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Emoji-Driven Sentiment Analysis for Social Bot Detection with Relational Graph Convolutional Networks.

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Summary
This summary is machine-generated.

This study introduces ESA-BotRGCN, a novel framework for detecting malicious social bots by analyzing emoji semantics and sentiment. The method significantly improves bot detection accuracy by leveraging multi-modal features and graph convolutional networks.

Keywords:
RGCNdeep learningemojisentiment analysissocial bot detection

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Malicious social bots threaten cybersecurity and online information integrity.
  • Current bot detection methods often neglect the semantic and emotional content of emojis in user-generated text.
  • Emojis offer valuable cues for distinguishing between authentic and automated social media activity.

Purpose of the Study:

  • To propose ESA-BotRGCN, an emoji-driven, multi-modal framework for enhanced social bot detection.
  • To integrate semantic enhancement, sentiment analysis, and multi-dimensional feature modeling for improved accuracy.
  • To address the limitations of existing methods by incorporating emoji-based emotional and semantic cues.

Main Methods:

  • Established emoji-text mapping and used GPT-4 for textual coherence, generating tweet embeddings via RoBERTa.
  • Extracted seven sentiment-based features to quantify emotional expression differences between bots and humans.
  • Employed an attention gating mechanism to fuse sentiment features with user descriptions, tweet content, and network topology using a Relational Graph Convolutional Network (RGCN).

Main Results:

  • Achieved a superior accuracy of 87.46% on the TwiBot-20 benchmark dataset.
  • Significantly outperformed existing baseline bot detection models.
  • Validated the effectiveness of emoji-driven semantic and sentiment enhancement strategies in improving detection performance.

Conclusions:

  • The proposed ESA-BotRGCN framework demonstrates superior performance in social bot detection.
  • Integrating emoji semantics and sentiment analysis is crucial for robust bot detection systems.
  • The multi-modal approach effectively models heterogeneous social network topology for enhanced cybersecurity.