What is the point of Twitter? The 11-year-old microblogging platform is a social network, a broadcasting tool, a public relations platform, a joke incubator, and a news aggregator. It’s a daunting medium, but with the help of a little AI, it doesn’t have to be. At least, that’s the premise of Post Intelligence, a social media assistant tool launched this week by a pair of former Google executives.
“We’re dubbing it the world’s first AI-based media assistant,” says Bindu Reddy, co-founder and CEO of Post Intelligence. “It focuses on problems in the realm of social media which humans find difficult to do, that require a lot of brain energy, like simply establishing a social media presence, getting followers, writing engaging things, making insightful comments.”
To this end, a user signs into Post Intelligence with a social media account, and within two minutes it scoops up a user’s data, and then suggests what sorts of things a user might want to share online. In my conversation with Reddy and in my own experience, I focused primarily on Twitter, but the tool is also configured for Facebook, and could be expanded to other social networks.
Post Intelligence (or PI for short; the company features a cartoon of a smiling robot with the greek letter on its torso) was born out of MyLikes, a tool that delivered sponsored content and advertising for celebrities on Twitter and other social media. To figure out what celebrities should share, what both matched the brand and follower interests, MyLife used AI and deep learning. PI is designed to take those tools and apply them more broadly, rather than just curating an online presence for a brand.
“We create a deep learning model for every user who joins, and that’s created pretty much on the fly, it takes 2 minutes from when you sign up, and it reads all your posts, whether they’re facebook or on twitter, and then it takes as much data as it can grab, and then it trains a model specific to you,” says Reddy. “It’s trying to learn what you post about and what resonates with your audience, if you’re a sports personality, it’s going to look through all your tweets and now we know you post about baseball and who’s succeeding and who’s failing.”
To demonstrate, Reddy showed a selection of recommendations from PI. Our call was on March 2nd, and so the suggestions hit the high notes of the day: the looming SnapChat IPO, the announcement that Attorney General Jeff Sessions would recuse himself from any investigations related to the last presidential campaign, and a viral video of turkeys circling a dead cat. This mercurial mix of topics is both the appeal and the barrier for entry to Twitter: all these conversations are happening at the same time, which is fascinating and confusing, and then by the next day it’s all gone. Coming up with what to say in the moment can be hard, especially for people who don’t spend hours and hours of every day checking Twitter. Reddy, tapping into the zeitgeist, used PI to pull the video of the circling turkeys, and made a joke about Sessions recusing himself.
I was intrigued. So, in preparation for the launch, I decided to turn my Twitter over to Post Intelligence, and see how, exactly, an AI could help me Tweet. I set a few rules for myself: for the two-day trial period, I would only using PI to tweet during work hours. I would keep this up for the two days, and I couldn’t let anyone besides my editor know that this was what I was doing. I’ve been on Twitter for a long time, and have developed what I like to think of as a somewhat distinct voice, so I was curious to see what changed.
The short version of the experiment is that Post Intelligence told me to tweet less. The day before I started the experiment, I sent 44 tweets. The first day of the experiment, PI recommended I tweet just 4 times (I ultimately tweeted 7, adding a few others through the tool). It recommended I tweet at 2:30, 3:30, 7:00, and 10:00pm, and when I asked it to schedule a fifth tweet, it put it at 3:00. One of the neat tools in PI is a prediction score, where it looks at the words and attached images or links to a tweet, and gives a score from 1 to 10 on how well it thinks that tweet will do. PI preferred the straightforward description for a story about a comet to my dated meme description for a tweet about anchors.
The second day, I leaned more into the suggestions. A couple gaps in PI’s processing were immediately apparent. It recommended I share tweets from a couple different accounts that I’d muted, and even let me schedule a retweet of a post from an account that I knew had me blocked. (That tweet did not go through, so it looks like Twitter’s own blocking tools caught it before it went live). Instead, I shared suggested tweets from people outside my normal feed, which I might not have seen otherwise, and had about the same level of engagement as if I’d shared from within my normal timeline.
For my second day, too, PI recommended I tweet just four time a say, which was a frequency I matched back when I was posting tweets via text message from a flip-phone. In that respect, the scheduling was a nice break: I felt like I was broadcasting observations on the world, rather than living and breathing with the pulse of a social network every second that news happened.
Which brought me to the first major understanding of what Post Intelligence does in practice. It’s a tool for those new to Twitter, and those with limited time to spend on tweets, to broadcast thoughts into the general news stream as it happens. But it’s not a great tool for interacting with others. Whenever someone replied to one of my tweets, there was no way to see that through the PI interface, and so no way to respond directly.
When I asked Reddy about mentions and notifications in our call before my trial, she suggested it as a possible future feature for PI. Without notifications, PI offers feedback on a few different metrics: first, there’s the likes and retweets of sent tweets themselves, displayed below each published tweet in a column in PI, just like they are on the Twitter app itself. And then there’s a whole analytics section, tracking Follower Growth, a Word Cloud, a Relationship Graph, Posting Patterns, and Sentiment. Sentiment is by far the most interesting, as it breaks tweets down into either “positive” or “negative” (with some falling in-between) and then displays a graph of how well tweets of each type performs.
“’Trump is a very funny guy, haha.’ Is that a negative sentiment or a positive one?,” says Reddy. To tackle sentiment, Post Intelligence has their own API to try and infer context. It’s a task that’s hard for AI and for people, too. “That’s something that social media struggles with, when I’m being sarcastic, people think I’m being literal. If you’re being tongue-in-cheek, people take it literally.”
In my brief trial, it wasn’t sentiment that tripped me up, but just the lack of interaction with followers. A joke made in a moment loses potency the next day, and “I’m sorry, it was funny, but I was testing a tool for work” isn’t the greatest excuse for answering a question a day late.
Still, I think there’s value to a tool like PI, especially for people who aren’t glued to the internet for over eight hours every day. The freedom to plan a day’s tweets in five minutes, with automatically supplied topical content, meant I could focus my attention elsewhere, confident that my online presence was intact.
“Twitter is very addicting, and it is very important, even as a company it may be only worth a few billion dollars,” says Reddy, “but it’s really important to the culture of humanity, in some way I know that’s a strong way to say, it’s proven itself as recently as November 9th, it can change the world. I think more people want to do well on it but don’t, because it’s just so difficult to do well on it.”
Viewed as the only way to experience Twitter, Post Intelligence is a little underwhelming, but as a tool to get into Twitter, without needing to spend hours a day following the news looking for good enough jokes and news to share, Post Intelligence makes a pretty good set of training wheels.