homes were built with different building techniques. The monitoring period took place under unoccupied conditions during a summer month with varying internal thermostat settings. Outside weather data was recorded and used in the simulation run. Overall, the simulation is very accurate in predicting energy consumption in entry level homes. Confidence intervals for the ratio of the variances and the difference of the means show very promising results. Additionally, the differences are centered around zero with no patterns or trends. These results will provide the developers of the software with a solid foundation of the accuracy of the software.

In today's business, a company must be able to make efficient use of its available resources. Decision making techniques play a significant role in enabling companies to optimize their limited resources. Discrete-Event Computer Simulation is one of the more widely used decision making tools. However, simulation in itself is a descriptive tool, and provides solutions to different alternatives that an analyst wishes to explore. Because running a simulation is computationally expensive, randomly trying different alternatives results in inefficient practice at a high cost. Simulation Metamodeling is an inexpensive method of exploring various alternatives that can be used with simulation models as decision-making tools. Simulation metamodels reduce the time needed to explore and optimize a simulated scenario. However the appropriate simulation metamodel to be used for a specific goal has not yet be identified. This thesis will investigate the construction of two types of metamodels for a given simulated scenario (regression metamodels and neural network metamodels), and compare their ability to accurately predict the simulation model. Conclusions will be drawn on the ability of each to accurately predict the simulation model, and suggestions will be made on the type of characteristics to which each method is suited.

This thesis focuses on language communication barriers in a medical emergency care environment, specifically a hospital Emergency Room. Language communication barriers can be the result of an uncommon language between those trying to communicate or medical problems harming the communication. The patient and the care provider may not speak the same language or may speak different dialects of the same language that have some different words with more than one meaning. Cultural difference may also hinder communication. Taboo gestures, words, and topics may also cause barriers. Any barriers between a patient and a care provider can hinder the care provided to the patient as well as affect the efficiency of the Emergency Room's operations and procedures. Many methods exist for overcoming communication barriers. Interpreters can be used, but accurate and effective interpretation in an emergency care environment requries many qualifications such as: being the same sex as the patient, being trained in interpretation, and having a medical background. There are computer based natural language translators in which a comptuer translates text from one language to another. There are other computerized methods such as the U.S. Customs Service Forms Translator Assistant (1992) developed at the University of Central Florida's Institute of Simulation and Training in Orlando, Florida. A computer program was developed to approach the problem of language communication barriers in the Emergency Room. The program is called ERCA - Emergency Room Communications Aid and the Proof-of-Concept demonstration of ERCA includes triage questions and statements geared toward Spanish male patients with vomiting complaints. Emergency-On-Line (EOL) is a recommendation to put ERCA on the Internet to be accessed by hospitals. EOL allows updates and amendments to be easily added and thus accessed in a timely manner. This and other recommendations allow ERCA to be more effective for the health care providers and patients. Another recommendation suggests to create a working map for ERCA so the users always know what sections of the program have been used and where they are in relation to the other sections. Other recommendations suggest including all aspects of the Emergency Room process and increasing the language base in ERCA as well as print the information gathered from ERCA in a report for the patient's medical records.

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This thesis presents a technique for modelling the 6-dimensional contact space of a 2-part assembly and planning compliant motion trajectories within that contact space. A nominal assembly motion plan is supplied a priori by a high-level planner, which derives moving part trajectories from kinematic constraints alone, disregarding the effects of uncertainty in the moving part's sensed pose and control velocity. The local contact space surrounding each critical configuration in the nominal trajectory is analyzed and represented as an adjacency graph of contact states, providing a search space for an appropriate sequence of contact state transitions. A heuristic graph search produces a sequence of reliable and recognizable contact state transitions. At a lower level of planning, a piecewise-linear trajectory is synthesized through uncertainty analysis. The resulting fine-motion plan is expressed as a sequence of hybrid position/force control strategy specifications.

<p>A software package for the identification of the modal parameters, of a n-degree of freedom system, in the time domain has been developed. A systematic method leading to an adequate discrete time series model from a set of discrete data has been summarized and the results of the testing and the use of the computer package have been presented.</p> / Master of Engineering (ME)