Football is a popular sport; however, it is a big business as well. From a managerial perspective, the important decisions that team managers make -Concerning player transfers, issues related to player valuation, especially the determination of transfer fees and market values, are of major concern. Market values can be understood as estimates of transfer fees-prices that could be paid for a player on the football market. Therefore, market values play an important role in transfer negotiations. The market has traditionally been estimated by football experts. However, expert judgments are inaccurate and not transparent. Data analytics may thus provide a sound alternative or a complementary approach to experts-based estimations of market value. In this study, we propose an objective quantitative method to determine football players' market values. The method is based on the application of machine learning algorithms to the performance data of football players. The data used in the experiment are FIFA 20 video game data, collected from sofifa.com. We estimate players' market values using four regression models that were tested on the full set of featureslinear regression, multiple linear regression, decision trees, and random forests. Moreover, we seek to analyze the data and identify the most important factors affecting the determination of the market value. In the experimental results, random forest performed better than other algorithms for predicting the players' market values. It has achieved the highest accuracy score and lowest error ratio compared to baseline. The results show that our methods are capable to address this task efficiently, surpassing the performance reported in previous works. Finally, we believe our results can play an important role in the negotiations that take place between football clubs and a player's agents. This model can be used as a baseline to simplify the negotiation process and estimate a player's market value in an objective quantitative way.INDEX TERMS Player value prediction, regression, Machine learning, Football analytics, FIFA Video game data.

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Social networks like Facebook and Twitter have become an important way for people to connect and share their thoughts. The most important feature of social networks is the rapid sharing of information. In this context, users often share fake news without even knowing it. Fake news affects people's daily lives and its consequences can range from mere disturbing to misleading societies or even countries. The aim of this study was to provide a literature review that investigates how artificial intelligence tools are used in detecting fake news on social media and how successful they are in different fields. The study was developed using the methodology presented by Keela (2007), which is a formal methodology in computer science. The results of the study show that artificial intelligence tools such as machine learning and deep learning are widely used to develop systems for detecting fake news in various fields such as politics, sports, business, etc. and that these two tools have proven to be effective in classifying fake news. This study is intended to guide researchers as well as people involved in this field. It is believed that this study will help fill a gap in this field by presenting the main tools used for this purpose and shed light on further research. It is also hoped that this study will be a guide for researchers and individuals interested in the detection of fake news.

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