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The impact of personal judgment on the assessment of an individual’s financial situation has been drastically reduced through the development of credit scoring. The systems are capable of deciding based on an applicant’s total score which is a combination of several factors and indicators. Over the past few decades, credit scoring has been considered an essential tool for evaluation in various institutions and has also been able to transform the industry as a whole. Most of the research conducted in the field has taken into account traditional credit scoring, but considering the ever-evolving technological world that we live in and the increasing emergence of new social media networks, such research has now become obsolete. Such technological advancements have not only paved the way for far more sophisticated credit scoring systems but also essentially rendered the previous generations useless. It should be noted that credit scoring and its features have widely been discussed across the globe but, considering the various aspects and models that have to be taken into account, no one best method has been designed or suggested for it so far. This study shows that social media channels tend to perform relatively well in predicting stock market trends when the overall index is growing positively. The research also illustrates that a higher number of days of activity and a large number of signals released do not necessarily mean that the channels can or have credited their offered stock return on a one-month time frame. The methodology used is "CRISP-DM," which consists of six steps. The main variables include social and financial variables that are examined for six months. In the research, we seek to identify, analyze and categorize active telegram channels in stock signals using the data mining model and the RFM method. The k-means algorithm is selected for this category. Then, in each cluster, the importance of social variables and the performance of the channels are extracted by the EXTRATREECLASSIFIER algorithm, and channel performance is measured by considering the changes in the total index.

The present study sought to find the most frequent lexical bundles, particularly the token of the bundles, in the introduction and discussion sections of Civil Engineering research articles. It also aimed to examine the forms or types of lexical bundles found in the introduction and discussion sections of the articles. To this end, a quantitative analysis was performed on the use of two-to-five-word lexical bundles, followed by a qualitative analysis of their functions and structures based on the structural taxonomy of lexical bundles proposed by Biber, Conrad, and Cortes (2004). AntConc, as a corpus analysis tool, was employed for the extraction of lexical bundles as well as the examination of their types and tokens. Likewise, concordancers and word and keyword frequency generators were used for the analysis of word clusters and lexical bundles.   A total of 790 lexical bundles in the introduction section and 279 lexical bundles in the discussion section were analyzed based on a set of pre-established criteria (e.g., the frequency cut-off of 20 per million words) . The findings revealed that the lexical bundles consisted largely of two-word strings, and the five-word strings were the fewest. Regarding the structural classification of bundles, phrasals were the most frequent ones, whereas clausal on es were the least frequent ones. The study carries important implications, especially for materials developers and course designers who may hopefully benefit from the results in designing EAP materials finely tailored to the linguistic needs of Civil Engineering students.