Cheerfulness (Before or after morning coffee?)
Orderliness (Because of my 3rd normal form sock drawer?)
Gregariousness (Before of after my 3rd beer?)
Agreeableness (I disagree! Watson needs debugging.)
I'm ranked dead last in:
Imagination (No one I know could imagine how this could be.)
Authority-challenging (My teachers & bosses would disagree.)
Intellect (Before or after my mother dropped me on my head?)
This could make a good movie plot. The all powerful AI analyses your characteristics, gets it all wrong and assigns you to the wrong line of work (Harry Potter?) :)
> In other words, you are ranked 148th out of like 100k really smart people.
Its not a top by category ranking of all HN posters, there's a merged list of top commenters and leaders, and then the ranking is from that. Being on the bottom of that (as I am for agreeability) doesn't mean you are still ahead of everyone on HN that didn't make the list.
You are being pedantic. I am being sort of humorous.
I think the main gist of my point stands though: Ranking dead last on this list hardly equates to "dropped on your head." That's like saying "I only won one of the less important Nobel Prizes. God, I'm such a loser." Or something.
I like edw. I like a lot of people here. But sometimes folks here really suffer from tunnel vision in a bad way.
Somebody please make a browser plugin that uses this data.
I know the data isn't perfect, but it would be nice to be able see who in a thread is a top HNer and which character traits are outliers from the norm. You get insulted by somebody ranked low in Sympathy? No need to worry.
I'm serious. Somebody do this. It would look great on the résumé.
Or, we could learn to do brief history searches and check out past user comments if it really matters.
I'm generally pretty suspicious of any sort of computer-generated personality profiles, and though I understand the appeal to techies of empathy-as-a-service I don't think it's something we should consider relying upon.
Hell, half the fun in life is trying to figure out who other people really are.
Hi. I built a Chrome extension for Hacker News that lets people follow others and get notifications when they are replied to or their karma changes. http://hackbook.club
The ext basically matches my original vision now, so I'm thinking hard about what features to add next, but I'm not quite sure I understand what you're asking for. Are you saying that when the extension says "So and so replied to you", you want, say, a sympathy score shown with that user as well?
On one level it could be a simple bookmarket (bookmark with JS). When clicked, it goes through the HN discussion page. When it finds a top HNer's username, it (1) colors it and (2) shows the category if that HNer is in the top or bottom 10% in any category.
For instance:
edw519 33 minutes ago | link
Top: Cheerfulness, Orderliness, Gregariousness, Agreeableness
Bottom: Imagination, Authority-challenging, Intellect
(I'd also something that shows me when a commenter is somebody important, even if not a karma king.)
Ok this actually makes a lot of sense. When someone replies to you on Facebook, you generally know who they are. On Hacker News, you have no idea. Including some basic (albeit imperfect) information along with a notification reply could be really useful.
This could even be true for the follow-feed mechanism. If you're following someone on Twitter or friends with them on Facebook and they write something that appears in your feed, you are already familiar with the author on some level. In the Hackbook extension, it's fairly common to follow people and not know much about them at all. Again, including some basic Watson-generated information along with the "a user you're following wrote a comment" newsfeed item could be helpful.
Let me give it some more thought and maybe I'll have time to work on it this week.
Observing something changes it. It'd probably be useful in the beginning, but as soon as people start replying based on these personality profiles, it won't be useful anymore.
I wonder if this data might be more interesting on the bottom 100 users with a longevity > 2 years. And of course it would be interesting to see the differences between the top 100 HN users and the top 100 Reddit users. That might provide some insight into the echo chamber effect.
I agree with that, one interesting question though is can you use data like this to characterize the echos. If so, then one could point it at an arbitrary forum, score the top 100 posters, and pull out a 'flavor' for the forum. That would be kind of neat.
I don't know if anyone else noticed this, but @Tichy ranks first in Emotional range, Fiery, Prone to worry, Melancholy,
Immoderation, Self-consciousness and 2nd in Susceptible to stress. I haven't read any of his/her comments but this makes me wonder how distinctly each of these measurements are calculated. Given another random set of 100 users, would one user come out on top in all of these categories too?
I first read about the "Big 5 personality Traits" in this Economist article last year. Very interesting read on how some researchers were using Twitter writings (specifically some keywords) to gauge a person's personality - http://www.economist.com/blogs/economist-explains/2013/05/ec...
One of the difficulties this kind of output has is that it runs very quickly into issues of semantics and model labeling.
We humans each build models kind of like these when we interact with one another, so when I ask somebody, "do you think <name> is an agreeable person?" and they reply "sure I think he is!" they're consulting that model to provide me an answer. Humans can even do a kind of pairwise sorting on that model and tell you if person1 is more or less agreeable than person2.
However, even if our individual models may differ a bit, and the results of these kinds of questions to each other might differ a bit, there's an inherent "humanness" to the results because people generally have a pretty similar semantic understanding of what "agreeableness" means.
However, what does Watson think agreeableness means? I have no idea, nobody really knows. Watson can't really explain it. All we know is that there's a model that produces a scored (and thus rankable output) when asked to score a corpus on that model and somebody somewhere labelled that model as the "agreeableness" model, perhaps based on some heuristics or parameters that were intended to define that notion.
It's thus very hard for humans to trust scoring like this because when it doesn't make sense, it doesn't make sense for reasons that no human would have about the matter. For example, I would personally say pg is far more agreeable than I am, yet Watson scores our respective collection of comments exactly the same. I can't explain it, Watson can't explain it, and thus it feels "wrong" and now I can't trust the scores that Watson provides me.
Well, in a sense, most of us all only know each other through our comments, and that's all we can ever base an assessment like this on. By proxy we have to assume that when people's inner thoughts leak out into the Internet on a forum like this (and in a sustained enough way to make them a top-100 karma earner) that their aggregate corpus of comments will be a reasonable insight into who they are.
So for all purposes that you, I or Watson can demonstrate, "do you think X is Y" and "do you think X's comments show Y" are functionally the same.
edit
I just checked what Watson thinks are my needs. Apparently I don't have many, and everybody on HN has an extreme need for Challenge.
I almost feel like these results require a lot of interpretation, and that interpretation is about as reliable as a horoscope.
> I almost feel like these results require a lot of interpretation, and that interpretation is about as reliable as a horoscope.
I got a similar feeling from this - that's why I'd love to see some hard data behind the algorithm, or at least bits and pieces about the methodology used to arrive upon it.
Does not seem correct. The top poster for practicality has some useless thank you posts that provide nothing useful to the reader. StackOverflow even tends to lock questions that just get a lot of useless thank you comments to prevent putting useless information on the page for people coming to find reference material.
2144 days into this experiement that is HN, I couldn't ask for a better analysis of myself than through empiricism. My online self may not be my "true self" but it certainly represents a portion of who I want to be.
Now to contextualize, I wonder how we trend together and apart from the median user model as individuals and the community? The distributions seem interesting -- for example at a glance we appear heavy on challenge seekers but light on stability!
I learned two things from this: I'm no longer in the top 100, and I don't recognize a lot of the names that are. I must not be spending as much time here as I used to.
These results look pretty spotty...the rankings seem about as random as picking out names from a Bingo machine. User ssciafani is among the top users in Cautiousness, Openness, and Adventurousness, Stability, and Practicality. Any "Yeah, user johndoe is totally some-characteristic! revelations may be no more the intentional result of a sophisticated algorithm than the confirmation bias that many have when reading a horoscope.
That's really cool. For some reason, the ranking changes every time I click on the same category. Edit: I just realised it's probably because the position of users who have the same score is randomised.
Libertatea's score was computed on a very low number of comments so it may contain large errors, pergaps we should remove him/her or show a warning there...
I had a small giggle at my rankings, not going to lie.
Without a definition for what each of these things are, it's a little unclear what this is actually saying. I'm assuming there's some documentation on this somewhere?
One thing that we're wondering is whether a score 1% means that it believes that the person has little of this treat, or that it has little proof to believe that the person has this treat. If I'm not mistaken it's the former.
I think that IBM Watson expects a general text no? I admit to having done some personality classification on OKC profiles (around 200) and found that disproportionately to the average population, there were a lot of people that were classified as caring, helpful, social, popular individuals.
It fits the medium. Just like one would expect HN comments to be full of more "head-y" discussions.
I didn't find such expectation in the docs, and someone else posted this, where it says that researchers from IBM were using their tech to analyse tweets:
I'm extremely relieved that sometime in the past few months I've dropped off the list.
In case you're wondering, IBM's BlueMix is a public installation of Cloud Foundry, which is an opensource PaaS. Disclaimer: I work on Cloud Foundry at Pivotal.
This is interesting. On the visualization, though, I'm not sure what the purpose of the inner ring is (it just seems to chart the first entry in the group at the next level out, which doesn't seem to be particularly meaningful.)
Very nice project, thanks for introducing me to IBM Watson. I will also play with it a little bit:)
I don't know how it determines Openness and stuff, pasted one of my texts in there, it gave out 'interesting' results:)
Are there any other services that offer similar functionality to the User Modeling API? I've come across a few Sentiment Analysis services, but nothing with this level of detail.
I don't think this kind of analysis would provide any insight there. A simple look at users who have high activity on certain topics would be a good first filter, though.
Thanks for doing this. It is hard to judge the data, but I just did a quick check for agreeableness, and it seems reasonable. The person rated highest for agreeableness top does in fact seem way more agreeable than the person at the bottom.
I'd be interested to learn what some of the headings really mean. For example, the only listing I'm near the top of is "Trust" (I'm #4) but I don't really know what that means in this context. I had a quick look at the IBM docs but couldn't figure it out.
Selfishly, it would also be pretty cool to be able to select a person and see their rankings across the categories :-)
Oh, and your "top 100 users" link on the front page is broken as it's not an absolute link.
Definitely looks like context is lacking here in some respects. I found it interesting that 'whoishiring' "needs love" more than almost everyone on the list.
Cool app! IBM is in the process of significantly updating their documentation on User Modeling, but meanwhile, here are some basic descriptions of some of the traits, as well as links to some of the research behind the service.
User Modeling analytics are developed based on the psychology of language in
combination with data analytics algorithms. User Modeling extracts three types of
personal characteristics from the data a person generates in social media or within
their written/digital communications:
Big 5 Personality - This is the most used personality model that generally
describes how a person engages with the world by the following five
dimensions:
– Openness-to-Experience - associated with curiosity, intellect, and an
appreciation for art and adventure
– Conscientiousness - associated with organization and industriousness
– Extraversion - associated with positive and outgoing attitudes toward other
people
– Agreeableness - associated with compassion and cooperation toward other
people
– Neuroticism - associated with a sensitivity to negative emotions
Each of the five top-level dimensions has six sub-facets that further characterize
an individual at a finer-grained level.
Basic Human Values - this model describes motivating factors that influence a
person's decision-making. Our current model includes five dimensions of human
values based on Schwartz's work in psychology:
– Self-Transcendence - motivated by helping others
– Self-Enhancement - motivated by increasing social status
– Hedonism - motivated by pleasurable experiences
– Openness-to-Change - motivated by experiencing new things in the world
– Conservation - motivated by tradition and conformity
Fundamental Human Needs - this model is based on Maslow's hierarchy of
needs and Ford's work on Marketing and consumer-related needs modeling. It
describes, at a high level, which aspects of a product will resonate most with a
person.
– Ideal - the person likes high-end, finely crafted products
– Self-Expression - the person likes products that express their individual
identity
– Closeness - the person likes products that help them establish closer
relationships with family and friends
– Excitement - the person likes products that provide exciting, adventurous
experience
– Practicality - the person likes products that simply get the job done
For more detailed information about the research and technical background behind
the User Modeling service, see the following:
You read what you value: understanding personal values and reading interests
Gary Hsieh, Jilin Chen, Jalal Mahmud, Jeffrey Nichols; CHI 2014. 983-986.
Understanding individuals' personal values from social media word use Jilin
Chen, Gary Hsieh, Jalal Mahmud, Jeffrey Nichols; CSCW 2014. 405-414
Recommending targeted strangers from whom to solicit information on social
media
Jalal Mahmud, Michelle X. Zhou, Nimrod Megiddo, Jeffrey Nichols,
Clemens Drews; IUI 2013: 37-48
Modeling User Attitude toward Controversial Topics in Online Social Media
Huiji Gao, Jalal Mahmud, Jilin Chen, Jeffrey Nichols, Michelle Zhou; ICWSM
2014.
Who will retweet this?: Automatically Identifying and Engaging Strangers on
Twitter to Spread Information Kyumin Lee, Jalal Mahmud, Jilin Chen, Michelle
Zhou, Jeffrey Nichols; IUI 2014. 247-256
KnowMe and ShareMe: understanding automatically discovered personality
traits from social media and user sharing preferences Liang Gou, Michelle Zhou,
Huahai Yang; CHI 2014.
Identifying User Needs from Social Media Huahai Yang, Yanyuo Li; IBM
Research Report.
PersonalityViz: a visualization tool to analyze people's personality with social
media Liang Gou, Jalal Mahmud, Eben M. Haber, Michelle X. Zhou; IUI
Companion 2013: 45-46
I'm ranked first in:
I'm ranked dead last in: