The process of gathering useful information from online messages has increased as more and more people use the Internet and other online applications such as Facebook and Twitter to communicate with each other. One of the problems in processing online messages is the high number of noisy texts that exist in these messages. Few studies have shown that the noisy texts decreased the result of text mining activities. On the other hand, very few works have investigated on the patterns of noisy texts that are created by Malaysians. In this study, a common noisy terms list and an artificial abbreviations list were created using specific rules and were utilized to select candidates of correct words for a noisy term. Later, the correct term was selected based on a bi-gram words index. The experiments used online messages that were created by the Malaysians. The result shows that normalization of noisy texts using artificial abbreviations list compliments the use of common noisy texts list.