Best Time to Post? It’s Irrelevant

While social media invent various algorithms to show relevant information to users, companies like Buffer are trying to understand how to circumvent these algorithms to promote their clients’ content. This is not necessarily a zero-sum game, as it may seem. Optimizers add more structure to the content, pick relevant addressees, and distribute content to the media where information overload is less extreme.

The simplest problem around is to pick the best time for posting when you already have certain content. I looked into this once for, and the optimal timing happened to depend a lot on the subsite in question. StackExchange is a network of Q&A websites built on a common technology but with somewhat segregated users and different rules. The subsites look alike, integrated, and you normally expect the common features to prevail over everything else. But according to the data, the patterns of performance, such as time-to-answer, vary across the subsites. The soft rules—those that are not engraved in the common software code—and people make them vary.

Here’s another example: Y-Combinator’s Hacker News, which has a solid community and transparent ranking algorithm. The rules are simple: a user submits a link and title, the community upvote this submission. Good submissions make the front page, bad submissions are unread and forgotten. The service receives more than 300,000 submissions annually. The question is the same: given a submission, what’s the best time to post it? I took the number of expected upvotes as the criterion.

Many studied the Hacker News dataset before. A good example is this one. There’s even a special app for picking the time (I didn’t get what it does exactly). They answered different questions, though.

Here’s my version of events. In this post, however, I’d make another point based on this data.

First, just looking at upvotes shows that weekends are the better days for posting (0 is Monday, 6 is Sunday):


In particular:


However, this approach can’t say much. Time affects not only users who read links submitted to Hacker News (demand), but also those who submit the links (supply). You have causation suspects right away. Like, maybe users submit better links on weekends because they have more time to pick the good ones. Then scheduling the same submission of yours to weekends would not increase the upvotes it gets.

For a bunch of typical reasons (few variables available, unstructured data, and no suitable natural experiments), the impact of time on upvotes is hard to separate from other factors. You have only indirect evidences. For example, less competition on weekends may increase expected upvotes:


It remains unclear how to sum up indirect evidences into conclusions. Statistical models would disappoint. Time-related variables explain less than 1% of variation—meaning, unsurprisingly, that the other 99% depend on something else. This something includes the page you link to, the readers, and nuances of Hacker News’ architecture.

My point is, even a simple algorithm can be efficient, meaning, its outcome is independent of irrelevant factors, like time. A complex algorithm may perform worse, in fact. If content promotion depends on the author’s social capital (followers, friends, subscribers), ranking relies on the author’s past submissions rather than the current one. So, Facebook’s or Quora’s algorithms for sorting things for users are not only harder to pass through; they also may distort important outcomes.

See also: Python notebook with Hacker News data analysis

The Source of Our Sources

Not today’s news, but The New York Times and other major newspapers have a great influence on public policy. Key government documents, like budgets and congressional hearings, mention “the new york times” about 38,000 times (see Government Printing Office website with Google Search), while an economist from the top 10—who studies his topic for decades but doesn’t write for the public regularly—gets mentioned in the same documents just 10 times. So, even if economists know something (like a big secret about inflation), it’s up to the media to deliver this knowledge.

Where do the media source their information from, in turn? The Times explains:

Mentions in NY Times articles, Source

If someone doesn’t see the black line for the references to researchers, it’s because the line had been drawn over the zero axis.

(That was a post of envy, of course.)


The inflation fear appears here and there, but mostly around government initiatives. One problem with this fear is: regardless of how you measure it, there’s no inflation in government initiatives. Economists spend a lot of time in the media repeating this.

But “inflation” is not even a public concern. You won’t find it in polls or, for instance, Google Search trends:


In fact, “inflation” is a problem in just one place:


Ok, in two places. Wall Street made traffic for NYC. Meanwhile, elsewhere:


Employment does remain a top problem in public polls for long. You might expect the media to keep the public updated on this, but:

NYT Chronicle
NYT Chronicle

The New York Times devotes about the same attention to inflation as to unemployment, despite unemployment being a much bigger concern.

What comes after ignoring both public and experts? Correlation in the tail of the plot is suggestive:


And anyone who disagrees is a witch!

Doing Harm by Way of Habit

Facebook’s experiment on emotions got more feedback than any academic research had before. Many quitted, some raged. The reaction concerned, first, experimenting, second, manipulating.

Facebook experimented with News Feed by reducing or increasing posts containing positive and negative emotions. Then it measured the users’ reaction, which happened to be small but statistically significant, mostly because of the huge sample.

So, Facebook once tried what reputable The New York Times and Washington Post do every day (not to mention TV, penny press, and advertising industry). In fact, traditional media scrutinized the study and maybe raised more emotions than the original researchers did in the experiment.

Facebook surely learned the lesson and won’t publish significant research readily. Just like any other relatively open private company, including Google and Microsoft. Going stealth is safer for employees, which also means less collaboration with outsiders from universities and less openness to the public in general.

If doing “experiments” is punishable, then it’s better to leave everything as is. But that does most harm.

Experiments test hypotheses. When you have no experiments, you have hypotheses alone, true and (more often) false. Meanwhile, decisions are still being made. In Facebook, in GE, or in the government. Policymakers have to. They have many hypotheses for that, even if they never mention the word.

If you’re a CEO of a large corporation, you have hypotheses about your employees. You may think that praise works better than salary, and praise more. Or increase the payroll, or threaten the employees. But without experiments you can’t know if you’re right. Having positive experience indicates a little, because anyone can be right by chance. Even coin flipping gives a 50% chance. Without systematic evidences, you end up doing Machiavellian stuff and hurting people who trust you.

Current practices do much harm because they came out of all the crazy theories authors had. Governments have their own, corporations do. Experiments in the present help find better solutions. And all attention to the Facebook study, Ebola, and Michael Jackson’s death you could devote to questioning what happens every day. It makes the difference.

Ordinary government failures

(comparing public policies against one of the deadliest diseases; source)

Governments make mistakes. But not those that typically get into the press.

Stories about government failures—sort of Brooking’s and Heritage Foundations stories here and there—are inconclusive. It’s unclear where a “failure” starts because you have no baseline for “success.” In result, the press and think tanks criticize governments for events anyone can be blamed for. Mostly because of huge negative effects.

The financial crisis of 2008 is a conventional government failure in public narratives. September 11 is. But neither was predicted by alternative institutions. Individual economists who forecasted the burst in 2008 came from different backgrounds, organizations, and countries. These diverse frameworks—though being very valuable dissent—are not a systematic improvement over mainstream economics. Predicting 9/11 has even a weaker record (Nostradamus and similar).

Governments make other, more systematic, mistakes. Studying and reporting these mistakes make sense because a government can do better in next iterations. The government can’t learn from the Abu Ghraib abuse, however terrible it was. But it can learn to improve domestic prisons, in which basically similar things happen routinely.

Systematic problems are easier to track, predict, and resolve. A good example unexpectedly comes from the least developed nations. Well, from international organizations and nonprofits that run their anti-poverty programs there. These organizations use randomized evaluations and quasi-experimental methods to separate out the impact of public programs on predefined goals. The results show manifold differences in efficacies of the programs—and it’s a huge success.

Organizations such as the MIT Poverty Action Lab and Innovations for Poverty Action evaluated hundreds of public policies over the last ten years. Now, guess how much press coverage they got. Zero. The NYT can’t even find the Lab mentions among its articles. Google returns 34 links for the same query, most of them to hosted blogs.

One explanation is the storytelling tradition in newspapers. Journalists are taught to tell stories (which is what readers like). Presenting systematic evidences makes a bad story. You have little drama in numbers, however important they are. And telling numbers reduce your readership, which is incompatible with a successful journalist career. Even new data journalism comes from blogs, not well-establised publishers.

More fundamentally, mass media’s choice of priorities leads to little attention to systematic problems in general. Each day brings hot news that sound interesting, however irrelevant and impractical they may be. Reporting public policy research can’t compete in hotness with political speeches and new dangerous enemies around. It took a couple of decades for climate change to become a somewhat regular topic. And survival rates of other important issues are much lower.

NYT-speak continued

The New York Times’ choice of words tells much about history and the media. As seen before, their Chronicle shows great snapshots of the newspaper’s wording evolution.

Here, a few more cases.

Information sourcing

As Robert Fisk once noted, the media now rely more on what officials said, rather than sourcing news by themselves. That’s a confirmation:

Money and knowledge in crises

Though in general money and knowledge move in the same directions, money moves in greater magnitudes. Also, notice that in the Great Depression, as well as in the Great Recession, the NYT mentioned money less frequently. And the opposite happened during stagflation in the 70s.

Inflation and unemployment

Mentions of unemployment and inflation went in different directions before the 70s: right until the economy happened to have both. But it was a short period and right now there’re no mutual relations (at least, in wording).

In- and equality

Inequality never was an issue for the NYT. Even in the late 20s, when inequality was extremely high. So, it’s a new topic. Meanwhile, previous mentions of equality are generally associated with civil rights movements, as in the 60s.

“Make war, not love”

At its peak, war themes took up to 30% of the newspaper materials. But local wars, like Iraq and Afghanistan, never draw so much attention.

Referring to minorities

A similar graph was in the previous post, but here changes in wording are clearer. Especially right after the Civil War, when politicians no longer needed support from the black population, and one hundred years later, when politicians and media had to update their vocabulary.

Sports becoming more popular