An empirical framework for event prediction in massive datasets

Certain events always trigger evolutionary changes in temporal Social Networks (SNs) communities. Machine Learning models make predictions for such events. The performance of these ML models largely depends on the dataset's features. Existing literature shows that the community features of the datasets have helped ML models predict the events with some accuracy. However, a temporal dataset has temporal and community features owing to its evolving structures. These temporal features also aid in improving the performance of the ML models. Thus, this work aims to compare the effectiveness of temporal and community features in improving the accuracy of ML models. This paper proposes a framework to extract the detected communities' community- and temporal- features in temporal data. This research also analyses ML models suitable for predicting events based on features and compares their performance. The experimental research shows that adding temporal features improves the prediction accuracy from 79.51 to 81.47% and saves 59.37% of the computational time of ML models.