On the Ground Validation of Online Diagnosis with Twitter and Medical Records

Social media has been considered as a data source for tracking disease. However, most analyses are based on models that prioritize strong correlation with population-level disease rates over determining whether or not specific individual users are actually sick. Taking a different approach, we develop a novel system for social-media based disease detection at the individual level using a sample of professionally diagnosed individuals. Specifically, we develop a system for making an accurate influenza diagnosis based on an individual's publicly available Twitter data. We find that about half (17/35 = 48.57%) of the users in our sample that were sick explicitly discuss their disease on Twitter. By developing a meta classifier that combines text analysis, anomaly detection, and social network analysis, we are able to diagnose an individual with greater than 99% accuracy even if she does not discuss her health.Comment: Presented at of WWW2014. WWW'14 Companion, April 7-11, 2014, Seoul, Kore

On the Ground Validation of Online Diagnosis with Twitter and Medical Records

Social media has been considered as a data source for tracking disease. However, most analyses are based on models that prioritize strong correlation with population-level disease rates over determining whether or not specific individual users are actually sick. Taking a different approach, we develop a novel system for social-media based disease detection at the individual level using a sample of professionally diagnosed individuals. Specifically, we develop a system for making an accurate influenza diagnosis based on an individual's publicly available Twitter data. We find that about half (17/35 = 48.57%) of the users in our sample that were sick explicitly discuss their disease on Twitter. By developing a meta classifier that combines text analysis, anomaly detection, and social network analysis, we are able to diagnose an individual with greater than 99% accuracy even if she does not discuss her health.Comment: Presented at of WWW2014. WWW'14 Companion, April 7-11, 2014, Seoul, Kore

A novel approach to track public emotions related to epidemics in multilingual data

Emergence of new epidemic and re-appearance of older diseases causes great impact towards public health. Surveys based techniques which are costly and time-consuming are the most popular methods to measure information related to public health and used in decision making. Early monitoring of these epidemics helps in rapid decision making. Social media platforms provide rich source of information related to public health in forms of blogs, tweets, public posts etc., but these data is in unstructured form contains multiple languages words. This research focused on developing an automatic system for detecting public emotions related to epidemics in multilingual unstructured data to gain deeper understanding of public emotions and health related information. This approach gives timely information related to epidemics, corresponding symptoms, prevention techniques and awareness, which can help government and health agencies for rapid decision making. Experimental analysis of data set provides results that significantly beat the baseline term counting methods used for sentiment analysis

Online diagnosis of diabetes with Twitter data

Innovation in technology enables people to communicate, share information and look for their needs by just sitting in rooms and going through some clicks. While social media has played a very important role in connecting people worldwide, its potential has stretched beyond the innovative idea of connecting people through their social networks. While many thought there was no meeting point for the healthcare sector and social media, it was a surprise when research and innovations have shown that social media could lay a very significant role in the health care sector. Research has been done in developing models that could use social media as the data source for tracking diseases. Most of these analyses are based on models that prioritize strong correlations with seasonal and pandemic kinds of diseases over the health conditions of a specific individual user. The aim of this research is to develop a diabetes detecting tool at the individual level using a sample of Twitter IDs that have been collected from the Twitter search using the query -- \u27recently diagnosed\u27 and \u27diabetes\u27\u27. Based on text analysis of social media posts using Fisher\u27s exact test, without any medical settings, this thesis investigates the feasibility of diagnosing and classifying diabetes via machine learning techniques, Naive Bayes and Random Forest classifiers. It was found that more than half (20/30 ≈ 67%) of the users in the sample mentioned being tested positive for diabetes, about 27% (8/30) of the users mentioned the symptoms and got involved in diabetes related discussions, but did not mention about being tested positive and rest 4% had no mention of symptoms or diabetes --Abstract, page iii

Preprocessing Techniques to Support Event Detection Data Fusion on Social Media Data

This thesis focuses on collection and preprocessing of streaming social media feeds for metadata as well as the visual and textual information. Today, news media has been the main source of immediate news events, large and small. However, the information conveyed on these news sources is delayed due to the lack of proximity and general knowledge of the event. Such news have started relying on social media sources for initial knowledge of these events. Previous works focused on captured textual data from social media as a data source to detect events. This preprocessing framework postures to facilitate the data fusion of images and text for event detection. Results from the preprocessing techniques explained in this work show the textual and visual data collected are able to be proceeded into a workable format for further processing. Moreover, the textual and visual data collected are transformed into bag-of-words vectors for future data fusion and event detection