Who Should We Be Listening to? Applying Models of User Authority to Detecting Emerging Topics on the EIN

Description: 

Emerging event detection is the process of automatically identifying novel and emerging ideas from text with minimal human intervention. With the rise of social networks like Twitter, topic detection has begun leveraging measures of user influence to identify emerging events. Twitter's highly skewed follower/followee structure lends itself to an intuitive model of influence, yet in a context like the Emerging Infections Network (EIN), a sentinel surveillance listserv of over 1400 infectious disease experts, developing a useful model of authority becomes less clear. Who should we listen to on the EIN? To explore this, we annotated a body of important EIN discussions and tested how well 3 models of user authority performed in identifying those discussions. In previous work we proposed a process by which only posts that are based on specific "important" topics are read, thus drastically reducing the amount of posts that need to be read. The process works by finding a set of "bellwether" users that act as indicators for "important" topics and only posts relating to these topics are then read. This approach does not consider the text of messages, only the patterns of user participation. Our text analysis approach follows that of Cataldi et al.[1], using the idea of semantic "energy" to identify emerging topics within Twitter posts. Authority is calculated via PageRank and used to weight each author's contribution to the semantic energy of all terms occurring in within some interval ti. A decay parameter d defines the impact of prior time steps on the current interval.

Objective

To explore how different models of user influence or authority perform when detecting emerging events within a small-scale community of infectious disease experts.

Primary Topic Areas: 
Original Publication Year: 
2011
Event/Publication Date: 
December, 2011

May 02, 2019

You voted 'down'.

Contact Us

NSSP Community of Practice

Email: syndromic@cste.org

 

This website is supported by Cooperative Agreement # 6NU38OT000297-02-01 Strengthening Public Health Systems and Services through National Partnerships to Improve and Protect the Nation's Health between the Centers for Disease Control and Prevention (CDC) and the Council of State and Territorial Epidemiologists. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC. CDC is not responsible for Section 508 compliance (accessibility) on private websites.

Site created by Fusani Applications