Before revision:  If the amount is sufficient (>500), a Rank 1 of 97-99\%is achieved. If data is scarce (e.g. 20 Tweetsfor testing), the Rank 1 with the best individual feature method ranges from 54.9\% (100 Tweetsfor enrolment)to 70.6\% (1000 Tweets). By combining the available features, a substantial improvement is observed, reaching a Rank 1 of 70\% when using 100 Tweets for enrolment and only 20 for testing. With a bigger hit list size, accuracy of the latter case increases to 86.4\% (Rank 5) or 95\% (Rank 20). This demonstrates the feasibility of identifying writers of digital texts, even with few data available .
After revision:  If the amount is sufficient (>500), a good accuracy (Rank-5>80\%) is achievable with only a few dozens of test Tweets, even with several thousands of authors. With smaller sample sizes (10-20 training Tweets), the search space can be diminished by 9-15\% while keeping a high chance that the correct author is retrieved among the candidates. In such cases, automatic attribution can provide significant time savings to experts in suspect search. For completeness, we report verification results. With few training/test Tweets, the EER is above 20-25\%, which is reduced to < 15\% if hundreds of training Tweets are available. We also quantify the computational complexity and time permanence of the employed features .