As I was reading An Xiao Mina’s recent (and excellent) piece for Nieman Lab, and it reminded me that I had not yet written here about why I’ve increasingly been talking about reputation as a core part of online digital literacy. Trust, yes, consensus, yes. But I keep coming back to this idea of reputation.
Why? Well, the short answer is Gloria Origgi. Her book, Reputation, is too techno-optimist in parts, but is still easily the most influential book I’ve read in the past year. Core to Origgi’s work is the idea that reputation is both a social relation and a social heuristic, and these two aspects of reputation have a dynamic relationship. I have a reputation, which is the trace of past events and current relationships in a social system. But that reputation isn’t really separate from the techniques others use to decode and utilize my reputation for decision-making.
This relationship is synergistic. As an example, reputation is subject to the Matthew Effect, where a person who is initially perceived as smart can gain additional reputation for brilliance at a fraction of the cost of someone initially perceived as mediocre. This is because quick assessments of intelligence will have to weight past assessments of others — as a person expands their social circle initial judgments are often carried forward, even if those initial judgments are flawed.
Reputation as a social heuristic maps well onto our methods of course — both Origgi and the Digital Polarization initiative look to models from Simon and Gigerenzer for inspiration. But it also suggests a theory of change.
Compare the idea of “trust” to that of “reputation”. Trust is an end result. You want to measure it. You want to look for and address the things that are reducing trust. And, as I’ve argued, media literacy programs should be assessing shifts in trust, seeing if students move out of “trust compression” (where everything is moderately untrustworthy) to a place where they make bigger and more accurate distinctions.
But trust is not what is read, and when we look at low-trust populations it can often seem like there is not much for media literacy to do. People don’t trust others because they’ve been wronged. Etc. What exactly does that have to do with literacy?
But that’s not the whole story, obviously. In between past experience, tribalism, culture, and the maintenance of trust is a process of reading reputation and making use of it. And what we find is that, time and time again, bad heuristics accelerate and amplify bad underlying issues.
I’ve used the example of PewDiepie and his inadvertent promotion of a Nazi-friendly site as an example of this before. PewDiepie certainly has issues, and seems to share a cultural space that has more in common with /pol/ than #resist. But one imagines that he did not want to risk millions of dollars to promote a random analysis of Death Note by a person posting Hitler speeches. And yet, through an error in reading reputation, he did. Just as the Matthew Effect compounds initial errors in judgment when heuristics are injudiciously applied, errors in applying reputation heuristics tend to make bad situations worse — his judgment about an alt-right YouTuber flows to his followers who then attach some of PewDiepie’s reputation to the ideas presented therein — based, mostly, on his mistake.
I could write all day on this, but maybe one more example. There’s an old heuristic about the reputation of positions on issues — “in matters indifferent, side with the majority.” This can be modified in a number of ways — you might want to side with the qualified majority when it comes to treating your prostate cancer. You might side with the majority of people who share your values on an issue around justice. You might side with a majority of people like you on an issue that has some personal aspects — say, what laptop to get or job to take. Or you might choose a hybrid approach — if you are a woman considering a mastectomy you might do well to consider what the majority of qualified women say about the necessity of the procedure.
The problem, however, from a heuristic standpoint, is that it is far easier to signal (and read the signal) of attributes like values or culture or identity than it is to read qualifications — and one underremarked aspect of polarization is that — relative to other signals — partisan identity has become far easier to read than it was 20 years ago, and expertise has become more difficult in some ways.
One reaction to this is to say — well people have become more partisan. And that’s true! But a compounding factor is that as reputational signals around partisan identity have become more salient and reputational signals around expertise have become more muddled (by astroturfing, CNN punditocracy, etc) people have gravitated to weighting the salient signals more heavily. Stuff that is easier to read is quicker to use. And so you have something like the Matthew Effect — people become more partisan, which makes those signals more salient, which pushes more people to use those signals, which makes people more partisan about an expanding array of issues. What’s the Republican position on cat litter? In 2019, we’ll probably find out. And so on.
If you want to break that cycle, you need to make expertise more salient relative to partisan signals, and show people techniques to read expertise as quickly as partisan identity. Better heuristics and an information environment that empowers quick assessment of things like expertise and agenda can help people to build better, fuller, and more self aware models of reputation, and this, in turn, can have meaningful impact on the underlying issues.
Well, this has not turned into the short post I had hoped, and to do it right I’d probably want to talk ten more pages. But one New Year’s resolution was to publish more WordPress drafts, so here you go. 🙂
3 thoughts on “Why Reputation?”
I also enjoyed Origgi’s book very much, and think the idea of reputation needs much more consideration as we sort out how to weigh what we discover online.
If you haven’t already encountered it, you should look into Kenneth Craik’s “Reputation: A Network Interpretation”. Published in 2008, it provides a mostly pre-digital consideration with one key idea: reputation is not located in a person, but in a more or less loosely connected network of other individuals who all exchange information by varying means and in whom information about the person of interest is stored.
I’ve also found John Cheney-Lippold’s 2017 “We Are Data” very useful in thinking about reputation online. By contrast with Craik, Cheney-Lippold has little to say about pre-digital social reputation. He focuses on how our digital traces can be algorthmically reduced to ‘measureable types’ which form a kind of datafied reputation.
Thank you for these recs! I will get on this immediately.
Since you read Reputation I’m curious — did you have the same weird experience I did, where so much of her thinking about technology felt very naive, but at the same time her overall theory was so well tuned to address the deeper problems?