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A CNN-based misleading video detection model.

Xiaojun Li1, Xvhao Xiao1, Jia Li2

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Summary
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This study developed a convolutional neural network (CNN) to detect misleading videos using content, uploader, and environment features. The approach achieved high accuracy (0.90) and F1 score (0.89), outperforming other models.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Media Studies

Background:

  • Short videos are a major information source, but misleading content poses social risks.
  • Automated detection of misleading videos is crucial for mitigating negative impacts.

Purpose of the Study:

  • To develop and evaluate a novel method for automatically identifying misleading videos.
  • To investigate the effectiveness of combining content, uploader, and environment features for this task.

Main Methods:

  • A convolutional neural network (CNN) model was constructed.
  • Three feature categories were engineered: content, uploader, and environment features.
  • The CNN integrated these features for misleading video detection.

Main Results:

  • All three feature categories significantly contributed to the detection accuracy.
  • The proposed CNN approach achieved an accuracy of 0.90 and an F1 score of 0.89.
  • The method demonstrated a substantial performance improvement (over 22%) compared to traditional machine learning models (SVM, k-NN, decision tree, random forest).

Conclusions:

  • Combining content, uploader, and environment features within a CNN is highly effective for misleading video detection.
  • The developed approach offers a robust solution for identifying misleading video content on online platforms.
  • This research contributes to combating the spread of misinformation in the digital media landscape.