Tens of millions of social media users have shared an "All eyes on Rafah" image, drawing attention to the mounting civilian death toll as a result of Israel's assault on the Gazan city. The image's viral reach has revived debate around the effectiveness of politically charged hashtags and campaigns on social media, often derisively called "slacktivism." 

Real-world action
In addition to being a "bland, AI-generated" image rather than footage from the conflict, some pro-Palestinian activists noted with frustration that "All eyes on Rafah" came with "no call to action beyond 'pay attention' — no donation links, no details on how to help," said James Greig at Dazed. Movements like #MeToo and Black Lives Matter raised millions of pounds in funding after going viral, said the University of Sussex. But whether a short-term spotlight translates into "meaningful and lasting social change" is a question that "remains unsettled."

Impact
A lot depends on the context. Resharing posts "might not count for much on its own," said Greig, but the "ubiquity" of the "All eyes on Rafah" image indicates a "shift in consensus" in the West. In a climate where "censorship and repression" of pro-Palestinian activism has been widespread, "we should welcome the movement becoming 'trendy,'" even in the "shallowest way possible." 

Is all digital activism 'slacktivism'? 
Digital activism can mean many different things, Dr. Kelly Lewis, a research fellow at Monash University, Melbourne, said at The Conversation. Social media is undeniably a powerful tool for organizing real-world action, both literal — protests around the world are now largely coordinated online — and through building "collective networks of solidarity and resistance." 

Pitfalls
It's all too easy for "verified facts" and sensitive reporting to be drowned out by partisan "misinformation, political propaganda and irresponsible journalism" pumped out on social media and uncritically reshared by users, said Lewis. As demonstrated by incidents of Islamophobic, racist and antisemitic aggression around the world motivated by the conflict in Gaza, the circulation of propaganda that fans the flames of hatred on social media can have "disastrous consequences."

Artificial intelligence has relied on high-quality language data to train its models, but supply is running low. That depletion is forcing companies to look elsewhere for data sourcing, as well as change their algorithms to use data more efficiently. 

What is the scope of the problem? 
Artificial intelligence needs to be trained, and data and information is used to accomplish that. The trouble is that the data is running out. A paper by Epoch, an AI research organization, found that AI could exhaust all the current high-quality language data available on the internet as soon as 2026. "The issue stems from the fact that as researchers build more powerful models with greater capabilities they have to find ever more texts to train them on," said the MIT Technology Review. The quality of the data used in training AI is important. "The [data shortage] issue stems partly from the fact that language-AI researchers filter the data they use to train models into two categories: high-quality and low-quality," said the Review. "The line between the two categories can be fuzzy," but "text from [high-quality data] is viewed as better-written and is often produced by professional writers." 

What are AI companies doing to combat this? 
The ticking clock on high-quality data has forced AI developers to think more creatively. For instance, Google has considered using user data from Google Docs, Google Sheets and similar company products. Other companies are "searching for content outside the free online space, such as that held by large publishers and offline repositories" and published before the internet existed, said Singularity Hub. More broadly, many companies have looked to synthetic data, which is generated by AI itself.

The other option is to rework AI algorithms to better and more efficiently use the existing high-quality data. One strategy being explored is called curriculum learning, which is when "data is fed to language models in a specific order in hopes that the AI will form smarter connections between concepts," said the Journal. If successful, the method could cut the data required to run an AI model by half.