We found that most of the videos labeled as benefiting the Trump campaign might be more accurately described as highly critical of Clinton. Many videos clearly leaned toward one candidate or the other. In order to judge this, we watched the content of the videos and considered their titles. The computer program written by Guillaume Chaslot overcomes that obstacle to force some degree of transparency. Each individual video was scrutinised to determine whether it was obviously partisan and, if so, whether the video favoured the Republican or Democratic presidential campaign. When all 1, videos were tallied — including the missing videos with very slanted titles — we counted videos had an obvious bias. Whichever combinations of searches, recommendations and repeats Chaslot used, the program was doing the same thing:
Here are the 25 most recommended videos, according to the above metric. Formulating a view on these videos was a subjective process but for the most part it was very obvious which candidate videos benefited. About a third of the videos were deemed to be either unrelated to the election, politically neutral or insufficiently biased to warrant being categorised as favouring either campaign. We were unable to watch original copies of missing videos. He has used his software to detect YouTube recommendations across a range of topics and publishes the results on his website, algotransparency. The type of content recommended was, in both cases, overwhelmingly beneficial to Trump, with a surprising amount of conspiratorial content and fake news damaging to Clinton. A video falsely claiming Clinton suffered a mental breakdown was categorised as benefiting the Trump campaign. His program also detected variations in the degree to which YouTube appeared to be pushing content. For example, some might consider this CNN clip , in which a Trump supporter forcefully defended his lewd remarks and strongly criticised Hillary Clinton and her husband, to be beneficial to the Republican. In order to judge this, we watched the content of the videos and considered their titles. The process was repeated thousands of times, collating more and more layers of data about the videos YouTube was promoting in its conveyor belt of recommended videos. In the end, this video was too difficult for us categorise. The data was probably influenced by the topics that happened to be trending on YouTube on the dates he chose to run the program: By keeping the algorithm and its results under wraps, YouTube ensures that any patterns that indicate unintended biases or distortions associated with its algorithm are concealed from public view. Disclosing that data would enable academic institutions, fact-checkers and regulators as well as journalists to assess the type of content YouTube is most likely to promote. By putting a wall around its data, YouTube, which is owned by Google , protects itself from scrutiny. During the election, it acted as a YouTube user might have if she were interested in either of the two main presidential candidates. There were a few exceptions. The precise formula it uses, however, is kept secret. The ex-Google engineer said his method of extracting data from the video-sharing site could not provide a comprehensive or perfectly representative sample of videos that were being recommended. Multiple recommendations between the same videos on the same day were not counted. He has made the code behind his program publicly available here. But on a handful of dates, Chaslot tweaked his program, starting off with three or four search videos, capturing three or four layers of recommended videos, and repeating the process up to six times in a row. Both of these were categorised, in the second round, as beneficial to the Trump campaign. When all 1, videos were tallied — including the missing videos with very slanted titles — we counted videos had an obvious bias. Whichever combinations of searches, recommendations and repeats Chaslot used, the program was doing the same thing: On most of those dates, the software was programmed to begin with five videos obtained through search, capture the first five recommended videos, and repeat the process five times.
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