Sentence-Level BERT and Multi-Task Learning of Age and Gender in Social Media
Table 1: Distribution of age and gender classes in our data splits
['[BOLD] Data split', '[BOLD] Under 25  [BOLD] Female', '[BOLD] Under 25  [BOLD] Male', '[BOLD] 25 until 34  [BOLD] Female', '[BOLD] 25 until 34  [BOLD] Male', '[BOLD] 35 and up  [BOLD] Female', '[BOLD] 35 and up  [BOLD] Male', '[BOLD] #tweets']
We make use of Arap-Tweet zaghouani2018arap, which we will refer to as Arab-Tweet. Arab-tweet is a dataset of tweets covering 11 Arabic regions from 17 different countries. For each region, data from 100 Twitter users were crawled. Users needed to have posted at least 2,000 and were selected based on an initial list of seed words characteristic of each region. The seed list included words such as ¡برشة¿ /barsha/ ‘many’ for Tunisian Arabic and ¡وايد¿ /wayed/ ‘many’ for Gulf Arabic. zaghouani2018arap employed human annotators to verify that users do belong to each respective region. Annotators also assigned gender labels from the set male, female and age group labels from the set under-25, 25-to34, above-35 at the user-level, which in turn is assigned at tweet level. Tweets with less than 3 words and re-tweets were removed. Refer to zaghouani2018arap for details about how annotation was carried out. We note that zaghouani2018arap do not report classification models exploiting the data. We shuffle the dataset and split it into 80% training (TRAIN), 10% development (DEV), and 10% test (TEST). For pre-processing, we reduce 2 or more consecutive repetitions of the same character into only 2 and remove diacritics. We have two baselines, (1) (maj-base), is the majority class in our TRAIN and (2) a small unidirectional GRU (small-GRU) with a single 500-units hidden layer. We train the small GRU with the same batch size = 8 and dropout =0.5 as our bigger BiGRUs We report our results in accuracy.