First, we used Google Trends to see how the online searches for the word “vegan” have grown in the last ten years (Rouse, 2013). This data analyzing method creates a clear graph that shows how often a particular search term is entered. We confirmed that “vegan” was a trend, particularly in 2015. Google Trends also gives the possibility to compare two search terms. We compared “vegan” and “vegetarian” and obtained interesting results.

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Finally, Google Trends creates a map that told how popular are search terms in different countries. Vegan was more popular in The United States, Canada and Australia.

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Before officially establishing veganism as our research topic, we searched for hashtags such as #vegan, #healthyvegan and #animalcruelty on social networks like Twitter and Instagram. We also find some vegan-related pages on facebook.That helps us to confirm that Veganism was a very popular topic on social media at that moment.


For our research, we used a methodology called Netnography. By using this method, we analysed data which we collected from certain online communities. Netnography is a good choice for our research topic since vegans are very active online. Hundreds of vegan blogs can be found on the internet (Rosen, 2014).

In order to collect data about the topic and answer our research question, we chose a community: The vegan Subreddit on the social network Reddit, a very active community which counts with the participation of people all over the world.

Inside this community, we analysed recent threads and discussions, as well as comments and answers, looking for remarks that suggested why those participants decided to go vegan. We searched for specific keywords among the community such as healthy, health, animal, animal cruelty, environment, society, animal rights, etc. We also actively participate in it by creating a post. Finally, we analysed the data we collected and tried to answer our research question based on the comments we had.

Besides Netnography, we used another method known as Social Network Analysis. There are two elements in a network, actors and the interactions between them. Social Network Analysis study this two elements. In our case, we analysed certain Instagram hashtags and how they were connected. To do this, we use an online tool called  Instagram Hashtag Explorer, which looks for given tags or locate posts in a specific area.

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In order to organise the collected data, we used the tool Gephi. Gephi is an open-source and free network analysis software. This tool transforms data collected from social networks into clear graphs. This helps the analysts to organise the information for a better understanding of it. The user interacts with the graph by manipulating the structures, shapes and colors in order to expose hidden patterns (The Open Graph Viz Platform, 2008).

When information gathered by Instagram Hashtag explorer is loaded in Gephi, the software shows hashtags and connections in a graph. Nodes represent hashtags and edge  the connections between them.

 

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Example of a Gephi graph from our own research

 


References

  1. Rouse, M. (2013, January). DEFINITION Google Trends. Retrieved February 29, 2016, from http://whatis.techtarget.com/definition/Google-Trends. Contributor: Heather Darcy
  2. Rosen, E. (2014). Top 50 Vegan Blogs. Retrieved March 1, 2016, from http://psychologyofeating.com/50-top-vegan-blogs/ . Institute For The Psychology of Eating
  3. Grandjean, M. (2015). GEPHI – Introduction to Network Analysis and Visualization. Retrieved March 20, 2016, from http://www.martingrandjean.ch/gephi-introduction/
  4. The Open Graph Viz Platform. (2008). Retrieved March 20, 2016, from https://gephi.org/
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