Twitter trends: A ranking algorithm analysis on real time data

Abstract Social media has recently become popular due to its vast applications. The common people all over the world uses its diverse channels to express personal views, experiences and opinions regarding diverse topics. Social media has revolutionized the way people interact and communicate with each other and overall, it has changed the methods and approaches in about all the aspects of life such as social issue, business, education, health, etc. Thus, sales and marketing departments of multinational industries are focusing on social media trends to analyze current trends and predict future trends by analyzing user generated content on Facebook, Flickr, Twitter, etc. However, the prediction process becomes challenging as the multiplicity of factors affect the popular elements in the social media content. This research paper aims to work on Twitter trend analysis and proposes a trend detection process over streams of tweets. The proposed approach detects the trending topics of the real-time Twitter trends along with ranking the top terms and hashtags. The paper further discusses the motivation for trend prediction over the social media; In addition to exploratory data analysis, the research paper explores the Term Frequency-Inverse Document Frequency (Tf-IDF), Combined Component Approach (CCA) and Biterm Topic Model (BTM) approaches for finding the topics and terms within given topics. In modern competitive world, this research provides investors, advertisers, industries and all the stakeholders. A detailed and comprehensive data analysis which may help them to focus their investment, area of work, marketing, and product.