Making a Tweet Bot With Microsoft Flow

If you subscribe to my Twitter feed, you will have noticed a lot more activity of late. This is because I have created a Tweet Bot to find me the most interesting Dynamics articles out there and Tweet them.

My inspiration for doing this was Mark Smith’s Twitter feed (@nz365guy). Every hour Mark pumps out a Tweet, sometimes in a different language, sometimes on related technologies, such as SQL Server. He also drops in quotes from the books he is reading, as well as the odd manual Tweet.

As you can see, this formula has been very successful for him. Over 11,000 followers and almost 69,000 likes on the back of 29,000 Tweets. That’s a little over two likes per Tweet. Good stuff.

Previously I had only really used Twitter to promote my blog articles so I thought it would be a perfect testbed to see if automated Tweeting, plus the odd promotion of my blogs and speaking engagements did anything to lift my own statistics.

In doing so I also found a curated list of Tweets was far more useful than browsing through the list of Tweets from the people I am Following because looking at my own list of Tweets is ad-free. Now I review the curated list and most days, if I find something I really like I post it to my LinkedIn feed. So, if you want to see something less automated, feel free to follow me on LinkedIn.

How It Works

Here it is. Essentially, the Flow:

• Triggers with a pre-determined frequency
• Initializes a bunch of variables and searches for candidate Tweets
• Loops through the Tweets to find the best one
• Stores the winning Tweet in a list of sent Tweets and then Tweets it

Let us go through these stages in more detail.

Recurrence

This seems pretty straightforward but there are a couple of things to consider. Firstly, if I did like Mark and scheduled to send one every hour, this would be around 24*30 = 720 Tweets per month which is close to my quota of 750 on a free plan. Do-able but this does not leave a lot of wiggle room for other Flows and experiments like my MondoDB integration.

Initially I set it to every two hours but even this had some troubles with the following error often appearing:

{

“status”: 429,

“message”: “This operation is rate limited by Twitter. Follow Twitter guidelines as given here: https://dev.twitter.com/rest/public/rate-limits.\r\nclientRequestId: 00776e5e-6e93-4873-bcf5-a1c972ba7d2a\r\nserviceRequestId: 597a00b83806f259127207b0a18797a0”,

“source”: “twitter-ase.azconn-ase.p.azurewebsites.net”

}

I went to the link suggested but it was broken. So I went to the rate limits in the Flow documentation for Twitter and I did not seem to be violating these limits so it was quite confusing. A little browsing revealed that others had also come across this problem and it does appear to be a bug in Flow.

A bit of testing suggests that as long as you do not Tweet more often than once every four hours you do not hit this error (unless you are Jukka).

Variables and the Candidate Tweets

Variables are really useful for debugging, as you can see the value assigned to them, but also for managing the information you pass around in your Flow. In my case, I defined the following variables:

• TweetBody: The body of the Tweet we will be posting
• TweetRank: A measure of how good the Tweet is. Initially I wanted to use ‘Likes’ but Flow does not allow you to access the number of Likes a Tweet has so I had to use another measure in the end.
• TweetAuthor: Who Tweeted the best Tweet. While Flow does not allow you to Retweet (or put the ‘@’ symbol in any Tweet you post), I wanted to give the original poster as much credit as I could
• TweetID: Every Tweet has a unique ID which is useful to make sure you are not posting the same popular Tweet more than once
• TweetMatch: A flag to say if a Tweet being reviewed has failed to make the cut of being the ‘best’ Tweet

The criterion for the candidate Tweets is pretty simple.

If the Tweet has the #msdyn365 flag, it is worth considering. You will notice my step limits the number of Tweets returned to 100. The reason for this is because it is the maximum allowed by Flow, which is a pity.

Loop Decision One: Has the Tweet Been Retweeted?

As mentioned above, it is not possible with Flow to check the number of Likes a Tweet has so I took inspiration from Google. While much more complex now, the original algorithm for ranking in the Google search engine was the number of links to a web site. The more people referenced you, the more likely you were to appear at the top of the search rankings. In my case, I used the number of retweets of the original Tweet being referenced as my measure of popularity. To clarify, this is not the number of retweets of the Tweet that the Flow search found but, if the search found a retweet, it is the number of retweets of that original Tweet. Going to the original Tweet as my source meant I removed the possibility of Tweeting two people’s retweet of the same original Tweet, no matter how popular the retweets were..

However, I soon discovered that testing the number of retweets of the source Tweet failed if the Tweet was not a retweet. I tried working around this by capturing null results but, in the end, it was easier just to test up front.

You will see that if the condition fails, we set our TweetMatch flag. If there is no retweet, the Tweet is no good.

Loop Decision Two: Will My Tweet Be Too Long?

Next I want to make sure that if I construct a Tweet from this candidate Tweet, it is not too long. Initially I just concatenated the resultant Tweet but I was partially cutting hashtags and I could see that being a problem if the wrong hashtag was cut the wrong way (#MSDYN365ISAWFULLYGOOD becoming #MSDYN365ISAWFUL, for example).

The format of my resultant Tweet is ‘<author> <Tweet body>’ so as long as this is under 280 characters, we are good to go. Again, if this test fails, we set the TweetMatch flag.

Loop Decision Three: Testing for Popularity and Filtering Out ‘Bad’ Tweets

Next we ask if the Original Tweet Retweet Count is bigger than the retweet count of our existing ‘best’ Tweet. If not, we raise our flag, if it is, we need to make sure that the Tweet in question has not been Tweeted by me before and that it is not from my blacklist of Twitter Accounts.

To manage the list of posted Tweets and the blacklist, I used an Excel sheet in OneDrive. I also included myself on the blacklist as, if I did not, it could lead to the situation where I am reposting my own Tweet, which, in itself could be reposted and so on. Again, if these tests fail, the flag is set.

Final Loop Decision: Is the Tweet Worthy?

If the Tweet gets through all those checks unscathed, the variables are set with the values from this new Tweet. Otherwise, we reset the TweetMatch flag in readiness for the next loop integration. We then repeat for the next candidate Tweet until we have gone through all of them.

Store and Send

With the winning Tweet selected, we store its ID in our Excel sheet to avoid sending it twice on subsequent runs and post our Tweet. Initially, rather than using an Excel sheet, I tried string matching to avoid resends but this proved too hard with the limited tools available in Flow. Keeping a list of IDs and looping through them proved to be a lot easier to implement in the end.

As mentioned before, Flow does not allow for retweeting, so I simply constructed a Tweet which looks similar to a retweet and off it goes.

Consequences of  Activating the Bot

I did have one Follower complain about the bot but, otherwise things have been positive as you can see below.

Impressions, visits, and mentions are significantly up with followers also getting a net gain. Moreover, as well as getting more exposure, I now have an ad-free list of interesting articles to read and promote on LinkedIn.

Conclusions

This has been a really interesting project from a Flow development perspective but also in forcing me to consider what I use Twitter (and LinkedIn) for and whether I should change my use of them.

Building the bot has given me lots of tips on how non-coding developers can think like their coding counterparts, which I will be talking about in Melbourne at Focus 18 and this conscious change in my use of Twitter has massively increased my audience reach.

I encourage all of you to think about Flow can solve that automation problem you have but also, if you use social media, seriously consider if you use it as effectively as you can and if it could serve you better.