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 naïve 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.

A survey of uncover misleading and cyberbullying on social media for public health

“…Karajeh et al [ 131 ], classified over 7000 heart attack tweets as informative and non-informative. The dataset consists of 11% informative tweets and 89% non-informative tweets.…”

“…As a result, this will affect our ability to achieve a large enough and representative sample of examples from the minority class negatively. Karajeh et al [ 131 ], faced this problem by using an unbalanced dataset that contains a higher percentage of non-informational tweets than informational tweets.…”

“…Misleading information can be found anywhere, but the researchers tend to use Twitter data because the tweets are considered open source and their use are legal, while other platforms such as Facebook and LinkedIn are considered private and illegal content. This problem has faced a lot of researchers such as Karajeh et al [ 131 ] since Twitter was the only platform used in this work.…”

Application of Machine Learning Techniques to Detect the Children with Autism Spectrum Disorder

“…In decision fusion, the features of each modality are independently classified, and the classification results of each modality are identified as subdecisions. To fuse the subdecisions of different modalities and obtain a comprehensive classification result, we adopted a naive Bayes algorithm based on attribute weighting to calculate the weight of the subdecisions [27,28]. In accordance with the Bayes algorithm, the probability of a child being identified as ASD can be defined as follows: can be expressed as the product of the probabilities of each attribute.…”

Wireless Internet, Multimedia, and Artificial Intelligence: New Applications and Infrastructures

“…A completely different but important domain is that of the work proposed in [4], where the authors propose a method for extracting implicit answers from large tweet collections, with the aim of collecting tweets related to heart attacks, which contain useful information. The contribution in [5], which is focused on network security, first provides an extensive overview of the scenario, then proposes a novel Local Feature Engineering (LFE) approach, which is based on a data pre-processing strategy, to face some wellknown problems that affect state-of-the-art solutions.…”