So, if the term malicious isn’t appropriat, what term is? English isn’t my native language you know.
I think likes, when looking at all of them and not just a single example, are more then just noise.
The summarized topic view for instance works pretty well for gameplay idea threads.
Also. Those post with above 5 likes generally do have more “vallue” by representing more people, including a good idea or just being funny.
When reading a post and agreeing with it slightly, there is usually an emotional reaction. The like button allows for an imidiate action without the fuss of constructively backing off that simple emotion with arguments.
I’ve looked at my profile to see which posts were liked the most. It was an fascinating encapsulation of forum member interest in various topics.
Only if you place value on receiving likes. To me, they are a feedback mechanism which is useful only in a relative fashion. Any moron can pander to get people to hit the like button, but if the poster is being sincere and the likes are sincere, then the feedback is useful. If the likes are not sincere, then the system goes into the scrap bucket.
We can’t have nice things in life because too many people aren’t interested in being sincere. Or far worse, of course.
I can’t have nice things because they’re more likely to get stolen or destroyed by inconsiderate a-holes. I’m very sincere about that.
On a more deliberate note, people can like a post for a variety of reasons, and as such, sincerity can’t be determined, let alone what they liked about the post. I mean I could like your post for the use of “moron” and “pander” in the same sentence or perhaps I’m dyslexic and thought you got an insane number of BJs. You wouldn’t know unless I told you.
That would fall under “far worse, of course”. “Nice things” in this context is stuff like likes or editing a post after a time limit is up, or countless other forum restrictions that are intended to stop people from doing destructive things.
Yes, you could. We can always come up with “what if” scenarios that are a couple standard deviations off the median, but the bottom line is that I can look at the likes on my posts and infer what gets people sufficiently enthused to click that button.
Of course. Suddenly posts that under usual circumstances would receive no likes are receiving dozens. The advantage of statistical analysis is precisely to be able to spot such irregular behaviour (and under ideal circumstances, filter it out).
Now you’re applying a subjective filter to your statistics. If you think your post would, under normal circumstances, receive more likes than average and you got trolled by a normal sample size during that same sampling, then your statistical conclusion fails.
The interpretation of the “like” button is as subjective as its usage. Statistics has no place here.
Actually, I’m having way more fun reading a debate about people talking about other people liking their posts and analysis and purpose thereof rather than actually liking their posts.
I’m going to have to create more slightly controversial threads in the future.
Of course it would tell you what likes are used for and how they’re used. It’s kind of in the definition to “like” something. Most people across a large enough random sample will use the button to indicate that they found something positive out of a post simply because that’s what “liking” something means.
But we haven’t been talking about what they’re using a like button for…we’re talking about WHY they like it. Any interpretation at that point is subjective. If you’re going to try and use statistics to divine that question, then you also have to start narrowing the topics of posts that are liked down to much narrower subject matters in order for any conclusion to have meaning. If you have to start purposely selecting and rejecting posts that have been liked in order to get a sensible meaning, then your entire statistical analysis is based on your subjective interpretation of that subject matter. In which case, you would be better suited to creating a questionnaire that asks people why they liked a post, and if you have to ask people why they liked a post, then doing a statistical analysis at that point to find out why they liked the post is rather redundant.
A researcher can objectively rate the posts being evaluated on things such as, humor, seriousness, complexity, sarcasm, helpfulness and many other criteria. They might find the reason why people “like” the most is helpfulness or humor. So I think you are underestimating the power of statistical analysis.
Welcome to the future! While you’ve been travelling through time, statistics have gotten scary. Procedurally analysing the contents of posts and cross referencing the number of likes received can not only tell you what types of content matter is most likely to get likes, but whether or not the people giving likes are pregnant and/or highly intelligent.
A subjective interpretation of objectiveness, as anyone determining whether the researcher’s interpretations are objective is subjective. Many people might find them to be objective, while others would think they are highly opinionated. Even developing standards of objectiveness is subjective in its own right.
I’m not underestimating its power. I believe you are underestimating the sampling size you would need to draw such conclusions, realizing that once you reach a such a size, you’re only going to have probabilities of your conclusion being accurate.
Again…requiring massive individualized statistics of individual people’s behaviors across the full spectrum of their likes, and also relying on the principle of homophily, which, considering its based around social tendencies, is probabilistic at best. This is less likely to show why any particular individual likes something and more that there is a probability within a given population size of people that someone will like a particular post.
If we assume you have a large history of data on an individual person or even people for that matter, then you are not determining why people like something, you are predicting patterns of behaviors of individuals, and thus a probabilistic chance that any individual will click a like button based on the subject matter.
Probabilities are more suited towards marketing. Companies don’t particularly care why people like something, only that a large percent of people are more likely to like something or that any individual exhibiting certain behaviors is more likely to like something and thus is a thing they should focus their sales efforts towards.
The point of all of this, is to not dismiss human cognition in favor of statistical trend analysis, personal or population. Determining why people like something based on a trend is still subjective and is still an exercise in cognition and potentially a significant amount of investigation.
I’m leaving it at this before it turns into some kind of Seldon Crisis.
First off, you can teach a machine how to identify certain concepts, either based on rules or just a ton of data. So your “researcher” that is objectively rating the posts, could really be as objective as they get, a machine.
Second, I think you are looking at statistics from the wrong point of view, it’s not about any specific post or any specific person, it is a law of averages basically, it will give you probabilities and their certainty given you have enough data.
I know you might not like the fact that an algorithm can conclude that you are an adict or have a 90% chance of becoming one within some time-frame with a 99% certainty, based on your facebook likes, but from the video @Runiat posted it seems very likely that this is today a possibility.
This is cognition, whether human or machine, it is used to answer the question of why.
I said as much, did you not understand my post? You are not seeing differences between determining a reason by cognitively understanding something versus determining a reason by trend analysis and probabilities. If the reason for a like on a post is never stated, you must cognitively infer or deduce that reason. This applies to large data sets as well. The why is not there, only the probability that people will like it. In fact there are probably a multitude of reasons why, but if you try to determine a ‘why’ based solely on even a near certain probability that someone will like a post, you are being subjective even with a standard methodology.
Trend analysis based on behavior and whatever other environmental factors are given, not cognitive conclusion of why.
You need to take a closer look at what was said during that video before you hold statistical conclusions as doctrinal truth. The assumptions made and data sets used can end up making wildly different relations if they are not scrutinized and constrained appropriately. The curly fries, while a nice clincher to get people to listen, should be a red flag. If you don’t constrain it in some way, you might as well conclude with Kevin Bacon every time.