Mobile Journalism Isn’t Just Producing Content. It’s Knowing How Mobile Content Affects Engagement.

Story Highlights

• Mobile analytics are good. Understanding how mobile structures affect mobile analytics is better.
• Teaching the production of good mobile video is only a part of mobile journalism.
• Stories with a photo or video followed by pages of text don’t inform scanning mobile users.

If you made it to this sentence and keep reading, that’s due, in part, to your specific interest in mobile and/or journalism. But if a scanning user without such an interest encounters this topic as one in a variety of topics, the structure of this post plays a critical role in user engagement.

Researching and teaching mobile journalism to undergraduates since 2011 has provided me with more insights into engagement than any book or webinar could. An audience analytics class later convinced me that the best writing or video alone may not engage the largest mobile audience possible.

Complicating the teaching of mobile journalism is “best practices” from five years ago aren’t always suited for today’s younger users, who are growing into the more experienced mobile users of tomorrow. By younger users, I’m referring to A.C. Nielsen’s February 2017 report finding nearly 45 percent of children obtained their first mobile service plan between the ages of 10 and 12.

This is why I teach, research and test how new structures for content can attract users who are now often in a state of “continuous partial attention.” This term doesn’t mean multitasking, but instead the feeling of being a “node” on a network always waiting for immediate information from texts and social media.

Combining quality mobile media with an understanding of users can increase engagement.

Having synthesized my interest in journalism with cognitive psychology, I’m the first to admit there are many variables that can determine engagement including what a report by Comscore lists as the “Mobile Hierarchy of Needs.”  But to be more specific here, my research focuses on three types of digital users.

1. Scanners, which make up the largest sector of the mobile audience
2. Seekers who are searching for specific content, and
3. Readers, the minority who reads all content.

The level of engagement by each type depends on his or her motivation, available time, and interest in the content. I teach my students that like the “inverted pyramid” for text, mobile users should be able to terminate engagement at any point and still obtain a “nugget” of news. Besides three types of users, two types of interest also help us to understand engagement.

There’s individual versus situational interest. A user with experience and/or expertise for content has individual interest, a strong motivator for engagement. For example, a Giants football fan scanning the ESPN app who sees a Giants headline requires less multimedia to engage in the story compared to a headline about the Seattle Mariners baseball team. Assuming the football fan has no individual interest in baseball, how the baseball headline and story are structured with photos, video, interactive graphics, etc. could increase situational interest in the content, or interest generated by how the content is presented. We’ve all sat through a talk about a topic in which we had no interest. Adding discussions, interactive polls, and even humor makes the presentation situationally interesting.

We know how headlines can affect initial engagement, but continued engagement depends on all the stuff below the headline. Even if a user with limited time spends a bit more time on content that they typically would scan over, the metrics increase. Countless stories and vague headlines and those I call “list headlines” that offer “10 reasons why you should do X” assume all users have the time to engage with more content. That strategy just left behind scanners who don’t have time to read pages of text.  In short, analytics measure how many users engaged but not how much situational interest the content generates. If the goal is to increase – not just measure – the number of users, structure offers room for improvement.

Let’s also consider the things in content that terminate engagement. Pages of text, long videos, broken links or just one term unfamiliar to the user define what I call cognitive “kick-outs.” Increasing users’ interest by replacing potential kick-outs with a better structure is akin to increasing students’ attention by replacing lectures with interactive projects.

Understanding Digital Audiences – University of Maryland from PBS MediaShift on Vimeo. (Video by Samuel Antezana)

Even headlines and tweets with at least two of the five Ws are more effective than vague headlines. “Teasing” headlines might work for some content but it’s impossible to tease a mobile scanner who doesn’t have the time or interest to engage with more content.

Integrating more “scannable” elements into longer mobile stories attracts more users.

I’m building a list of story elements that generate situational interest for general audiences. Even relatively subtle elements can make a difference. For example, eye tracking studies by the Norman Nielsen Research Group found that simple bullet points significantly increased users’ attention, so I’ll use them here to list some elements:

• An explanatory headline (like the one in this story) informs ALL users. Why are many headlines still vague?
• A summary of story highlights (as featured here) for scanners. CNN stories include this.
• Explanatory subheads with declarative sentences (not titles) to inform scanners who won’t read the text.
• Combine a photo with the first paragraph. (Video isn’t as effective for scanners who like to know what the video is before playing it.)
• Chunk all paragraphs into two or three sentence and offer “read more” for those with the time and interest to continue.
• Strategically embed short videos clips  (:30 or less) throughout the text, not one long video package.
• Replace traditional data charts with simple explanatory elements.

This list is just a sample of what my teaching and researching have revealed about mobile communication. There’s no doubt the list will continue to grow and change. Mobile journalism courses, books, and webinars that synthesize good production techniques with an understanding of how users cognitively process information will best serve future journalists who must prepare for an increasingly mobile world. Yesterday’s techniques don’t always work today and today’s techniques won’t always work tomorrow. I expect future research will change how we will produce mobile content and interpret mobile metrics.

Ronald Yaros @ryaros (Ph.D. University of Wisconsin-Madison) is a former TV science reporter and developer of educational software, and now an associate professor of mobile journalism and director of explainmynews.org at the University of Maryland. Yaros is a 2017 Apple Distinguished Educator.