The effects of algorithmic content selection on user engagement with news on Twitter

Social media platforms use content selection algorithms to help their users cope with the vast amount of information available online, typically by selecting the kind of content that users find most interesting and relevant.

A recently published paper by MMTC members Erwan Dujeancourt Opens in new window. and Marcel Garz Opens in new window. titled "The effects of algorithmic content selection on user engagement with news on Twitter", investigates how Twitter’s switch from a reverse-chronological timeline to algorithmic content selection in March 2016 influenced user engagement with tweets published by German newspapers. To mitigate concerns about omitted variables, Dujeancourt and Garz use the Facebook postings of these newspapers as a counterfactual. They find that the number of likes increased by 20% and the number of retweets by 15% within a span of 30 days after the switch. Importantly, their results indicate a rich-get-richer effect, implying that initially more popular outlets and news topics benefited the most. User engagement also increased more for sensationalist content than quality news stories.

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