# Enhancing Social Media Platforms with Machine Learning Algorithms and Neural Networks
## 4 Analysis of the Literature  
### Synthesis of Findings  
Out of 955 articles, 26 were chosen. Machine learning and neural networks are two topics of AI that have received significant interest in recent years because of their potential to automate decision making and enhance accuracy in a variety of applications. Machine learning is the process of teaching computers to learn from data, and neural networks are a form of machine learning algorithm inspired by the structure of the human brain. Machine learning and neural networks have had a profound impact on reducing costs, improving user engagement, and enhancing decision-making processes. By leveraging large volumes of data and sophisticated algorithms, machine learning techniques enable businesses to automate processes, optimize resource allocation, and identify cost-saving opportunities. Additionally, neural networks have revolutionized user engagement by powering personalized recommendations, targeted advertisements, and interactive chatbots, thus enhancing the overall user experience. Moreover, these technologies enable data-driven decision making, providing organizations with valuable insights, predictive analytics, and actionable recommendations that facilitate more informed and efficient choices, leading to improved outcomes and competitive advantages in today's fast-paced and data-driven world. According to the included studies, machine learning and neural networks are increasingly being utilized to tackle complicated problems and make more accurate and efficient decisions. These technologies can automate human-performed jobs such as picture identification, language translation, and even car driving. According to several of the featured papers, machine learning and neural networks are also being utilized to improve the safety  
Figure 3: Process of selecting articles. and security of various systems. The study by [PERSON] et al. [50] looked at using machine learning to detect abusive comments on Instagram, while another considered utilizing neural networks to construct an intrusion detection system for cloud environments. Other studies have claimed that machine learning and neural networks can help to improve healthcare results. The study by [PERSON] et al. [51] covered the use of machine learning to predict the success of psychiatric medicines, whereas another study [52] investigated the use of neural networks to forecast illness risk and improve patient safety. However, the implementation of machine learning and neural networks is not without difficulties. [PERSON] et al. [53] examined the need to quantify uncertainty in machine learning models, while [PERSON] [54] investigated the topic of \"naturalness\" in machine learning and RNNs. These names imply that, while machine learning and neural networks have the potential to change many disciplines, crucial considerations and constraints need to be considered. In summary, the included studies demonstrate how machine learning and neural networks can be used to improve decision making, safety, healthcare outcomes, and other elements of modern life. However, it is equally critical to be aware of the constraints and limitations of these technologies, as well as to carefully examine their uses in a variety of contexts. There has been a considerable increase in the number of publications discussing machine learning and neural networks over the last five years, particularly regarding social networks, media, and related factors (Figure 4). The trend began with a dearth of publications in 2017 and 2018, but gained traction in subsequent years. This rising tendency could be explained by several factors. Firstly, rising interest and investment in AI and machine learning have catapulted these subjects into the mainstream. As a result, there is a greater demand for information and publications that delve into the complexities of these topics. During this time, advances in machine learning and neural networks have also been important.