Information professionals spend a lot of time worrying about the quality of information. This is nothing new—we’ve always been dedicated to providing high-quality information. However, the scope of what constitutes quality information has escalated along with the increase in formats, sources, trolls, and tricksters. The trend toward misleading information, both as misinformation and disinformation, is disheartening.
There was a time when a simple checklist would suffice. Does the information come from a valid source? Is it created by someone who knows what they’re talking about? Has bias affected it? Is it believable and reliable? The web has obscured some of those points, making it difficult for people to ascertain who is actually behind the source, what biases exist, and whether it’s accurate.
In an era when every news story can be labeled “fake news,” and social media companies can’t distinguish between real people and bots, the ability to delineate good information from bad information is hugely challenging. With academia plagued by fake, predatory journals, the problem worsens. We have long questioned statistics presented by third-party publishers, but we are now a bit suspicious of government statistics. Market forecasts can differ widely from one analyst to another depending on how they gather and interpret data. Business researchers and competitive intelligence specialists have always been wary of the information contained in corporate press releases, as it may be designed to mislead the competition. Then again, it may not. But skepticism rules the day.
I’ve always advocated being skeptical toward information. I still do, but when healthy skepticism morphs into distrust of everything on the web (and maybe even in print) and not only the general public but professionals in all disciplines greet any piece of information with doubt, we sow the seeds of catastrophe. If we believe nothing, how can we function as information professionals?
The phrase “Seeing is believing” has become “Seeing is deceiving.” Noting factual distortions in text is easier (sometimes) than discerning alterations in images. We’ve known for decades that changing the scale of the X axis and the Y axis on a graph changes the message of the numbers. Changing colors to emphasize one experimental result over another has a similar effect. Image manipulation in scientific articles has grown to where it can be considered researcher malfeasance.
Whether image manipulation happens inadvertently or by design and whether it is the responsibility of editors or reviewers to catch the mistake raise important questions; for information professionals, the issue is about our role. We can raise the alarm about shady publications, indications of inaccurate data sources, and outdated information. But do we really have the expertise—or the time—to examine images for possible errors? I think this is one area that we must defer to publishers to identify misleading and deliberately incorrect information. We can only hope they do it well. Otherwise, doubt will overtake our ability to do our jobs.