increasing twitter click- t hroughs with images
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Increasing Twitter Click-Throughs with Images
Michael Levi
UNECE Work Session on the Communication of Statistics
June 19, 2014
Background
BLS began tweeting news release headlines in June, 2012
Expanded to other publications in January, 2013
Added images to selected tweets beginning in January, 2014
2
Tweets with Images
3
Motivation
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Why images? Cheap and easy way to entice users
to access underlying content
Expectation of success We know that visually appealing
layout increases readership of publications
It stands to reason that the same would hold true for tweets
Beware the Intuitively Obvious!
Michael Levi
UNECE Work Session on the Communication of Statistics
June 19, 2014
Results
Did adding images increase click-throughs? Before looking at data: “Absolutely!” After looking at data: “Not the way we
expected.”
Metric Used
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Number of “click-throughs”
Each tweet has a unique shortened URL, for which we can get reliable counts
Measures how many users are navigating to underlying data and analysis
Click-throughs vs. Retweets
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0 50 100 150 200 250 3000
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
Click-throughs vs. Retweets
Retweets
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Click-throughs vs Retweets: Bottom 90%
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0 5 10 15 20 25 300
200
400
600
800
1,000
1,200
Click-throughs vs. Retweets, Zoomed
Retweets
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Click-throughs by date
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1 25 49 73 97 1211451691932172412652893133373613854094334574815055295530
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
Click-throughs in Chronological Order
Sequence
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Click-throughs by level
110
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
Click-throughs by level
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Click-throughs by level – Top 35
120
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
Click-throughs by level - Top 35
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Employment Situation news releaseTweet with imageOther
Comparing Apples to Apples: The Editor’s Desk (TED)
A daily post featuring recent data – news releases, reports, etc.
One or two charts with summary text and link
Before Jan 2014, tweets without images
Since Jan 2014, tweets with images
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TED Click-throughs
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 310
500
1,000
1,500
2,000
2,500
3,000
Click-throughs: TED only
Sequence
Clic
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No image
With image
TED Click-throughs Summary Statistics
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7 highest-volume tweets have images
4 lowest-volume tweets have images
Mean Median
No Image 355 331
With Image 688 343
Other Contributing Factors?
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No particular correlation by Type of chart (map, bar, line, etc.) General topic (employment, prices,
etc.) Day of week Time of day
Content: Tweets with relatively many click-
throughs
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“Find out which occupation has the highest relative concentration in your state”
“Differences in union and nonunion compensation, 2001–2011”
“Productivity and Costs by Industry: Selected Service-Providing and Mining Industries”
Content: Tweets with relatively few click-throughs
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“College graduates more likely than those with less education to be married at age 27”
“Investment in higher education by race and ethnicity”
“In honor of Workers’ Memorial Day, trends in fatal workplace injuries”
Conclusions
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Tweeting images has ambiguous results When tweets with images catch on,
they garner relatively high click-throughs
Most tweets with images have comparable click-through rates as tweets without images
It is not obvious what will interest the Twitterverse, but content appears to be more important than presentation
So Why Did I Believe Otherwise?
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Expectations Confirmation bias A narrative, once established, is
hard to overturn
Conclusions II
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Just because something is obvious does not mean it is true
Data analysis may lead to unexpected results
Contact Information
Michael D. Levi
Associate Commissioner for Publications and Special Studies
U.S. Bureau of Labor Statistics
202-691-5100levi.michael@bls.gov
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