Identifying value through its fakes

Fascinating article (via Schneierblog) originally published in the New Republic on how social media currency (value) is being faked. Forget spam emails etc. The way to do it is to exploit differential labor costs, exploit phone verification with mountains of SIM cards, and scrape data from dating websites.

Here’s why this is important: if you want to know what’s valuable, look at how it’s being faked, and what those fakes are selling for. So $29.99 will get 1,000 Facebook likes etc.

Furthermore, examine what the big social media companies are doing about it. On the one hand they too receive value by having more likes, users, and engagement they can sell. And remember that both Facebook and Twitter are more than 90% dependent on advertising for their revenue. If they can offer increased visibility (increased attention) they can help maintain that revenue. On the other hand, this stream of fake users, activity, clicks and so on undermines their purpose because it devalues real social engagement. Why buy an advertising campaign (or “boost” your post) if all it does it get put in front of ghost users?

You could see this as a contradiction of capitalism: “true” value being undermined by cheaper knockoff values; the necessity for cheap labor (globally cheap not locally, the workers in the story earn above average wages locally). Then you could see capitalism as accommodating (or not) such contradictions in the way it has usually done, trying to stamp out the fake product (think: champagne, Gucci handbags) while denying and obfuscating the scale of the problem. (Here’s some good research on it though, and the SEC still plays a role in terms of disclosures–more social scientists should use SEC disclosures I believe).

Or you could see it  as an arms race, with each side trying to outdo the other. This arms race might continue in perpetuity. Here’s where this becomes important. What these companies need is to distinguish a fake user (even a premium one created by hand as described in the story) from a real user. This is hard to do simply looking at the profile, admits the writer. But what’s the one huge distinguishing aspect? It’s that a fake user doesn’t move around and leave a geolocational trail. There’s no spatial media aspect to their profile (bots don’t move, and their behaviours in general are often highly coordinated for ease of management). Real people move through the environment in varying degrees.

So Facebook needs to be able to get users’ geolocational traces. There are various ways to do this; a lot of spatial profiles are available online (think: Strava, Twitter geotags). The government would also want these data for not entirely dissimilar purposes of distinguishing possible threat individuals; known as “Co-traveler analytics.” (Hey, maybe FB could get the gov data?) But spy vs. spy: the creators of fake users will start adding geolocational trails to their profiles, won’t they?

Once that happens we’ll perhaps begin a general undermining of the value of geolocational data. Privacy advocates may cheer at that. Nevertheless, it seems a great research opportunity. I’m not aware of work on fake geolocational data and economy, though there must be stuff more generally. Is value undermined by counterfeits? Or is it part of the cost of doing business? And if so, what is the cost? The article states:

Researchers estimate that the market for fake Twitter followers was worth between $40 million and $360 million in 2013, and that the market for Facebook spam was worth $87 million to $390 million. Italian Internet security researcher Andrea Stroppa has suggested that the market for fake Facebook likes could exceed even that.

So, similarly, what is the potential market for fake geolocational data? I don’t think anyone’s looked into this. And indeed, how would you research it? One way would be to estimate the proportion of fake accounts and the degree of “long tail” usage on social media. (The Oxford Internet Institute has done work on the uneven geographies of the information economy long tail aka the digital divide.) So the number of bots and the disproportionate concentration of activity that we see on Wikipedia, OSM, etc. Stroppa and Di Micheli have taken this approach. (Some of the numbers on that 2013 may be out of date already.) One finding: FB likes cost $1.07 from Facebook, but only 5c on the black market.

Anyway, some potential lines of research here. Time for an article!

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