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Reference-Based Spam Detection Using Longest Common Substring
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AI
DOI：10.1002/itl2.70179.png)
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With the rise of social media chat platforms, spam, particularly in the form of text-embedded images has become increasingly disruptive. This study proposes a lightweight, reference-based, unsupervised spam detection method that offers both computational efficiency and high adaptability. Unlike traditional supervised models, the approach leverages string similarity algorithms to detect phrases resembling user-defined spam references. Specifically targeting overlooked categories like festive wishes, this method allows users to customize spam detection based on contextual needs. By focusing on low-resource processing and dynamic adaptability, the framework effectively identifies clutter-inducing spam while (with 80% accuracy) remaining scalable for broader applications.
Keyword:
festive wishes spam
longest common substring
reference-based
spam detection
期刊
I
IF:
0.5
论文数:
179
被引数:
423
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