Abstract:
"An engagement and reach prediction system for text-based Tweets is the main goal of this
project. The need for a social media engagement and reach forecast system using the post
content and user account features is the issue this project attempts to solve. However, it can be
difficult to predict engagement and reach with accuracy, so a system that can offer trustworthy
and precise predictions is required to help users improve their social media performance. The
project makes predictions regarding tweet engagement using machine learning methods. The
model was trained using a dataset that included details on user accounts on social media and their
activities. The dataset contains information on users' followers, following, total likes, user
verification status, and tweet content. As for the prototype, sentiment models, Neural Network
models, LDA topic modeling, KeyBert, and Decision Tree Regression models were used to
approach predicting numerical values.
The system's performance can be enhanced by using advanced NLP techniques and deep
learning be enhanced by the inclusion of more features and a larger variety of data. In terms of
estimating the audience and reach of social media activities, the project's early implementation
has generally produced good results. However, there is still potential for progress, and the
accuracy and usability of the prediction system need to be improved by the inclusion of more
in-depth systems and methodologies in the upcoming advancements.
Keywords: Social media, Reach prediction, Audience estimation, Web application, Tweet text
content, Account features, Sentiment analysis, Keyword extraction, Topic detection
Subject descriptors:
● Information Systems: social media, web-based applications
● Human-centered computing: User interfaces, User experience
● Software and its engineering: Software development process, software architecture,
software algorithms
● Computing methodologies: Machine learning, Predictive modeling, data analysis
● Computer applications: Digital marketing, audience reach prediction."
