Besides these manual approaches, several (semi-)automated approaches have been developed by social scientists and computer scientists. These approaches identify differences in news coverage, which potentially resulted from media bias, by analyzing the text and meta data, such as author and publishing date. For instance, NewsCube is a news aggregator that extracts key phrases that describe a topic differently. Other approaches make use of text- and meta-data, e.g., matrix-based news aggregation spans a matrix over two dimensions, such as "publisher countries" (in which articles have been published) and "mentioned countries" (on which country an article reports). As a result, each cell contains only articles that have been published in one country and that report on another country. Particularly in international news topics, matrix-based news aggregation helps to reveal differences in media coverage between the involved countries. Attempts have also been made to utilize machine-learning to analyze the bias of text.