Application Case 5.3- Mining for Lies

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Grand Canyon University *

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600

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Information Systems

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Dec 6, 2023

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docx

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Uploaded by MagistrateJellyfishPerson3768

Application Case 5.3: Mining for Lies Questions for Discussion 1. Why is it difficult to detect deception? Humans, in general, do perform poorly at distinguishing truth from lies, which makes detecting deception challenging. This problem is even more prominent when communication is done through text since there are no non-verbal indicators like tone of voice and body language. When assessing someone's credibility, people often look for both verbal and nonverbal signs. When those cues are absent, as in written text, it becomes more challenging to identify deception accurately. Consequently, there is a need for automated methods to aid in deception detection. 2. How can text/data mining be used to detect deception in text? Text and data mining techniques can be used to identify deception in text by analyzing linguistic and structural characteristics. These techniques involve converting text data into a machine- readable format, after which it is determined whether word choices, emotional overtones, or complexity of language might point to deceit. After that, automated systems can use machine learning techniques to evaluate these characteristics and predict if the text is true or not. The study in the case used the previously mentioned techniques to analyze the person of interest statements for deception. It achieved promising accuracy rates. 3. What do you think are the main challenges for such an automated system? Dealing with the complexity of human language, cultural differences in communication, changing deception techniques, and ensuring adaptability to various circumstances are the key issues facing an automated text-based deception detection system. In addition, one of the biggest

challenges in monitoring personal conversations is maintaining privacy and ethical issues. Maintaining accuracy and avoiding mistakes requires ongoing training and development. It is also critical to address any biases in data and algorithms in order to prevent prejudice in deception detection.

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Application Case 5.3- Mining for Lies

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