Why is it Difficult to Detect Sudden and Unexpected Epidemic Outbreaks in Twitter?

11/10/2016
by   Avaré Stewart, et al.
0

Social media services such as Twitter are a valuable source of information for decision support systems. Many studies have shown that this also holds for the medical domain, where Twitter is considered a viable tool for public health officials to sift through relevant information for the early detection, management, and control of epidemic outbreaks. This is possible due to the inherent capability of social media services to transmit information faster than traditional channels. However, the majority of current studies have limited their scope to the detection of common and seasonal health recurring events (e.g., Influenza-like Illness), partially due to the noisy nature of Twitter data, which makes outbreak detection and management very challenging. Within the European project M-Eco, we developed a Twitter-based Epidemic Intelligence (EI) system, which is designed to also handle a more general class of unexpected and aperiodic outbreaks. In particular, we faced three main research challenges in this endeavor: 1) dynamic classification to manage terminology evolution of Twitter messages, 2) alert generation to produce reliable outbreak alerts analyzing the (noisy) tweet time series, and 3) ranking and recommendation to support domain experts for better assessment of the generated alerts. In this paper, we empirically evaluate our proposed approach to these challenges using real-world outbreak datasets and a large collection of tweets. We validate our solution with domain experts, describe our experiences, and give a more realistic view on the benefits and issues of analyzing social media for public health.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/29/2019

Towards Automatic Bot Detection in Twitter for Health-related Tasks

With the increasing use of social media data for health-related research...
research
08/27/2022

An event detection technique using social media data

People post information about different topics which are in their active...
research
09/22/2017

Characterizing Diabetes, Diet, Exercise, and Obesity Comments on Twitter

Social media provide a platform for users to express their opinions and ...
research
03/14/2019

Survey of Text-based Epidemic Intelligence: A Computational Linguistic Perspective

Epidemic intelligence deals with the detection of disease outbreaks usin...
research
11/20/2019

Scalable and Generalizable Social Bot Detection through Data Selection

Efficient and reliable social bot classification is crucial for detectin...
research
05/06/2022

Domain-Level Detection and Disruption of Disinformation

How, in 20 short years, did we go from the promise of the internet to de...
research
06/12/2021

BIOPAK Flasher: Epidemic disease monitoring and detection in Pakistan using text mining

Infectious disease outbreak has a significant impact on morbidity, morta...

Please sign up or login with your details

Forgot password? Click here to reset