Spamord

Spamord is a term used in the field of spam detection to describe a class of representation techniques that encode textual content into ordinal features and apply sequence-aware classifiers to separate unsolicited messages from legitimate ones.

The word is a blend of "spam" and "ordinal" (or "order"), reflecting the method’s emphasis on preserving ordering information in text. It emerged in academic discussions during the 2010s as researchers sought alternatives to conventional bag-of-words representations for better capture of token order and frequency patterns.

In typical implementations, tokens are mapped to ordinal values based on criteria such as frequency ranks, positional information, or contextual scores, forming an ordered sequence rather than a simple bag of words. This sequence is then fed into machine learning models such as logistic regression, support vector machines, or neural networks, sometimes in combination with standard features.

Variants of spamord may emphasize different sources of order information, including spamord-frequency, which emphasizes global token frequency, or spamord-context, which incorporates local neighborhood ordering. Hybrid approaches combine ordinal sequences with traditional features like TF-IDF.

Applications include email spam filtering, SMS spam detection, and moderation of user-generated content on social platforms. While spamord can improve performance on datasets with strong ordering signals, it may require more careful preprocessing and can be sensitive to domain shifts or adversarial manipulation.

See also: Spam filtering; Text representation in natural language processing; Ordinal encoding; Machine learning for text classification.