Detecting Tweets Reporting Birth Defect Pregnancy Outcome Using Two-View CNN RNN Based Architecture (2020)

Abstract: This research work addresses a new multi-class classification task (fifth task) provided at the fifth Social Media Mining for Health Applications (SMM4H) workshop. This automatic tweet classification task involves distinguishing three classes of tweets that mention birth defects. We propose a novel two view based CNN-BiGRU based architectures for this task. Experimental evaluation of our proposed architecture over the validation set gives encouraging results as it improves by approximately 7% over our single view model for the fifth task. Code of our proposed framework is made available on Github

## introduction twitter is a micro-blogging system, which allows its users to publish tweets of up to 280 characters in length to tell others what they are doing, what they are thinking, or what is happening around them. over the past few years twitter has become very popular, for every second, on average, around 6,000 tweets are tweeted on twitter. twitter is also considered as "it's what's happening". social media can be considered as an enormous corpus since last decade many researcher started exploring different tasks such as sentiment analysis @xcite , named entity recognition @xcite and disambiguation @xcite using this data. more recently twitter data is used to identify and study a small cohort of twitter users whose pregnancies with birth defect outcomes @xcite . due to unique characteristics of social media there are few challenges associated with using social media data for health care research @xcite , including informal texts, colloquial expressions and misspellings of clinical concepts, noisy text, data sparsity, ambiguity, and multilingual posts. ## proposed framework hashtags are one the most important aspects of twitter. hashtag lets users to apply dynamic, usergenerated tagging that helps other users easily find posts on twitter with a specific theme or content. users create and use hashtags by placing a hash symbol in front of a word or unspaced phrase in a tweet. the hashtag may contain symbols, digits and letters(capitalized & uncapitalized). many tweets contain at least one hashtag, and often they provide context which motivated us to incorporate them in our framework. ## conclusion our main goal is to show the potentiality of our two view framework which employs semantic views of both tweet and hashtag. our approach work better when the percentage of tweets having atleast one