Nieman Foundation at Harvard
HOME
          
LATEST STORY
How journalists can avoid amplifying misinformation in their stories
ABOUT                    SUBSCRIBE
April 13, 2016, 12:11 p.m.
Audience & Social
LINK: www.parsely.com  ➚   |   Posted by: Joseph Lichterman   |   April 13, 2016

Parsely1Twitter generates 1.5 percent of traffic for typical news organizations, according to a new report from the social analytics company Parse.ly that examined data from 200 of its client websites over two weeks in January. (You’ll need to give Parse.ly your email address to access the full report.) Parse.ly’s network includes publishers like Upworthy, Slate, The Daily Beast, and Business Insider.

The median publisher saw roughly 8 tweets per post, 3 clicks per tweet, and 0.7 retweets for each original tweet, Parse.ly said. The top five percent of publishers performed better on Twitter, averaging 11 percent of their traffic from the network.

(We here at Nieman Lab are among those outliers, not least because our audience is made up of digitally savvy journalists — a prime Twitter demo. During the first three months of 2016, about 15 percent of Nieman Lab’s traffic came from Twitter.)

The key to success on Twitter — and social media in general — is to know what your audience “finds interesting and make sure that you are creating content that reflects this,” the company said:

There is no “secret sauce” for digital publishers looking to improve their success on Twitter. Sites that are doing well on the platform — achieving high levels of engagement — are not necessarily the most active; rather, they are sites that are producing interesting and shareable content that appeals to a large number of people.

There are two main types of posts on Twitter, Parse.ly says: conversational news and breaking news:

Typical content on Twitter tends to be conversational in nature, with thousands of people engaging with a particular topic for an extended period of time. Breaking news stories, on the other hand, often drive large spikes in traffic over shorter periods of time.

The 2016 presidential election is an example of a conversational topic that generates ongoing reporting and discussion. In March, there were 1.9 million tweets with links to news coverage about the race in its network, the company said. Election tweets made up more than 6 percent of all the tweets Parse.ly studied in March.

Last month’s terrorist attacks in Brussels, meanwhile, demonstrated how breaking news spreads on Twitter. More than 92,000 tweets with links to stories on the attacks were posted within the first 24 hours after they occurred, according to Parse.ly’s data. About a third of those tweets were sent within the first six hours after the attacks.

Parsely2

Despite its conversational and breaking news value, Twitter remains a relatively small source of traffic for most publishers. According to Parse.ly, less than 5 percent of referrals in its network came from Twitter during January and February 2016. Twitter trails Facebook, Google, and even Yahoo as sources of traffic, the report said (though it does edge out Bing!):

Parsely3

“Though Twitter may not be a huge overall source of traffic to news websites relative to Facebook and Google, it serves a unique place in the link economy,” the report said. “News really does ‘start’ on Twitter.”

Show tags Show comments / Leave a comment
 
Join the 50,000 who get the freshest future-of-journalism news in our daily email.
How journalists can avoid amplifying misinformation in their stories
We need new tools to ensure visual media travels in secure ways that keep us safer online. Overlays are among these tools.
How China used the media to spread its Covid narrative — and win friends around the world
China’s image plummeted in North America, but over half of 50 nations surveyed at the end of 2020 reported coverage of China had become more positive in their national media since the onset of the pandemic.
From deepfakes to TikTok filters: How do you label AI content?
How should we label AI media in ways that people understand? And how might the labels backfire?