Infovigil: An Open Source Infodemiology and Infoveillance System
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Abstract
How effective did public health officials communicate with the public
during the H1N1 pandemic? How did Canadians react to public health and
health care system responses? Can we answer these questions by
analyzing what Canadian talked about in Web-based social media? Should
we, in the future, monitor in real time what people say on the
Internet, to optimize our public health knowledge translation and
communication strategies as well as our health care system responses?
What role should social media play in the overall communication
strategy of public health agencies? Can Twitter be used to detect
epidemics early?
Such questions can be answered by "infodemiologists". Infodemiology,
an emerging area of research at the crossroads of consumer health
informatics and public health informatics, can be defined as the
science of distribution and determinants of information in an
electronic medium, specifically the Internet, with the ultimate aim to
inform public health and public policy. Infodemiology data can be
collected and analyzed in near real time. Examples for infodemiology
applications include: the analysis of queries from Internet search
engines to predict disease outbreaks (eg. influenza); monitoring
peoples' status updates on social media and microblogs such as Twitter
for syndromic surveillance; detecting and quantifying disparities in
health information availability; identifying and monitoring of public
health relevant publications on the Internet (eg. anti-vaccination
sites, but also news articles or expert-curated outbreak reports);
automated tools to measure information diffusion and knowledge
translation, and tracking the effectiveness of health marketing
campaigns. Moreover, analyzing how people search and navigate the
Internet for health-related information, as well as how they
communicate and share this information, can provide valuable insights
into health-related behavior of populations.
In this talk the author introduces the idea and basic metrics of infodemiology and infoveillance by presenting some longitudinal data his group collected during the H1N1 outbreak, specifically data collected from Twitter. We developed a proof-of-concept infoveillance system called Infovigil, which can
identify, archive, and analyze health-related information from Twitter
and information streams from other sources. Between May and Dec 2009 our group collected over 2 million H1N1-related tweets. Some of the research questions to be addressed include the potential of
infoveillance and social media for syndromic surveillance and for
knowledge translation research, and for "biosurveilance 2.0"", which
actively engages the public. Infoveillance has the potential to not
only detect outbreaks and other public health relevant events early,
but can also help us to understand the pulse of the Canadian public
during a pubic health emergency, ultimately also informing us how
public health agencies and policy makers should use social media for
future public health threats and disasters in the context of their
existing information dissemination and knowledge translation
strategies.
during the H1N1 pandemic? How did Canadians react to public health and
health care system responses? Can we answer these questions by
analyzing what Canadian talked about in Web-based social media? Should
we, in the future, monitor in real time what people say on the
Internet, to optimize our public health knowledge translation and
communication strategies as well as our health care system responses?
What role should social media play in the overall communication
strategy of public health agencies? Can Twitter be used to detect
epidemics early?
Such questions can be answered by "infodemiologists". Infodemiology,
an emerging area of research at the crossroads of consumer health
informatics and public health informatics, can be defined as the
science of distribution and determinants of information in an
electronic medium, specifically the Internet, with the ultimate aim to
inform public health and public policy. Infodemiology data can be
collected and analyzed in near real time. Examples for infodemiology
applications include: the analysis of queries from Internet search
engines to predict disease outbreaks (eg. influenza); monitoring
peoples' status updates on social media and microblogs such as Twitter
for syndromic surveillance; detecting and quantifying disparities in
health information availability; identifying and monitoring of public
health relevant publications on the Internet (eg. anti-vaccination
sites, but also news articles or expert-curated outbreak reports);
automated tools to measure information diffusion and knowledge
translation, and tracking the effectiveness of health marketing
campaigns. Moreover, analyzing how people search and navigate the
Internet for health-related information, as well as how they
communicate and share this information, can provide valuable insights
into health-related behavior of populations.
In this talk the author introduces the idea and basic metrics of infodemiology and infoveillance by presenting some longitudinal data his group collected during the H1N1 outbreak, specifically data collected from Twitter. We developed a proof-of-concept infoveillance system called Infovigil, which can
identify, archive, and analyze health-related information from Twitter
and information streams from other sources. Between May and Dec 2009 our group collected over 2 million H1N1-related tweets. Some of the research questions to be addressed include the potential of
infoveillance and social media for syndromic surveillance and for
knowledge translation research, and for "biosurveilance 2.0"", which
actively engages the public. Infoveillance has the potential to not
only detect outbreaks and other public health relevant events early,
but can also help us to understand the pulse of the Canadian public
during a pubic health emergency, ultimately also informing us how
public health agencies and policy makers should use social media for
future public health threats and disasters in the context of their
existing information dissemination and knowledge translation
strategies.
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