I’ve Tweeted for 120 days in a row.

And added +22K followers without spending $10,000 on Ghoastwritters.

My secret: This list of 8 Hidden Twitter Metrics (and how you can use them to grow like crazy):

Before we dive in...

1. What you’re about to learn is priceless—Put it to work.

2. I've used these secrets to get 8,396 followers in 28 days.

Let's GO 👇
1/ Consistency Streak (CS)

Twitter needs a consistent flow of content.

The avg lifetime of a tweet is ONLY 30 mins.

Give it consistency & you'll get reach & followers in return.

→ Schedule your tweets and have 1 EVERY single day.
2/ Activities per week (APW)

Just like in a videogame, every time you do an activity:

• Like
• DM
• Reply
• Retweet

Twitter assigns you an activity point for these.

→ Higher APW = Higher chances of Twitter sharing your content.
3/ Comments per tweet (CPT)

How many replies do you get on your Tweet(s)?

Why it's important?

• CPT for each tweet = Reach of your tweet

• Your Avg CPT = Initial reach of your tweets/replies.

→ Ask more questions in your Tweets. https://t.co/T2iMGuHBxP
4/ Retweets per tweet (RPT)

This is how many retweets you get on your Tweet(s).

This is all about the virality of your content.

My advice:

• Use Emotions
• State benefits
• Include numbers https://t.co/SUa63sxTJb
5/Self retweets per tweet (sRPT)

Be careful with this one!

Retweeting your own tweets might give you extra reach in the short term.

In the long run, however, this will hurt you.

→ Limit your self-retweets to 25% of your tweets
6/ Engaged people per week (ePPW)

Twitter is a social platform, it promotes engagement.

Keep people engaged, and the algorithm will:

• Favor your content
• Push it to new audiences

→ Be interesting; Use your own contrarian/hot takes.
7/ Two-way engagements per week (tEPW)

Two-way engagements are powerful in real life & on Twitter.

Here's how it works:

• You engage with a user
• They engage back with your content
•You GROW 🚀

→ Build genuine relationships with others.
8/ Profile Quality and Virality (PQV):

I've saved the best for last!

This secret metric is an avg of your metrics (last 28 days).

PQV= CS + APW + CPT + RPT + sRPT+ ePPW (28 day avg)

→ Stay consistent for 28 days & watch your reach explode.
TLDR; 8 Hidden Twitter Metrics:

Consistency Streak (CS)
Retweets per tweet (RPT)
Activities per week (APW)
Comments per tweet (CPT)
Self retweets per tweet (sRPT)
Profile Quality and Virality (PQV)
Engaged people per week (ePPW)
Two-way engagements per week (tEPW)
If you enjoyed this thread:

• Follow me @albadawee on my journey from 0 to $1M
• Like/Comment/ RT to help your friends up thier Twitter game https://t.co/JxIJixmLZL

More from All

How can we use language supervision to learn better visual representations for robotics?

Introducing Voltron: Language-Driven Representation Learning for Robotics!

Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z

🧵👇(1 / 12)


Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.

Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)

The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (
https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).

The secret is *balance* (3/12)

Starting with a masked autoencoder over frames from these video clips, make a choice:

1) Condition on language and improve our ability to reconstruct the scene.

2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)

By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.

Why is the ability to shape this balance important? (5/12)

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