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Twitter trends have enabled the speedy dissemination of information with the ability to affect public opinion. Unfortunately, fake trends are also generated by malicious users to mislead the public. In general, Twitter users are studied in depth to identify humans, bots, spam, and fake accounts. However, artificial intelligence algorithms are not developed for the identification of &#x2018;trend promoters&#x2019; generating fake trends. In this paper, we propose Push-To-Trend &#x2013; a novel framework to detect &#x2018;trend promoters&#x2019; in trending hashtags. For this purpose, first, we develop a dataset of TREP-21 containing 3,900 users labelled into two categories of &#x2018;trend promoters&#x2019; and &#x2018;normal users&#x2019;. In addition, we design four discerning features of number of total tweets, duplicate tweets, overlapping ngram, and peak-to-mean ratio for trend promoters classification. Moreover, we thoroughly examine the features used for spam and bot accounts classification to filter three efficacious features for trend promoters identification. Leveraging these seven features, Push-To-Trend achieves the accuracy of 0.97 for TREP-21. Furthermore, we leverage our framework to identify and analyze trend promoters from the Urdu tweets repository &#x201C;Anbar&#x201D; which consists of 106.9 million tweets and 1.69 million users. The analysis of 602 most frequent hashtags in Anbar reveals that 15.7&#x0025; of trend promoters generate 68.1&#x0025; of total tweets related to hashtags. To the best of our knowledge, this is the first attempt to design machine learning models for the automatic classification of trend promoters. As such, our framework is generic and adaptable for tweets posted in different natural languages as it utilizes language-independent features.

Push-to-Trend: A Novel Framework to Detect Trend Promoters in Trending Hashtags