The aim of this study was to develop a methodology to link mortality data from Internet sources with administrative data from electronic health records and to assess the performance of different record linkage methods. We extracted the electronic health records of all adult patients hospitalized at Rennes comprehensive cancer center between January 1, 2010 and December 31, 2015 and separated them in two groups (training and test set). We also extracted all available online obituaries from the most exhaustive French funeral home website using web scraping techniques. We used and evaluated three different algorithms (deterministic, approximate deterministic and probabilistic) to link the patients' records with online obituaries. We optimized the algorithms using the training set and then evaluated them in the test set. The overall precision was between 98 and 100%. The three classification algorithms performed better for men than women. The probabilistic classification decreased the number of manual reviews, but slightly increased the number of false negatives. To address the problem of long delays in the publication or sharing of mortality data, online obituary data could be considered for real-time surveillance of mortality in patients with cancer because they are easily available and time-efficient.