Effectiveness of Social Media analytics counterterrorism Technology (smact) on Terrorism containment rates in Lamu county beach tourism destination, Kenya

Abstract

Fear of terrorism has led to social and economic decline particularly in beach tourism destinations. The main approaches used in managing terrorism include hard and soft strategies such as military and counter-radicalization. Previous studies have reported the capabilities of social media analytics in detecting terror threats through media streams. However, the effectiveness of this technology has not been examined in beach tourism destinations. This study examines the effectiveness of social media analytics counterterrorism technology (SMACT) in identifying terror threats in tourism destinations. The aim of the research is to determine the effectiveness of SMACT technology on terrorism containment rates in Lamu County beach tourism destination. Descriptive research and machine learning is used to process Twitter dataset using Python. A dataset of 9,572 tweets is preprocessed. Naive Bayes and Recurrent Neural Network (RNN) sentiment analysis models are implemented and evaluated to categorize terrorism-related tweets as positive or negative. A dataset of 5840 tweets is processed after preprocessing and split 80:20 into train and test sets models are developed and trained. The trained models are used to predict sentiment on the 20% test set. A total of 1168 tweets counts are predicted on the test set for each model. Naive Bayes model predicts 719 positive and 449 negative tweets. The RNN model predicted 829 positive and 339 negative tweets. The Naive Bayes and RNN models demonstrate highly accurate detection of extremist sentiment in tweets, though the Naive Bayes classifier outperformed the RNN model. The Naive Bayes model predicted 61.6% of tweets as positive sentiment and 38.4% as negative. In contrast, the RNN model predicted a 71:29 split between positive and negative tweets. Frequently occurring terrorism related terms include 'attack', 'kill', 'bomb' among others. Twitter terrorism and counterterrorism tweets sentiments are successfully analyzed, giving important insights into the prevalent opinions on the platform. The sentiment distribution and correlations between positive and negative feelings are clustered. The results help researchers, policy makers and

other stakeholders in counterterrorism efforts gain a better grasp of public opinion on Twitter.