Computer scientists say meme research doesn't threaten fre
Source: Patrick Thibodeau
In a letter to lawmakers Tuesday, five of the nation's top computing research organizations defended a research grant to study how information goes viral. The groups were responding to claims that the government-funded effort could help create a 1984-type surveillance state.
The controversy arises over a nearly $1 million research grant to researchers at Indiana University (IU) to investigate "why some ideas cause viral explosions while others are quickly forgotten," particularly on Twitter.
This information diffusion analysis project, dubbed "Truthy," is under attack by a number of lawmakers, including U.S. Rep. Lamar Smith (R-Texas), the chairman of the House Committee on Science, Space and Technology. Smith said the government "has no business using taxpayer dollars to support limiting free speech on Twitter and other social media.
Kevin McCarthy (R- Calif.), the House majority leader, said last week that Truthy's goal is to "evaluate users' 'partisanship' and to track 'subversive propaganda.'"
The research project is well under way, and the principal investigator, Filippo Menczer, professor of informatics and computer science director at the Center for Complex Networks and Systems Research at IU, pointed, in an email to the more than 30 papers already published about it.
There's also a demo site, with models illustrating memes.
As for why these attacks are arising now, Menczer declined to speculate “because that would drag us into politics, and we would rather keep focusing on our research,” he said. “But we were caught completely by surprise by both the timing and the deliberately misleading nature of the attacks in some online blogs and news sites, which were eventually echoed by the lawmakers.”
In the Tuesday letter to Smith, J Strother Moore, the head of the Computing Research Association; Thomas G. Dietterich, the head of the Association for the Advancement of Artificial Intelligence; Alexander L. Wolf, who heads the Association for Computing Machinery; Irene Fonseca, head of the Society for Industrial and Applied Mathematics; and Brian Noble, head of the USENIX Association, all countered the claims.
"We are dismayed by recent mischaracterizations and misplaced criticisms of research on information diffusion in online social networks," said the computer scientists in their letter.
The research project "can help internet users discover where information they glean from the web or social networks has come from -- did it arise organically, did it originate from authoritative sources, or has it been spread by bots designed to "game" social networks and spread misinformation?"
The work will help researchers understand how information flows, why some memes travel faster than others, "and how bad actors can game the network to their advantage.
"We do not believe this work represents a threat to free speech or a suppression of any type of speech over the internet," the letter said. "The tools developed in the course of this research are capable of making no political judgments, no prognostications, and no editorial comments, nor do they provide any capability for exerting any control over the Twitter stream they analyze," the wrote.
The controversy over Truthy is just another sign of ongoing deterioration between the science community and lawmakers over basic research funding as well as the science itself.
Climate science researchers, in particular, have been on the defensive with lawmakers. Smith, for instance, dismissed the just-released United Nations' Intergovernmental Panel on Climate Change (IPCC) warning about the impacts of climate change on the planet, as "re-packaged rhetoric." (See IPCC report PDF.)
The Truthy project is not sitting back and letting the lawmakers' attacks turn into memes.
It is offering its own defense, and writes in a blog post: "The Truthy platform is not informed by political partisanship. While it provides support to study the evolution of communication in all portions of the political spectrum, the machine learning algorithms used to identify suspicious patterns of information diffusion are entirely oblivious to the possibly political partisanship of the messages.”
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