Data Analysis and Mathematical Modeling of Social Media Content
Our study focuses on discerning between automated and genuine user-generated interactions, such as likes, retweets, and replies, prevalent on Twitter. Leveraging statistical modeling and machine learning techniques, we aim to develop a robust method for this discrimination. By analyzing various features and patterns in interactions, including user details, and tweet characteristics, our framework seeks to accurately identify automated processes amidst vast user-generated data. The significance of our work lies in enhancing the authenticity of social media engagement by prioritizing genuine user interactions, thereby fostering trust and transparency among platform users. In summary, our research contributes to advancing the understanding of social media dynamics and offers practical solutions to mitigate the impact of automated processes, preserving the authenticity of online interactions.
Keywords: Data,Analysis,Mathematical Modeling,Machine Learning ,Statistics
Tools: python,selenium,Twitter
Department: Department of Mathematics
Project Team Members
| Name |
Email |
|
Ayesha Sidiqa
|
ayeshasidiqa2020@namal.edu.pk |
|
Muhammad Aqeel
|
aqeel2020@namal.edu.pk |
Project Poster
