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Riddell's algorithm begins with the Wikipedia entries of all authors in the English language edition (PDF)—more than a million of them. His algorithm extracts information such as the article length, article age, estimated views per day, time elapsed since last revision, and so on. This produces a "public domain ranking" of all the authors that appear on Wikipedia. For example, the author Virginia Woolf has a ranking of 1,081 out of 1,011,304 while the Italian painter Giuseppe Amisani, who died in the same year as Woolf, has a ranking of 580,363. So Riddell's new ranking clearly suggests that organizations like Project Gutenberg should focus more on digitizing Woolf's work than Amisani's. Of the individuals who died in 1965 and whose work will enter the public domain next January in many parts of the world, the new algorithm picks out TS Eliot as the most highly ranked individual. Others highly ranked include Somerset Maugham, Winston Churchill, and Malcolm X.
How long you were dating: (Couples who dated 1-2 years before their engagement were 20% less likely to end up divorced than couples who dated less than a year before getting engaged. Couples who dated 3 years or more are 39% less likely to get divorced.); How much money you make: (The more money you and your partner make, the less likely you are to ultimately file for divorce. Couples who earn $125K per year are 51% less likely to divorce than couples making 0 — 25k); How often you go to church: (Couples who never go to church are 2x more likely to divorce than regular churchgoers.); Your attitude toward your partner: (Men are 1.5x more likely to end up divorced when they care more about their partner's looks, and women are 1.6x more likely to end up divorced when they care more about their partner's wealth.); How many people attended the wedding: ("Crazy enough, your wedding ceremony has a huge impact on the long-term stability of your marriage. Perhaps the biggest factor is how many people attend your wedding: Couples who elope are 12.5x more likely to end up divorced than couples who get married at a wedding with 200+ people."); How much you spent on the wedding: (The more you spend on your wedding, the more likely you'll end up divorced.); Whether you had a honeymoon: (Couples who had a honeymoon are 41% less likely to divorce than those who had no honeymoon)
Of course correlation is not causation. For example, expensive weddings may simply attract the kind of immature and narcissistic people who are less likely to sustain a successful marriage and such people might end up getting divorced even if they married cheaply. But "the particularly scary part here is that the average cost of a wedding in the U.S. is well over $30,000," says Olson, "which doesn't bode well for the future of American marriages."
This study is also significant for the sample size, which allows it to address whether the data from such large sets has a tendency to converge or diverge on genetic pathways; this study particularly favors the latter, which is of great utility toward studying other polygenetic conditions in the future. The original paper is likely paywalled, however the abstract is available for free and some of the collaborators behind it have other bits available for free in the meantime.
The current "user rating" ranking system seems to divide everything into big semantic buckets ("Overwhelmingly Positive", "Positive", "Mixed", etc.), stack those in order, then sort each bucket's contents by the total number of reviews per game. Given that Steam reviews skew massively positive, (about half are "very positive" or higher), this is virtually indistinguishable from a standard "most popular" chart.
Luckily, there's a known solution to this problem — use statistical sampling to account for disparate numbers of user reviews, which gives "hidden gems" with statistically significant high positive ratings, but less popularity, a fighting chance against games that are already dominating the charts.