Twitter has been a prominent social media platform for mining population-level health data and accurate clustering of health-related tweets into topics is important for extracting relevant health insights. In this work, we propose deep convolutional autoencoders for learning compact representations of health-related tweets, further to be employed in clustering. We compare our method to several conventional tweet representation methods including bag-of-words, term frequency-inverse document frequency, Latent Dirichlet Allocation and Non-negative Matrix Factorization with 3 different clustering algorithms. Our results show that the clustering performance using proposed representation learning scheme significantly outperforms that of conventional methods for all experiments of different number of clusters. In addition, we propose a constraint on the learned representations during the neural network training in order to further enhance the clustering performance. All in all, this study introduces utilization of deep neural network-based architectures, i.e., deep convolutional autoencoders, for learning informative representations of health-related tweets.
In contrary to abovementioned feature extraction methods which are not specific to representation of tweets but rather generic in natural language processing, various works propose custom feature extraction methods for certain health-related information retrieval tasks from Twitter. For instance, engineered sentiment analysis features to discover latent infectious diseases from Twitter @cite_7 . In order to track public health condition trends from Twitter, specific features are proposed by Parker at al. employing Wikipedia article index, i.e., treating the retrieval of medically-related Wikipedia articles as an indicator of a health-related condition @cite_1 . Custom user similarity features calculated from tweets were also proposed for building a framework for recommending health-related topics @cite_27 .
@cite_27 @cite_1 @cite_7
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Traditional public health surveillance requires regular clinical reports and considerable effort by health professionals to analyze data. Therefore, a low cost alternative is of great practical use. As a platform used by over 500 million users worldwide to publish their ideas about many topics, including health conditions, Twitter provides researchers the freshest source of public health conditions on a global scale. We propose a framework for tracking public health condition trends via Twitter. The basic idea is to use frequent term sets from highly purified health-related tweets as queries into a Wikipedia article index -- treating the retrieval of medically-related articles as an indicator of a health-related condition. By observing fluctuations in frequent term sets and in turn medically-related articles over a series of time slices of tweets, we detect shifts in public health conditions and concerns over time. Compared to existing approaches, our framework provides a general a priori identification of emerging public health conditions rather than a specific illness (e.g., influenza) as is commonly done. Recent work in machine learning and natural language processing has studied the health content of tweets and demonstrated the potential for extracting useful public health information from their aggregation. This article examines the types of health topics discussed on Twitter, and how tweets can both augment existing public health capabilities and enable new ones. The author also discusses key challenges that researchers must address to deliver high-quality tools to the public health community. A microblogging service like Twitter continues to surge in importance as a means of sharing information in social networks. In the medical domain, several works have shown the potential of detecting public health events (i.e., infectious disease outbreaks) using Twitter messages or tweets. Given its real-time nature, Twitter can enhance early outbreak warning for public health authorities in order that a rapid response can take place. Most of previous works on detecting outbreaks in Twitter simply analyze tweets matched disease names and or locations of interests. However, the effectiveness of such method is limited for two main reasons. First, disease names are highly ambiguous, i.e., referring slangs or non health-related contexts. Second, the characteristics of infectious diseases are highly dynamic in time and place, namely, strongly time-dependent and vary greatly among different regions. In this paper, we propose to analyze the temporal diversity of tweets during the known periods of real-world outbreaks in order to gain insight into a temporary focus on specific events. More precisely, our objective is to understand whether the temporal diversity of tweets can be used as indicators of outbreak events, and to which extent. We employ an efficient algorithm based on sampling to compute the diversity statistics of tweets at particular time. To this end, we conduct experiments by correlating temporal diversity with the estimated event magnitude of 14 real-world outbreak events manually created as ground truth. Our analysis shows that correlation results are diverse among different outbreaks, which can reflect the characteristics (severity and duration) of outbreaks.