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Deep learning-based credibility conversation detection approaches from social network.

Imen Fadhli¹, Lobna Hlaoua¹, Mohamed Nazih Omri¹

¹MARS Research Laboratory LR17ES05, University of Sousse, Sousse, Tunisia.

Social Network Analysis and Mining

|April 3, 2023

Summary

This summary is machine-generated.

This study introduces CreCDA, a deep learning method to detect credible conversations on social media by analyzing user and post features. The approach effectively identifies false information, enhancing online discourse credibility.

Keywords:

Credibility detection Deep learning Post features Sentiment analysis Twitter conversation User features

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Area of Science:

Computer Science
Artificial Intelligence
Social Media Analysis

Background:

Social networks are primary information sources but also spread non-credible information.
False information negatively impacts online conversation credibility.

Purpose of the Study:

To propose a novel deep learning approach, CreCDA, for credibility conversation detection in social networks.
To enhance the accuracy of identifying credible and non-credible online discussions.

Main Methods:

Developed CreCDA, integrating post and user features using multi-dense layers.
Incorporated sentiment analysis by aggregating tweet sentiments.
Evaluated performance on the standard PHEME dataset.

Main Results:

CreCDA achieved high performance metrics: 79% mean precision, 79% mean recall, 79% mean F1-score, 81% mean accuracy, and 79% mean G-Mean.
Demonstrated the effectiveness of combining text and user-level analysis.
Highlighted the significant contribution of sentiment analysis in credibility detection.

Conclusions:

The proposed CreCDA approach is effective for detecting credible conversations on social media.
Combining multi-level features and sentiment analysis significantly improves credibility detection accuracy.
This method offers a robust solution for combating misinformation on social platforms.