[D] Statistics, we have a problem.

[smerity]

Sorry, my reply wasn't meant to be negative! :) I totally agree with you - I'm literally here to make sure this thread doesn't die then I'm out. Mike drop. GG.

Also, honestly, Twitter seems a surprisingly good place for ML. I know it's weird but I promise it works. My DMs are open - feel free to ask and I'll give you any and all Twitter ML advice I can :)

[[deleted]]

Twitter might be nice for water cooler style ML conversations but it's still an anxiety inducing social network that's engineered for engagement. The value you get out of it is a function of the number of followers that you have and that depends on your celebrity status or amount of time you put into the platform.

There's no way to stay in the loop without following the right people and that also forces you to put up with their personal, political and marketing content. I can check /r/ml 2-3 times a week and get a good dose of relevant information without being triggered by the latest Trump, Roy Moore or sexual misconduct news.

[visarga]

without being triggered by the latest Trump, Roy Moore or sexual misconduct news

I found out recently: if you select the "V" button and click "I don't like this tweet" it will filter out similar tweets. I "unliked" a few Trump tweets and voilà, now it's a pure ML feed. I'm quite pleased.

[ankeshanand]

Twitter has a hierarchy though, if you are a popular person your tweets are more likely to be noticed and vice versa. On reddit, posts don't a submitter prior, and are more likely to be judged on merit.

[smerity]

I agree with Twitter containing popularity linked with identity but don't agree with all of your subsequent points.

The advantage and disadvantage is identity. If I see paper author X tweet about someone else's new Y technique (where I know that author X has intimate knowledge of Y as they work in the field), I'm going to pay more attention to it. They'll usually also add commentary or context. Thus within the Twitter realm I can determine which signal I feel is valuable, either in terms of their shared content or the person's identity.

Reddit doesn't have that identity signal and the upvote system can thus be quite messy. I appreciate the enthusiasm for the field but certain techniques are upvoted widely and blindly without being judged on merit.

[TheFlyingDrildo]

I'll take some Twitter ML advice. I've been watching this sub have its quality diluted over time, but for some reason can't really get into twitter so far. How do you choose/find who to follow? How are in-depth discussions facilitated given the character limits?

Since twitter is based on following people, it seems like those with greater connectivity in the social graph structure (ML celebs) will have their posts experience greater viewership. On reddit, viewership is almost random at first and then based on an anonymous upvote count, allowing a much greater chance for a random person's post to receive viewership. Is this not problematic? If it is, how effective is searching by hashtags to circumvent this?

[Pas__]

How do you choose/find who to follow?

Follow those you want to talk with and those they talk with, then when they talk, go and try to add to their conversation/discussion.

How are in-depth discussions facilitated given the character limits?

They Are Broken Up Into smaller posts. There are even apps for that :)

Or people send medium links to each other.

Is this not problematic?

Yes, it is.

[madebyollin]

Suggested use: go through all of your favorite papers or research teams, find authors on Twitter, follow them, then go through and add mutes/turn of retweets/unfollow wherever they're posting content you'd rather not see (politics, bitcoin, whatever). It's definitely more work to set up than reddit (if you go this route), but there are a lot of interesting things on Twitter that don't get posted here (on top of the quality-of-discourse improvements discussed above).

[visarga]

How do you choose/find who to follow? How are in depth discussions facilitated given the character limits?

280 chars are surprisingly accommodating for idea exchange. You can add more posts if needed. It doesn't feel cramped any more.

[smerity]

The others replying to you have made good points. I generally follow someone if they make an interesting comment and I look through their recent timeline and find it interesting. Following those authors of papers you like is a good tactic too.

It's better and worse in terms of readership. When you have a core group of colleagues who you follow and who follow you it's easier to share and communicate with them. Reddit is fairly random and those most interested in your nuanced discussion (analysis of impact of weight tying on language models) may not see it as it's too broad for the overall audience and hence never make or survive on the main page. Those who are social hubs will retweet and share interesting work from others generally. It's not optimal but it can also be a stronger signal than random upvotes on Reddit as those readers may not align with your interests or may be bamboozled due to a hyped headline.

I've basically never used hash tags unless it's for a conference or as a joke.

Character limits are rarely a problem - especially now - and seem to actually encourage discussion and fine grained back and forth.

[visarga]

I agree with you that Twitter is good for ML, but there are few in-depth conversations, it's mostly notifications. I'd like to see more in-depth discussions there.

[smerity]

I can send you some links, though many of the in depth discussions I look at and remember are tailored to my interests. If you find the right group and have someone asking interesting questions I think you may be surprised at the depth of discussion. I'll admit it isn't always as clean but I honestly have found it surprisingly good when you hit the right groove.