It can be difficult to identify bias in a text. Meanwhile, a large part of the Swedish population reads their news online and according to Statistics Sweden (SCB), only 1/3 verifies information they find false or questionable online (SCB, 2021). Although methods for identifying indicators of bias exists, there are currently no tools available for Swedish texts providing the same analysis to an end user. This study targeted the problem of identifying bias in news articles and designing a tool with this purpose by addressing the research goal: develop a tool that can assist users in identifying bias in news. This goal was approached by using Design Science Research (DSR) where experiments were used as a method to demonstrate and evaluate the developed artifact. DSR provided a framework for structuring the design work into different phases which made it a suitable choice for this study. The artifact was developed and different indicators of bias implemented as functions using dictionary analysis method and language model based classifiers. An evaluation was performed where the artifact got to analyse news, political and biased texts. The results suggests the artifact can identify the targeted indicators of bias although the political language function had poor accuracy. It was concluded that the artifact to some extent managed to solve the problem of identifying bias in text. However, evidence from previous research suggests the analysis could have been more accurate if metadata was included in the analysis. Furthermore, the generalizability of the study was limited as the data collections used were relatively narrow. It was proposed future studies continue to develop the different analysis functions for the indicators, primarily by using larger data sets.