Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/86664
Title: A study on real-time low-quality content detection on Twitter from the users’ perspective
Authors: Chen, Weiling
Yeo, Chai Kiat
Lau, Chiew Tong
Lee, Bu Sung
Keywords: Adult
Adolescent
Issue Date: 2017
Source: Chen, W., Yeo, C. K., Lau, C. T., & Lee, B. S. (2017). A study on real-time low-quality content detection on Twitter from the users’ perspective. PLOS ONE, 12(8), e0182487-.
Series/Report no.: PLoS ONE
Abstract: 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.
URI: https://hdl.handle.net/10356/86664
http://hdl.handle.net/10220/44184
DOI: http://dx.doi.org/10.1371/journal.pone.0182487
Rights: © 2017 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
metadata.item.grantfulltext: open
metadata.item.fulltext: With Fulltext
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