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Simekit t1_iw27qoi wrote

Reply to comment by Simekit in [OC] Anime Sources - Where does Anime come from? by Smooth_Trifle7561

And for the 2nb graph why did you use "favorites" + "score_by" instead of "members" which represent the number of people who add the anime in their MAL (and which is used to rank anime by "popularity") ?

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Smooth_Trifle7561 OP t1_iw2bqkk wrote

Spent quite an amount of time cleaning the data I retrieved from Kaggle. Didn't retrieve it on my own but used what others did. Thanks for the comment!

Still improving my design skills - will look into adding a thin layout next time.

As for the 2nd graph, it was rather hard for me to decide. Initially, I titled it engagement level instead of popularity. By engagement level, I looked into the number of ratings and favourites. Perhaps, I could have factored in 'members' as well - but the effect is negligible as an anime with a high number of 'members' usually have a relatively high favourites/score_by. In that respect, you are right, using members would be less cumbersome to illustrate popularity. However, I wasn't sure how an Anime viz would do on dataisbeautiful, considering demographics, so I thought favourites and score_by would be more intuitive for everyone, without having to justify the 'members' part.

TL:dr: Agree with your points!

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Simekit t1_iw2k6g1 wrote

Yeah, "member" is a weird name to appoint the "views". Furthermore, with "popularity" we never know if it includes a notion of good or not. For example, Sword Art Online is one of the most-seen anime but its score is not that high (7,22) so is it "popular" or just "known"?

Maybe the term "famous" is more appropriate to designate something known+good.

So :

  • Sword Art Online is "popular" (high members and normal score)
  • Fullmetal Alchemist: Brotherhood is "famous" (high members and high score)
  • Gintama° is "good" and "unpopular" (few members but high score)

It is nearly like playing with words, but doing it in a foreign language is really hard :|

In the end, what is important is not the definition we have of these words (as data analysts) but the definition others have of these words (people who use the graph).

This comment makes no sense, sorry for the digression...

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