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Do Social News Sites Deteriorate? (Analysis of 1.8M HN Comments)

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11–20 of 57 posts

Re: Do Social News Sites Deteriorate? (Analysis of 1.8M HN Comments)

#11
Certainly we can all argue about the underlying structure of the analysis of the data. However, I think that @tansey has done two great services here:

1.He's proposed a full service taxonomy with by proposing a name for the phenomenon SND. Which is much a much better choice than JTS - Jumping the Shark or otherwise.

2. He's asking how we can evaluate that phenomenon. Proposing one solution.

So the question becomes, how else can we evaluate the phenomenon and what can we do to reduce SND?

Well having spent time elsewhere, here are a number of clear indicators of SND:

1. Shorter, less thoughtful responses, often veering into humor or the absurd. With chuckles getting the most upvotes

2. Less fact checking and less source linking in both posts and comments

3. More image / pic posting

4. Linkjacking, with materials not linked to the original materials.

5. More community centered posts aka AMA etc.

6. Fewer news links.

So perhaps simply evaluating the length of comments of that same 1.8M HN data could support PG allegations.

The next question is what can be done to prevent SND?

I think that would be clear: Don't support the characteristics that lead to the decline.

Re: Do Social News Sites Deteriorate? (Analysis of 1.8M HN Comments)

#12
post #9

Hi everyone, So there seems to be a lot of skepticism about whether our algorithm can actually measure emotional impact accurately. For the long answer, I'll refer you to the about page[1] for EffectCheck. For the short answer: My co-founders are an AI PhD and a Clinical Psychology PhD. They spent three years curating a huge dictionary of words using a methodology similar to the Harvard Psychosocial Dictionary [2], b…

This sounds like solid and worthy research. I would be especially interested to see your Amazon words v. stars results. Just off the top of my head, I can see a ton of uses for your research -- More accurate real time political polling / Better product assessments across the vast internet landscape for both designers and brand managers / Policy analysis for new policy proposal and communication evaluations. This has fantastic potential. Please show us the testing and results when you get to it. Thank you for sharing.

Re: Do Social News Sites Deteriorate? (Analysis of 1.8M HN Comments)

#13
post #9

Hi everyone, So there seems to be a lot of skepticism about whether our algorithm can actually measure emotional impact accurately. For the long answer, I'll refer you to the about page[1] for EffectCheck. For the short answer: My co-founders are an AI PhD and a Clinical Psychology PhD. They spent three years curating a huge dictionary of words using a methodology similar to the Harvard Psychosocial Dictionary [2], b…

You claim that context is not important, but surely in a domain-specific corpus such as HN comments you have to take domain, if not context, into account?

For example, words that are generally positive may be only used in a snarky context in hacker circles, whereas words that are generally negative may similarly be used with positive affect (e.g. the word "hacker" itself). Did you customise your lexicon at all for this genre?

Also, in my experience, it's not the lexicon of words -- heck, SentiWordNet has existed for long enough -- it's what you do with that input signal that counts. Many "bag of words" approaches are on the shelf now, and very few of them are particularly accurate or clever, but most of them do the job just well enough that they get away with it.

Re: Do Social News Sites Deteriorate? (Analysis of 1.8M HN Comments)

#14
post #13
post #9

Hi everyone, So there seems to be a lot of skepticism about whether our algorithm can actually measure emotional impact accurately. For the long answer, I'll refer you to the about page[1] for EffectCheck. For the short answer: My co-founders are an AI PhD and a Clinical Psychology PhD. They spent three years curating a huge dictionary of words using a methodology similar to the Harvard Psychosocial Dictionary [2], b…

You claim that context is not important, but surely in a domain-specific corpus such as HN comments you have to take domain, if not context, into account? For example, words that are generally positive may be only used in a snarky context in hacker circles, whereas words that are generally negative may similarly be used with positive affect (e.g. the word "hacker" itself). Did you customise your lexicon at all for th…

...surely in a domain-specific corpus such as HN comments you have to take domain, if not context, into account?

That's a great insight and I believe you are correct. In general, I think the domain does have an impact and I am working on some automatic demographic profiling technology. However, for a first-look, I think our general word dictionary covers an abundant set of terms that do not vary much between domains. For instance, your comment has the following anxiety-evoking words:

not, important, but, example, positive, used, hacker, negative, long, bag, but, away

Of all those words, only "hacker" would likely need to be adjusted for the HN domain. For a first look, we'll chalk that up to some minor added noise in the data.

EffectCheck does include the ability to automatically profile the document type (e.g., "Press Release" or "Motivational Speech") based on a training corpus. For example, most press releases have a high degree of confidence-- based on a corpus of press releases, we normalize the levels so that "Very High" on confidence for a press release requires much higher confidence elicited per-word than it would in a complaint letter.

Re: Do Social News Sites Deteriorate? (Analysis of 1.8M HN Comments)

#15
post #9

Hi everyone, So there seems to be a lot of skepticism about whether our algorithm can actually measure emotional impact accurately. For the long answer, I'll refer you to the about page[1] for EffectCheck. For the short answer: My co-founders are an AI PhD and a Clinical Psychology PhD. They spent three years curating a huge dictionary of words using a methodology similar to the Harvard Psychosocial Dictionary [2], b…

I can easily believe that you can predict quite well ratings of Amazon reviews by a simple keyword-counting algorithm. Just by counting words like "good", "enjoyed", "fantastic" vs "terrible", "boring", "awful" you're gonna get a very strong correlation in that limited domain.

But what tests have you done on your broader methodology? What experiments can you really do to figure out the extent to which the use of the word "nosegay" is correlated with actual depression?

Also, as someone else said, where are the error bars? If there really is a correlation between word choice and other metrics, then some simple statistics should give you error bars on your other metrics, right?

Oh, one more thing: in the example on your website you say that the sentence:

"That joke kills me!"

is "subconsciously" aggressive. My question: would your algorithm rate that at exactly the same level of aggression as the sentence:

"I'm gonna kill you!"?

cuz, y'know, intuitively one seems rather more aggressive than the other.

Re: Do Social News Sites Deteriorate? (Analysis of 1.8M HN Comments)

#16
post #13
post #9

Hi everyone, So there seems to be a lot of skepticism about whether our algorithm can actually measure emotional impact accurately. For the long answer, I'll refer you to the about page[1] for EffectCheck. For the short answer: My co-founders are an AI PhD and a Clinical Psychology PhD. They spent three years curating a huge dictionary of words using a methodology similar to the Harvard Psychosocial Dictionary [2], b…

You claim that context is not important, but surely in a domain-specific corpus such as HN comments you have to take domain, if not context, into account? For example, words that are generally positive may be only used in a snarky context in hacker circles, whereas words that are generally negative may similarly be used with positive affect (e.g. the word "hacker" itself). Did you customise your lexicon at all for th…

I'm pretty sure that even when we don't know how accurately it can measure the 'absolute' values, we can clearly see the change which is relative to the comments on the same context and domain. It can be argued if it really measures happiness etc., but I'm sure there's correlation in the change even if it's not accurate.

Re: Do Social News Sites Deteriorate? (Analysis of 1.8M HN Comments)

#17
post #7

I don't mean to come across as hostile or undermine the effort that's been invested, but I can't help but be a tad skeptical about these visualizations, especially given the fact that the author (in what might be construed as cavalier fashion) presents seemingly nebulous metrics like they are absolute matter-of-fact ("Anxiety/Confidence Ratio", "Hostility/Compassion Ratio", "Depression/Happiness Ratio"). It would cer…

>And with that, I throw down the gauntlet. Analyze this! Sure. Scored with EffectCheck: Anxiety - Very High Hostility - High Depression - Very High Confidence - Low Compassion - Low Happiness - Very Low I will post a longer explanation detailing how/why it works, since others have had this question as well.

This is excellent. Would you be willing to check this text from my blog please?

"It's said that great military commanders, chess players and Go players feel physical pressure on their stomachs when their game pieces are threatened, and the pressure indicates the moves to make. This full-body thinking communicates much more rapidly than purely deductive mental reasoning. The intuition is the result of thousands of prior episodes where such reasoning was employed, acted on and the outcome experienced in all its pain or glory.

Other than hours of practicing the game, or whatever one does, the only other way to improve the chance of learning this physical intuition is to be sure one's body is not sending conflicting signals. Please eat well and exercise."

Re: Do Social News Sites Deteriorate? (Analysis of 1.8M HN Comments)

#19
post #9

Hi everyone, So there seems to be a lot of skepticism about whether our algorithm can actually measure emotional impact accurately. For the long answer, I'll refer you to the about page[1] for EffectCheck. For the short answer: My co-founders are an AI PhD and a Clinical Psychology PhD. They spent three years curating a huge dictionary of words using a methodology similar to the Harvard Psychosocial Dictionary [2], b…

I know this stuff works. Some friends of mine from Cornell were able to train an SVM to predict people's moods on Livejournal.
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