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In addition, the recognition is of course also influenced by our particular selection of authors, as we will see shortly. With these main choices, we performed a grid search for well-performing hyperparameters, with the following investigated values: These statistics are derived from the users profile information by way of some heuristics. As in our own experiment, this measurement is based on Twitter accounts where the user is known to be a human individual. From this material, we considered all tweets with a date stamp in and In all, there were about 23 million users present. Bigrams Two adjacent tokens. Slightly more information seems to be coming from content The conclusion is not so much, however, that humans are also not perfect at guessing age on the basis of language use, but rather that there is a distinction between the biological and the social identity of authors, and language use is more likely to represent the social one cf. An alternative hypothesis was that Sargentini does not write her own tweets, but assigns this task to a male press spokesperson. Unigrams are mostly closely mirrored by the character 5-grams, as could already be suspected from the content of these two feature types. In this paper, we start modestly, by attempting to derive just the gender of the authors 1 automatically, purely on the basis of the content of their tweets, using author profiling techniques. This corpus has been used extensively since. Starting with the systems, we see that SVR using original vectors consistently outperforms the other two. In this way, we derived a classification score for each author without the system having any direct or indirect access to the actual gender of the author. Normalized 5-gram About K features.
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