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Related Concept Videos

False Memories01:18

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False memories represent a cognitive distortion in which individuals recall events that did not happen, or remember them in an altered form. This phenomenon highlights the brain's constructive nature in processing and recalling memories, emphasizing that memory is not a perfect representation of past events but rather a dynamic reconstruction influenced by various factors.
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Updated: Jun 13, 2025

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Fake social media news and distorted campaign detection framework using sentiment analysis & machine learning.

Akashdeep Bhardwaj1, Salil Bharany2, SeongKi Kim3

  • 1School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India.

Heliyon
|September 10, 2024
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Summary

This study introduces a novel framework using emotion-based sentiment analysis to detect fake news and bot accounts on social media. The model achieves 99.68% accuracy, outperforming existing methods for identifying disinformation campaigns.

Keywords:
BotsDistorted campaignsFake newsSentiment analysisSocial media

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Social media platforms facilitate global information exchange but are increasingly exploited for spreading fake news and spam.
  • Manual verification of vast amounts of social media content is infeasible, necessitating automated solutions.
  • Existing methods struggle to accurately distinguish genuine content from disinformation and malicious bot activity.

Discussion:

  • This research proposes a framework leveraging sentiment analysis based on emotions to analyze social media content.
  • The model computes sentiment scores for content entities to identify fake or spam profiles and bot accounts.
  • Sentiment analysis provides a nuanced approach to understanding user emotions and detecting deceptive content.

Key Insights:

  • The developed framework effectively detects fake news, spam, and bot accounts with high precision.
  • Sentiment analysis based on emotions proves to be a powerful tool for uncovering disinformation campaigns.
  • The machine learning algorithm achieved a remarkable accuracy of 99.68%, significantly surpassing other methodologies.

Outlook:

  • Further research can explore incorporating multimodal sentiment analysis (text, image, video) for enhanced detection.
  • The framework has the potential to be integrated into social media platforms to improve content moderation.
  • Continued development of AI-driven solutions is crucial for combating the growing threat of online disinformation.