## 2 Overview of Fake News and Fake Profiles
### Detection Methods  
Internet and social media made access to news information much easier and more comfortable. On social media platforms, every user from around the world can publish and disseminate any kind of statement, or set of statements, to spread fake news to achieve different goals, which may be fraudulent or illegal [3]. Aside from individuals spreading  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref.** & **Models Used** & **Dataset/Data Type** & **Main Contribution** & **Best Performance Value** & **Limitation** \\ \hline
[10] & GPT-3.5, GPT-4 disinformation & LLM-generated of LLMs in detecting LLM-generated disinformation & Explores the efficacy of ILMs in detecting LLM-generated disinformation & Misclassification rate: GPT-4 (all scale)—4.7\% & Existing detection techniques struggle with LLM-generated disinformation; relies heavily on advanced prompts \\ \hline
[9] & ChatGPT, OPT-IML-Max, Vicuna, GPT-4, etc. & News articles generated by LLMs of LLMs of detection models & Evaluates disinformation generation capabilities of LLMs and effectiveness of detection models & ChatGPT: 0.97 AUC, 0.82 Macro F1-score & High variability in model performance; detection models may not generalize well across different LLMs \\ \hline
[6] & BERT, RoBERTa, Grover, DualEmo, Llama2-7b, etc. & Fake news detection & Proposes VLPrompt for generating fake news and evaluates different prompting strategies & Llama2-7b + LoRA: ACC 0.971, F1 0.955, PRC 0.986, RCL 0.927 & Limited exploration of other prompting strategies; high computational cost for fine-tuning LLMs \\ \hline
[20] & RoBERTa, BERT, ELECTRA, ALBERT, DeBERTa & GossipCop++ and PolitiFact++ datasets & Examines the performance of detectors on different types of news (real, fake, machine-generated) & Large RoBERTa: 99.51\% accuracy with 100\% machine-generated texts; requires balancing dataset composition for optimal training \\ \hline
[14] & Transformer models, classical ML models & Text, audio, video datasets & Proposes a taxonomy for fake news detection and evaluates various feature representation and classification methods & Context-dependent models with transformers perform best & Optimal feature extraction techniques vary with dataset characteristics; limited exploration of audio and video data \\ \hline
[8] & FactAgent utilizing pre-trained LLMs & News claims & Introduces FactAgent for structured workflow in fake news detection & N/A & Dependent on structured workflow and domain-specific tools; does not involve model training \\ \hline
[17] & ChatGPT, various LLMs & Human-written and LLM-generated misinformation compared to human-written misinformation & Investigates the detection difficulty of LLM-generated misinformation compared to human-written misinformation & LLM-generated misinformation harder to detect requires extensive empirical validation across more models \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of key studies on Fake News Detectionfake news, some sources are considered to be authentic and are popular sources for informational services, such as Wikipedia, which are also prone to false information or fake news [21]. Mass media may also manipulate the information in different ways to achieve their goals. As the sources of spreading fake news are too many, the detection of fake news is essential. One important detection mechanism is the fact-checking sites, but unfortunately, they are insufficient to combat the amount of disinformation disseminated [22]. Fake news is increasingly sophisticated, adopting complex characteristics that differ between different types of social networks. Therefore, it is essential to develop detection mechanisms using artificial intelligence or machine learning algorithms. The paragraphs that follow present the two main fake news detection and mitigation types: (i) Rule-based Approaches and (ii) Human Fact-Checking and Moderation and the key solutions of each type.