This post is by Information Retrieval Specialist Ann Feeney.

Marketers, PR professionals and advocacy groups need to move beyond the traditional ways of performing outreach and measuring results.

An organization can get hundreds or thousands of media mentions, but that doesn’t necessarily measure whether or not a campaign changed minds or even effectively exposed people to a new idea. Instead, we need to dig deeper, beyond mention counts and share of voice. There are dozens of ways that you can do this, but one way is to segment your media universe by its audience and stance toward a topic.

Many social scientists say that today’s media is an “echo chamber,” that people get their news only from sources that support what they already think and feel and discuss it primarily among those who agree with them. As a result, we’re not regularly exposed to new ideas and once we’ve made up our minds, they stay made up.

For example, several research studies have shown that on Twitter, both politicians and everyday citizens tend to follow those who share their political mindset more than those who do not. As the Internet becomes more personalized—Amazon recommends books and magazines, Netflix recommends shows and Google News presents news stories based on what news stories one has read recently—the echo chamber effect will grow stronger and less conspicuous.

The proliferation of cable television stations and websites means that we can get our news from sources that match, rather than challenge, our preconceptions and ideologies. Not only that, but there’s physiological evidence that we tune out opposing viewpoints when we encounter them. There’s even a technical term for this: “disconfirmation bias.” It doesn’t mean that people won’t ever listen to new ideas; we know that people are open to new information from their second-degree or “weak” connections, showing that despite the echo chamber effect, people will take in new information when it’s available to them. We also know that people can be open to new ideas when their sense of self is neutralized by some kind of affirmation.

This means, for example, an organization that wants to spread news about the health benefits of yoga could categorize publications according to their coverage as consistently exposed (such as Yoga Journal), regularly exposed (Natural Health), occasionally exposed (Christian Science Monitor), and rarely exposed (Wired). Then it can categorize articles by factors such as word length (brief, medium, extensive), tone (positive, neutral, skeptical), or other factors, based on its particular message or goal.

You can do this within Cision’s PR Software  by custom-tagging and assigning tone, or you can do it in Excel or another spreadsheet program.

Here’s an example of a pivot table showing publication stance and the tone of coverage. Here you can see that while negative stories tend to appear in unfavorable outlets and positive stories in favorable outlets, there was neutral coverage in both favorable and unfavorable outlets.

annfeenygraph1

Chart by Ann Feeney

Or for a quick summary, you might assign potential to change impressions to each story, with negative stories in favorable or neutral media receiving low scores, positive or neutral stories in unfavorable or neutral media receiving high scores, and other stories not receiving a score. Using a scale that goes from -1 to 1 for each story’s potential to change perceptions, this story has a score of 4, indicating that overall, the story likely had a positive effect on public perception. If you track all your stories like this, you can track impact over time, compare campaigns or approaches, or measure which publications have been the most fruitful for your campaigns. As always, you have to control for other variables (or at least keep the variables in mind when you draw conclusions), but this nonetheless provides a consistent measurement for your efforts.

Chart by Ann Feeney

Chart by Ann Feeney

As you can see, Cision provides you with the tools for additional insights into your media coverage, no matter what you want to measure.

 

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This post was written by a guest Cision contributor.