M a NLP @ SMM 4 H ’22: BERT for Classification of T witter Posts (2022)

Abstract: The reported work is our straightforward approach for the shared task “Classification of tweets self-reporting age” organized by the “Social Media Mining for Health Applications (SMM4H)” workshop. This literature describes the approach that was used to build a binary classification system, that classifies the tweets related to birthday posts into two classes namely, exact age(positive class) and non-exact age(negative class). We made two submissions with variations in the preprocessing of text which yielded F1 scores of 0.80 and 0.81 when evaluated by the organizers.

## introduction determining the exact age of an individual is crucial to increasing the use of social media data for research purposes. in this contemporary world, adolescents use social media to the extent that it can have some very severe effects on their overall well-being if not monitored. hence, a few applications like twitter have set up some age restrictions for the well-being of an individual. they automatically detect the age of an individual trying to protect them from viewing unnecessary and harmful content. in this work, we determine the exact age of an individual based on their tweets on twitter. this helps in validating if a particular user has faked their age or not. one of the major challenges we faced while working with the data is that some of them could tweet about their friend's or relative's birthday which was getting misclassified. we have used bert model which helps in the binary classification of our data. while developing our system for this task, we have discovered bert that outperforms traditional training models. ## evaluation in the validation phase, our model produced satisfactory results with about 90%. in the test data, 10,000 tweets were provided by the organizers. we have first pre-processed with pronouns and then removed pronouns in the next round of preprocessing. after evaluation, our models generated an f1-score of 0.80 and 0.81. recall, and f1-score as provided by the organizers. model 1 shows scores of pre-processing with pronouns and model 2 shows scores of pre-processing with pronouns removed. ## conclusion we discussed our approach to fine-tuning our bert model on task 4 of the 2022 social media mining for health applications shared task. as we observe from the results, the given training data was inadequate to train on a bert model. there was an imbalance in the number of positives and negatives given in our dataset(refer to figure 1 ). an interesting observation drawn from this work is that bert models rely on huge and balanced datasets for learning patterns. future work might consider collecting more data points for training, fine-tuning our bert model, and applying other state-of-the-art methods like roberta. 0 1 5,966 2,834