Millions of people around the world are connected to and interact with one another on the internet through personal devices. Social media platforms, blogs and forums are just some of the platforms that are being used by individuals to share their thoughts and experiences online in real time. During unexpected, breaking events, such as natural disasters, social media content can act as an early signal and provide up-to-date information of the dynamics of a situation as it develops. This publicly available data can also contain a more accurate reflection of the situation on the ground than official data sources. However, the speed of turnover and vast amount of posted information and media make it difficult to separate useful material from noise, losing an opportunity for the public sector to make use of these early signals to plan and co‑ordinate responses.

Dataminr’s First Alert system integrates numerous non-conventional data sources, such as citizen-generated content in the form of images and free text posted on social media (which tend to increase during unexpected crises), to isolate the early signals that anticipate emergency events. The platform combines this crowdsourced data with other data streams, such as audio broadcasts from first responders and sensor data (including data from the aviation industry).

Dataminr uses a hybrid AI approach to detect anomalies and events that could have widespread public impact in the data that it scrapes. The methods are a combination of natural language processing (NLP) for text classification, computer vision for identifying images and machine‑learning for audio streams. Finally, the platform uses natural language generation to issue text summaries as alerts to public sector organisations that are tasked with responding in crises.

Thanks to Dataminr, organisations can leverage and improve their abilities to manage risks. The AI speeds up response times and increases the capacity of teams to detect, classify and determine the significance of information generated by the crowd. More recently, Dataminr has started to use transfer learning to enable faster training of high‑quality models for new first response contexts. This development has been enabled by the original high‑quality labelled dataset of crowd-generated social media data, built over 10 years.

Organisations that use Dataminr’s First Alert service range from public sector clients who need to prioritise where to deploy aid in a crisis, to private companies who might need to make arrangements to keep their workforce and supply chains secure during environmental disasters.

When the Category-4 Hurricane Harvey hit the American South, Dataminr was able to issue an advance warning to clients about the planned closure of Houston’s port, so they could make arrangements to mitigate the impact on any of their logistics and supply chains. In 2019, Dataminr announced a partnership with the UN aimed at enhancing humanitarian response.

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AIDR (Artificial Intelligence for Digital Response)