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Increasing Twitter Click- T hroughs with Images

Increasing Twitter Click- T hroughs 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

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Increasing Twitter Click- T hroughs with Images

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  1. Increasing Twitter Click-Throughs with Images Michael Levi UNECE Work Session on the Communication of Statistics June 19, 2014

  2. 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

  3. Tweets with Images

  4. Why images? Cheap and easy way to entice users to accessunderlying 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 Motivation

  5. Beware the Intuitively Obvious! Michael Levi UNECE Work Session on the Communication of Statistics June 19, 2014

  6. Results • Did adding images increase click-throughs? • Before looking at data: “Absolutely!” • After looking at data: “Not the way we expected.”

  7. 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 Metric Used

  8. Click-throughs vs. Retweets

  9. Click-throughs vs Retweets: Bottom 90%

  10. Click-throughs by date

  11. Click-throughs by level

  12. Click-throughs by level – Top 35

  13. 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

  14. TED Click-throughs

  15. 7 highest-volume tweets have images 4 lowest-volume tweetshave images TED Click-throughsSummary Statistics

  16. No particular correlation by Type of chart (map, bar, line, etc.) General topic (employment, prices, etc.) Day of week Time of day Other Contributing Factors?

  17. “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 many click-throughs

  18. “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” Content: Tweets with relatively few click-throughs

  19. 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 Conclusions

  20. Expectations Confirmation bias A narrative, once established, is hard to overturn So Why Did I Believe Otherwise?

  21. Just because something is obvious does not mean it is true Data analysis may lead to unexpected results Conclusions II

  22. Michael D. LeviAssociate Commissioner for Publications and Special StudiesU.S. Bureau of Labor Statistics202-691-5100levi.michael@bls.gov

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