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Related Experiment Video

Updated: Feb 25, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

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A study on real-time low-quality content detection on Twitter from the users' perspective.

Weiling Chen1, Chai Kiat Yeo1, Chiew Tong Lau1

  • 1School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.

Plos One
|August 10, 2017
PubMed
Summary

This study introduces a real-time method for detecting low-quality content on social media, improving user experience. The approach achieved high accuracy in identifying and filtering undesirable tweets.

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Area of Science:

  • Computer Science
  • Social Media Analysis
  • Natural Language Processing

Background:

  • Existing detection methods for malicious content on Online Social Networks (OSN) overlook other low-quality content types impacting user experience.
  • There is a need for real-time detection of diverse low-quality content from the user's perspective on social media platforms.

Purpose of the Study:

  • To develop and evaluate a real-time system for detecting low-quality content on Twitter.
  • To improve the overall content browsing experience for users on social media.

Main Methods:

  • Utilized the Expectation Maximization (EM) algorithm for initial coarse classification of low-quality tweets into four categories.
  • Conducted a user survey to gather opinions and define low-quality content categories.
  • Identified and combined direct, indirect, and newly proposed features with word-level analysis and a keyword blacklist for detection.
  • Manually labeled a dataset of 100,000 tweets and employed a random forest classifier for real-time detection.

Main Results:

  • Achieved a high detection accuracy of 0.9711.
  • Obtained a good F1 score of 0.8379.
  • Demonstrated effective real-time performance in detecting low-quality content on Twitter.

Conclusions:

  • The proposed method effectively detects low-quality content in real time, significantly enhancing the user experience on social media.
  • The combination of feature engineering, user-centric definitions, and machine learning provides a robust solution for content quality management.