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Detecting when a title is put in an incorrect data category can be of interest for commercial digital services, such as streaming platforms, since they group movies by genre. Another example of a beneficiary is price comparison services, which categorises offers by their respective product. In order to find data points that are significantly different from the majority (outliers), outlier detection can be applied. A title in the wrong category is an example of an outlier. Outlier detection algorithms may require a metric that quantify nonsimilarity between two points. Text similarity functions can provide such a metric when comparing text data. The question therefore arises, "Which text similarity function is best suited for detecting incorrect titles in practical environments such as commercial digital services?" In this thesis, different text similarity functions are evaluated when set to detect outlying (incorrect) product titles, with both efficiency and effectiveness taken into consideration. Results show that the variance in performance between functions generally is small, with a few exceptions. The overall top performer is Sørensen-Dice, a function that divides the number of common words with the total amount of words found in both strings. While the function is efficient in the sense that it identifies most outliers in a practical time-frame, it is not likely to find all of them and is therefore deemed to not be effective enough to by applied in practical use. Therefore it might be better applied as part of a larger system, or in combination with manual analysis.