For example, Twitter can serve as a platform to help with the crisis management process by looking for specific hashtags. Twitter allows users to create a network of people to disseminate information and allow the mass communication of the information to a widespread audience (Boukes 2019). With the availability of the Internet and web-based information, platforms such as Twitter are widely used to support the distribution of information. These results show that using profile, content, user, and hybrid features for suspicious tweets detection helps build better prediction models. In most cases, the values are above 90% for different performance measures. The results showed that the used different sets of learning techniques have achieved a higher performance for the tweet spam classification. We performed a comprehensive evaluation of different techniques over the collected dataset and assessed the performance for accuracy, precision, recall, and f1-score measures. Further, we apply different machine learning, ensemble, and deep learning techniques to build the prediction models. ![]() Then, we create a set of rich features by extracting various features from the collected dataset. In the first step, we collect and label a large dataset from Twitter to create a spam detection corpus. In this work, we develop various models that utilize different features such as profile-based features, content-based features, and hybrid features to identify malicious content and classify it as spam or not-spam. Many previous works have used different strategies to overcome this challenge and combat spammer activities on Twitter. It poses the challenge to the researchers to identify malicious content and user profiles over Twitter such that timely action can be taken. © 2023 NYP Holdings, Inc.The wide popularity of Twitter as a medium of exchanging activities, entertainment, and information is attracted spammers to discover it as a stage to spam clients and spread misinformation. I think we should have the capacity to forgive people - whether a podcaster or a mayor - if they mess up. “I like to believe the best of people - especially if I’ve met and spent time with that person,” Yang tweeted early Sunday. After deleting his initial tweet on the Spotify host, Yang recast his support of Rogan in a series of follow-up messages. In 2019, Yang appeared on Rogan’s podcast during his upstart campaign for president. “Do I know black friends of Joe’s who would swear by him? Yes I do.” ![]() “I don’t think Joe Rogan is a racist - the man interacts with and works black people literally all of the time,” Yang tweeted Sunday before deleting it, according to The Hill. Yang, 47, on Sunday defended “The Joe Rogan Experience” host, 54, over widespread criticism he’s facing for anti-coronavirus vaccine remarks as well as for using racial slurs almost two dozen times in newly unearthed clips. UFC faces boycott calls after announcing tie-up with Bud Light reportedly worth $100Mįormer presidential candidate Andrew Yang stood up for Joe Rogan amid backlash over his past use of racial slurs, claiming the embattled Spotify podcast host is “not racist” before deleting his comments. ![]() ![]() Joe Rogan said he ‘poisoned’ himself eating too many sardines: Can arsenic in fish make you sick? Joe Rogan holds all the cards as his Spotify podcasting deal is set to expire Spotify’s CFO announces exit days after company lays off 17% of workforce
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