# Learning Word Embeddings with Chi-Square Weights for Healthcare Tweet Classification
## 1 Introduction  
More and more researchers have realized that Internet data could be a valuable and reliable source for tracking and extracting healthcare-related information. For example, in 2008, Google researchers found that they can \"forecast\" flu prevalence in real time based on search records [1]. Google later turned this research into one of their projects called Google Flu Trends (GFT) ([[https://en.wikipedia.org/wiki/Google_Flu_Trends](https://en.wikipedia.org/wiki/Google_Flu_Trends)]([https://en.wikipedia.org/wiki/Google_Flu_Trends](https://en.wikipedia.org/wiki/Google_Flu_Trends))). However, GFT later failed by missing the peak of the 2013 flu season by 140 percent [2]. One reason is the presence of too much noisy data [2]: people who search using the keyword \"flu\" might know very little about the symptoms of the flu. And some disease, whose symptoms are similar to the symptoms of the flu, is not actually the flu. The failure of GFT does not negate the value of the data but highlights the importance of classification of truly healthcare-related data from irrelevant and noisy data. Healthcare researchers desire to extract more healthcare information from information that people have shared online. Thus, we are more interested in tweets that talk about real disease symptoms as shown in Examples 4-6 which we name healthcare-related tweets, rather than those tweets that simply highlight healthcare information as seenin Examples 1-3 which we name healthcare-noise tweets. The classification task in the healthcare field is a challenging one since both healthcare-related tweets and healthcare-noise tweets might contain some keywords such as \"flu\" and \"health\" which makes basic filtering approaches unworkable. Below, we show examples of healthcare-noise tweets (Examples 1-3) versus the truly healthcare-related tweets (Examples 4-6). (The example tweets are all drawn from a published dataset by [PERSON] et al. [3].) Compared to the healthcare-related tweets, we found that although the healthcare-noise tweets all have keywords such as \"swine flu\" and \"flu shots\", they are not really talking about the symptoms of the flu of individuals. With this motivation, the task of this work is to classify truly healthcare-related data from healthcare-noise data that is typically collected through the keyword filtering approach provided by the Twitter API ([[https://dev.twitter.com/streaming/overview](https://dev.twitter.com/streaming/overview)]([https://dev.twitter.com/streaming/overview](https://dev.twitter.com/streaming/overview))). 1. Worried about swine flu? Here are 10 things you need to know: Since it first emerged in April, the global swine..
2. Swine Flu - How worried are you? - Take our poll now and check out how others feel! 3. Missed getting a FREE FLU SHOT at Central last night? You've got three more \"shots\" at it. 4. feels icky. I think I'm getting the flu...not necessarily THE flu, but a flu. 5. Resting 2 day ad my mthly blood test last 1 ok got appoint 4 flu job being 1 ky so far not getting swine flu thats something
6. 38 degrees is possible swine flu watching the thermometer go up. at 36.9 right now im scared :/  
Social media, such as Facebook and Twitter, have been widely used by individuals to share real-life data about a person's health. It made the popular social media platforms, such as Twitter, a major source of healthcare-related data. Twitter provides support for accessing tweets via the Twitter API. Healthcare researchers have long been utilizing social media data to conduct their research [3; 4; 5; 6; 7]. Because of the popularity of social media platforms such as Twitter, the number of healthcare-related posts is growing fast. To extract further healthcare information, the most basic and crucial task is to discriminate and extract healthcare-related tweets from the massive pool of tweets. Researchers have made efforts to collect healthcare-related tweets [3; 8].