7 ms·

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

110 points by tansey 15y ago

elblanco 15y ago
At risk of coming out of HN retirement, a friend forwarded this topic to me. I wrote an extensive commentary before signing-off: http://news.ycombinator.com/item?id=2059012 http://news.ycombinator.com/item?id=2059012 (now back to peaceful slumber)
nbashaw 15y ago
How does the author measure anxiety, hostility, depression, confidence, compassion, and happiness? That seems to be left out.
- tansey 15y ago
  Hi! My startup [1] is based on an algorithm that can measure the emotional impact of text. Our blog is where we post interesting results from applying our algorithm. [1] http://effectcheck.com http://effectcheck.com
  - hugh3 15y ago
    Uhhhhh-huh. But how does the algorithm work, and (more importantly) how do we know whether it works or not?
  - pavel_lishin 15y ago
    I assume the actual algorithm itself is not open sourced. I'm wondering if there's anything that trips it up, like adding points to a comment's "hostility" score for profanities, which some people use when they're fucking excited about an idea.
  - gammarator 15y ago
    Trying the sample EffectCheck script on http://effectcheck.com/about http://effectcheck.com/about : the sidebar says it's counting occurrences of phrases which score on each dimension. How is the association of word/topic determined? The measurement is valuable if and only if "Anxiety" defined as the count of ("different", "trigger", "taken out", "over", "carefully"...) corresponds to anxiety on some other psychologically relevant scale.
  - raganwald 15y ago
    Speaking of your startup, it appears to have "Oracle Pricing" as its revenue model. You have to talk to a salesperson to get a price, which is often code for "We need to know how much you can afford and how much you are willing to pay before we quote a price." I like everything else I see.
noelsequeira 15y ago
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 certainly help if the algorithm used to compute these metrics were shared and dissected. I tend to believe that sentiment analysis is much more art than science, given how tricky and profound context can be. To conclude, I'm not going to end this comment on a negative note. I'd much rather reserve my vitriol and caustic criticism for another thread and day and abstain from calling this an attempt at gaming HN to promote a startup. I do this not out of a lack of indignation towards what I've just read, but because I'd like to end this seemingly hateful comment on a note that isn't bitter or negative but is instead quite the opposite (without using a single word that would help the OP's algorithm figure this out). And with that, I throw down the gauntlet. Analyze this!
- tansey 15y ago
  >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.
  - michaeldhopkins 15y ago
    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."
tansey 15y ago
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], but with the twist that they were focused on lexical impact of words. The dictionary is pretty accurate at measuring both impact and sentiment [3]. For example, we can predict Amazon reviews as being positive or negative, using the stars to validate if we are correct-- blog post on that coming soon. Regarding context of the word usage: Both behavioral studies and fMRI scans have confirmed that context is not as important as one might believe. Our brains process multiple meanings of words in parallel, and the emotions associated with those words linger in our subconscious even after we know the correct context. Similarly, in cases of reviews and comments, people who use hostility-evoking words are often hostile themselves (angry people tend to make others angry) and the same is true of the other five fundamental emotions we measure. Happy to answer more questions. Also happy to analyze any data that you would like to see in order to verify accuracy of the algorithm-- just give me a link or the text. :) [1] http://effectcheck.com/about http://effectcheck.com/about [2] http://www.wjh.harvard.edu/~inquirer/homecat.htm http://www.wjh.harvard.edu/~inquirer/homecat.htm [3] Note that sentiment is correlated to impact only if the writer or speaker is writing without detailed attention to word choice. For example, political speech writers comb over every word to make sure they have the desired impact-- thus, the final text likely has little correlation to the sentiment of the orator.
- ChuckFrank 15y ago
  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.
- jlees 15y ago
rryan 15y ago
Where are the confidence intervals and error bars? In order to be taken seriously when aggregating 1.8MM comments, you need those. The variability in pg's plots makes me think the data for any individual is going to be just as noisy.
- tansey 15y ago
  We are looking at a time-based analysis of the monthly means of PG and the HN community. The null hypothesis is that the slope of a regression line should be 0. Since we are dealing with a time-based analysis of the mean, the variance of each bucket is irrelevant. I re-ran the analysis to make sure that my results were statistically significant: Anxiety/Confidence PG: p <= 0.0007 HN: p <= 0.0001 Hostility/Compassion PG: p <= 0.0005 HN: p <= 0.0001 Depression/Happiness PG: p <= 0.0067 HN: p <= 0.0001 Note that for the HN comments, even though I used a 2nd order (parabola) fit for the graphs, the p values above are for a linear regression as that is the more appropriate fit for determining statistical significance here.
  - vecter 15y ago
    p values only make sense when the residuals are normal and independent. Is that true for this data set?
ChuckFrank 15y ago
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.
- recoiledsnake 15y ago
  > 5. More community centered posts aka AMA etc. I consider AMA the best kind of self post... I don't see community centered posts as necessarily a bad thing except if there is too much of drama or he-did, she-did type content.
- stcredzero 15y ago
  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 I think "Evaporative Cooling" is descriptive and very apt.
bhousel 15y ago
Any plans to compare against corpora from other social news sites? BTW, I think what you're doing is brilliant, and I can think of about a dozen practical applications for your technology.. Ignore the naysayers.
- tansey 15y ago
  Thanks! I'd be happy to analyze of corpora like reddit or Digg, but I don't want to scrape the sites as that is a great way to have my IP banned. Are there publicly-available datasets that I can download somewhere?
sidww2 15y ago
Are the number of votes for a posts available? It could be interesting to use the metrics given to compare posts with high vs low number of votes and could also help validate the use of given metrics.
- tansey 15y ago
  I did originally have a similar idea, where I started out by looking at the top 10% of comments each month (ranked by upvotes). However, the resulting trends looked pretty similar to the overall community: http://blog.effectcheck.com/top_dep_hap.png http://blog.effectcheck.com/top_dep_hap.png
mef 15y ago
Thanks for this interesting analysis. I would challenge the assumption that the symptoms of SND include a decrease in the positive emotion measured in the average HN comment. Is it not possible that there is a growing segment of the HN user base which posts and enjoys the kind of comment that induces negative sentiment in people like pg? Would that not raise the sentiment of the average HN comment, while at the same time lowering the sentiment of the average "good old days" user?
ignifero 15y ago
The solution: divide the community to more specialized subgroups. That's what http://textchannels.com http://textchannels.com is doing
pgroves 15y ago
What jumps out at me is that the community curves all peak in the months following May 2009, which is when the stock market bottomed. The community was least happy when the economy was at it's "darkest before the dawn" moment. In other words, I wonder if these graphs are just tracking general sentiment, and would look the same for any site.
- tansey 15y ago
  It's a good question, and I guess we'll see how well the correlation holds. It's worth noting that the stock market bottomed in March 2009, so this may be a lagging indicator.
bzupnick 15y ago
solution for the deterioration- why cant we simply disable the ability for people to post who get X amount of "off topic" votes?
brudgers 15y ago
Hypothesis: PG's mood is improving because YC of which HN is a part is increasingly successful. Hypothesis: The net mood of HNer's is deteriorating because it the ratio of financially successful members is lowered as a more people join. In addition as HN grows the ratio of preexisting and external relationships among members gets smaller, e.g. the number of HN'ers affiliated with YC companies has not kept pace with the overall growth in HN membership.
pg 15y ago
"Thus, if we look at the PG analysis as a measure of his mental state, we can speculate this is due to the prosperity he’s experienced in these last few years." Actually it's because the site has become so large that I have to be more diplomatic. When it was small I could say harsh-sounding things without worrying about being misinterpreted. Now I have to be more of a politician.
- tansey 15y ago
  That was my other hypothesis, but it didn't really correlate well with the growth in the site's user base. HN has experienced an exponential growth rate, but your comments show a linear decline in negative impact. I went with the "better life" theory because it seems like personal prosperity and happiness would be more likely to show gradual, linear change. This is also supported by the trends I saw in other famous top HN users, particularly those that I know have experienced increased financial prosperity.
  - djcapelis 15y ago
    Maybe they decided to become more diplomatic as the community has gotten larger and begun to have real impact on people's reputation. Why do you think that would just be limited to pg? I would think HN-famous people would especially be prone to become more diplomatic as their comments began to have more impact.
  - staunch 15y ago
    Instant link bait if you let us look up our own charts.
topomorph 15y ago
In case anyone wants to make their own trends (less sophisticated, but maybe more transparent), I have an app at http://hn-trends.heroku.com http://hn-trends.heroku.com that plots word percentages on HN over time. For example, some emotive words that appear to have increased since the beginning: * sad: http://hn-trends.heroku.com/trends?q=sad http://hn-trends.heroku.com/trends?q=sad * fuck: http://hn-trends.heroku.com/trends?q=fuck http://hn-trends.heroku.com/trends?q=fuck * shit: http://hn-trends.heroku.com/trends?q=shit http://hn-trends.heroku.com/trends?q=shit Some words that appear to have decreased: * great: http://hn-trends.heroku.com/trends?q=great http://hn-trends.heroku.com/trends?q=great * cool: http://hn-trends.heroku.com/trends?q=cool http://hn-trends.heroku.com/trends?q=cool * lol: http://hn-trends.heroku.com/trends?q=lol http://hn-trends.heroku.com/trends?q=lol * reddit: http://hn-trends.heroku.com/trends?q=reddit http://hn-trends.heroku.com/trends?q=reddit * digg: http://hn-trends.heroku.com/trends?q=digg http://hn-trends.heroku.com/trends?q=digg I haven't looked at any of this closely, so make of it what you will. (I was meaning to wait until I had time to dig deeper and add extra features like time series smoothers and trend/slope metrics before "releasing" this, but figured this might be useful now given the post. I still plan on adding those later.)
recoiledsnake 15y ago
Others have done a good job at critiquing the automated analysis, so I will concentrate on the hypothesis. >There is a perceived phenomenon among online social news communities (e.g. Digg, reddit) that as the popularity of the community increases, the average member becomes baser and the overall quality of discourse decreases. I think that's not an accurate portrayal. Having been through the "decline" of Reddit (been reading it even before it had comments!), I observed that the real decline was in the quality(as perceived by me) of stories that made the frontpage(s) and in the quality(as perceived by me) of comments that were upvoted. In 2006, I would literally read almost every story on the first few pages and the content was very interesting, there were long essays and articles that were on the front page. Now? After the influx of the general public and the juveniles(not saying this in a demeaning manner), quality has dropped in every way. Pun and argumentative threads were the ones that were on top. I do like humor but not just endless pages of snark and humor. Pictures or story-in-headline posts requiring an attention span of 3 seconds tend to get the most votes, >If a community were suffering from SND, there are a few symptoms we might observe: >Anxiety, hostility, and depression would rise. >Confidence, compassion, and happiness would fall. >The ratios of anxiety/hostility, hostility/compassion, depression/happiness would rise. >Since each of the negative emotions has an opposite emotion, the last point enables us to measure the general negative/positive trend in each of the three main categories. How does this analysis address the above points? All the supposed-to-be-funny short comments probably would count as a positive under this analysis. Reddit's community now is pretty happy, confident and compassionate. The analysis is pretty lacking when applied to a supposed-to-be intellectually stimulating social site. Anyway I see that Slashdot(although not as popular now) and HN seem to have bucked the trend. Although there is some decline on HN, it's not as as steep or as bad as Reddit so far. Edit: Here's an idea, do your analysis of all the comments of the first x posts of Reddit vs. the same on HN. Could be interesting since they both have almost the exact same format.
- AJ007 15y ago
  Here is a metric that should be measured, reading level: http://www.google.com/support/websearch/bin/answer.py?hl=en&answer=1095407 http://www.google.com/support/websearch/bin/answer.py?hl=en&... In the 16 or so years I've been online I've watched many communities grow, expand, and vanish. For better or worse a large community never functions the same as a small one. Beyond theories and conjectures, it would be interesting to know why.

110 points by tansey 15y ago

At risk of coming out of HN retirement, a friend forwarded this topic to me. I wrote an extensive commentary before signing-off: http://news.ycombinator.com/item?id=2059012 http://news.ycombinator.com/item?id=2059012 (now back to peaceful slumber)

nbashaw 15y ago

How does the author measure anxiety, hostility, depression, confidence, compassion, and happiness? That seems to be left out.

tansey 15y ago
Hi! My startup [1] is based on an algorithm that can measure the emotional impact of text. Our blog is where we post interesting results from applying our algorithm. [1] http://effectcheck.com http://effectcheck.com
- hugh3 15y ago
  Uhhhhh-huh. But how does the algorithm work, and (more importantly) how do we know whether it works or not?
- pavel_lishin 15y ago
  I assume the actual algorithm itself is not open sourced. I'm wondering if there's anything that trips it up, like adding points to a comment's "hostility" score for profanities, which some people use when they're fucking excited about an idea.
- gammarator 15y ago
  Trying the sample EffectCheck script on http://effectcheck.com/about http://effectcheck.com/about : the sidebar says it's counting occurrences of phrases which score on each dimension. How is the association of word/topic determined? The measurement is valuable if and only if "Anxiety" defined as the count of ("different", "trigger", "taken out", "over", "carefully"...) corresponds to anxiety on some other psychologically relevant scale.
- raganwald 15y ago
  Speaking of your startup, it appears to have "Oracle Pricing" as its revenue model. You have to talk to a salesperson to get a price, which is often code for "We need to know how much you can afford and how much you are willing to pay before we quote a price." I like everything else I see.

noelsequeira 15y ago

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 certainly help if the algorithm used to compute these metrics were shared and dissected. I tend to believe that sentiment analysis is much more art than science, given how tricky and profound context can be. To conclude, I'm not going to end this comment on a negative note. I'd much rather reserve my vitriol and caustic criticism for another thread and day and abstain from calling this an attempt at gaming HN to promote a startup. I do this not out of a lack of indignation towards what I've just read, but because I'd like to end this seemingly hateful comment on a note that isn't bitter or negative but is instead quite the opposite (without using a single word that would help the OP's algorithm figure this out). And with that, I throw down the gauntlet. Analyze this!

tansey 15y ago
>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.
- michaeldhopkins 15y ago
  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."

tansey 15y ago

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], but with the twist that they were focused on lexical impact of words. The dictionary is pretty accurate at measuring both impact and sentiment [3]. For example, we can predict Amazon reviews as being positive or negative, using the stars to validate if we are correct-- blog post on that coming soon. Regarding context of the word usage: Both behavioral studies and fMRI scans have confirmed that context is not as important as one might believe. Our brains process multiple meanings of words in parallel, and the emotions associated with those words linger in our subconscious even after we know the correct context. Similarly, in cases of reviews and comments, people who use hostility-evoking words are often hostile themselves (angry people tend to make others angry) and the same is true of the other five fundamental emotions we measure. Happy to answer more questions. Also happy to analyze any data that you would like to see in order to verify accuracy of the algorithm-- just give me a link or the text. :) [1] http://effectcheck.com/about http://effectcheck.com/about [2] http://www.wjh.harvard.edu/~inquirer/homecat.htm http://www.wjh.harvard.edu/~inquirer/homecat.htm [3] Note that sentiment is correlated to impact only if the writer or speaker is writing without detailed attention to word choice. For example, political speech writers comb over every word to make sure they have the desired impact-- thus, the final text likely has little correlation to the sentiment of the orator.

ChuckFrank 15y ago
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.
jlees 15y ago

rryan 15y ago

Where are the confidence intervals and error bars? In order to be taken seriously when aggregating 1.8MM comments, you need those. The variability in pg's plots makes me think the data for any individual is going to be just as noisy.

tansey 15y ago
We are looking at a time-based analysis of the monthly means of PG and the HN community. The null hypothesis is that the slope of a regression line should be 0. Since we are dealing with a time-based analysis of the mean, the variance of each bucket is irrelevant. I re-ran the analysis to make sure that my results were statistically significant: Anxiety/Confidence PG: p <= 0.0007 HN: p <= 0.0001 Hostility/Compassion PG: p <= 0.0005 HN: p <= 0.0001 Depression/Happiness PG: p <= 0.0067 HN: p <= 0.0001 Note that for the HN comments, even though I used a 2nd order (parabola) fit for the graphs, the p values above are for a linear regression as that is the more appropriate fit for determining statistical significance here.
- vecter 15y ago
  p values only make sense when the residuals are normal and independent. Is that true for this data set?

ChuckFrank 15y ago

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.

recoiledsnake 15y ago
> 5. More community centered posts aka AMA etc. I consider AMA the best kind of self post... I don't see community centered posts as necessarily a bad thing except if there is too much of drama or he-did, she-did type content.
stcredzero 15y ago
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 I think "Evaporative Cooling" is descriptive and very apt.

bhousel 15y ago

Any plans to compare against corpora from other social news sites? BTW, I think what you're doing is brilliant, and I can think of about a dozen practical applications for your technology.. Ignore the naysayers.

tansey 15y ago
Thanks! I'd be happy to analyze of corpora like reddit or Digg, but I don't want to scrape the sites as that is a great way to have my IP banned. Are there publicly-available datasets that I can download somewhere?

sidww2 15y ago

Are the number of votes for a posts available? It could be interesting to use the metrics given to compare posts with high vs low number of votes and could also help validate the use of given metrics.

tansey 15y ago
I did originally have a similar idea, where I started out by looking at the top 10% of comments each month (ranked by upvotes). However, the resulting trends looked pretty similar to the overall community: http://blog.effectcheck.com/top_dep_hap.png http://blog.effectcheck.com/top_dep_hap.png

mef 15y ago

Thanks for this interesting analysis. I would challenge the assumption that the symptoms of SND include a decrease in the positive emotion measured in the average HN comment. Is it not possible that there is a growing segment of the HN user base which posts and enjoys the kind of comment that induces negative sentiment in people like pg? Would that not raise the sentiment of the average HN comment, while at the same time lowering the sentiment of the average "good old days" user?

ignifero 15y ago

The solution: divide the community to more specialized subgroups. That's what http://textchannels.com http://textchannels.com is doing

pgroves 15y ago

What jumps out at me is that the community curves all peak in the months following May 2009, which is when the stock market bottomed. The community was least happy when the economy was at it's "darkest before the dawn" moment. In other words, I wonder if these graphs are just tracking general sentiment, and would look the same for any site.

tansey 15y ago
It's a good question, and I guess we'll see how well the correlation holds. It's worth noting that the stock market bottomed in March 2009, so this may be a lagging indicator.

bzupnick 15y ago

solution for the deterioration- why cant we simply disable the ability for people to post who get X amount of "off topic" votes?

brudgers 15y ago

Hypothesis: PG's mood is improving because YC of which HN is a part is increasingly successful. Hypothesis: The net mood of HNer's is deteriorating because it the ratio of financially successful members is lowered as a more people join. In addition as HN grows the ratio of preexisting and external relationships among members gets smaller, e.g. the number of HN'ers affiliated with YC companies has not kept pace with the overall growth in HN membership.

pg 15y ago

"Thus, if we look at the PG analysis as a measure of his mental state, we can speculate this is due to the prosperity he’s experienced in these last few years." Actually it's because the site has become so large that I have to be more diplomatic. When it was small I could say harsh-sounding things without worrying about being misinterpreted. Now I have to be more of a politician.

tansey 15y ago
That was my other hypothesis, but it didn't really correlate well with the growth in the site's user base. HN has experienced an exponential growth rate, but your comments show a linear decline in negative impact. I went with the "better life" theory because it seems like personal prosperity and happiness would be more likely to show gradual, linear change. This is also supported by the trends I saw in other famous top HN users, particularly those that I know have experienced increased financial prosperity.
- djcapelis 15y ago
  Maybe they decided to become more diplomatic as the community has gotten larger and begun to have real impact on people's reputation. Why do you think that would just be limited to pg? I would think HN-famous people would especially be prone to become more diplomatic as their comments began to have more impact.
- staunch 15y ago
  Instant link bait if you let us look up our own charts.

topomorph 15y ago

In case anyone wants to make their own trends (less sophisticated, but maybe more transparent), I have an app at http://hn-trends.heroku.com http://hn-trends.heroku.com that plots word percentages on HN over time. For example, some emotive words that appear to have increased since the beginning: * sad: http://hn-trends.heroku.com/trends?q=sad http://hn-trends.heroku.com/trends?q=sad * fuck: http://hn-trends.heroku.com/trends?q=fuck http://hn-trends.heroku.com/trends?q=fuck * shit: http://hn-trends.heroku.com/trends?q=shit http://hn-trends.heroku.com/trends?q=shit Some words that appear to have decreased: * great: http://hn-trends.heroku.com/trends?q=great http://hn-trends.heroku.com/trends?q=great * cool: http://hn-trends.heroku.com/trends?q=cool http://hn-trends.heroku.com/trends?q=cool * lol: http://hn-trends.heroku.com/trends?q=lol http://hn-trends.heroku.com/trends?q=lol * reddit: http://hn-trends.heroku.com/trends?q=reddit http://hn-trends.heroku.com/trends?q=reddit * digg: http://hn-trends.heroku.com/trends?q=digg http://hn-trends.heroku.com/trends?q=digg I haven't looked at any of this closely, so make of it what you will. (I was meaning to wait until I had time to dig deeper and add extra features like time series smoothers and trend/slope metrics before "releasing" this, but figured this might be useful now given the post. I still plan on adding those later.)

recoiledsnake 15y ago

Others have done a good job at critiquing the automated analysis, so I will concentrate on the hypothesis. >There is a perceived phenomenon among online social news communities (e.g. Digg, reddit) that as the popularity of the community increases, the average member becomes baser and the overall quality of discourse decreases. I think that's not an accurate portrayal. Having been through the "decline" of Reddit (been reading it even before it had comments!), I observed that the real decline was in the quality(as perceived by me) of stories that made the frontpage(s) and in the quality(as perceived by me) of comments that were upvoted. In 2006, I would literally read almost every story on the first few pages and the content was very interesting, there were long essays and articles that were on the front page. Now? After the influx of the general public and the juveniles(not saying this in a demeaning manner), quality has dropped in every way. Pun and argumentative threads were the ones that were on top. I do like humor but not just endless pages of snark and humor. Pictures or story-in-headline posts requiring an attention span of 3 seconds tend to get the most votes, >If a community were suffering from SND, there are a few symptoms we might observe: >Anxiety, hostility, and depression would rise. >Confidence, compassion, and happiness would fall. >The ratios of anxiety/hostility, hostility/compassion, depression/happiness would rise. >Since each of the negative emotions has an opposite emotion, the last point enables us to measure the general negative/positive trend in each of the three main categories. How does this analysis address the above points? All the supposed-to-be-funny short comments probably would count as a positive under this analysis. Reddit's community now is pretty happy, confident and compassionate. The analysis is pretty lacking when applied to a supposed-to-be intellectually stimulating social site. Anyway I see that Slashdot(although not as popular now) and HN seem to have bucked the trend. Although there is some decline on HN, it's not as as steep or as bad as Reddit so far. Edit: Here's an idea, do your analysis of all the comments of the first x posts of Reddit vs. the same on HN. Could be interesting since they both have almost the exact same format.

AJ007 15y ago
Here is a metric that should be measured, reading level: http://www.google.com/support/websearch/bin/answer.py?hl=en&answer=1095407 http://www.google.com/support/websearch/bin/answer.py?hl=en&... In the 16 or so years I've been online I've watched many communities grow, expand, and vanish. For better or worse a large community never functions the same as a small one. Beyond theories and conjectures, it would be interesting to know why.