BuzzFeed Counts Only The CEN Stories That Supported Its Fake-News Frame

This document is significant because it goes to the selection method behind BuzzFeed’s CEN article. By this stage, BuzzFeed had looked at a wider pool of CEN-linked stories, but the material seen across the disclosure record suggests that stories which appeared true, harmless, properly sourced or otherwise unsuitable for the fake-news framing were dropped from the working list rather than counted in the overall sample. The result was a distorted evidential base: the published article could present a high proportion of problematic or allegedly false CEN stories because the wider universe of CEN material reviewed by BuzzFeed had already been filtered down to the examples that supported the planned narrative. This matters because the issue was not merely whether some CEN stories were disputed, exaggerated or wrong, but whether BuzzFeed’s method fairly represented CEN’s output. If positive, neutral or verified examples were removed from the count while negative examples were retained and totalled, the numbers would inevitably make CEN look disproportionately unreliable. The document is therefore useful as evidence of selection bias in the investigation: BuzzFeed was not simply measuring CEN accuracy across a representative sample, but building a case from stories that confirmed the “fake news” frame while leaving out material that weakened or complicated it.

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Part of Central European News & Mike Leidig v BuzzFeed (US Second Circuit Libel Case) – BF Case Documents

Europe Media Published 1 May 2026 at 21:09 Source: 1 November 2016

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This document is significant because it goes to the selection method behind BuzzFeed’s CEN article. By this stageBuzzFeed had looked at a wider pool of CEN-linked storiesbut the material seen across the disclosure record suggests that stories which appeared trueharmlessproperly sourced or otherwise unsuitable for the fake-news framing were dropped from the working list rather than counted in the overall sample. The result was a distorted evidential base: the published article could present a high proportion of problematic or allegedly false CEN stories because the wider universe of CEN material reviewed by BuzzFeed had already been filtered down to the examples that supported the planned narrative. This matters because the issue was not merely whether some CEN stories were disputedexaggerated or wrongbut whether BuzzFeed’s method fairly represented CEN’s output. If positiveneutral or verified examples were removed from the count while negative examples were retained and totalledthe numbers would inevitably make CEN look disproportionately unreliable. The document is therefore useful as evidence of selection bias in the investigation: BuzzFeed was not simply measuring CEN accuracy across a representative samplebut building a case from stories that confirmed the “fake news” frame while leaving out material that weakened or complicated it.

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