التفاصيل البيبلوغرافية
| العنوان: |
A study on real-time low-quality content detection on Twitter from the users’ perspective. |
| المؤلفون: |
Chen, Weiling, Yeo, Chai Kiat, Lau, Chiew Tong, Lee, Bu Sung |
| المصدر: |
PLoS ONE; 8/9/2017, Vol. 12 Issue 8, p1-22, 22p |
| مصطلحات موضوعية: |
SOCIAL interaction, ONLINE social networks, EXPECTATION-maximization algorithms, WEB browsing |
| Reviews & Products: |
TWITTER (Web resource) |
| مستخلص: |
Detection techniques of malicious content such as spam and phishing on Online Social Networks (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 content comprehensibly, Expectation Maximization (EM) algorithm is first used to coarsely classify 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 characterize 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 performance. 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 performance in the detection of low-quality content in tweets. Our work therefore achieves a positive impact in improving user experience in browsing social media content. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
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