We trained an SVM model on tweets to perform user profiling, in terms of gender and age, on non-Twitter social media data. The system exploits features that we deemed appropriate to profile authors on social media, and that do not characterise too closely the specific usage of Twitter. Our system works on English, Dutch, and Spanish data without any language-specific tuning of features or parameters. Results on the cross-validated training set seem to indicate that features contribute rather equally to the model’s performance.
|Title of host publication||Working Notes of CLEF 2016|
|Editors||Krisztian Balog, Linda Cappellato, Nicola Ferro, Craig Macdonald|
|Publication status||Published - Sep-2016|
|Event||Conference and Labs of the Evaluation forum (CLEF) - Evora, Portugal|
Duration: 5-Sep-2016 → 8-Sep-2016
|Conference||Conference and Labs of the Evaluation forum (CLEF)|
|Period||05/09/2016 → 08/09/2016|