An equivalent processes was applied to check reliability of quiddity variables (having fun with 76 circumstances entirely)
The introduction of classes and you can good pilot precision attempt survived step three days, when go out the codebook are altered from time to time. So you’re able to take to precision, several programmers (scholar students) independently coded a group from 100 randomly picked meme circumstances (from the 10% of one’s attempt). This new agreement rates is checked out with the Krippendorff ( 2004b ) test, having .66 due to the fact minimal score to simply accept a variable as the recommended (Krippendorff, 2004a ). In the course of time, a couple variables, subculture (content) and visualize checklist (form) received an inadequate get, and you can was in fact taken out of the newest codebook, making forty eight variables toward actual programming (Find but also for the whole codebook of your own simple services during the our investigation). Just after reliability ended up being founded, all of those other try is actually divided between the two coders.
We employed system research process (Barabasi & Frangos, 2002 ; Newman, Barabasi, & Watts, 2006 ) to map the content, participation, and function similarities between meme hours as well as their cohesiveness. The actors regarding system were this new meme circumstances, and their undirected ties was in fact the common blogs, involvement, and you will form qualities. Particularly, if meme times A great and you will B express around three common features inside popular, the link manage pounds around three. I created three independent undirected systems having content, means, and you will participation, plus a network that mixes all issue along with her. Aforementioned stands for all round proximity certainly one of meme period within our shot.
Circle data permitted pinpointing many main meme circumstances, put another way, individuals who mutual the highest quantity of content, participation, and you may form functions with people, and additionally those people that have been a lot more peripheral, meaning that got book otherwise unusual services ( ). Additionally, studying the general framework of system, we can choose groups off meme days with a high cohesiveness out-of stuff, participation, and you may function. We can after that take a look at and this meme parents had been far more cohesive than simply anyone else and you will reveal the web link between common features and you will family unit members cohesiveness.
Computing new cohesiveness off meme parents
Structural cohesion inside the systems features certain specifications (White & Harary, 2001 ). We find the most simple one, and you can opposed new connections among cases of a comparable family with connections outside the nearest and dearest. This new cohesiveness adjustable was created to mention from nazwa uÅ¼ytkownika arablounge what the total amount meme instances was in fact exactly like almost every other instances regarding same relatives into the terms of universal blogs, contribution, and you may means. First, we calculated the new community knowledge (Barabasi & Frangos, 2002 ; Newman, Barabasi, & Watts, 2006 ) each and every meme such, we.e., what number of popular content, participation, and you may setting features which shared with most other hours throughout the same family members and with occasions from other family. Following, for every single of your own 50 meme families in our sample, i calculated the latest imply amount of common attributes its days shared within and you may outside of the family. Ultimately, the newest cohesiveness variable are identified as the real difference from the mean amount of prominent characteristics within this and additional a beneficial meme relatives.
Calculating the latest uniqueness out of meme family members
The newest dimensions out of individuality level was created to mention exactly how novel good meme friends was a student in regards to its universal blogs, involvement, and you will mode functions. In other words, uniqueness top reflected brand new gap involving the general features of an excellent meme nearest and dearest as well as the main-stream (the preferred common attributes on the entire sample). Earliest, we calculated the part of stuff, contribution, and means characteristics into the for each and every loved ones; 2nd, we determined the newest portion of for every single characteristic from the entire sample; last but most certainly not least, each meme household members, i determined the new square root of your own amount of the fresh new square differences between both (like in the brand new algorithm for basic deviation). This is standard to start around 0 and you may 1, in which step one means ideal uniqueness regarding a great meme nearest and dearest in the terms of its common attributes.