Establishing the authorship of online texts is a fundamental issue to combat several cybercrimes. Unfortunately, some platforms limit the length of the text , making the challenge harder. Here, we aim at identifying the author of Twitter messages limited to 140 characters. We evaluate popular stylometric features, widely used in traditional literary analysis, which capture the writing style at different levels (character, word, and sentence) . We use a public database of 93 users, containing 1142 to 3209 Tweets per user. We also evaluate the influence of the number of Tweets per user for enrolment and testing. 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 .
Establishing authorship of online texts is fundamental to combat cybercrimes. Unfortunately, text length is limited on some platforms , making the challenge harder. We aim at identifying the authorship of Twitter messages limited to 140 characters. We evaluate popular stylometric features, widely used in literary analysis, and specific Twitter features like URLs, hashtags, replies or quotes . We use two databases with 93 and 3957 authors, respectively. We test varying sized author sets and varying amounts of training/test texts per author. Performance is further improved by feature combination via automatic selection. With a large number of training Tweets (>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 .
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some platforms limit the length of the text
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which capture the writing style at different levels (character, word, and sentence)
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a public database of
two databases with
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users, containing 1142 to 3209 Tweets per user. We also evaluate the influence of the number of Tweets per user for enrolment and testing. If the amount is sufficient
and 3957 authors, respectively. We test varying sized author sets and varying amounts of training/test texts per author. Performance is further improved by feature combination via automatic selection. With a large number of training Tweets
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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
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