Cakoluchiam comments on 2012 Survey Results - Less Wrong

80 Post author: Yvain 07 December 2012 09:04PM

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Comment author: Cakoluchiam 29 November 2012 10:23:55PM *  4 points [-]

TROLL TOLL POLICY: Disapprove: 194, 16.4% Approve: 178, 15%

So more people are against than for. Not exactly a mandate for its use.

Hypothesis: those directly affected by the troll policy (trolls) are more likely to have strong disapproval than those unaffected by the troll policy are to have strong approval.

In my opinion, a strong moderation policy should require a plurality vote in the negative (over approval and abstention) to fail a motion to increase security, rather than a direct comparison to the approval. (withdrawn as it applies to LW, whose trolls are apparently less trolly than other sites I'm used to)

Comment author: gwern 29 November 2012 11:15:29PM *  16 points [-]

Hypothesis: those directly affected by the troll policy (trolls) are more likely to have strong disapproval than those unaffected by the troll policy are to have strong approval.

Hypothesis rejected when we operationalize 'trolls' as 'low karma':

R> lwtroll <- lw[!is.na(lw$KarmaScore),]
R> lwtroll <- lwtroll[lwtroll$TrollToll=="Agree with toll" | lwtroll$TrollToll=="Disagree with toll",]
R> # disagree=3, agree=2; so:
R> # if positive correlation, higher karma associates with disagreement
R> # if negative correlation, higher karma associates with agreement
R> # we are testing hypothesis higher karma = lower score/higher agreement
R> cor.test(as.integer(lwtroll$TrollToll), lwtroll$KarmaScore, alternative="less")
Pearson's product-moment correlation
data: as.integer(lwtroll$TrollToll) and lwtroll$KarmaScore t = 1.362, df = 315, p-value = 0.9129
alternative hypothesis: true correlation is less than 0 95 percent confidence interval:
-1.0000 0.1679 sample estimates:
cor 0.07653
R> # a log-transform of the karma scores doesn't help:
R> cor.test(as.integer(lwtroll$TrollToll), log1p(lwtroll$KarmaScore), alternative="less")
Pearson's product-moment correlation
data: as.integer(lwtroll$TrollToll) and log1p(lwtroll$KarmaScore) t = 2.559, df = 315, p-value = 0.9945
alternative hypothesis: true correlation is less than 0 95 percent confidence interval:
-1.0000 0.2322 sample estimates:
cor 0.1427

Plots of the scores, regular and log-transformed:

<code>plot(lwtroll$TrollToll, lwtroll$KarmaScore)</code>

<code>plot(lwtroll$TrollToll, log1p(lwtroll$KarmaScore))</code>

Comment author: Cakoluchiam 29 November 2012 11:25:52PM 15 points [-]

If this were anywhere but a site dedicated to rationality, I would expect trolls to self-report their karma scores much higher on a survey than they actually are, but that data is pretty staggering. I accept the rejection of the hypothesis, and withdraw my opinion insofar as it applies to this site.

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