December 6, 2024

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No, cellphone data used to arrest suspected Idaho killer doesn’t give ‘2,000 Mules’ credibility

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John Rich, half the country music duo Big & Rich, gained huge social media attention by tying the arrest of a suspected quadruple-killer in Idaho to “2,000 Mules,” the widely debunked election denier film.

“They caught the Idaho killer using cellphone ping data, the exact same method used to track the #2000Mules,” Rich told his 832,000 Twitter followers in a Jan. 5 post that quickly spread across Facebook. The tweet has received more than 100,000 engagements, including likes and retweets.

“One’s praised as ‘brilliant detective work,’ the other has been vilified as ‘not credible,’” he wrote. “See how this works? We live in a mirrored fun house where every image is bent.”

The Facebook post was flagged as part of Facebook’s efforts to combat false news and misinformation on its News Feed. (Read more about our partnership with Meta, which owns Facebook and Instagram.)

This comparison falls flat: The methods to track down information using cellphone data were not exactly the same and the Idaho arrest didn’t give credibility to the movie.

To find the suspected killer, law enforcement investigators used cellphone data specific to his phone and corroborated it with other evidence, including DNA. 

The “2,000 Mules” movie used aggregated and anonymized cell data to claim that people dumped 400,000 illegal ballots into drop boxes in the 2020 election. But the data showed only that people with cellphones were near the boxes, which could have been for any number of possible reasons. 

“What ‘2,000 Mules’ tried to do was say that we know these people went to these ballot boxes, and you cannot definitively say that,” said Aaron Striegel, a University of Notre Dame computer science and engineering professor. “What you know is they might have been around them, within the city block.”

Who is Rich?

Rich and “Big” Kenny Alphin comprise Big & Rich, whose hit songs include “Save A Horse (Ride A Cowboy).” 

Rich is politically active. He wrote “Raisin’ McCain,” a song promoting the 2008 presidential campaign of John McCain, a Republican Arizona senator who died a decade later. In 2017, the duo were the headline performers at a gala ahead of Donald Trump’s presidential inauguration. 

Rich previously has raised questions on Twitter about fraud and voting irregularities in the 2020 and 2022 elections. The tweet we’re fact-checking received praise from election denier Kari Lake, the Republican who lost in the November Arizona governor’s race. 

Evidence beyond cell data led to Idaho arrest

Bryan C. Kohlberger, 28, is charged with first-degree murder in the stabbing deaths of four University of Idaho students in a home near campus Nov. 13. 

Seniors Madison Mogen, 21, and Kaylee Goncalves, 21; junior Xana Kernodle, 20; and freshman Ethan Chapin, 20, were killed.

Before his arrest Dec. 30 at his family’s Pennsylvania home, Kohlberger was a doctoral criminology student at Washington State University, which is several miles from the Idaho campus.

To show probable cause that Kohlberger committed the killings, law enforcement investigators used cellphone tracking, video surveillance and DNA evidence on a knife sheath at the scene of the killings, a police affidavit filed in court said.

That’s different from what “2,000 Mules” did.

Cell data didn’t support ‘2,000 Mules’ illegal voting claim

“2,000 Mules,” released in May, was produced by far-right commentator Dinesh D’Souza, who has a history of spreading falsehoods. 

The film alleged that “mules” — people it defined as collecting and returning completed mail ballots — submitted 400,000 illegal ballots in five swing states in the 2020 election. 

The claim was based on cellphone data conservative group True the Vote said it had purchased. The group said it bought $2 million worth of anonymized cellphone geolocation data — the “pings” that track a person’s location based on app activity.

Experts told PolitiFact the movie’s evidence was inherently flawed. The film was also debunked by FactCheck.org, Reuters, The Associated Press and The Washington Post, which said it “offers the least convincing election-fraud theory yet.”

Cellphone location data can determine only whether a person came near a ballot drop box, and accuracy depends on factors such as cell tower capability and topography, Striegel said. 

Using a comparison, he said the data can tell “you went to a big-box store like Home Depot or Walmart, but it would be hard to say if you visited, say, a strip mall to say which store you actually went into.”

People could have passed near the drop boxes, which are typically in busy areas, for any number of reasons.

“That’s the problem with the ‘2,000 Mules,’ there just are a lot of other explanations that are viable” for why people were near a ballot box, Striegel said, “and that’s the primary evidence they provide.”

Cell tracking can place the location of a phone within 5 meters (16 feet) under ideal conditions, said Cooper Quintin, senior technologist with the Electronic Frontier Foundation, a nonprofit digital rights group.

“But you can’t show what (the people with the phones) were doing, whether they were putting ballots in or whether they were committing a murder. You have to have additional evidence for that,” he said. 

We tried to reach Rich by emailing addresses on the band’s website and YouTube channel, and on Rich’s business website, but received no reply.

Our ruling

Rich said cellphone data used to arrest a suspected Idaho quadruple-killer proves that criticism of the “2000 Mules” movie’s use of cell data is unfounded.

Idaho police obtained data specifically for the suspect’s cellphone and used corroborating evidence, including DNA at the crime scene, to arrest him.

“2,000 Mules” used purchased aggregated and anonymous cellphone location data, which signaled that people were near ballot drop boxes, but did not prove that the boxes were stuffed with illegal ballots. The data does not show what people were doing in those geographic areas. 

We rate the statement False.



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