#FinTwit Micro-Influencer & Community Analysis

We have discovered that #FinTwit is actually a collection of 6 communities

We analyzed 46.4 million tweets from 50,860 Twitter accounts to identify 6 very different communities within the online network traditionally called #Fintwit. We believe we are the first firm to explore the structure behind #FinTwit and to analyze the dynamics of the conversations between users, not just the content of the tweets.

We named them based on the occurrence of #hashtags or $tickers like $SPY used within each group. Click here to download the PDF report. We named them based on the occurrence of #hashtags or $tickers like $SPY used within each group

$Tickers
#PoliticalTwitter
#FinTech
#Advisors
#Crypto
$TSLA

We found that there are 8,581 twitter users that are engaged in a solid dialogue across 50,343 conversations. We were able to isolate and we were able to parse each tweet within 6 categories of discussion:

Gratitude (72%)
Discussion (15.5%)
Stock-Specific (7%) ,
Products and Data Science (4%)
Investing (~1%)
Risk-Return (~1%)

We could only then tease out how financial information is being discussed throughout #FinTwit as seen in the video above

TOP INFLUENCERS BY COMMUNITY

From all of this data we generated lists of the top 100 influencers for each of these 6 communities with the top influencer for each topic below

Download the full 23-page PDF report with the full lists and the methodology here:

$ticker – Top 10 Influencers

#PoliticalTwitter – Top 10 Influencers

#FinTech – Top 10 Influencers

#Advisors – Top 10 Influencers

#Crypto – Top 10 Influencers

$TSLA – Top 10 Influencers

But Why?

We’re use our discovered influencer lists to help us more quickly discover the experts, the micro-influencers, the gurus who have their finger on the pulse of very esoteric, financially wonky topics. In essence, in the noise of 330 million twitter users and 500 million tweets a day, can we find other like-minded folks looking to improve the financial technology landscape?

As an investor, a former hedge fund portfolio manager and technology geek, I need information to help me get a better sense of the world. However social media platforms these days are primarily designed to entertain you, not inform you. It is a dangerous game to use social media for research.

You first could be scrolling through a 14 part piece by @choffstein about momentum strategies and timing luck,…

1/ So this week's research note (https://t.co/WTjZhxwAX5) was pretty brief, so I want to write a few more words about this graph, which was buried within. pic.twitter.com/eYIXCvAK2P
— Corey Hoffstein (@choffstein) December 24, 2019

…and then click on a GIF by by @NateGerace of a bunch of kids jumping over hurdles pretending to be “active managers”!

Active managers trying to clear their fee hurdle… pic.twitter.com/4ANkHy7YQ7
— Nate Geraci (@NateGeraci) December 16, 2019

Part of the reason why social media networks are not great for professionals (of any industry not just finance), is that it is difficult to tailor how information flows into your feed. Sometimes we want to be entertained, sometimes we want all the information we can on a trending topic (this week it was the #SECUREACT) and other times we want to have a conversation with other experts and learn from the community.

As the end of the year approaches. We wanted to be deliberate in how we find information and debate topics in the new year. This article is an exercise in finding the best conversations occurring online in esoteric financial topics.

Social Media Analysis: Discovery vs Discourse

Honestly, I want my social media activity to drive my business but not in the way most businesses think about using social media to promote their work. Moreover I don’t want to be entertained; I want to learn new things, be inspired and connect with great people across the world. To do this I look at social media through the lens of Information Discovery and Discourse / Conversations, not clicks or retweets.

Most social media analysis used in the industry currently is designed to help you get your message to the person with the loudest megaphone. This type of influencer analysis is very common and brands pay healthy amounts to get celebrities to endorse products online (I’m not going to pay The Rock $880,000 to post Portformer on Instagram). The focus here is on getting eyeballs on content and to do this well you need to understand who is following who and use algorithms like ‘Page-Rank’ to see who has the loudest and most effective megaphone. Other people have used these algorithms and have created lists of Top FinTwit influencers and here are there lists of the people with megaphones: (some have pretty questionable methodologies but c’est la vie)

I’m not saying this type of social media promotion isn’t important for us as a business. When Josh Brown @ReformedBroker (with 1M+ followers) responded to our video, the response from advisors was a watershed moment for our company.

However, for this exercise we are not looking at social media as a marketing tool, we are looking at it as an information gathering tool and a community detection tool. How can we use it to talk to the smartest people out there? It turns out that with a weekend of programming, a lot of bandwidth and a some semi-fancy algorithms we took a quick stab at this.

Methodology for Conversational Discovery

Rather than focusing on who is following whom, and only focusing on how information is spreading from follower to follower. We want to discover the interesting two-way conversations among experts that are emerging online.

Version 1 Dataset

	(As of 2019-12-27)	Number
Twitter Accounts	50,860
Tweets	46,400,081
Tickers Mentioned (total / unique)	893,414 / 21,208
Hashtags Mentioned (total / unique)	24,9 million / 2,30 million
Other Users Mentioned (total / unique)	50,2 million / 4,76 million
Tweet Thread detected*	10,047,833
Conversations detected**	50,343

* Tweet Thread is a set of tweets that start with an original tweet and can contain many replies, retweets and replies of the replies, etc..

** Conversation occurs when at least two users reply to each other within the same thread. As you can see less than 1% of tweets are part of the back and forth conversation we care about here.

We started out with the twitter handles of a couple thousand financial advisors as well as the followers of our social media accounts @Portformer, @Astrocyte_Rsrch and @SeanKruzel and began searching for additional accounts that have engaged with this initial set of several thousand Twitter accounts. We limited the search to the last 1,000 tweets per account.

There are other ways we could have seeded this list. For example we could have used users that have used the #FinTwit hashtag or are part of other established ‘influencer lists’. Reach out to us if you have thoughts on ways you’d like to see this list grown or filtered.

If there is interest, we will continue to expand the coverage of our list in the coming weeks and months.

A deeper look at the data

We looked through these millions of tweet to give me a sense of what topics are being discussed and to give a quick sense that we haven’t veered too far from the #FinTwit and #AdvisorTwit social networks.

Although it makes me a little sad to see Bitcoin ($BTC) as the top ticker, the rest of the top 20 lists look about right.

Top 20 Tickers on Twitter (by count)

$BTC 44,083
$TSLA 25,511
$SPX 19,647
$SPY 18,041
$AAPL 14,909
$AMZN 11,980
$ETH 10,833
$FB 9,753
$TSLAQ 9,659
$XRP 9,651
$NFLX 7,439
$VIX 7,285
$QQQ 5,994
$TWTR 5,212
$LTC 4,893
$TRX 4,716
$GLD 4,632
$MSFT 4,417
$DIS 4,100
$ES_F 3,916

Most common #hashtags

This set of 46 million tweets clearly skews towards the AI / data science part of the twittersphere.

Most Discussed Users

Our “relative mention frequency” looks is the number of ‘@mentions’ divided by the sum of twitter followers and twitter friends. We dropped corporate accounts and only included users with at least 6,500 mentions in our dataset.

Top Users by Mention Frequency

@TR401 Tyrone V. Ross Jr. 66%
@enricomolinari Enrico Molinari 31%
@TESLAcharts TeslaCharts 37%
@EricBalchunas Eric Balchunas 34%
@EconomPic Jake 34%
@MichaelKitces MichaelKitces 29%
@DiMartinoBooth Danielle DiMartino Booth 23%
@dougboneparth Douglas A. Boneparth 22%
@psb_dc Theo 19%
@OphirGottlieb Ophir Gottlieb 21%
@ThinkAdvisor ThinkAdvisor 19%
@RampCapitalLLC Ramp Capital 15%
@jposhaughnessy Jim O’Shaughnessy 13%
@SpirosMargaris Spiros Margaris 11%
@awealthofcs Ben Carlson 12%
@michaelbatnick Michael Batnick 11%
@chigrl 11%
@MikeQuindazzi Mike Quindazzi 9%
@patrick_oshag Patrick OShaughnessy 9%
@JimMarous Jim Marous 8%

Conversational Data

In the world of Twitter, actual back and forth conversations are quite rare. If they do happen, they tend to be between isolated sets of friends. Based on our analysis, these users are the exception to the rule; they engage with the greatest number of users.

TOP ‘SOCIAL BUTTERFLIES’

Rank User # users conversed with

@BrentBeshore Brent Beshore 288
@michaelbatnick Michael Batnick 203
@danielcrosby Daniel Crosby 181
@TR401 Tyrone V. Ross Jr. 177
@EricBalchunas Eric Balchunas 175
@WCInvestor White Coat Investor 168
@katie_martin_fx Katie Martin 164
@PeterLBrandt Peter Brandt 157
@crypto_rand Crypto Rand 147
@choffstein Corey Hoffstein 143
@patrick_oshag Patrick OShaughnessy 140
@AustinLieb Austin Lieberman 139
@karpathy Andrej Karpathy 136
@ReformedBroker Downtown Josh Brown 135
@CPAPlanner Jeff Levine 128
@AlexChalekian Alex Chalekian 125
@HonestlyCrypto The Crypto Profit 122
@ROIChristie ROI Christie 121
@lhamtil Lawrence Hamtil 119
@jus10castelli Justin Castelli 114

DOWNLOAD THE FULL 23 PAGE PDF REPORT

This 23 page report goes into the following:

Methodology

Twitter tweet topics analysis
#FinTwit subcommunity discovery
Deeper look at $tickers vs #advisors community micro-influencers (AKA ‘Connectors’)
#FinTwit Top 100 Influencers by Subcommunity: ($Tickers, #PoliticTwitter, #FinTech, #Advisors, #Crypto, $TSLA)