We propose an algorithmic approach for ranking of d iffering textual descriptions (accounts) of the sam e event or story according to their likeliness to bes t describe the source. Application domains include the ranking of eyewitness reports, historical accounts, and news reports. For this, we developed the conce pt of “semantic centrality” as a measure of how centra l a text is among a collection of texts, in terms o f its semantic overlap or similarity with all other texts . This measure is based on natural language process ing theory, as well as graph theory. Using three different collections of humanly genera ted texts (gathered through a recall task, “Chinese Whispers” task, and real-world news reports), we evaluated the proposed method for algorithmic ranking of textual accounts by their trustworthiness to des cribe source events. In one experiment algorithmic ranking is compared to human ranking. Results indica te that semantic centrality as a measure for trustworthiness of textual accounts is promising and deserves further research attention.