A 7.8-magnitude earthquake [1] struck the central Luzon region of the Philippines earlier this month, sparking a surge of viral disaster footage online.

The spread of these videos complicates emergency response and public perception by mixing authentic damage with misleading content. This trend highlights the growing difficulty in verifying real-time disaster data as synthetic media becomes more prevalent.

While some clips accurately depict the destruction from the June event, others have been identified as fraudulent. The CBC Fact-Check Team said they are working to separate real footage from AI-generated content [2]. These synthetic videos can mimic the chaos of a natural disaster, potentially misleading the public about the scale or location of the damage.

Beyond AI-generated content, some users shared genuine footage from previous disasters. One widely circulated video was misattributed to the current crisis. The PolitiFact Fact-Check Team said the footage was not taken during the June earthquake; it captured a different earthquake that shook the Philippines two years ago [3].

This pattern of misattribution often occurs during high-stress events when users prioritize speed over verification. The 7.8-magnitude [1] quake caused extensive damage, which prompted a wave of social media sharing that outpaced official reporting.

Fact-checkers noted that the overlap of old footage and new AI tools creates a volatile information environment. The misattributed video from two years ago [3] served as a primary example of how legacy content can be weaponized to create a false narrative of current events.

“The footage was not taken during the June earthquake; it captured a different earthquake that shook the Philippines two years ago.”

The intersection of high-magnitude natural disasters and generative AI creates a significant 'verification gap.' When authentic footage is mixed with AI-generated imagery and archived clips from previous years, it undermines the reliability of social media as a tool for situational awareness, potentially delaying aid or causing unnecessary panic.