Trust me, I'm an influencer

In this episode Mike and John discuss the pernicious use of paid-for social signals by non-political actors. Many entrepreneurs and those who wish to be perceived as influential use fake approval markers such as likes and shares from fictitious social accounts. We identify a number of risks that come with this kind of gaming of social platforms, including possible reputational damage to real people caused by account hijacking. Also the increased risk of spreading malware infections, when links are shared by accounts which may enjoy misplaced trust through being apparently connected with the supposed great and the good. We also discuss the risks to your own online reputation posed by automating your own follow back policies.

Photo by Nick Fewings on Unsplash  

Photo by Nick Fewings on Unsplash

 

Show note links

The New York Times feature The Follower Factory

The Bureau of Investigative Journalism feature Astroturfing, Twitterbots, Amplification - Inside The Online Influence Industry

 

How Mentionmapp analysed the 'influencers' buying influence:

Episode #8 Show Notes: “Bot Influence”

 

Revealing potential 10 “influencers” all buying Bot services

No mistaking patterns of behavior.

 

Our process/methodology -

From these 10 people:

  • 1: we exported 23 snapshots (200 tweets per snapshot) with data about the profiles that mentioned or retweeted them.

  • 2: from this we collected 2461 spreadsheet rows (includes duplicates) of Bots/Fake profiles (many we can find their twins, their real profile owners) Total Number of Unique Accounts: 2159  

  • 3: created a list of 232 Bots… the duplicates, as they have retweeted more than 1 of our featured 10 bot users.

  • 4: from these 232 bots, we then gathered every profile mentioned and hashtag used by 100 Bots from their previous 200 tweets..

  • 5: from these 100 Bots we collected 19,236 spreadsheet rows (includes duplicates) of profiles mentioned. Total Number of Unique Mentions: 9419… the customers, and the astroturf “farmers” ( and unsuspecting/unwitting provides of content… layer of legitimate and attractive content to give these Bot feed the look of real)

  • 6: from these 100 Bots we collected 17,267spreadsheet rows (includes duplicates) of hashtags used. Total Number of Unique Hashtags: 6785

 

The Who:

Already published:

@CraigBrownPhd *Onalytica’s #2 Big Data influencer 2016

https://www.patreon.com/posts/dr-duplicitous-15616191

@CHRISVOSS

https://www.patreon.com/posts/top-influencer-14788432

Grabbed data: Dec. 31, ‘17; Jan. 4, ‘18; Jan. 8 ‘18

@AlvinLindsay21

https://www.patreon.com/posts/fake-friends-14933022

Grabbed data: Dec. 31, ‘17; Jan. 4, ‘18; Jan. 8 ‘18

@DavidPapp

https://medium.com/mentionmapp/the-bullsh-ters-the-socialbots-vol-iv-verified-tweeting-a-good-game-b8977270cb4d

Grabbed data: Dec. 31, ‘17; Jan. 4, ‘18;  Jan. 8 ‘18

Via Onalytica Top VR Influencers 2017

http://www.onalytica.com/blog/posts/virtual-reality-2017-top-100-influencers-brands-publications/

#1 Influencer @RickKing16

Grabbed data: Dec. 31, ‘17; Jan. 4, ‘18; Jan. 8 ‘18

#16 influencer @Sanemavcil

Grabbed data: Dec. 31, ‘17; Jan. 4, ‘18 Jan. 8 ‘18

@LeteciaJohnson

Grabbed data: Jan. 4, ‘18; Jan. 8 ‘18

@Insights2Growth

*massive follower increase… less than 3000 in December.

Grabbed data: Dec. 31, ‘17; Jan. 4, ‘18; Jan. 8 ‘18

@TheAvaCantrell (verified)

Grabbed data: Dec. 31, ‘17;Jan. 4, ‘18;Jan. 8 ‘18

@redragdolly (verified)

Grabbed data: Jan. 8 ‘18