However, it also has a dark side which can be seen in the vicious and cruel comments which appear on Twitter as well as platforms such as Facebook and Youtube. Many such comments are those few would dare to say face to face, so what is it about social media that brings out our dark side? Is it simply that anonymity makes us “braver” or is there more to it?

On TV3’s the Nation yesterday there was a fascinating interview by Jon Ronson, a journalist and documentary maker who was at the forefront of some examples of Twitter “shaming” where Twitter was used to bludgeon those who were perceived to make racist, sexist or other privileged comments on Twitter, or in public after which the comments were distributed (and amplified) via Twitter. I use the word “bludgeon” because I can’t think of a more appropriate term when the recipient of such shaming receives thousands if not hundreds of thousands of messages attacking them, often with messages that are so vile they make the recipients original comment seem innocuous by comparison.

Recently Mr Ronson has finding out what has happened to those who have been shamed and has been finding that for many it has had quite a devastating affect on their lives.

A link to the Nation interview can be found here

This interview got me thinking about why so many seemingly nice people in real life can utter the cruellest comments via social media. Here are some of my own thoughts, mixed in with those of Mr Ronson

- For most of our history interactions have been face to face, allowing us to understand each other not only through verbal but also through body language. Over time we have developed the ability to recognise and empathise with the feeling of others. This allows us to temper our behaviour towards others. Also, in real life if someone gets too offensive physical violence is a potential response. On line not only are we unable to read body language, or adjust for cultural differences, we are making a snap judgement on what someone else’s intention is (And on Twitter this is done using 140 characters or less). And of course there is little chance of physical retaliation if we offend someone
- Mob mentality. It would be nice to think that the modern human being has evolved enough to understand and avoid mob mentality, however, there are many examples on social media and in real life which show this is not the case. We still seek validation from our social peers, and in the Twittersphere we have a potential pool of millions of them, who can encourage us to cross boundaries we wouldn’t normally cross.
- Competing for recognition. If thousands of other people are deriding someone, how do you stand out from the crowd? Raise the ante by getting mean? Shaming on Twitter can delve into depths where most normal people normally won’t go – yet the people who make such comments are often surprisingly normal.
- Failure to see the person. When the object of our offence or disgust is a screen name it is easy to forget that there is a living, breathing, fallible human being behind it. Someone who may be very similar to your sibling, father, best friend or grandmother.
- Diffusion of responsibility. When you are one voice in many, you may feel little responsibility for how that person feels in response to your comments. Unfortunately this is not true, as part of an online mob, the accumulated abuse potentially has a synergetic effect. One insult can be dismissed, one hundred or one thousand is far more devastating.

It is my belief that as we come to understand the benefits and dangers of online social media, we will evolve (and where necessary regulate and legislate) to make better use of such media. In the meantime, those who choose to use social media to pile abuse on or attack someone else should be very wary as he (or she) who lives by the sword can easily find themselves facing the pointy end. It only takes one mistake to go from being one of thousands of baying hounds to being the desperate fox.

A fascinating TED Talk by Jon Ronson can also be found here.

]]>Now let us apply my Rule #1 and visualise the data (see previous Chris Martin post).

Plot A is a histogram in which I have grouped for each of the two sets of data (the partnership scores when Chris was Out and the scores when he was Not out) into bins. Each bin is 5 runs wide except for the first. That is the first bin is from 0 to 2.5 (really to 2), the second from 2.5 to 7.5 etc. What can be seen from this is that there appear to be more very low partnerships when Chris was Out than when the other batsman was Out. However, don’t be fooled by histograms like this. Remember, there were not the same number of innings in which he was out (52) compared to when the other batsman was Out (49). This may distort the graph.

Plot B is better, but harder to read. Each black or red dot is a score. The coloured boxes show the range called the “Interquartile range”. That is, 25% of the scores are below the box, and 25% are above. The line in the middle of the box is the median – that is 50% of score are below and 50% of scores are above. The “Whiskers” (lines above and below the box) show the range of scores.

Plot C is less often used in the medical literature (at least), but is really very useful. It plots cumulatively the percentage of scores below a particular score for each of the two sets of data. For example, we can read off the graph that about 27% of the partnership scores for when Chris Martin was out were zero. If we look a the dashed line at 50% and where it intersects the blue line, then we see that 50% of the scores for when Chris Martin was out were 2 or below. This is a bit more informative than plot B.

What all the plots show is that the distribution of scores in both data sets is highly skewed. That is, there are many more scores at one end of plot A than the other, or the lines in plot C are not straight lines. This is very important because it tells us what tests we can not use and how we should not present data. Quite often when I referee papers, and in papers I read I see the averages (means) presented for data like this. This is wrong. They are presented like:

Chris Out: 8.4±13.9

Chris Not Out: 10.8±11.8

The first number is the mean (ie add all the scores and divide by the number of innings). The second number after the “plus-minus” symbol is called the standard deviation. It is a measure of the spread of the numbers around the mean. In this case the standard deviation is large compared to the mean. Indeed anything more than half the size of the mean is a bit of a give away that the distribution is highly skewed and that presenting the numbers this way is totally meaningless. We should me able to look at the mean and standard deviation and conclude that about 95% of the scores are between two standard deviations below the mean and two above. However two below (8.4 – 2*13.9) is a negative score! Not possible.

What should be presented is the medians with interquartile range (ie the range from where 25% are below and 75% are below).

Chris Out: 2.0 (0-12.8)

Chris Not Out: 8 (1-16.5)

We are now ready to apply a statistical test found in most statistical packages to see if Chris being out or the other batsmen being out was better for the partnership. The test we apply is called the Mann-Whitney U test (or Kruskall-Wallis test if we were comparing 3 or more data sets). Some people say this is comparing the medians – it is not, it is comparing the whole of the two data sets. If you don’t believe me, see http://udel.edu/~mcdonald/statkruskalwallis.html.

So, I apply the test and it gives me the number p=0.12. What does this mean? It means that if Chris Martin were to bat in another 104 innings, and another, and another etc, then 12% of the time we would see the difference (or greater) between the Outs and Not Out partnerships that we do actually see (see significantly p’d for more explanation of p). 12% for a statistician is quite large and so we would suggest that there is no overall difference in partnerships whether Chris Martin was Out or was Not Out. Alas, Chris Martin’s playing days are over and we have the entire “population” of his scores to assess his batting prowess. The kind of statistical test I’ve presented is only really useful when we are looking at a sample from a much greater population. However, in the hope that Chris may make a return to Test cricket one day, then what is presented here should give pause for thought for the next batsman who goes out to bat with him… perhaps there is not a lot to gain by swinging wildly, and thereby increasing their chances of getting out; they are probably not improving the chances of the team.

Tagged: Average, Chris Martin, cricinfo, Cricket, ESPN CricInfo, Mann-Whitney, Mean, Median, Statistics

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