Social media plays a major part in everyone’s lives in which it aids the people to interact and connect with them. Social media has created large communities to influence what everyone reads, does, or even thinks. The influencing mechanism of Twitter trends is exploited using bots and spam accounts where several tweets on a topic are boosted into trends based in an artificial way. The fake trends are common in the social media domain which is discovered through promoting gambling, information phishing, disinformation campaigns, political slogans, and hate speech. The manipulation of Twitter trends has great implications because Twitter trends get more attention. The broader media channels cover trends through utilizing as a stand-in for what people are talking about. Unfortunately, this stand-in is manipulated, clouding the public’s perception of the topics being discussed. It is important to detect these fake trends and ensure that the public is not exposed to fake trends. The proposed fake trend detection system classifies the Twitter accounts into bot and human accounts. TwiBot-20 benchmark dataset is trained to classify the bot accounts using Random Forest model which achieves 94% accuracy. The metadata features and influence entropies are extracted from bot accounts. The influence entropy is measured by constructing an influence graph using igraph and NetworkX through analyzing the retweet count and user mentions in each tweet of an account. The trending topic of the tweets is analyzed and the authenticity of the trend is categorized using one-dimensional clustering. A web application is developed to facilitate the proposed fake trend detection system for the users without technical knowledge.