The rise of social bots has become a significant concern in the digital age, particularly on social media platforms like Twitter. Social bots are automated software agents designed to mimic human behavior, often interacting with real users in ways that can be harmful or misleading. These bots can be benign, such as those used for customer service or content aggregation, but they can also be malicious, spreading misinformation, spam, or even influencing political outcomes. The presence of social bots poses a threat to online ecosystems and society, as they can distort public opinion, manipulate markets, and compromise the integrity of social media. The challenge of detecting social bots is complex, as they have become increasingly sophisticated, capable of mimicking human behavior in terms of content, network, sentiment, and temporal patterns. Current detection methods include analyzing network structures, leveraging human intelligence through crowdsourcing, and using machine learning to identify patterns that distinguish bots from humans. However, these methods face challenges, including the difficulty of detecting bots that have evolved to mimic human behavior more closely and the need for large amounts of training data. Efforts to combat social bots include the development of detection systems that combine multiple approaches, such as network-based analysis, behavioral pattern recognition, and human-in-the-loop verification. These systems aim to identify and mitigate the impact of social bots, which can be used for various malicious purposes, including political manipulation, cybercrime, and market manipulation. Despite these efforts, the problem remains challenging, with new forms of bot behavior emerging that require ongoing research and adaptation of detection strategies. The future of social media may involve a shift towards environments where machine-machine interactions are more common, and humans must navigate a world increasingly populated by bots. The need for effective detection and mitigation strategies is critical to maintaining the integrity and trustworthiness of social media ecosystems.The rise of social bots has become a significant concern in the digital age, particularly on social media platforms like Twitter. Social bots are automated software agents designed to mimic human behavior, often interacting with real users in ways that can be harmful or misleading. These bots can be benign, such as those used for customer service or content aggregation, but they can also be malicious, spreading misinformation, spam, or even influencing political outcomes. The presence of social bots poses a threat to online ecosystems and society, as they can distort public opinion, manipulate markets, and compromise the integrity of social media. The challenge of detecting social bots is complex, as they have become increasingly sophisticated, capable of mimicking human behavior in terms of content, network, sentiment, and temporal patterns. Current detection methods include analyzing network structures, leveraging human intelligence through crowdsourcing, and using machine learning to identify patterns that distinguish bots from humans. However, these methods face challenges, including the difficulty of detecting bots that have evolved to mimic human behavior more closely and the need for large amounts of training data. Efforts to combat social bots include the development of detection systems that combine multiple approaches, such as network-based analysis, behavioral pattern recognition, and human-in-the-loop verification. These systems aim to identify and mitigate the impact of social bots, which can be used for various malicious purposes, including political manipulation, cybercrime, and market manipulation. Despite these efforts, the problem remains challenging, with new forms of bot behavior emerging that require ongoing research and adaptation of detection strategies. The future of social media may involve a shift towards environments where machine-machine interactions are more common, and humans must navigate a world increasingly populated by bots. The need for effective detection and mitigation strategies is critical to maintaining the integrity and trustworthiness of social media ecosystems.