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In Indonesia, mental health awareness is not as prominent as physical health, and many adolescents have experienced mental health problems. Some people hide their mental and emotional problems by appearing cheerful an...
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Rubber plantations are an integral part of economy in Malaysia. However, inadequate soil moisture poses a significant problem for rubber trees as it can lead to water stress, ultimately hindering their growth, leaf de...
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Accurate ground truth annotation plays a vital role in training and evaluating deep learning models for forest and non-forest classification tasks. This paper introduces a robust algorithm designed to automate the ann...
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This paper studies the complexity of operations on finite automata and the complexity of their decision problems when the alphabet is unary and n the number of states of the finite automata considered. The following m...
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Sa ltwa ter intrusion is one of the prima ry sources of groundwa ter conta mination, especia lly nea r the coa sta l line, a nd it is crucia l to monitor sa ltwa ter intrusion to protect fresh groundwa ter. Due to the...
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In recent years, the exponential growth of e-commerce has transformed consumer purchasing behavior, with online reviews playing a crucial role in shaping buying decisions. Unfortunately, the prevalence of manipulated ...
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ISBN:
(纸本)9798350341737
In recent years, the exponential growth of e-commerce has transformed consumer purchasing behavior, with online reviews playing a crucial role in shaping buying decisions. Unfortunately, the prevalence of manipulated reviews has become a significant challenge, undermining the integrity of consumer feedback and eroding trust in e-commerce platforms. This research paper focuses on developing an advanced solution to address the issue of manipulated reviews using the innovative language model, GPT-4. The primary objective of this study is to investigate the effectiveness of GPT-4 in identifying and flagging manipulated reviews within the context of e-commerce platforms. GPT-4 is a state-of-the-art language model with superior natural language processing capabilities, making it an ideal candidate for automated review analysis. The research methodology encompasses a large-scale data collection process, where diverse e-commerce reviews are gathered from various platforms. To simulate real-world scenarios, manipulated reviews are artificially injected into the dataset, representing different degrees of sophistication. The dataset is then annotated by expert reviewers to establish ground truth labels for comparison. Next, GPT-4 is fine-tuned using transfer learning to specialize in detecting manipulated reviews. The fine-tuning process involves exposing the model to both genuine and manipulated review samples, allowing it to learn patterns and features indicative of manipulation. The fine-tuned model's performance is then evaluated using various metrics, including precision, recall, F1 score, and accuracy, against the ground truth dataset. The results of the experiment demonstrate the efficacy of GPT-4 in distinguishing between authentic and manipulated reviews. GPT-4 showcases remarkable accuracy and robustness in detecting increasingly sophisticated manipulation techniques, outperforming previous iterations of language models and traditional detection methods. Furthermore,
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