Twitter and Policy Outcomes- Europe

I have been examining the possibility of a link between social media presence and policy outcomes, region by region, around the world.  Today we’re looking at Europe.

Social Media

Almost 100% of states are represented on 100% of the social media platforms.  There are 160 active, verified Twitter accounts. 60% are institutional.

The presence of a supranational government with an additional 9 Twitter accounts complicates the picture: how does the European Union use social media differently from national governments?  How are their policy effects different?  How might they interact with each other?  These questions are beyond the scope of a post like this, and will have to be explored in later, more specific studies.  As such, I will be setting those accounts aside for the time being.

Other than the EU, 11 countries have 5 or more accounts.  France tops the list with 11, with the Vatican on its heels with 10.  Russia has 9.  No surprisingly, these countries are also frequenters of the top ten lists.  They take half of the tweets/day spots and 7 of the most followed.  France has the highest interaction rate by a long shot: their prime minister’s personal account has a 2.22% rate versus Iceland’s prime minister’s personal account in second at 1.59%.

Interestingly, European top interactors also have low tweet averages, from 0-2 a day.  Accounts with a high number of followers also have relatively few tweets and a low interaction rate, with 3 Vatican exceptions.  The tweetingest accounts, on the other hand, averaged 718,536 followers (not enough to approach the millions following the top ten, but still high) with extremely low interaction rates (excepting the Maltese government account which made top 10 at 1.28%.}  The tweeters don’t interact but do get followed; the interactors don’t tweet; and the popular don’t reach out.  As seen before, the tweeters are predominately institutions, the followed mixed, and the interactors are all (but one- Malta’s) personal.

Policy

There is much less data on poverty rates than inequality.  I thought it might be more useful to depart from my previous metric(s) ($3.10 or $4/day 2011/2005 PPP) to look at the headcount at the national line.  However, both measures only had 21 countries with any data, and the $3.10 metric had several more years available.  In comparison, 40 countries have some Gini data.   So I will be sticking to the same metrics and methods of my previous posts.

Moldova and Belarus had the most dramatic, more than 10 point, falls in poverty.  Most of the poverty data comes from Eastern Europe.  40% of countries with sufficient data saw a rise in poverty.  The average nation saw a 1.4% fall overall, although the 2014 average total poverty was 4.33% lower than in 1997.

Moldova also saw 10.11 point fall in Gini index since 1997.  Norway, Belarus, and Estonia saw nearly or greater than 5 point falls as well.  On the other end of the spectrum, Macedonia had the greatest increase in inequality, and overall 19 countries (47.5%) saw increases.  Total average inequality fell about 4 points, but on average individual countries only saw a less than a one point difference.

Comparison

Moldova’s one active, verified account (foreign ministry institutional, established 2012) doesn’t make any of the top ten lists.  Neither does either of Belarus’ similar accounts.  None of Norway’s 6 accounts make it, and Estonia makes a single appearance (3rd in interaction rate) with a personal prime minister’s account created just last year.  Those countries with the most impressive outcomes weren’t the ones dominating the Twittersphere.  Russia, with its 9 accounts and 5 total top ten list appearances, was 6th in largest poverty decline, but was 9th in greatest inequality increase.  France’s inequality also increased, by 2.3 points, and Iceland’s only fell by a little over a point.  Neither has poverty data, and Malta doesn’t have any for either poverty or inequality.

Conclusions and Questions

I don’t think this analysis has brought forth any good case study contenders, despite Europe’s overwhelming social media presence.  Overall it demonstrated more how wealthy countries interact with social media than how the region’s social media impacts policy.

The different interaction behaviors seem like they might describe an American TV high school.  They bring up a lot of questions: what do you do on Twitter to get followers?  To be influential?  What should a government’s goals on social media be?  How should private accounts behave versus institutional?  How should their goals be tied together?  It might be interesting to compare the behavior of these accounts to major brands.  Would personal or institutional accounts look more similar?  What would that say about their strategies and goals?

It is interesting that in the European case, the countries with the greatest positive poverty and inequality changes had primarily foreign ministry accounts.  Albania, 3rd for poverty decline, follows this pattern as well, with 2 foreign ministry accounts and one prime minister account.  But their Twitter behavior isn’t, relatively, remarkable.  For future analysis, it may be more helpful to subdivide European regions, as Western European accounts dominate an aggregated analysis.

The European Union also brought up some great questions for further research, as noted above.

As always, if you have ideas, data, or advice, feel free to reach out!

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2 thoughts on “Twitter and Policy Outcomes- Europe

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