8 research outputs found
A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data
Over the last decade, hundreds of thousands of volunteers have contributed to science by collecting or analyzing data. This public participation in science, also known as citizen science, has contributed to significant discoveries and led to publications in major scientific journals. However, little attention has been paid to data quality issues. In this work we argue that being able to determine the accuracy of data obtained by crowdsourcing is a fundamental question and we point out that, for many real-life scenarios, mathematical tools and processes for the evaluation of data quality are missing. We propose a probabilistic methodology for the evaluation of the accuracy of labeling data obtained by crowdsourcing in citizen science. The methodology builds on an abstract probabilistic graphical model formalism, which is shown to generalize some already existing label aggregation models. We show how to make practical use of the methodology through a comparison of data obtained from different citizen science communities analyzing the earthquake that took place in Albania in 2019
TriggerCit: Early Flood Alerting using Twitter and Geolocation - A Comparison with Alternative Sources
Rapid impact assessment in the immediate aftermath of a natural disaster is
essential to provide adequate information to international organisations, local
authorities, and first responders. Social media can support emergency response
with evidence-based content posted by citizens and organisations during ongoing
events. In the paper, we propose TriggerCit: an early flood alerting tool with
a multilanguage approach focused on timeliness and geolocation. The paper
focuses on assessing the reliability of the approach as a triggering system,
comparing it with alternative sources for alerts, and evaluating the quality
and amount of complementary information gathered. Geolocated visual evidence
extracted from Twitter by TriggerCit was analysed in two case studies on floods
in Thailand and Nepal in 2021.Comment: 12 pages Keywords Social Media, Disaster management, Early Alertin
Image-based Social Sensing: Combining AI and the Crowd to Mine Policy-Adherence Indicators from Twitter
Social Media provides a trove of information that, if aggregated and analysed
appropriately can provide important statistical indicators to policy makers. In
some situations these indicators are not available through other mechanisms.
For example, given the ongoing COVID-19 outbreak, it is essential for
governments to have access to reliable data on policy-adherence with regards to
mask wearing, social distancing, and other hard-to-measure quantities. In this
paper we investigate whether it is possible to obtain such data by aggregating
information from images posted to social media. The paper presents VisualCit, a
pipeline for image-based social sensing combining recent advances in image
recognition technology with geocoding and crowdsourcing techniques. Our aim is
to discover in which countries, and to what extent, people are following
COVID-19 related policy directives. We compared the results with the indicators
produced within the CovidDataHub behavior tracker initiative. Preliminary
results shows that social media images can produce reliable indicators for
policy makers.Comment: 10 pages, 9 figures, to be published in Proceedings of ICSE Software
Engineering in Society, May 202