references, picture count and rating
today i’m continuing my series on the connection between measurable profile variables on CS and my own personal rating of the experience. since it is a personal subjective rating, consider all of this data as merely informative.
again, i used the same database, and this time loaded the whole thing into octave. then i calculated the correlation coefficients between each variable and my personal rating. octave is much better to work with, so i’m abandoning “pretty” graphs for more accurate ones.
correlation factors found
| variable | correlation with rating |
|---|---|
| number of photos | -0.013480 |
| number of positive references | 0.068074 |
| number of neutral references | 0.039177 |
| number of negative references | 0.0037717 |
it’s interesting to note that of all, the strongest indicator is the total number of positive references, even though it is very weak. it is also interesting that the correlation factor between negative references and rating is positive. i would say it is a small sample size for me to conclude anything, but it’s interesting nevertheless since one would expect a negative correlation for both neutral and negative, if these were good predictors of bad experiences
graphs of the data distribution
as usual, bell curves slightly tilted to the positive experience. since the distribution seems to be consistent with a bell curve, the correlation coefficient is appropriate enough.
sources for replication
besides the usual database, here is the octave script. make sure you edit the shebang line and the database path according to your needs.
discussion
by now i’m a bit tired of getting always the same “random variable” results. it would be interesting to look at datasets of people that believe references, images, etc are predictors. since i controlled for these variables at my own place, my dataset is the “raw” version of the experience. my guess is that depending on the variable chosen, each person would selectively bias their own perspective on the experience, when if controlled for, the variables amount to very little in terms of predictive power.
this would allow us to see how is it that things really feel like they work when they don’t: we shape it ourselves via our own perceptions of the other. by assigning arbitrary characteristics to them that come from some outside narrative and not a real, factual analysis of who they really are. prejudices come in many forms, and these online status symbols are themselves tools of prejudice and social differentiation. it is by now clear that these symbols have no evidence to support their validity, however, they remain essential tools for this group’s stability. the seeking and valuing of these virtual status symbols creates a shared belief that one should behave appropriately, which causes a safer behavior in general.
so, in a somewhat contradictory way, even though these status symbols are not relevant in practice in terms of quality of the experience, they become so through common beliefs in their validity, and this makes the community as a whole work more harmoniously.
as a side effect, anyone who unknowingly ignores the social codes of CS will be treated as an example of the things these symbols are trying to protect people from, even though there is no evidence this is true, creating a whole set of people that will suffer injustice and prejudice on this network. these people will rapidly leave the network due to the overwhelming unfriendliness that they might encounter. this filtering out then leaves only the people that comply with the social codes, rendering the network functional.
nowhere in this chain of filtering and reasoning is any account to facts. there is no critical analysis of any of this data. but that shared irrational belief is what keeps the community functional. in a way, these status symbols and social rules are essential to properly filter out users on this network, not because they work, but because they are only accepted and tolerated by those who believe them, and those who don’t are progressively outcast or eliminated.
many times i ended up hosting people that were clueless about how to message someone and that felt great injustice about it. fact is that CS works very well, but only for a group of people. for the rest of the “scum” that uses it, the frequent failures to find a place, the angry or disrespectful replies and judging encounters will cause people to leave the network.
i’ll do another one on this: my hypothesis is that CS is a temporary phase in people’s lives, and that after some time or a bad experience people will simply leave it. my guess is that there are nowhere near 3 million CSers. i’ll dig for the real numbers soon.