Spatio-Temporal-Thematic Analysis of Citizen Sensor Data: Challenges and Experiences

We present work in the spatio-temporal-thematic analysis of citizen-sensor observations pertaining to real-world events. Using Twitter as a platform for obtaining crowd-sourced observations, we explore the interplay between these 3 dimensions in extracting insightful summaries of social perceptions behind events. We present our experiences in building a web mashup application, Twitris that extracts and facilitates the spatio-temporal-thematic exploration of event descriptor summaries

What Information about Cardiovascular Diseases do People Search Online?

The objective of this study is to understand the types of health information (health topics) that users search online for Cardiovascular Diseases, by performing categorization of health search queries (from Mayoclinic.com) using UMLS MetaMap based on UMLS concepts and semantic types

An Analysis of Mayo Clinic Search Query Logs for Cardiovascular Diseases

Increasingly, individuals are taking active participation in learning and managing their health by leveraging online resources. Understanding online health information searching behavior can help us to study what health topics users search for and how search queries are formulated. In this work, we analyzed 10 million cardiovascular diseases (CVD) related search queries from MayoClinic.com. We performed semantic analysis on the queries using UMLS MetaMap and analyzed structural and textual properties as well as linguistic characteristics of the queries

Online Information Searching for Cardiovascular Diseases: An Analysis of Mayo Clinic Search Query Logs

Since the early 2000’s, Internet usage for health information searching has increased significantly. Studying search queries can help us to understand users “information need” and how do they formulate search queries (“expression of information need”). Although cardiovascular diseases (CVD) affect a large percentage of the population, few studies have investigated how and what users search for CVD. We address this knowledge gap in the community by analyzing a large corpus of 10 million CVD related search queries from MayoClinic.com. Using UMLS MetaMap and UMLS semantic types/concepts, we developed a rule-based approach to categorize the queries into 14 health categories. We analyzed structural properties, types (keyword-based/Wh-questions/Yes-No questions) and linguistic structure of the queries. Our results show that the most searched health categories are ‘Diseases/Conditions’, ‘Vital- Sings’, ‘Symptoms’ and ‘Living-with’. CVD queries are longer and are predominantly keyword-based. This study extends our knowledge about online health information searching and provides useful insights for Web search engines and health websites

Social Health Signals

Recently Twitter, has emerged as one of the primary medium for sharing and seeking of the latest information related to variety of the topics including health information. Recently, Twitter has emerged as one of the primary mediums for sharing and seeking the latest information related to a variety of topics, including health information. Although Twitter is an excellent information source, identification of useful information from the deluge of tweets is one of the major challenge. Twitter search is limited to keyword based techniques to retrieve information for a given query and sometimes the results do not contain real-time information. Moreover, Twitter does not utilize semantics to retrieve results. To address these challenges, we developed a system (Social Health Signals) by leveraging rich domain knowledge to extract relevant and reliable health information from Twitter in near real-time. We have used semantics based techniques 1) to retrieve relevant and reliable health information shared on Twitter in real-time 2) to enable question answering 3) to rank results based on relevancy, popularity and reliability 4) to semantically categorize the information

What Information about Cardiovascular Diseases do People Search Online?

The objective of this study is to understand the types of health information (health topics) that users search online for Cardiovascular Diseases, by performing categorization of health search queries (from Mayoclinic.com) using UMLS MetaMap based on UMLS concepts and semantic types

What Information about Cardiovascular Diseases do People Search Online?

The objective of this study is to understand the types of health information (health topics) that users search online for Cardiovascular Diseases, by performing categorization of health search queries (from Mayoclinic.com) using UMLS MetaMap based on UMLS concepts and semantic types

Understanding Events through Analysis of Social Media

Users are sharing vast amounts of social data through social networking platforms accessible by Web and increasingly via mobile devices. This opens an exciting opportunity to extract social perceptions as well as obtain insights relevant to events around us. We discuss the significant need and opportunity for analyzing event-centric user generated content on social networks, present some of the technical challenges and our approach to address them. This includes aggregating social data related to events of interest, along with Web resources (news, Wikipedia pages, multimedia) related to an event of interest, and supporting analysis along spatial, temporal, thematic, and sentiment dimensions. The system is unique in its support for user generated content in developed countries where Twitter is popular, as well as in support for SMS that is popular in emerging regions