#ThanksToVideoGames shows Twitter gaming community has a sincere side

National Video Games Day was Thursday 7th July, 2020. A day where people in the gaming community can reflect on what video games mean to them. For me, video games are a way of staying in touch with friends I no longer live close to and provides an escape from the everyday stresses of the world. 

It's clear that I'm not the only one, with #ThanksToVideoGames making waves on Twitter, people from all over the world shared their own reasons to be grateful for having video games in their lives. My curiosity got the better of me; I wanted to find out more about why people were grateful for video games and I wanted to show people that (shockingly) gamers can also be very sincere online. 

I created this visualisation of the most common words that appeared in tweets with #ThanksToVideoGames to show the different themes coming through in discussions on Twitter. Make sure you hit the full screen icon to show the full picture; you can find also find this Viz on my Tableau Public Profile.

You can see from the treemap on the left that topics such as connecting with others, positivity, and learning appeared in tweets most frequently. Connecting with others was by far the most common topic, and made up 3 of the top 4 words used across all tweets:  "friends", "meet" and "people", as you can see on the right of the Viz. 

The COVID-19 pandemic will have certainly highlighted the importance of video games as a means of staying connected virtually, while our ability to physically connect has been severely reduced across the globe. 

Topics that were associated with the highest frequency of unique words included connecting with others, positivity, while themes of time, learning and escapism were also frequent. 

But as you can see, video games are not only about socialising with friends. For many, video games have played a significant role in some of the most important periods in their lives. For some, a love of video games has translated into a dream career; for others, they met the love of their lives through gaming; video games can be an important release or escape from stresses of reality; and, for some, video games have been a lifeline, helping them through difficult times and struggles in coming to terms with who they are.

You can explore the individual words and how often they occurred for each topic in the handy graphic below.

It wasn't all too surprising to me that themes like connecting with others or escaping to a different world were common in these tweets. It was, however, perhaps a surprise that there were very few references to themes like competition and violence - themes that are very common in mass media portrayals of gamers as hyper-competitive nerds that spend their days locked away shooting zombies or aliens in ultra-violent shooter games.

My own experience with video games is that this only captures a very small part of the range of video games out there, and the reasons for playing them. For many, shooter games are not about the violence at all, and they are often played just because it's what their friends are all playing, and that opportunity to connect with their friends is what really draws them to these games in the first place.

My approach to data collection and analysis

To get to the above visualisations, I had to first collect and then analyse the relevant data from Twitter and then produce the visualisations themselves. To collect the data, I used the Public Twitter API and along with Tweepy to scrape the data from Twitter. I adapted these code snippets from Vicky Qian and TowardsDataScience as a starting point. I first collected all tweets containing #ThanksToVideoGames and posted on 7th July 2020, which gave me 3,075 unique tweets. The public Twitter API aims for relevance rather than completeness, which means that these 3,075 tweets are not a complete list of all tweets on this topic, but a selection of what Twitter thinks are the most relevant. I passed the tweet text through a script to create a dictionary of every unique word used and was returned with the following: 

Not that informative, right? 

If this were a much larger data set, I would have needed to use some natural language processing to get to a meaningful set of words and then assign them to different themes or topics. Thankfully, as this data set is not too onerous, I was able to use a  simple, manual process in Excel. From the original Twitter scrape, I identified 9,471 unique words. I then selected just those words that occurred on at least 10 different occasions, which got me down to just 209 words (most of the words that occurred less often were either UTF codes for emojis, typos or usernames). I then manually removed connecting words like "and" or "that" and combined highly similar words, e.g "friend" and "friends" to get me to a highly manageable set of 143 words.  

From there, I used Tableau to create the initial Viz. The Tableau Public community is fantastic and a great source for inspiration, whether you plan on using Tableau or not. Finally, the interactive bar graph embedded in this blog post was created using the amazingly flexible D3 library in Observable, you can see the full code here. 

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