Volume 18, Issue 2 (Summer 2016)
Advances in Cognitive Sciences 2016, 18(2): 26-40 |
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1- Department, Science and Research Branch Islamic Azad University, Tehran, Iran.
2- Amirkabir University of Technology, Teran, Iran.
2- Amirkabir University of Technology, Teran, Iran.
Abstract: (3180 Views)
Objective: Audience trust is considered as a main asset for the news media, hence assessment of the influence of audience trust in the news is of great importance. An ultimate goal is to design a machine capable of anticipating the state of trust in the news, based on an emotional cognitive model, known as brain emotional learning (BEL) machine, and also determining the factors contributing to audience trust in the news.
Method: In this study, factors contributing to audience trust were evaluated by means of a survey conducted among 110 news experts in IRIB. Each record comprised a set of features in six categories which were in fact the six factors of audience trust in this study. Accordingly, a cognitive model was designed and implemented using the brain emotional learning machine. Furthermore, Whitney and Kolmlgorov-smirnov statistical tests were employed to analyze the difference between the dataset from experts and the test outputs of the machine. The task was simulated using the MATLAB software.
Results: Findings demonstrated that the six components of “impartiality and objectivity”, “censorship”, “integrity”, “accuracy”, “source credibility” and “broadcast attractiveness” had a very strong correlation coefficient with the audience trust in the news. The results of training data showed an error of less than 0.002 and output of test data showed insignificant difference between the results of the designed cognitive model and the results of the survey, suggesting the proper performance of the machine.
Conclusion: Taking the significance of audience trust assessment in the news , information technology can help design a machine to predict the audience trust in the news before broadcast
Method: In this study, factors contributing to audience trust were evaluated by means of a survey conducted among 110 news experts in IRIB. Each record comprised a set of features in six categories which were in fact the six factors of audience trust in this study. Accordingly, a cognitive model was designed and implemented using the brain emotional learning machine. Furthermore, Whitney and Kolmlgorov-smirnov statistical tests were employed to analyze the difference between the dataset from experts and the test outputs of the machine. The task was simulated using the MATLAB software.
Results: Findings demonstrated that the six components of “impartiality and objectivity”, “censorship”, “integrity”, “accuracy”, “source credibility” and “broadcast attractiveness” had a very strong correlation coefficient with the audience trust in the news. The results of training data showed an error of less than 0.002 and output of test data showed insignificant difference between the results of the designed cognitive model and the results of the survey, suggesting the proper performance of the machine.
Conclusion: Taking the significance of audience trust assessment in the news , information technology can help design a machine to predict the audience trust in the news before broadcast
Type of Study: Research |
Subject:
Special
Received: 2016/11/25 | Accepted: 2017/02/23 | Published: 2017/06/26
Received: 2016/11/25 | Accepted: 2017/02/23 | Published: 2017/06/26
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