Is Social Media turning us into monsters?

By Graham

Antisocial Media
I started using social media a year and a half ago, mainly to promote my freshly published book, The Hydra. It took me a few days to figure out the main functions, a week to hone my marketing strategy and within a month, I found myself embroiled in bitter, acrimonious exchanges with anonymous trolls over subjects I had only a passing interest in. It all came to a head when, one day in July, I found myself about to publish a tweet in which I was calling someone an asshole. I paused, deleted the tweet without sending, took a deep breath and asked myself what the hell was wrong with me. This was not a part of my marketing strategy. And even beyond that, I am not the kind of person who calls other people assholes; not in social situations, not even when cycling to work and some aggressive, rude driver nearly runs me off the road. Yet there I was, about to commit to a bare knuckle, ad hominem insult of someone I didn’t even know. For no reason.

This was the moment I re-evaluated my approach to social media and found some helpful advice. The first and most important rule I learned was: If you don’t have something nice to post, don’t post anything. Before you send a tweet or leave a comment, ask yourself this: Is this a positive sentiment? Does this show me from my best side? Would I say this to the person’s face in a crowded room of mutual acquaintances? And if not: Cancel. Delete. Or at very least, reword. It’s okay, I discovered, to give your interlocutor the last word, even if you don’t agree with them. It takes more courage to walk away from a troll in silence than to descend to their level.

This strategy is working for me, and I’ve managed to avoid the worst of the trolls so far. Thankfully, 4,300 Tweets later, I haven’t had to block anyone and have only been blocked by a very few people, all without compromising on my opinions. Yet it does not answer the question as to why social media brings out the worst in otherwise normal people.

 

The tweets above from Nathan and Christa are nothing exceptional. I found them in – quite literally – seconds of searching for an example of hateful tweets. In fact, one might easily scroll past a dozen such tweets without batting an eyelid. Yet if you step back and consider their content, they don’t reflect particularly well on their authors. To wish ‘rabies’ on any person, whether you like his/her politics or not, is contemptible. To liken Donald Trump’s face with an ape’s posterior is puerile, untrue and unkind. I’m sure neither person, if they were introduced to Mr Trump at a dinner party, would say these things to his face. Moreover, I’m sure they never speak this way to anyone they interact with outside of Twitter. I don’t know them, but I’m willing to bet both Nathan and Christa are really nice, polite people if you meet them in the queue at the supermarket or in a local Starbucks. Most people are. So why be hateful online?

Anonymity
One explanation that is often advanced to describe extreme online behaviours is that the shield of anonymity encourages impoliteness. The detachment and privacy afforded by the cyber-world removes any accountability for our words. It also allows us to plug into a wider debate from the very private spaces where we are prone to let our guard down: One can tweet from the living room, from bed, even from the toilet.

This leads to a debate as to whether people are at their ‘realest’ when they are not being their ‘real selves’. Is Dave McKenzie, sales manager from Dayton Ohio, being fake when he wishes his clients a nice afternoon? Is the real Dave McKenzie the guy who goes home, logs in as “Knuckler1776” and abuses Hillary Clinton supporter’s for being ‘fat pigs’ with smelly body parts? It’s tempting to think so, yet I would take a more optimistic view. In life, we are always playing roles. When we go back to our families we revert to our childhood roles, at work we behave differently than we do when out for a night with our friends. None of these roles are any more ‘real’ than the others. What’s new about anonymous internet usage is that it allows us to play a new role. Adding this persona doesn’t make the other roles we play any less real. And conversely, taking it away won’t make us phonies either.

Algorithms of Hate
Another possible explanation is that the way content is sorted and customised by the various platforms encourages more and more extreme opinions. Custom sorted content means we see what we want to see, except often a more extreme version of what we first thought. So, for example, if I click on a Youtube video showing a refugee assaulting a German woman on the street, the algorithms used to suggest content to me will select other such videos. I find myself watching video after video showing African or Middle Eastern men verbally abusing, assaulting or harassing white, German women. As a user, it is easy for me to (erroneously) assume this content is representative and I quickly leap to the conclusion that refugees are everywhere, hurting ‘our’ women. A sense of panic engulfs me. Despite the fact that my feed is overflowing with clear examples of this kind of thing, the mainstream media appears unwilling to give it due attention. A conspiracy!

Now imagine my anger when someone I ‘meet’ on Twitter has the gall to suggest the vast majority of refugees are nice people, and that the instances of violence are relatively few. “Idiot, moron!” I think to myself. He, in turn, has been watching videos that confirm his previously held convictions, and my last tweet “Round them up at gunpoint and deport them! Claim our country back!” incites him to call me a “Racist turd.”

In reality, both he and I are good people with good intentions. We don’t really want Syrians refugees to suffer and we don’t want German women to feel unsafe on the streets. But the nature of the platform has made our views more extreme, our positions more entrenched and lowered our threshold for issuing gratuitous insults. Instead of bringing us to a possible common ‘middle ground’ position, we end up hurling insults until one of us blocks the other. Communication failure.

Putting the Social back into Social Media
Is there anything we can do to counteract this? I feel there is; a great deal in fact. Mostly, it’s about being mindful of how we interact. One method is the ten second rule. Before you reply to anything online, count to ten and then ask youself, can I make this message kinder? Another trick is ‘killing your adversary with kindness’. The troll culture has made us so aggressive that it can be quite a powerful argument if you simply turn to someone who is being aggressive and say something like “I know you are a decent, kind person. Even if we disagree, I respect you.”

Another idea I would suggest is not trying to use Social Media to push your ideas. Why not search for things you are less certain, but possibly interested in knowing more about? For example, I might have strong views about abortion policy, but I might not know very much about whether nuclear power is good or bad. I could interact with experts who have real knowledge in an open curious way. That benefits me far more and tends to lead to much more pleasant social interactions online.

My final thought takes us back to the idea of anonymity. I would encourage anyone who hasn’t already done so, to set up a real account, with your real picture and real name. You will very quickly find that the content you are prepared to put into the world is quite different, more civil, more human and kinder. And that, I choose to believe, is the ‘real’ you.

 Category: Media

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As with birth rates, we use data for 4 categories of countries from 1990 to 2015 (100 observations total). We have two explanatory variables, AGE and Y, where AGE is defined as the percentage of the population aged over 65 and Y is per capita GDP.

After eyeballing the scattergrams, we test the following functional form:

d = (minY^a)/Y^a * (1/AGE^g)

Where minY is the constant equal to the smallest value of Y in the series.

Logarithmic transformation gives:

ln(d) = ln(minY^a) – a*ln(Y) – g*ln(AGE)

which we test on the data using OLS. Here are the results:

Adjusted R square: 75.191

Intercept coefficient: 7.37384
t-Stat: 20.4011

Y coefficient: -1.01444
t-Stat: -13.1059

AGE coefficient: 2.0097
t-Stat: 11.5208

The estimated intercept is a good, but not perfect, approximation of ln(minY^a)

Here are the fitted against actual values of the scattergram for death rate against per capita GDP:

fitted-death-rates-against-actual-values

While the results are not as good as with the birth rates calculations, it is nevertheless a good enough fit and the explanatory variables have a strong enough confidence factor to be usable in our estimations.

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We begin by examining the scatter of data for 100 observations of per capita GDP and per capita emissions for 4 categories of countries, over 25 years (1990 – 2015).

The scatter suggests a cubic functional form, so we test:

GHG = a + b*Y + c*Y^2 + d*Y^3

where GHG are per capita emissions of GHG, and Y is per capita GDP.

The results from OLS regression are:

Adjusted R square: 0.980438073

coefficient a: 1090
t-stat a: 3.06

coefficient b: 0.709310153
t-Stat b: 8.241453

coefficient c: -0.0000047025
t-Stat c: -1.01233

coefficient d: -0.000000000105314
t-Stat d: -1.47005

While the t-scores on the squared and cubed terms are low, the number of observations are also limited.

Here is the plot of the fitted against actual values:

fitted-emissions-to-gdp-against-actual-values

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