This study adopts a corpus-based computational linguistic approach to measure individual differences (IDs) in visual word recognition. Word recognition has been a cardinal issue in the field of psycholinguistics. Previous studies examined the IDs by resorting to test-based or questionnaire-based measures. Those measures, however, confined the research within the scope where they can evaluate. To extend the research to approximate to IDs in real life, the present study undertakes the issue from the observations of experiment participants' daily-life lexical behaviors. Based on participants' Facebook posts, two types of personal lexical behaviors are computed, including the frequency index of personal word usage and personal word frequency. It is investigated that to what extent each of them accounts for participants' variances in Chinese word recognition. The data analyses are carried out by mixed-effects models, which can precisely estimate by-subject differences. Results showed that the effects of personal word frequency reached significance; participants responded themselves more rapidly when encountering more frequently used words. People with lower frequency indices of personal word usage had a lower accuracy rates than others, which was contrary to our prediction. Comparison and discussion of the results also reveal methodology issues that can provide noteworthy suggestions for future research on measuring personal lexical behaviors.