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The Like Log

The Rankings

Numbers to Know

Social Engagement Per 1000 Pageviews

Lifespan of a Story

Engagement Trends

Few likes A lot of likes

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1/27 New York Times

2/27 Yahoo! News

3/27 Huffington Post

4/27 Wall Street Journal

5/27 FOX News

6/27 AOL News

7/27 CNN

8/27 BBC

9/27 TechCrunch

10/27 Mashable

11/27 Wired Magazine

12/27 Engadget

13/27 All Things D

14/27 Venture Beat

15/27 Business Insider

16/27 Business Week

17/27 Forbes

18/27 Good Magazine

19/27 Change.org

20/27 paidContent.org

21/27 Gawker

22/27 eHow.com

23/27 Associated Content

24/27 Yahoo! Music

25/27 Yahoo! OMG

26/27 Yahoo! Sports

27/27 Yahoo! Shine

Engagement for Entites

Engagement for Topics

Voting Trends at Hacker News

Engagement trends can be computed from different signals. Instead of likes one can use tweets or upvotes. On this chart we show most votable topics at Hacker News site.

few upvotes a lot of upvotes big: a lot of submissions small: few submissions

These voting trends are based on a few thousands recent submissions to Hacker News (news.ycombinator.com).

Five Recommendations

• Put significant effort in your top stories. According to our study, most websites can capture 30% of their total enagement by publishing only ONE story per week! One story per day can capture 70-80% of your audience reactions. But we rarely see web journalists working weeks on a single story. Here are some examples of "big effort for big story": events coverage (Oscars, Superbowl), nominations, awards and voting (Fast Company Influence Project, TIME Magazine Person of the Year readers choice), exclusives (Gizmodo iPhone 4 leak), celebrity Q&A (Obama at Youtube, Beckham at Yahoo! Sports), lists (Forbes Billionaires, Vanity Fair 'Best Dressed'), high-quality infographic & interactives (Good Magazine Transparency blog, NYT 'You Fix the Budget'), trends and predictions (Read Write Web), liveblogging from top events (Apple announcements), op-eds on most controversial issues. Yahoo! Labs have recently published a paper with detailed analysis of hit/niche balance of interests.
• Improve promotion of your best content. According to our measurements, web stories are practically lost 24 hours after publications. Only 20% likes are coming after the first day. This engagement pattern discourages production of "big stories". To get maximum return on your hits, change your frontpage policy. Best stories should be highly visible. Consider hits-only RSS and twitter feeds, month-in-review / year-in-review programs. TechCrunch Classics is another example of hit promotion. And internal efforts are not enough. Breakout success comes when other media (top TV networks, newspapers and magazines) are picking your story and link back to it.
• Improve your median story. Sort all your stories by engagement and pick a story right in the middle of the list. This is called a "median". A median story has less than 50 likes for majority of websites in our study. In other words, every second story takes more effort from a writer than it brings value to the readers. Recently leaked "The AOL Way" reports their median story to have only 1500 pageviews, and they aim to grow it four times. Publishers should ask themselves: Why do we write so many weak stories?
• Use both intuition and algorithms for demand analysis. Some say that best writing can only come from internal compass. Others are writing new articles in direct response to search queries. The key to success is likely somewhere in between. In ideal settings the writers have full control but they are equiped with great tools to navigate content demand. Up to this moment most demand tools were based on search data and internal metrics. The Like Log presented here is a new approach to demand analysis. Likes represent so called "interest graph", the map of world interests. You can understand your readers better by using our engagement trends technology. And you can get insights into audience of your competitors, too.
• Invest in social media optimization. According to our numbers, you should get around 5-20 tweets and likes per 1000 pageviews. If you have less than that, your social distribution is suboptimal. Create different designs for sharing and subscription functionality and split-test them to find the optimal one. Also, make sure to have just one URL for every story and slideshow. Otherwise, your enagement numbers get scattered among article duplicates. Social networks are likely to be a bigger traffic driver in the future. Combining our data with Comscore, we see that average New York Times reader makes only ONE like per year. We expect this number to go up significantly.

Pointers