Open Thoughts

The primary way I use Twitter is through lists. I have two lists (both currently private): one for history and one for what I call “Future Web,” which is an assortment of people who are thinkers and practitioners that are working on the semantic web, tools for thought, web3 (though generally not the blockchain version but the IPFS and similar technologies version), and web archival.

I use lists because I mostly use Twitter for learning. I have many different interests and at certain times my mind is more focused on one area. So when I am in the mood for history, I can open that list (props to Twitter for the new design on mobile that puts lists on the top of the screen so they are easy to access).

I slowly add to these lists by checking out retweets from people already on my list. I also occasionally remove people from the list because the subject matter falls outside of my main interests. I add someone to Future Web because I want to see their thoughtful ideas about tech. If they start talking too much about politics, do too much self-promotion, or go on too many rants, I remove them from the list. This is hard to do because you feel a little guilty. Also, I don’t want to put myself in an echo chamber and only hear what I already believe about things, but I also have to acknowledge that my mental capacity is limited and I cannot read everything from everyone.

I have used mute features at certain times when certain things had Twitter in an uproar. (I set a mute filter on the word “Twitter” after Twitter was bought by Musk. How meta is that?) But these filters are limited in their functionality. They can only block certain words, but cannot block ideas.

Let’s give an example. If I didn’t want to have any tweets about Large Language Models (LLMs), such as ChatGPT in my feed, how would I do it? It would be pretty hard, because everyone, including people who you never would have thought would care about AI are talking about them. I could start by muting tweets with “ChatGPT” in them. But there are other language models, so I would also need to add words such as “GPT-3,” “ESMFold,” “MT-NLG,” “OpenAI,” and “AI.” But some companies use generic words for their products. There is one language model named “Bloom.” If you added that to your list of muted words, you would probably block content that had nothing to do with LLMs.

Manually blocking keywords probably wouldn’t be enough to filter out content. There are so many words used for any subject and there is overlap of words which make it impossible to filter out a subject completely in this manner.

Artificial intelligence, including (somewhat ironically, considering the example I chose) LLMs, is probably part of the solution. Though, I do have a hard time blindly putting my trust in AI. Facebook has the option to click on a post and “see fewer posts like this.” What exactly does that mean? It gives no clue of how it will filter out the content. And it might be thinking of different criterial than you are thinking. The post might be a picture of a cat in a cardboard box, and you click the button thinking it will show you fewer posts about cats, but the algorithm interprets it to mean you want to see fewer posts about cardboard boxes.

I keep saying it, but I think the semantic web can help with this. It allows content to be concretely associated with concepts. I don’t think a writer of a social media post should have to tag each word with RDFa tags, but what if after a user posts, then AI can identify the concepts in the post. When I click on “see fewer posts like this” it can bring up a list of concepts found in the post. You can then manually select the concepts to remove from your feed.