ULD - NUIG at Social Media Mining for Health Applications (# SMM 4 H ) Shared Task 2021 (2021)

Abstract: Social media platforms such as Twitter and Facebook have been utilised for various research studies, from the cohort-level discussion to community-driven approaches to address the challenges in utilizing social media data for health, clinical and biomedical information. Detection of medical jargon’s, named entity recognition, multi-word expression becomes the primary, fundamental steps in solving those challenges. In this paper, we enumerate the ULD-NUIG team’s system, designed as part of Social Media Mining for Health Applications (#SMM4H) Shared Task 2021. The team conducted a series of experiments to explore the challenges of task 6 and task 5. The submitted systems achieve F-1 0.84 and 0.53 score for task 6 and 5 respectively.

## introduction in recent decades, social media has proved to be one of the greatest sources of information exchange. when the world was overtaken by the covid-19 outbreak, social media became the greatest platform for the general public to exchange different information about the pandemic. with the widespread digitization of behavioural and medical data, the emergence of social media, and the internet's infrastructure of large-scale knowledge storage and distribution, there has been a breakthrough in our ability to identify human social interactions, behavioural patterns, cognitive processes and their relationships with healthcare. at the same time, it has also induced a different level of challenges in the natural language processing field such as detection of medical jargons, named entity recognition, multi-word expressions. furthermore, the informal nature of tweets and short length, which often contain non-standard grammar, frequent misspellings, many contractions, extensive slang, and use of emojis/emoticons to express emotion exacerbate the challenges @xcite . the scenario becomes even more complicated since social media covers very large populations and the geographical location. despite several issues, social media data has been used to monitor human health and disease (the recent pandemic outburst) all over the world. many promising methodologies are being developed. in this paper, we describe two different systems trained on the data provided by the social media mining for health applications (#smm4h) shared task 2021 organisers @xcite namely task 5: classification of tweets self-reporting potential cases of covid-19 and task 6: classification of covid19 tweets containing symptoms (see section 2). we conduct a series of experiments to explore the challenges of both the tasks. we use multilingual pre-trained bert model for task 5 and generative morphemes with attention (genma) model for task 6 (see section 3). ## evaluation we use shared task organizers' validation and test data-set for evaluation. the standard evaluation metrics, precision, recall and f-1 score, were used for automatic evaluation. it gives a quantitative picture of particular differences across different systems, especially with reference to evaluation scores. on the validation data-set, task 5 and 6 systems' f-1 score were 0.89 and 0.95 respectively. while on the test data-set, f-1 score were 0.53 and 0.84 respectively. the detailed results are given in ## summing up the entire series of experiments gave us various types of insights to deal with social media data for mining medical information. we observed that pre-trained language models such as bert do not provide good results for extraction of medical information for covid-19. one of the reasons that we could think is that these models are trained on various domain data set but it is very unlikely that these data-sets contain information regarding covid-19. on the other hand our characters based attention model outperform the bert model. in future, we would like to explore more models with word features, specific linguistic features in order to deeply understand the characteristics of social media mining for clinical information.