UniNE at PAN-CLEF 2019: Bots and Gender Task

When participating in the “bots and gender” subtask (both in English and Spanish), our aim is to automatically detect different text sources (sequence of tweets sent by a bot or a human). When a text is identified as being sent by humans, the system must determine the author’s gender (author profiling). To solve these questions, we focus on a simple classifier (k-NN, k = 5) usually able to produce a correct answer but not in an efficient way. Thus, we apply a feature selection procedure to reduce the number of terms (around 200 to 500). We also propose to apply a Zeta model to reduce the number of decisions taken by the kNN classifier. In this case, we focus on terms used in one category and ignored or used rarely by the second. In addition, the Type-Token Ratio of the lexical density (LD) presents some merit to discriminate between tweets sent by a bot (TTR < 0.2, LD ≥ 0.8) or humans (TTR ≥ 0.2, LD < 0.8).