## 3 Methodology  
To determine the related scope of the current work, we utilized the PRISMA 2020 guidelines [50]. These guidelines outline the systematic approach adopted for reviewing literature on the usage of LLMs for generating fake news, and fake profiles, and also the methods of detection based on LLMs. **Search Strategy:** The initial search was conducted across multiple academic sources, including Google Scholar and IEEE Xplore, but mainly focused on Scopus due to the better quality of related results and improved control during the filtering process. In Scopus, we used a set of search parameters that look for specific keywords in the titles, abstracts and keywords, and limit the search range to works published from 2018 onwards. We targeted published works in journals, conferences, and review papers. The query used for Scopus is presented below. ```
!TITLE-ABS-KEY((\"fakenews\"OR\"=misinformation\"or\"disinformation\")AND(\"llm\"OR\"=\"largelanguagemodels\"))ANDPUBYEAR>2018 ANDPUBYEAR<2025 AND(LIMIT-TO(DOCTYPE,\"re\"ORLIMIT-TO(DOCTYPE,\"ch\")ORLIMIT-TO(DOCTYPE,\"ar\"ORLIMIT-TO(DOCTYPE,\"cp\"))AND(LIMIT-TO(LANGUAGE,\"English\"))
```  
**Search Criteria:** To gather relevant works, we extended our search to include Google Scholar and IEEE Xplore. In addition to the initial keywords, we used composite phrases such as \"LLM in fake news generation\", \"fake profile detection using LLM\", and \"security challenges in detecting fake news with LLM\". We conducted several queries and manually validated the results to find more relevant papers, including pre-prints. By screening abstracts, assessing the credibility of sources, and prioritizing peer-reviewed articles, we selected works that address the various aspects of using LLMs for generating and detecting fake news and profiles. We also employed a snowballing technique, which involves examining the references of initially identified papers to discover additional relevant works. This method allowed us to uncover papers that might not have surfaced during our initial search. By exploring citations within these papers, we could track the development and interconnections of research topics over time. We also utilized forward snowballing, looking at newer papers that cited our core set of articles. Iterative refinement of our search strategies further helped us in this process. The methodology we followed in selecting papers for inclusion in the survey is depicted in Figure 1. The results of our literature review are detailed in the following sections. **Inclusion and Exclusion Criteria:** The search was restricted to English-language articles. We limited the search to works published in the years after 2018 and further filtered the papers using additional criteria. The inclusion criteria were as follows: peer-reviewed articles focusing on using large language models (LLMs) to generate fake news, detect fake profiles, and address security challenges associated with these activities. We also considered papers that discuss various techniques for improving the effectiveness and accuracy of LLMs in these contexts. Exclusion criteria included non-peer-reviewed articles and publications not specific to LLMs or their application in fake news and profile detection. In total, 117 publications met the inclusion criteria. The distribution of these publications over the years is presented in Figure 2, while Figure 3 shows the types of the accepted publications. **Quality Assessment:** The quality of the selected papers was meticulously evaluated based on several criteria: their relevance to the research topics, methodological rigor, the impact factor of the journals in which they were published, and their citation counts. This comprehensive evaluation ensured that only the most credible and significant studies were included in our survey, thereby enhancing the reliability and depth of our findings.