Fake social media accounts spread pro-Iran, anti-Trump messages during 2018 election

The fake accounts targeted Donald trump campaigning for the 2018 congressional elections. (AFP/File photo))
Updated 28 May 2019

Fake social media accounts spread pro-Iran, anti-Trump messages during 2018 election

  • The operation focused on promoting “anti-Saudi, anti-Israeli, and pro-Palestinian themes”

LONDON: A network of fake social media accounts impersonated political candidates and journalists to spread messages in support of Iran and against US President Donald Trump around the 2018 congressional elections, cybersecurity firm FireEye said on Tuesday.
The findings show how unidentified, possibly government-backed, groups could manipulate social media platforms to promote stories and other content that can influence the opinions of American voters, the researchers said.
This particular operation was largely focused on promoting “anti-Saudi, anti-Israeli, and pro-Palestinian themes,” according to the report by FireEye.
The campaign was organized through a series of fake personas that created various social media accounts, including on Twitter and Facebook. Most of these accounts were created last year and have since been taken down, the report said.
Spokespersons for Twitter and Facebook confirmed FireEye’s finding that the fake accounts were created on their platforms.
Lee Foster, a researcher with FireEye, said he found some of the fake personas — often masquerading as American journalists — had successfully convinced several US news outlets to publish letters to the editor, guest columns and blog posts.
These writings displayed both progressive and conservative views, the report said, covering topics including the Trump administration’s designation of Iran’s Islamic Revolutionary Guard Corps (IRGC) as a terrorist organization.
“We’re assessing with low confidence that this network was organized to support Iranian political interests,” said Foster. “However, we’re not at the point where we can say who was doing it or where it’s coming from. The investigation is ongoing.”
Twitter said in a statement that it had “removed this network of 2,800 inauthentic accounts originating in Iran at the beginning of May,” adding that its investigation was ongoing.
Before the 2018 midterms election, the nameless group created Twitter accounts that impersonated both Republican and Democratic congressional candidates. It is unclear if the fake accounts had any effect on their campaigns.
The imposter Twitter accounts often plagiarized messages from the politicians’ legitimate accounts, but also mixed in posts voicing support for policies believe to be favorable to Tehran. Affected politicians included Jineea Butler, a republican candidate for New York’s 13th District, and Marla Livengood, a republican candidate for California’s 9th District. Both Livengood and Butler lost in the general election.
Livengood and Butler could not be immediately reached for comment.
Facebook said it had removed 51 Facebook accounts, 36 Pages, seven Groups and three Instagram accounts, connected to the influence operation. Instagram is owned by Facebook.
The activity on Facebook was less expansive and it appeared to be more narrowly focused, said Facebook head of cybersecurity policy Nathaniel Gleicher. The inauthentic Facebook accounts instead often privately messaged high profile figures, including journalists, policy makers and Iranian dissidents, to promote certain issues.
Facebook similarly concluded the activity had originated in Iran, although it’s not clear whether the operation was backed by the Iranian government.
Foster said the research demonstrates how groups will use a variety of different techniques and methods to push an agenda online.


Facebook researchers use maths for better translations

Updated 13 October 2019

Facebook researchers use maths for better translations

  • Facebook researchers say rendering words into figures and exploiting mathematical similarities between languages is a promising avenue
  • Allowing as many people as possible worldwide to communicate is not just an altruistic goal, but also good business

PARIS: Designers of machine translation tools still mostly rely on dictionaries to make a foreign language understandable. But now there is a new way: numbers.

Facebook researchers say rendering words into figures and exploiting mathematical similarities between languages is a promising avenue — even if a universal communicator a la Star Trek remains a distant dream.

Powerful automatic translation is a big priority for Internet giants. Allowing as many people as possible worldwide to communicate is not just an altruistic goal, but also good business.

Facebook, Google and Microsoft as well as Russia’s Yandex, China’s Baidu and others are constantly seeking to improve their translation tools.

Facebook has artificial intelligence experts on the job at one of its research labs in Paris. Up to 200 languages are currently used on Facebook, said Antoine Bordes, European co-director of fundamental AI research for the social network.

Automatic translation is currently based on having large databases of identical texts in both languages to work from. But for many language pairs there just aren’t enough such parallel texts.

That’s why researchers have been looking for another method, like the system developed by Facebook which creates a mathematical representation for words.

Each word becomes a “vector” in a space of several hundred dimensions. Words that have close associations in the spoken language also find themselves close to each other in this vector space.

“For example, if you take the words ‘cat’ and ‘dog’, semantically, they are words that describe a similar thing, so they will be extremely close together physically” in the vector space, said Guillaume Lample, one of the system’s designers.

“If you take words like Madrid, London, Paris, which are European capital cities, it’s the same idea.”

These language maps can then be linked to one another using algorithms — at first roughly, but eventually becoming more refined, until entire phrases can be matched without too many errors.

Lample said results are already promising. For the language pair of English-Romanian, Facebook’s current machine translation system is “equal or maybe a bit worse” than the word vector system, said Lample.

But for the rarer language pair of English-Urdu, where Facebook’s traditional system doesn’t have many bilingual texts to reference, the word vector system is already superior, he said.

But could the method allow translation from, say, Basque into the language of an Amazonian tribe? In theory, yes, said Lample, but in practice a large body of written texts are needed to map the language, something lacking in Amazonian tribal languages.

“If you have just tens of thousands of phrases, it won’t work. You need several hundreds of thousands,” he said.

Experts at France’s CNRS national scientific center said the approach Lample has taken for Facebook could produce useful results, even if it doesn’t result in perfect translations.

Thierry Poibeau of CNRS’s Lattice laboratory, which also does research into machine translation, called the word vector approach “a conceptual revolution.”

He said “translating without parallel data” — dictionaries or versions of the same documents in both languages — “is something of the Holy Grail” of machine translation.

“But the question is what level of performance can be expected” from the word vector method, said Poibeau. The method “can give an idea of the original text” but the capability for a good translation every time remains unproven.

Francois Yvon, a researcher at CNRS’s Computer Science Laboratory for Mechanics and Engineering Sciences, said “the linking of languages is much more difficult” when they are far removed from one another.

“The manner of denoting concepts in Chinese is completely different from French,” he added.
However even imperfect translations can be useful, said Yvon, and could prove sufficient to track hate speech, a major priority for Facebook.