# Detection of Inappropriate Tweets Linked to Fake Accounts on Twitter
## 2 Related Work
[22], suggest a security architecture for Twitter while it is operating in catastrophe mode. The study found that when there is a natural catastrophe, people have a tendency to rely significantly on the usage of Twitter to communicate and organize in emergency situations. However, the research also found that the communication may be vulnerable to security issues. To defend themselves, they have proposed new security extensions that will provide the same level of security in the disaster mode as they do in the normal mode. Studies on the identification of the broken points that lead to security difficulties have demonstrated some great influence, despite the fact that Twitter's security concerns mostly center on the platform's general safety. One of these is the work conducted by [PERSON] and colleagues [23]. The research presented a method to evaluate the veracity of tweets pertaining to news by concentrating on contrasting several kinds of machine learning algorithms. In a similar fashion, [PERSON] and [PERSON] [24] provided a model for detecting phony Twitter accounts by utilizing a blacklisting strategy. In this model, an inspection was carried out by generating a blacklist using topical modeling and keyword extraction methods that were based on the content of tweets. In addition, [PERSON] and [PERSON] [25] devised a method for identifying bogus profiles on Twitter. The model was developed based on a collection of rules that can effectively classify false and actual profiles using machine learning classifiers. [PERSON] et al. [26] proposed a model for detecting fake news on Twitter by making use of a repetitive neural model and a variety of distinct semantic and stylistic features that fit the model. In this model, a group of features extract the timelines of Twitter news accounts by reading their posts in fragments, rather than treating each tweet independently. The research demonstrates the empirical benefits of modeling the latent stylistic characteristics of mixed false and true news using a large-scale sequential model of strong baselines. This study finds it appropriate to continue from there because the previously mentioned research literatures do not associate their detection technique with the detection of inappropriate tweets linked to bogus accounts on Twitter. Several recent studies have illuminated the urgency of addressing the issue of fake content in social media; in particular, [PERSON] [27] establishes the fact that bots increasingly tamper with political elections and economic discussions, tracing trends in detection strategies and key suggestions for how to win the fight against the menace, especially in relation to social media. Several other recent studies have also highlighted the urgency of dealing with the issue of bogus information on social media. Many studies highlighting the necessity of addressing the problem of bogus content on social media were published fairly recently. [PERSON] and [PERSON] [28] claim that spammers have taken notice of the rise in popularity of social media platforms such as Twitter and Facebook over the past decade. With more people using social media, more phony personas are being created. These fictitious characters engage in spamming and the dissemination of false material in an effort to sway public opinion. Real users can be safeguarded by identifying these impostors. The authors in this study used a feature-based method to spot phony accounts on social media. With these 24 indicators, bogus accounts were easily spotted. Results from these classification methods are triple checked. Analysis using the random forest model found a 97.9% success rate in identifying phony profiles. Spam activity has grown on Twitter, according to research by [PERSON] et al. [29]. In the case of Twitter spam, most studies have been conducted in a controlled environment and very few have used machine learning in real-world situations.