Human Information Interaction

Purpose: The purpose is to present an open government data evaluation method by considering comprehensive and complete dimensions and criteria - calculating the weight and importance of each criterion, examining the country in this area, clustering organizations and presenting a classification model to predict the situation.
Methodology: Library studies was used to extract the dimensions and criteria of evaluation. Population includes articles related to open government data evaluation criteria. Ten articles were reviewed by simple random sampling method. Multiple attribute decision making techniques was used to calculate the weight and importance of each criterion. Data mining techniques was incorporated to cluster and create a classification model.
Findings: By reviewing the articles 15 criteria of open government data evaluation including:  Data originality, license openness, up-to-datedness, data access rate, metadata completeness, number of data sets, format openness, non-discriminatory, comprehensible, number of data fields, free, no missing data, data request ability, visual and feedback, were extracted. Using AHP technique, the weights of the criteria were calculated, which after normalization, the total weight of the 15 extracted criteria was equal to one. "Data originality" with a weight of 0.165, " license openness " with a weight of 0.124 and " up-to-datedness" with a weight of 0.109 were ranked first to third among 15 evaluation criteria, respectively. Weight of evaluation criteria obtained and data extraction of 358 organizations in harmony with 15 evaluation criteria, the weight of organizations was calculated. The sum of the weights was equal to one. "East Azerbaijan Agricultural Jihad Organization" with a weight of 0.088, "Statistics Center of Iran" with a weight of 0.062 and "Geological Survey" with a weight of 0.058 were the first to third ranks among 358 organizations and government institutions, respectively, based on the combination of criteria and the weight of criteria.
Conclusion: Evaluation criteria obtained, calculating the weight and importance of each criterion, examining the current situation of government organizations and institutions in the country and the classification model created can help managers to understand the current situation and improve it and thus increase citizens' interaction with open government data as a kind of human information interaction.

Findings: Findings showed an increase in the score of all types of individual competencies, knowledge, and skills at the advanced level compared to the basic level. Moreover, the average merit scores in both levels were higher than the average level. This finding indicates that despite the high importance of all competencies at both professional levels, the importance of many competencies is higher at the advanced level, where more complex responsibilities are envisaged for IT consultants.
Conclusion: In the research model, the types of individual competencies, knowledge, and skills required at both the basic and advanced professional levels are introduced. It should be noted that providing specialized training to job applicants such as information counseling can strengthen their desirable job competencies. Therefore, when starting to work in professions such as intelligence consulting, applicants should have an acceptable level of competence and experience the additional training, knowledge, and skills needed to take on higher and more complex levels of responsibilities

Introduction: Errors in data collection and failure to pay attention to data that is noisy in the collection process for any reason cause problems in data-based analysis and, as a result, wrong decision-making. Therefore, solving the problem of missing or noisy data before processing and analysis is of vital importance in analytical systems. The purpose of this paper is to provide a method to identify noisy data, outliers, and missing data and provide a suitable solution for these data.
Methods: This study is applied research. Data mining techniques including binning smoothing and regression models have been used to identify and replace outlier and noisy data.
Results: The results of the tests performed in the real environment related to the data of social networks show the proper performance of the proposed method. It has also been shown that the proposed method has higher accuracy compared to the methods of binning smoothing, average and linear regression. So that for the data related to the tweet section, the mean squared error obtained for the proposed method was equal to 0.04, the binning smoothing method was equal to 0.38, the linear regression method was equal to 0.05 and the average method was equal to 0.06.
Conclusion: The method presented in this article can initially identify outlier data through one-third and two-thirds normal, and then replace the outlier data with a linear regression model, which results in improving the performance of using and processing information and improving human-information interaction

Social networks and their increasing influence among different users in all parts of the world have made these networks become suitable tools for advertising and e-commerce. Today, businesses have come to understand that social networks are and will continue to be a means of doing business. Instagram is a popular social network based on video and images. This social network is known as one of the powerful marketing tools. The number of views, likes and comments on social networks, including Instagram, plays a significant role in customer decision-making; Because they pay attention to the opinions and reception of other audiences towards that product or post and are influenced. This research analyzes what factors create posts with different levels of popularity. For this purpose, the factors affecting the number of views, likes and comments in an Instagram social network post are extracted and their weight and importance are calculated based on the regression model. Finally, the decision tree model is presented for forecasting and management in order to increase the number of visits, likes and comments.
Methods and Materoal
In this research, the type of research is based on the purpose of applied research. At first, library studies have been used in order to extract factors affecting the amount of visits, likes and comments in Instagram social network marketing posts. The statistical population includes all articles related to the factors affecting visits, likes and comments. The probability sampling method of simple random samples has been used and 30 articles in this field have been reviewed. Then, the data related to the factors identified from the previous stage have been extracted from the pages of big marketers on the Instagram social network. Then, using the extracted data and using the regression model, the weight and importance of each factor affecting the number of visits, likes, and comments of Instagram social network marketers' posts has been calculated. Finally, a decision tree model has been created to predict the status (rate of visits, likes and comments) of a marketing post on the Instagram social network based on the characteristics of that post.
Resultss and Discussion
Directly, factors such as the number of posts, the number of followers, the type of post, the content of the post and the time of the post are potential factors that affect the number of views, likes and comments. According to the obtained results, the "post content with survey" factor with a positive sign and a coefficient of 420,290.616 had the most positive effect on the label, which is the number of visits to a post. The factor "discount post content" with a positive sign and a coefficient of 5417.751 has had the most positive effect on the label, which is the liking of a post. The factor "discount post content" with a positive sign and a coefficient of 2164.016 has had the most positive effect on the label, which is the amount of comments on a post. Also, the type of image post with a regression coefficient of 565.153 and a negative sign in the investigation of factors affecting the number of comments shows that the use of video posts will increase the comments and interaction of customers.

Conclusion
Most of the researches conducted, such as Gkikas et al (2022), Torbarina, Jelenc & Brkljačić (2020), Wahid & Gunarto (2022), etc., only investigated the influence of a few specific factors on the likes and comments of social media posts, and a comprehensive set of factors has not been investigated. Also, these factors were only for checking likes or opinions and not checking both cases. Most importantly, in the studies conducted, only the positive or negative impact of a factor on the number of likes and opinions has been discussed, and their importance has not been determined. In this research, various factors affecting the number of visits, likes and comments of social network posts were investigated. Also, the importance of each factor was determined. In addition, a decision tree model was presented to manage related pages and posts in order to achieve increased likes and comments. Based on the extracted effective factors, calculating the weight and importance of each factor and the created decision tree model, posts can be managed to increase the number of visits, likes and comments