# A Classifier to Detect Informational vs. Non-Informational Heart Attack Tweets
###### Abstract  
Social media sites are considered one of the most important sources of data in many fields, such as health, education, and politics. While surveys provide explicit answers to specific questions, posts in social media have the same answers implicitly occurring in the text. This research aims to develop a method for extracting implicit answers from large tweet collections, and to demonstrate this method for an important concern: the problem of heart attacks. The approach is to collect tweets containing \"heart attack\" and then select from those the ones with useful information. Informational tweets are those which express real heart attack issues, e.g., \"Yesterday morning, my grandfather had a heart attack while he was walking around the garden.\" On the other hand, there are non-informational tweets such as \"Dropped my iPhone for the first time and almost had a heart attack.\" The starting point was to manually classify around 7000 tweets as either informational (11%) or non-informational (89%), thus yielding a labeled dataset to use in devising a machine learning classifier that can be applied to our large collection of over 20 million tweets. Tweets were cleaned and converted to a vector representation, suitable to be fed into different machine-learning algorithms: Deep neural networks, support vector machine (SVM), J48 decision tree and naive Bayes. Our experimentation aimed to find the best algorithm to use to build a high-quality classifier. This involved splitting the labeled dataset, with 2/3 used to train the classifier and 1/3 used for evaluation besides cross-validation methods. The deep neural network (DNN) classifier obtained the highest accuracy (95.2%). In addition, it obtained the highest F1-scores with (73.6%) and (97.4%) for informational and non-informational classes, respectively. machine learning; classification; support vector machine; deep neural networks; tweets; heart attack; health +
Footnote †: journal: _Future Internet_  
0000-0001-999  
health issues [5]. For example, Twitter is considered one of the largest resources used by people on a daily basis to post their updates and life events. While there are many social media networks nowadays, the most recent research has focused on Twitter for knowledge discovery data-mining, and tweets semantic analysis [6; 7] for the following reasons: First, Twitter has a limitation of 280 characters for each message, which enables quick posting of activities and updates. Consequently, this allows messages to be easily shared and forwarded to other users in the network. This feature causes information and news to rapidly spread over the Internet. The ease of access to people updates is the second reason which makes Twitter a well-known network for crawling and collecting datasets. While in most social media networks, mutual friendship is required to access other profiles, Twitter allows users to follow each other without this restriction [8]. The third reason is Twitter exploits the use of hashtags, which are labels used within social networks to make it easier for users to search for text with specific content or topic. This facilitates the search for tweets with specific subjects using hashtags [9]. Our research project is focusing on the heart attack field. While surveys provide explicit answers to specific questions, posted texts in social media have the same answers implicitly occurring in the text. Therefore, Twitter is used in our research project for extracting information regarding the heart attack problem. The estimated annual incidence of heart attacks in the United States is 720,000 new attacks [10]; hence different science disciplines should give a hand to those working in the medical field to study the heart attack problem. Computer science is one of them. Therefore, this research aims to construct a dataset of tweets with information related to the heart attack problem to help shed light on this issue. Useful information could be _when_ or _where_ the heart attack has occurred (Time or place).