The (Mis)Information Age: Measuring and Improving ‘Digital Literacy’

31 Aug

The information age has bought both bounty and pestilence. Today, we are deluged with both correct and incorrect information. If we knew how to tell apart correct claims from incorrect, we would have inched that much closer to utopia. But the lack of nous in telling apart generally ‘obvious’ incorrect claims from correct claims has brought us close to the precipice of disarray. Thus, improving people’s ability to identify untrustworthy claims as such takes on urgency.

Before we find fixes, it is good to measure how bad things are and what things are bad. This is the task the following paper sets itself by creating a ‘digital literacy’ scale. (Digital literacy is an overloaded term. It means many different things, from the ability to find useful information, e.g., information about schools or government programs, to the ability to protect yourself against harm online (see here and here for how frequently people’s accounts are breached and how often they put themselves at risk of malware or phishing), to the ability to identify incorrect claims as such, which is how the paper uses it.)

Rather than build a skill assessment kind of a scale, the paper measures (really predicts) skills indirectly using some other digital literacy scales, whose primary purpose is likely broader. The paper validates the importance of various constituent items using variable importance and model fit kinds of measures. There are a few dangers of doing that:

  1. Inference using surrogates is dangerous as the weakness of surrogates cannot be fully explored with one dataset. And they are liable not to generalize as underlying conditions change. We ideally want measures that directly measure the construct.
  2. Variable importance is not the same as important variables. For instance, it isn’t clear why “recognition of the term RSS,” the “highest-performing item by far” has much to do with skill in identifying untrustworthy claims.

Some other work builds uncalibrated measures of digital literacy (conceived as in the previous paper). As part of an effort to judge the efficacy of a particular way of educating people about how to judge untrustworthy claims, the paper provides measures of trust in claims. The topline is that educating people is not hard (see the appendix for the description of the treatment). A minor treatment (see below) is able to improve “discernment between mainstream and false news headlines.”

Understandably, the effects of this short treatment are ‘small.’ The ITT short-term effect in the US is: “a decrease of nearly 0.2 points on a 4-point scale.” Later in the manuscript, the authors provide the substantive magnitude of the .2 pt net swing using a binary indicator of perceived headline accuracy: “The proportion of respondents rating a false headline as “very accurate” or “somewhat accurate” decreased from 32% in the control condition to 24% among respondents who were assigned to the media literacy intervention in wave 1, a decrease of 7 percentage points.” The .2 pt. net swing on a 4 point scale leading to a 7% difference is quite remarkable and generally suggests that there is a lot of ‘reverse’ intra-category movement that the crude dichotomization elides over. But even if we take the crude categories as the quantity of interest, a month later in the US, the 7 percent swing is down to 4 percent:

“…the intervention reduced the proportion of people endorsing false headlines as accurate from 33 to 29%, a 4-percentage-point effect. By contrast, the proportion of respondents who classified mainstream news as not very accurate or not at all accurate rather than somewhat or very accurate decreased only from 57 to 55% in wave 1 and 59 to 57% in wave 2.

Guess et al. 2020

The opportunity to mount more ambitious treatments remains sizable. So does the opportunity to more precisely understand what aspects of the quality of evidence people find hard to discern. And how we could release products that make their job easier.

This site uses Akismet to reduce spam. Learn how your comment data is processed.