Just how useful is Twitter as a measure of broad sentiment?
One group's study of Brexit and the U.S. presidential election claims that for those looking at the right signals, the surprising outcomes of a number of recent elections were not so surprising at all.
“Our analysis was showing something, but our beliefs were different,” says Vishal Mishra, CEO of Right Relevance, which sells a research platform focused on influence. “We thought, how is Trump going to win? But our analysis kept showing us.”
Leading up to the U.S. election, the company was raising the possibility of a surprise outcome.
Among the signals they tracked was the number of supporters on Twitter associated with each side, adjusted for bots. Just as important is a follower’s influence, measured in part by retweets, mentions, and replies, as well the quality of their network of connections and whether they bridge different communities.
In both the Brexit vote and the presidential contest, these signals looked better ahead of the vote for the side that eventually won, Mishra says.
Deb Roy, a professor at MIT and chief media scientist at Twitter who has been closely tracking the election on Twitter as well, agrees that there were signals on Twitter, but resists the temptation to label them as “clear.”
In an e-mail response to questions from MIT Technology Review, Roy wrote, “We have entered new territory over the past year in terms of media dynamics” among social media networks, mainstream media sources, and fringe sources. He also wrote that there are “unique elements” to the events that “make it very difficult to claim clear interpretability of Twitter signals.”
On issues, there has been a long-term divergence between what mainstream media tweets about and what others do. In early November, the Right Relevance analysis found the trending terms on Twitter to be increasingly anti-Clinton, with “FBI,” “Podesta emails,” and “Comey” among the most discussed and shared terms.
This wasn’t true among the Twitter accounts from large media sources.
This new data poisoning tool lets artists fight back against generative AI
The tool, called Nightshade, messes up training data in ways that could cause serious damage to image-generating AI models.
Rogue superintelligence and merging with machines: Inside the mind of OpenAI’s chief scientist
An exclusive conversation with Ilya Sutskever on his fears for the future of AI and why they’ve made him change the focus of his life’s work.
The Biggest Questions: What is death?
New neuroscience is challenging our understanding of the dying process—bringing opportunities for the living.
Driving companywide efficiencies with AI
Advanced AI and ML capabilities revolutionize how administrative and operations tasks are done.
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.