eurovisionBias

Distributional distances ranked from score data in the Eurovision song contest and what associations with score exists with the preferential links.

Mantzaris, A. V., Rein, S. R., & Hopkins, A. D. (2018). Preference and neglect amongst countries in the Eurovision Song Contest. Journal of Computational Social Science, 1(2), 377-390. https://link.springer.com/article/10.1007/s42001-018-0020-2

and is an extension of the code and research found in https://github.com/mantzaris/eurovision and published in http://jasss.soc.surrey.ac.uk/21/1/1.html where this extension provides the bias sampling on both sides of the distribution. Key is that the preferential bias (positive bias) as well as the negative bias (neglect) are both accounted for here. Also that the investigation of the association of the amount of bias found from participants and their competition ranking is displayed as graphs. The results are also aggregated into name folders for the user.

The code for the paper "Preference and neglect amongst countries in the Eurovision Song Contest. in Journal of Computational Social Science

• It looks at not only the preferential biases where countries allocate statistically higher than expected scores, but also those associations which are statistically lower . Separate networks for the preferential links and links of neglect are drawn.
• It also analyzes for the period of time chosen and the windows size in years, the association of the number of links (preferential or from neglect) and the scores countries accumulate.

What is the reasoning for these extensions and why are they important? (what is the motivation?)

• Since the scores a country can allocate are constrained; yes a country can choose to distribute more of them to a select set of countries due to preference, but then the question remains 'from which countries were those points taken that would normally have gotten them?'

• The average audience member is not keeping a log of the scores allocated between countries year over year but they may remember certain biases whether they were preferential (Country X always gives country Y lots of points) or whether they are based on neglect (Country X hardly ever gives country Z any points). That set of associations is maybe easier to remember and then the question arises whether there are any patterns between these opposite biases and the scores countries accumulate.

• This can be used to question whether the biased votes emerge because audience members see these biases as means to the end of succeeding in the competition. The results show that neglect does not associate with reduced total score so there is no perception of cost for neglecting countries and results also show that preferential biases associate with larger score accumulations probably for reciprocal behavior. (more in the paper can be found)

Running the code

(from Linux) simplest way is to run main.jl function main(startYr = 1980, endYr = 1990, windowSize = 5, alpha = 0.05), have graphviz installed that is called from Julia, in the Julia REPL (prompt of Julia) via:

include("main.jl")

main(1980,1990,5,0.05)

the first number is the starting year, the second param the end year of the analysis, the 3rd param is the window size to segment the time line, and the last is the alpha for the thresholding (p-value). The smaller the alpha the more conservative it is (less edges as only the strongest biases and neglect get used) and larger alpha values allow more edges. You can find the output for the overall results in the same directory as the main.jl file and the rest in the graphs and plots folders

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Looking at years 2000-2005 and the edges of NEGLECT (significant low votes) between countries

Looking at the years 1980-2000 and the edges of NEGLECT (significant low votes) between countries

Looking at the years 2012-2017 and the edges of PREFERENCE (significant high votes) between countries

Looking at the years 2000-2016 and the edges of PREFERENCE (significant high votes) between countries that collude as mutually high votes

Looking at the years 1985-2015 and the TOTAL SCORE RECEIVED vs the TOTAL PREFERENCE produced (biased links to other countries over the years where each dot is a country's accumulation each decade in that time period

Looking at the years 1985-2015 and the TOTAL SCORE RECEIVED vs the TOTAL NEGLECT produced (biased links to other countries over the years where each dot is a country's accumulation each decade in that time period