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Stanford researchers crack the math behind successful Reddit submissions

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It's a social media marketer's dream: a formula for a successful Reddit submission. A team of statisticians at Stanford has spent the past few months analyzing 16,700 pictures on Reddit in order to analyze the impact of content, title, community to which it was submitted, and time of submission. Each picture was submitted an average of 7.9 times, which helped the researchers isolate each factor's impact.

What makes a popular submission to the link aggregator that drives more than 4.8 billion pageviews a month? The answer, of course, is "it depends." The interplay between factors turned out to be hugely important, and different strategies worked for different subreddits, the topic-centric communities on Reddit.

Good content "speaks for itself" and may become popular regardless of title, the researchers concluded. However, the title also makes a big difference, as does choice of community. For example, for r/gifs, r/pics, and r/gaming, titles did better when they were somewhat similar to other titles that get submitted to those verticals, but not too similar. For r/atheism and r/gaming, the titles are more formulaic. In those communities, a submission will perform better the more closely its title mirrors other titles in the vertical.

Good content "speaks for itself"

Submissions are also more likely to succeed when submitted around 8AM or noon UTC and least successful when submitted around 4AM UTC. That was true of all the subreddits the researchers examined. Content also became less popular each time it was it was submitted. This "resubmission penalty" gradually lessens, although it is still present after 140 days (the community for r/atheism was a unique exception; resubmission seemed to have no negative effect after 140 days).

The data could be used to develop models to name products for different markets, the researchers said, and a natural next step would be to create a generator to spawn optimized titles. "Current work on title generation is generally concerned with whether generated titles are accurate, in that they meaningfully summarize the content they refer to," the researchers wrote. "Our methods could be combined with such approaches, to identify titles that are accurate, yet are also interesting."

The Verge
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