درخت حوزه‌های تخصصی

Purpose: Despite the widespread diffusion and interest aroused by content marketing, little attention has been paid until this moment to building a framework that presents the main currents and studies of the field. Hence, the main aim of this study was to cover this gap by analyzing bibliographic information as complementary sources and enable a wider understanding and grasp of the content marketing field.Method: For this purpose, a bibliometric study of the publications indexed in Web of Science (WoS) between 1985-2022 was conducted. The search process used in this review was informed by PRISMA guidelines. During the search process, a set of 371 documents (research  and review articles) were obtained. Also, the bibliometrix R-package and VOSviewer software were used for quantitative analysis and visualizing bibliometric networks.Findings: The descriptive statistics showed that content marketing studies have rapidly grown since 2011. The US and Spain are the countries with the most publications of the field. The most prominent journal concerning content marketing research is Brand Journalism (with 11 articles), and the most prolific author is Bull A (with 11 articles).The results of the thematic analysis showed that ‘digital marketing’ and ‘brand storytelling’ are emerging themes and have replaced ‘content marketing’. The co-word analysis of author’s keywords defines 8 clusters: 1) platforms and techniques 2) content marketing concepts, 3) influencer marketing and advertising, 4) digital and social media marketing, 5) brand management and brand storytelling, 6) brand journalism, 7) private and native media, and 8) corporate and public communication.Conclusion: Simultaneously with the development of content creation platforms, these platforms have been welcomed in the field of content marketing. Content preparation has undergone changes in recent years. The style of information content based on news and specialized knowledge has shifted its focus to storytelling and narrative messages from the brand. This paper introduces the main areas of interest and possible gaps. It also contributes to the body of knowledge by providing a comprehensive overview of content marketing literature.

For investors, predicting stock market changes has always been attractive and challenging because it helps them accurately identify profits and reduce potential risks. Deep learning-based models, as a subset of machine learning, receive attention in the field of price prediction through the improvement of traditional neural network models. In this paper, we propose a model for predicting stock prices of Tehran Stock Exchange companies using a long-short-term memory (LSTM) deep neural network. The model consists of two LSTM layers, one Dense layer, and two DropOut layers. In this study, using our studies and evaluations, the adjusted stock price with 12 technical index variables was taken as an input for the model. In assessing the model's predictive outcomes, we considered RMSE, MAE, and MAPE as criteria. According to the results, integrating technical indicators increases the model's accuracy in predicting the stock price, with the LSTM model outperforming the RNN model in this task.