The Scouting of Original and Influent InsurTech Voices

In May 2016 we published our #50insurtech influencer list for the first time and ever since it has registered 44.600 page views on our website. Every month we have scouted the web for the most prominent voices on Linkedind, Twitter and the main online insurance website. In the past two years more than 150 people have been featured on the list.

The noise around insurtech has grown significantly due to the amount of money invested and the increasing number of conferences specializing on this topic. This has made our job even more complex, that is to find the original influent voices in the crowded panorama. It has become more and more difficult to separate the opinion leaders from all the other people which are only sharing or re-tweeting other opinions or adding the hashtag “insurtech” in their posts and tweets.

Our criteria for assessing the online relevancy has evolved due to that evolution of the context. One tool we love is NodeXL, a tool that enables the collection, analysis and visualization of social media data.

In this article we show some of the queries we have done since the beginning of this month.

The graph above represents a network of 474 Twitter users whose recent tweets contained "insurtech -fintech", or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18.000 tweets. The network was obtained from Twitter on Thursday, 10 May 2018 at 19:04 UTC. It can be found also here http://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=151365

The tweets in the network were tweeted over the 4-hour, 29-minute period from Thursday, 10 May 2018 at 14:33 UTC to Thursday, 10 May 2018 at 19:03 UTC.

Additional tweets that were mentioned in this data set were also collected from prior time periods. These tweets may expand the complete time period of the data.

There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, and a self-loop edge for each tweet that is not a "replies-to" or "mentions".

The graph is directed.

The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.

The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.

The edge colors are based on edge weight values. The edge widths are based on edge weight values. The edge opacities are based on edge weight values. The vertex sizes are based on followers values. The vertex opacities are based on followers values.

Overall Graph Metrics

Vertices: 475

Unique Edges: 664

Edges With Duplicates: 272

Total Edges: 936

Self-Loops: 97

Reciprocated Vertex Pair Ratio: 0,0786163522012579

Reciprocated Edge Ratio: 0,145772594752187

Connected Components: 105

Single-Vertex Connected Components: 39

Maximum Vertices in a Connected Component: 208

Maximum Edges in a Connected Component: 621

Maximum Geodesic Distance (Diameter): 12

Average Geodesic Distance: 3,977606

Graph Density: 0,00304685765045525

Modularity: 0,606341

NodeXL Version: 1.0.1.396

Top 10 Vertices, Ranked by Betweenness Centrality:

Top URLs in Tweet in Entire Graph:

[7] https://www.amazon.co.uk/INSURTECH-Book-Technology-Entrepreneurs-Visionaries/dp/1119362210/ref=sr_1_1?ie=UTF8&qid=1525103586&sr=8-1&keywords=insurtech

[5] https://www.pscp.tv/w/bcRD7zFvTlFsRGRlR0x5UXd8MXJtR1BtWURWcXlKTsMsI8za7Ur91aC6gUeVhTXyUI7KKEVtRB2n4Vci3Woj?t=4m52s

[4] https://www.investmentexecutive.com/building-your-business/practice-management/the-insurtech-gap/?utm_source=twitter&utm_medium=social&utm_campaign=byb&utm_content=fintech

[4] https://twitter.com/Imanchev/status/994110842361798656

[4] https://insurtechnews.com/insights/africa-making-microinsurance-work

[4] https://bootcamp-hack2in.eventbrite.com.ar/

[3] https://www.guidewire.com/about-us/news-and-events/press-releases/20180510/guidewire-announces-20181-release-pc-insurance

[3] http://theonebrief.com/collaboration-future-insurtech/?utm_source=social&utm_medium=twitter&utm_campaign=aonsocial

[3] http://www3.asiainsurancereview.com/News/View-NewsLetter-Article/id/41616/Type/AirPlus/Ecosystems-and-platforms-to-determine-insurance-evolution?utm_content=buffere1193&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

[3] https://www.digitalscouting.de/interview-chris-cheatham-riskgenius-insurtech-ai/

Top Domains in Tweet in Entire Graph:

[23] twitter.com

[13] insurtechnews.com

[9] linkedin.com

[9] co.uk

[7] pscp.tv

[5] dig-in.com

[5] investmentexecutive.com

[5] boltinc.com

[5] com.ar

[4] medium.com

Top Hashtags in Tweet in Entire Graph:

[232] insurtech

[77] insurance

[41] ai

[25] iot

[19] blockchain

[17] startups

[16] machinelearning

[14] bigdata

[13] innovation

[12] digital

Top Words in Tweet in Entire Graph:

[280] Words in Sentiment List#1: Positive

[112] Words in Sentiment List#2: Negative

[0] Words in Sentiment List#3: Angry/Violent

[7819] Non-categorized Words

[8211] Total Words

[286] insurtech

[132] insurance

[45] ai

[35] industry

[34] startups

Top Word Pairs in Tweet in Entire Graph:

[17] insurance,insurtech

[15] insurance,industry

[13] thanks,author

[13] contribution,editors

[13] editors,sabine

[12] sabine,vanderlinden

[11] psb_dc,db_entrepreneur

[11] insurtech,startups

[10] gt,gt

[10] jamesjwyork,psb_dc

Top Replied -To in Entire Graph:

[4] matt_tsou

[2] nigelwalsh

[2] insurtech_book

[2] ctdecd

[1] guzmand

[1] nick_lamparelli

[1] garetheggle

[1] charliehalkett

[1] apexa_corp

[1] SMMBrightBlueH

Top Mentioned in Entire Graph:

[21] mcins_

[21] sabinevdl

[17] nigelwalsh

[17] psb_dc

[16] guzmand

[14] stratorob

[14] susannechishti

[12] charliehalkett

[12] k_feldborg

[12] mikequindazzi

Top Tweeters in Entire Graph:

[652318] evankirstel

[302385] s_schwartzmann

[210758] nancyrubin

[148624] josefreedomve

[138772] motolin5

[130657] barclaysuk

[126467] juliosilvajr

[113903] ipfconline1

[103404] spirosmargaris

[101925] infopronetwork

The graph above was obtained from Twitter on Tuesday, 08 May 2018 at 23:59 UTC and all the details can be seen at http://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=151387

It represents a network of 1.827 Twitter users whose recent tweets contained "Insurtech -fintech", or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18.000 tweets.

The tweets in the network were tweeted over the 1-day, 17-hour, 20-minute period from Monday, 07 May 2018 at 06:21 UTC to Tuesday, 08 May 2018 at 23:42 UTC.

Additional tweets that were mentioned in this data set were also collected from prior time periods. These tweets may expand the complete time period of the data.

There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, and a self-loop edge for each tweet that is not a "replies-to" or "mentions".

The graph is directed.

The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.

The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.

Overall Graph Metrics:

Vertices: 1827

Unique Edges: 2737

Edges With Duplicates: 1867

Total Edges: 4604

Self-Loops: 927

Reciprocated Vertex Pair Ratio: 0,0647648419429453

Reciprocated Edge Ratio: 0,121650977552498

Connected Components: 446