## 4 Methods That Use Large Language Models
### LLM-Based Fake Profile Detection  
Profile Content Analysis  
MedGraph is a multi-head attention-based graph neural network (GNN) approach to detect malicious behaviors, i.e., edges from a temporal reciprocal graph [85] in online dating applications, considering both the user's features and their interactive behaviors. The method is based on four components. The first component is the motif-based GNN component that defines reciprocal user features through a bipartite graph and uses a motif-based random walk algorithm to sample neighbors based on predefined motifs. A temporal user behavior embedding component discovers abnormal user behaviors based on feature representations of historical interaction data. Then, a co-attention component captures the interactive behavior attributes between users, especially focusing on distinguishing the behavior of abnormal users. Finally, there is the prediction layer to predict if an interaction (edge) is malicious.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Study** & **Models Used** & **Dataset/Data** & **Main Advantage** & **Best Performance Value** & **Limitations** \\ \hline
[75] & Fine-tuned BERT, GPT-3.5, ARG, ARG-D & Two real-world datasets & ARG and ARG-D outperform baseline methods & Macro F1: 0.784 & LLM’s inability to select and integrate rationales properly \\ \hline
[78] & ERNIE, KnowBert, KEPLER, K-ADAPTER & LIAR, COVID-19 K-ADAPTER & LIAR, COVID-19 K-ADAPTER & Knowledge-enhanced models improve detection on LIAR & KnowBert-W+W & on LIAR: 28.95\% & Mixed results on COVID-19, computational cost \\ \hline
[79] & BERT, LSTM, Gradient Boosted Tree & NewsFN data & Fine-tuned BERT model performs well with minimal text pre-processing & Accuracy: 97.021\% & Limited comparative context \\ \hline
[80] & Logistic regression, Decision tree & GossipCop++, PolitiFact++ & Mitigation strategy improves detection accuracy for human and LLM-generated news & F1 score (PolitiFact++): 0.83 & Bias in existing detectors \\ \hline
[8] & FactAgent & PolitiFact, GossipCop, Snopes & Transparent, structured workflow without training process & F1 (PolitiFact): 0.88 & Initial implementation without extensive real-world validation \\ \hline
[69] & ChatGPT, Google Gemini & LIAR & High performance in real-world applications & AUC-ROC: High (exact value not specified) value not specified & Needs ongoing refinement for optimal performance \\ \hline
[81] & Neural networks with explainable framework & Two real-world benchmarks & Provides high-quality justifications & Outperforms SOTA baselines (exact values not specified) & Dependent on the quality of crowd-sourced data \\ \hline
[82] & RoBERTa & Fake News Challenge Stage 1 (FNC-I) & State-of-the-art performance in stance detection & Weighted accuracy: 90.01\% & Limited to stance detection \\ \hline
[20] & RoBERTa, BERT, ELECTRA, PGLBERT, DeBERTa & GossipCop++, PolitiFact++ & Adapts to LLM era, robust performance on mixed news & Accuracy (GossipCop++): 98.22 (ALBERT) & Detector bias against machine-generated texts \\ \hline
[83] & M-DRUM, GPT-4, LLaVA & Human-centric and Fact-related Fake News (HFFN) & Multi-modal detection and reasoning & Outperforms SOTA models & Limited by the benchmark dataset \\ \hline
[84] & Llama model & 70,000 instructions dataset & Parameter-efficient fine-tuning & High accuracy (exact value not specified) & Hard to distinguish deepfake images \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of Studies on Fake News Detection.