Data scraping & visualization

In order to make a good research on a hashtag #veganism in the USA, we picked a social network analysis software Gephi to analyze online discourse on Instagram.
Since our research question is: “What motivates people to become vegan?” we can see what hashtags people search for together with #veganism.

We searched for #veganism with 100 interactions and for a preview media, we used the thumbnail. Since one interaction gets 20 items, we got 1952 media items that were posted between 016-03-4 and 2016-03-15. In the Instagram case, nodes represent hashtags and edges the connections between them. That means that we got 7229 hashtags and 192593 connections between them.

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Hashtag #veganism reached 1812 counts, other hashtags which followed it were #vegan, #vegansofig, #veganfoodshare, #plantbased, #veganfood, #whatveganseat, #govegan, #crueltyfree, #veganlife, #healthy, #veganfoodporn, #vegansofinstagram, #vegetarian, #foodporn, #food, #veganlifestyle, #vegans, #vegangirl. #healthyfood, #love, #health, #rawvegan, #eatclean, #eatcleaning.
Also, after skimming through all hashtags we got an answer why actually people go vegan. According to Instagram hashtag explorer,  main reasons were animal rights and healthy lifestyle. For animal rights we got following hashtags: #crueltyfree, #animalrights, #veganfortheanimals, #animallover, #fitness, #organic, #raw, #plantpower, #nutrition, #HCLF (stands for high carb low fat), #healthy, health, #healthychoices, #workout.

By use of Gephi graph, we found out that our network is too big, so first we went for 10 interactions instead of 100 and it was still big.

With 5 interactions we finally got 854 hashtags and 16516 connections between them. Data laboratory looked this way.

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And Graph was still huge. Then we colored the nodes via a specific “Attribute and we picked “count”, so nodes were colored by their frequency. So, it is possible to see what nodes occur the most often (the darker the node, the more often it occurs).

For the layout, we used ForceAtlas 2 with Scaling 35.0 and Prevent Overlap.

In order to find out the main clusters we changed Range (count) settings to 14 – 96. Now it’s easy to see that #veganfood, #food, veganism, #veganfoodshare, #plantbased, #whatveganseat are the central hashtags. Other important clusters are: #veganlifestyle, #govegan, #vegansofinstagram, #foodporn, #foodie, #crueltyfree, #veganlife, #vegans, #healthy, #vegetarian, #healthyfood, and health.

 

 

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This is a link for PDF  of this image.

final vegan

 

After zooming in we found some small clusters in this Graph. Most of the information we got on the graph were relevant to veganism trend, but not all of it. We got a cluster from #awesome with hashtags #instacool, #queen, #gayswithbeards and another one connected to #awesome with #darkchocolate, #eatingcarrots, #blueverries, #chia. Also, we got some hashtags in Russian language. Those were #финики, #перекусы, #чай, #вкуснополезно.

The most often occurred nodes were colored by their frequency. Screen Shot 2016-03-25 at 9.28.27 am

Other clusters were related to veganism.Screen Shot 2016-03-17 at 2.28.07 pm

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What was surprising we found out a cluster with #whatvegansdrink where was mostly included alcohol: #nightspot, #fun, #cocktail, #cognac, #rum.

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Also, we chose an option to see how nodes are connected to each other. That means that nodes are colored by the amount of edges/connections they have. The darker the node, the more connection it has.

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The darkest nodes (nodes with most of connections) were vegan and veganism. Then were plantbased, vegansofg, veganfood, healthy and govegan. Less connected were vegan lifestyle, vegans, vegansofinstagram, veganlife, veganfoodporn, whatveganseat, crueltyfree, foodie, vegetarian and health.

 

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