Current Volume 7
Abstract
Twitter has become an effective tool for sharing information and conducting real-time conversations in the era of social media. A tweet's impact and engagement can be greatly increased by timing its release, which adds to its effectiveness beyond just its content. Using data science and machine learning, this project, "Breaking up the Twitter sphere: Predicting the Optimal Time to Tweet," identifies the best times for Twitter users to interact with their followers. We use a robust machine learning technique called Random Forest in our work to analyze a dataset that we obtained from Kaggle. The dataset is made up of detailed tweet information that is stored in a CSV file .The behavior of the Twitter sphere is complex and multidimensional, influenced by a wide range of factors such as daily routines, time zones, geographic diversity, and user demographics. Our research takes a multidisciplinary approach, combining big data analytics, machine learning, and social psychology to interpret the complex patterns of user activity and engagement. Using a large dataset that includes tweet timestamps, user interactions, and historical context, contemporary analytical techniques are used to find correlations between tweet timing and performance. The study uses clustering techniques to classify user groups based on comparable patterns of activity, providing tailored insights for recommendations regarding the best timing.
Keywords
Twitter, social media, Engagement, Optimal Time, Tweet Timing, Machine Learning, Random Forest, Kaggle Dataset, Data Science, Predictive Analytics.
Citations
IRE Journals:
Akshada Shinde , Roshni Poojary , Mithilesh Vishwakarma , Dr. Santosh Singh
"Breaking Up the Twittersphere: Predicting the Optimal Time to Tweet" Iconic Research And Engineering Journals Volume 7 Issue 8 2024 Page 175-180
IEEE:
Akshada Shinde , Roshni Poojary , Mithilesh Vishwakarma , Dr. Santosh Singh
"Breaking Up the Twittersphere: Predicting the Optimal Time to Tweet" Iconic Research And Engineering Journals, 7(8)
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