• Ashutosh Game
• Kanishk Vashisth
• Lavesh Mittal
• Sudeep Mutha

Detection techniques of malicious content such as spam and phishing on Online Social Net works (OSN) are common with little attention paid to other types of low-quality content which actually impacts users’ content browsing experience most. The aim of our work is to detect low-quality content from the users’ perspective in real time. To define low-quality con tent comprehensibly, Expectation Maximization (EM) algorithm is first used to coarsely clas sify low-quality tweets into four categories. Based on this preliminary study, a survey is carefully designed to gather users’ opinions on different categories of low- quality content. Both direct and indirect features including newly proposed features are identified to charac terize all types of low-quality content. We then further combine word level analysis with the identified features and build a keyword blacklist dictionary to improve the detection perfor mance. We manually label an extensive Twitter dataset of 100,000 tweets and perform low quality content detection in real time based on the characterized significant features and word level analysis. The results of our research show that our method has a high accuracy of 0.9711 and a good F1 of 0.8379 based on a random forest classifier with real time perfor mance in the detection of low-quality content in tweets. Our work therefore achieves a posi tive impact in improving user experience in browsing social media content

"A study on real-time low-quality content detection on Twitter from the users’ perspective", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 5, page no.f90-f103, May-2021, Available :http://www.jetir.org/papers/JETIR2105675.pdf

"A study on real-time low-quality content detection on Twitter from the users’ perspective", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 5, page no. ppf90-f103, May-2021, Available at : http://www.jetir.org/papers/JETIR2105675.pdf