Most characteristic words in successful and unsuccessful petitions









“Gun” is the most characteristic word in successful petitions. (Again, “successful” means there were enough signatures for the White House to write a response, few if any of the petitions’ requests were enacted.) There were both successful pro- and anti-gun control petitions; this is an issue that people on both sides feel passionate enough about to participate in this project.

There are also four names from Netflix’s Making a Murderer series: Avery (the defendant), Halbach (the victim) and both first and last names of Brendan Dassey, the other defendant.

The Westboro Baptist church is a popular item, as are “tragedy,” “Connecticut” and “CT” from the Newtown shootings. (Interestingly, “Newtown” had a somewhat lower log-likelihood keyness of 8.54, indicating it was a little more common in unsuccessful petitions than the other terms.)

Some superlative words seem to be common in successful petitions: “imperative” and “definitely”.

As for the unsuccessful petitions, the most overrepresented word is “say”. A skim through these petitions (and there are a lot of them), reveals that many of them are motivated by some perceived injustice of perception, e.g. “people say X, but I believe Y”.

The word “I” is associated with unsuccessful petitions, as well. Those who write (possibly rant) in the first person don’t get a lot of support; perhaps the fact that they reference themselves is an indication they don’t have enough organizational support to get a lot of signatures.

“Genocide” turns up a lot in unsuccessful petitions. Most of the time, it’s in the phrase “white genocide”.




So, since we have two corpora (words used in successful petitions, words used in unsuccessful petitions), and a frequency metric, why not do a simple keyness analysis? Dunning log-likelihood keyness is one of two approaches (the other being chi-squared) to determining the significance of differences in item frequencies between two datasets; it’s used a lot in corpus linguistics, although it’s falling out of favor as more advanced techniques become computationally feasible.

The nice thing about keyness is that it is a measure of significance, combining the effect of ratio and absolute difference between frequencies, and the size of the corpora. This makes it unnecessary to eliminate stopwords, which is an arbitrary process that always rubs me the wrong way. If “and” is used very often, it still might be more significantly found in one corpus than another, but the threshold of difference in frequencies is much higher than for a less common word. Conversely, if an extremely rare word is found 10 times in one large corpus and once in another, it’s unlikely to pass the threshold of significance even though the ratio between the frequencies is high.


Here’s the 4.4 MiB csv file made from the SQL dump in March 2016.


2 Replies to “Most characteristic words in successful and unsuccessful petitions”

  1. I converted the MySQL dump to csv on a Windows desktop with 16 GB RAM and an i5 processor, and it took several hours (More than 3, less than 12, I let it run overnight … it converted all the tables in the dump, not just the table with the petition text; this includes a table with every single signature, which is quite a bit larger.)


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