July 7th 2019
For a while now we’ve all known one can guess the generation from the given name. And there are sites and reports that will tell which names are most likely to be liberal or conservative, for example here and here.
Well, we can do the same thing with article authors/journalists.
In the first list, we are sampling our most productive 10k authors rated (recent articles, one article minimum), and requiring at least 10 authors with the same name. We are not (yet) giving special weight to being left or well to the left, but we are omitting anyone just slightly left. Same with slightly to the right. (Below we will pay more attention to extent.) Also, we are rating language used by these authors recently, not the stances they are taking. There is sometimes an important difference. Probably not, but possibly.
Author given names most likely to be rated left.
0.6875 Nicholas LEFT 0.684211 Claire LEFT 0.68 Simon LEFT 0.654545 Sam LEFT 0.631579 Jesse LEFT 0.625 Anna LEFT 0.621212 Sarah LEFT 0.621212 Ben LEFT 0.62069 Hannah LEFT 0.578947 Rob LEFT 0.571429 Kate LEFT 0.555556 Alex LEFT 0.551724 Emma LEFT 0.541667 Molly LEFT 0.540984 Peter LEFT 0.53125 Jamie LEFT 0.525 Amy LEFT 0.52 Alan LEFT 0.519231 Rachel LEFT 0.516667 Nick LEFT 0.513514 Rebecca LEF 0.5 Jordan LEFT 0.5 Jeremy LEFT 0.483871 Nicole LEFT 0.482759 Sean LEFT 0.48 Karen LEFT 0.477273 Jon LEFT 0.47541 Matthew LEFT 0.466667 Richard LEFT 0.466667 Patrick LEFT 0.466667 Erin LEFT 0.458333 Larry LEFT 0.454545 Phil LEFT 0.454545 Adam LEFT 0.452381 Julie LEFT 0.451613 Emily LEFT 0.449275 Tim LEFT 0.447761 Eric LEFT 0.447368 Stephanie LEFT 0.441558 Steve LEFT 0.433333 Will LEFT 0.430108 Mark LEFT 0.428571 Ken LEFT 0.428571 Gary LEFT 0.425 Elizabeth LEFT 0.421053 Lauren LEFT 0.418182 Jessica LEFT 0.416667 Chris LEFT 0.407767 David LEFT 0.406593 Matt LEFT 0.4 Ryan LEFT 0.4 Melissa LEFT 0.4 James LEFT
These are all the names where at least 40% of authors with that name rated left.
On the other side:
0.578947 Brandon RIGHT 0.545455 William RIGHT 0.53125 Justin RIGHT 0.525641 Kevin RIGHT 0.52381 Jacob RIGHT 0.5 Taylor RIGHT 0.481481 Tony RIGHT 0.481481 Kyle RIGHT 0.466667 Will RIGHT 0.454545 Bob RIGHT 0.447368 Andy RIGHT 0.444444 Jason RIGHT 0.433333 Mary RIGHT 0.431373 Dave RIGHT 0.428571 Mike RIGHT 0.428571 Ken RIGHT 0.423077 Aaron RIGHT 0.413793 Susan RIGHT 0.413043 Bill RIGHT 0.4 Melissa RIGHT 0.4 Andrew RIGHT
These are all the first names where the author is at least 4/10 rated right, or well-to-the-right.
Note that Melissa is 40% left, 40% right.
Most likely to be center-left or center-right?
0.517241 Steven CENTER 0.444444 Kelly CENTER 0.423077 Aaron CENTER 0.416667 Michelle CENTER 0.4 Katie CENTER
Actually Melissa is not even close to most non-center, with this list showing those top names:
0.96 Simon NON-CENTER 0.947368 Jesse NON-CENTER 0.9 Will NON-CENTER 0.894737 Claire NON-CENTER 0.875 Nicholas NON-CENTER 0.863636 Ben NON-CENTER 0.857143 Ken NON-CENTER 0.851852 Alex NON-CENTER 0.846154 Kevin NON-CENTER 0.837838 Rebecca NON-CENTER 0.833333 Larry NON-CENTER 0.825 Amy NON-CENTER 0.818182 Sam NON-CENTER 0.816667 Nick NON-CENTER 0.815789 Andy NON-CENTER 0.806452 Nicole NON-CENTER 0.803279 Peter NON-CENTER 0.8 Richard NON-CENTER 0.8 Patrick NON-CENTER 0.8 Melissa NON-CENTER
But we know Nicholas is up there because that is our top left-biased first name. So if we do a mathematical sum-of-squares trick, and limit the list to those who are no more that 50% left nor 50% right, here are the truly bipolar but balanced-bias names:
0.405556 Will 0.433333 0.466667 NON-CENTER 0.367347 Ken 0.428571 0.428571 NON-CENTER 0.350694 Larry 0.458333 0.375 NON-CENTER 0.338189 Nicole 0.483871 0.322581 NON-CENTER 0.335873 Andy 0.368421 0.447368 NON-CENTER 0.32937 Sean 0.482759 0.310345 NON-CENTER 0.328889 Richard 0.466667 0.333333 NON-CENTER 0.328889 Patrick 0.466667 0.333333 NON-CENTER 0.32595 Adam 0.454545 0.345455 NON-CENTER 0.32 Melissa 0.4 0.4 NON-CENTER
Much more important are the names with biases well to one side or the other. Well to the left:
0.5 Anna WELL LEFT 0.381818 Sam WELL LEFT 0.35 Amy WELL LEFT 0.344262 Peter WELL LEFT 0.333333 Nick WELL LEFT 0.324324 Rebecca WELL LEFT 0.318182 Sarah WELL LEFT 0.307692 Rachel WELL LEFT 0.30303 Ben WELL LEFT
And well to the right:
0.27451 Dave WELL RIGHT 0.266667 Mike WELL RIGHT 0.264151 Josh WELL RIGHT 0.25 Jonathan WELL RIGHT 0.242718 Tom WELL RIGHT 0.229167 Stephen WELL RIGHT 0.217949 Kevin WELL RIGHT 0.2 Andrew WELL RIGHT
As noted by other studies, the right extremes are populated by men’s names. Note that Nicholas is highly probably left, but not highly probably well to the left. To signal far left, not just probably left, one should use Nick, not Nicholas.
Unless one works for the CIA, this might not be the most important information. One wants to know bias, and the ratios of probabilities can be massive. But as betting odds in un-scaled terms, one might better leave the bias-assignment to machine learning accounting using a few more features. Used to be you could tell a hippie by his hair.
An article by a Nicholas is better than 2/3 likely left, but even a Brandon is in an important respect (right or not) just better than a coin flip. Anna, though, is a coin-flip good for being well left, which is probably a good life hack or at least a good thing to know before plowing into a random article by an Anna. Still, if one does not have a more precise bias rating on an author, people already have some tutored intuitions that they bring to the read.
Truly beware of an article by an Ana, if one is not in a lefty mood (note that Ana did not make our minimum-of-10-rated-names cut, above); again, just using more left-rated phrases than right-rated, and doing so more than half or 2/3 of the time, is not a comment on merit or fact. It may of course be a badge of honor, or simply the preference of the publication/readership.
Ana Suarez AVG IS 70.0958% WELL TO THE LEFT Ana Campbell AVG IS 68.75% WELL TO THE LEFT Ana Valens AVG IS 60.8696% WELL TO THE LEFT Anastasia Basil AVG IS 42.8571% WELL TO THE LEFT Ana Col AVG IS 38.8889% WELL TO THE LEFT Anabel Pasarow AVG IS 36.5809% WELL TO THE LEFT Ana Calderone AVG IS 27.4004% WELL TO THE LEFT Ana Rivera AVG IS 13.0787% LEFT Anastasia Dawson AVG IS 7.4719% SLIGHTLY C-RIGHT Anastasia Tsioulcas AVG IS 26.0796% WELL TO THE RIGHT
Probably the Russians are already gaming the names when they do their disinformation campaigns.
It is quite interesting that the Rush’s and Glenn’s and Laura’s and Donald’s don’t show here. Some would say the correlation simply isn’t there. Others might say that the brand-recognition requires a certain deviation from the mean. I think it’s because the strategy of the far right is simply to echo — even quote — the language of the far left, usually in a mocking or provocative fashion. It’s definitely a problem for bias rating when they do this, and a lot of them do it a lot of the time.
Perhaps Nicholas, Claire, and Simon should change names to Steven, or vice versa, depending on the readership. Or change one’s name to Melissa, just to keep everyone guessing. A world where all journalists use the name Melissa. That’s a bias-betting dystopia for sure!
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