Using social networking evidence to examine the impact of environmental factors on social Followings: An innovative Machine learning method

Abstract

Social media platforms have changed the way of communication. They are making the world more connected and provide information more quickly, which has resulted in beneficial and harmful effects on society. Analyzing data from multiple social media platforms to spot patterns and trends in user behavior in response to environmental changes is part of using social networking evidence to investigate the effects of environmental variables.The infinite collection of personal data generates privacy issues and ethical questions about data security and utilization.We present a novel framework called meta-heuristic atom search optimized dynamic artificial neural network (MASO-DANN) method for examining the impact of environmental factors using social networking evidence and the complementary abilities between MASO as well as DANN. The study aims to determine the connections between the dynamics of users' social relationships and involvement by examining the data from various online social platforms. We collected a Twitter dataset and preprocessed the data using the stop word removal preprocessing method.With the use of MASO-DANN, extensive social networking data analysis has been rendered achievable, which offers an exhaustive overview of the complex dynamics influencing online communities. The result findings examined various metrics, such as accuracy (95.07%), precision (89%), recall (88%), F1-score (90%) and RMSE (1.6).These metrics demonstrate the robustness along with the dependability of MASO-DANN and its capacity to provide more accurate predictions that evaluate the effects of environmental factors based on social networking evidence.