KCE DALab-APDA@FIRE2019: Author profiling and deception detection in Arabic using weighted embedding

Abstract

This paper explaining the work submitted on Author Pro- filing and Deception Detection in Arabic Tweets shared task organized at the Forum for Information Retrieval Evaluation (FIRE) 2019. The first task Author profiling illustrates identifying the categories of au- thors based on the Arabic tweets. In the second task, the aim is to Detect deception in Arabic for two genres such as Twitter and News. Deception detection means that the automatic way of identifying false messages in the text content on social network or news. For each task, we have submitted three different systems. For submission 1, we have used the Term Frequency and Inverse Document Frequency (TFIDF) based Support Vector Machine classification and in submission 2, we have used fastText classifier. For submission 3, we have proposed a low dimensional weighted document embedding (TFIDF + Word embedding) with SVM classification. We have attained second place in the Deception detection and third in Author profiling. The performance difference between the top team results and the submitted runs are only 3.34% for Author pro- filing and 1.16% for Deception detection. � Copyright 2019 for this paper by its authors.