Visualizing MyAnimeList Binh Tran Abstract MyAnimeList users use myanimelist.net to track the anime they watch. The users see a lot of simple visualizations on the website. There is not a lot of understanding on how MyAnimeList users use the website. The users could be incorrectly using their Anime lists. This paper analyzes a new visualization tool aimed at understanding MyAnimeList users usage patterns of their Anime List. 1 Introduction MyAnimeList is a useful tool for the anime enthusiast. It started out as a tool for anime enthusiasts to track what they have watched as an alternative to storing all that data in Excel spreadsheets. As more users signed up to the site, the tool evolved to meet user s demands. The initial anime list-tracking tool progressed to allow importing of data from Excel spreadsheets. Users could search for anime on MyAnimeList s vast databases to add to their list. The licensing companies were responsible for updating the anime database. Anime could be categorized into different types. The types of anime are TV, OVA, Movie, Special, ONA, and Music. Anime with the type TV are ones shown on television at least once in Japan. Anime with the type OVA (Original Video Animation) are standalone episodes. These are usually less than 10 episodes. They use to be typed as OAV for being mistaken as Original Adult Video. Anime with the type Movie are full feature films. They classified as having a total of 1 episode. Anime with the type Special are unique. Anime with the type ONA (Original Net Animation) are ones that are directly released onto the Internet. They could still be aired on television and typically have a smaller total number of episodes. Anime with the type music are a series of music videos. They tend to have a smaller number of total episodes. Anime could have different airing statuses. The airing statuses are currently airing, finished airing, and not yet aired. Anime currently airing are ones that have ongoing episodes, so the total number of episodes is unknown. Anime finished airing are ones that have a definite total number of episodes. Anime that have not yet aired do not have a known total number of episodes, and the number of watched episodes should be 0. Anime are categorized with different watch statuses. The watch statuses are watching, completed, on hold, dropped, and plan to watch. Anime the user is watching is exactly that. Anime the user has completed are ones where they have watched all the episodes. This means a ratio of watched episodes to total episodes should be equal to 1. Anime that are on hold are ones the user has stopped watching, but plan on watching when they have the time. The on hold anime should eventually be changed back to watching status. Anime that are dropped are ones that the user chooses to stop watching completely. Anime that are planned to watch are ones that are interesting and pending for watching. This category has watched episodes count of 0. Usually anime would be scored when they are completed. This is only sensible for an unbiased score. Anime are scored from 1 to 10. An anime that is not yet scored has a score value of 0 in MyAnimeList s database. The scores 1 to 10 are understood as 1. Unwatchable 2. Horrible 3. Very Bad 4. Bad 5. Average 6. Fine 7. Good 8. Very Good 1
9. Great 10. Masterpiece This paper will discuss a MyAnimeList visualization tool used to get an understanding of how a user keeps track of their anime. It will see if people use the tool in some incorrect way. 2 Technical Detail The visualization was written in JavaScript and interfaces with WebGL. The JavaScript libraries jquery, three.js, and d3.js were used. jquery is a general purpose JavaScript library that attempts to ease the cross-browser compatibility with JavaScript interpretations. The library three.js is a 3d scene graph that uses WebGL as the underlying technology. The library d3.js is a 2d InfoVis toolkit that utilizes SVG. Figure 1 - MyAnimeList Visualization with all options set To accomplish the visualization, numerous processes occur. First, the user chooses which MyAnimeList user s anime list they wish to view and clicks the Get Anime List button. Data is then retrieved from the unofficial MyAnimeList api at http://mal-api.com/animelist/xinil with an AJAX GET request sent with jquery s ajax function. This returns the anime list data as JSON. Usually, hardcore anime watchers have a few hundred anime on their list. For example, xinil, the creator of MyAnimeList, has 374 anime in his list. The JSON is a key-value data structure. There are 2 initial keys anime and statistics. The key anime has value of a JSON array storing anime data. A process then takes the JSON array and initializes the 3d scene graph on the left and the 2d scatterplot on the right. The data for each anime includes: airing status type user score watched number of episodes watch status title total number of episodes The x-axis of the 3d scene graph contains the!"!#$!"#$%&!"!"#$%!"#$%&!$. The y-axis of the 3d 2
scene graph contains the!"#$h!"!"#$%&!"!"#$%!"#$%&!$. These square rooted values help compact the data to fit into the 3d scene graph. The z-axis of the 3d scene graph contains the anime score from 0-10. The score 0 represents an anime without a score. The anime nodes are than colorcoded from red to purple to blue. The red represents a very biased anime. This means the anime has not been fully watched or the watched number of episodes is less than the total number of episodes. The purple coloring signals that about half of the total number of episodes has been watched. There is a threshold for a different shades of purple, which is a ratio of watched episodes to total episodes is greater than 0.3 but less than 0.7. The ratios less than equal to 0.3 are color-coded red. The ratios greater than equal or equal to 0.7 are color-coded blue. An anime that is colored blue represents an unbiased anime or one where almost all the episodes have been watched. These thresholds for color mapping were decided based on the usual use cases of the Anime list. A ratio 0.7 or higher represents that a good majority of episodes have been watched. This means that it is pretty valid for scoring. A ratio of 0.3 and lower means that there was a mere watching of the anime. The 3d scene graph is viewed in a 2d format to the right. For better anime data visibility, the x-axis represents the square root of the total number of episodes. The y-axis represents the square root of the number of watched episodes. The color-coding remains the same. Their type, airing status, watch status, and their scores can filter out certain anime. The unbiased plane shows where the episodes watched compared to total episodes equals to 1 that can be displayed. The 2d scatterplot shows the actual range of the anime data with the x-axis representing the number of total episodes. The y-axis represents the number of watched episodes. The color-coding remains the same, but is adjusted for a 256-color scheme function in d3. 3 Results The visualization tool was used to analyze xinil s, the creator of MyAnimeList and a hardcore anime watcher, anime list. The results were determined from using the filter controls on the large collection of 374 anime. The 374 anime were too big to really get meaningful results. Figure 2 - xinil's unscored anime Based on xinil s unscored anime, it would make sense that most of the anime are color coded red. Not many of the anime episodes have been watched. There are some anime that are reaching the unbiased plane and are coming to completed status. Most likely, within the month, xinil would change the blue colored anime to completed status with a score. 3
Figure 3 - xinil's score 1 (Unwatchable) anime MyAnimeList user xinil does not have any anime scored 1 (Unwatchable). This makes sense when hardcore anime users tend to try to watch all of their anime. Because they have the ability to watch through all the anime, it is unlikely that the hardcore users would rate an anime so low. A user would not continue watching a poor anime. Figure 4 - xinil's score 2 (Horrible) anime 4
Xinil does not like to score his anime at 2. His lowest scoring threshold is greater than 2. Figure 5 - xinil's score 3 (Very Bad) anime This is the lowest scoring threshold xinil has for his anime. Out of 374 anime, he only scores 2 anime as very bad. He seems to be a very positive anime watcher when only 2 out of his 374 anime or about 0.005% of his anime have this bad of a rating. Suprisingly, this shows he has unbiased scores for these anime because they are right on the unbiased plane and they are blue. These anime are however short on episodes. They could potentially represent movies. Movie ratings can be an easier hit or miss compared to a length of an anime of type TV. Figure 6 - xinil's score 4 (Bad) anime 5
It is becoming apparent that xinil doesn t generally score his anime low. If the anime has a low score like a 2 or 4, they tend to be fully watched. It looks like some of these anime are movies because they are hovering around the one mark. Figure 7 - xinil's score 5 (Average) anime It is becoming an apparent trend that xinil that does not really rate his anime low. Out of 374 anime, 20 of the anime are rated average. That is an average rating for only 5% of his anime. Strangely, the average ratings are showing some unbiased scores. It would make sense that if xinil gives an average, he would have watched a little bit more of the anime where it would be color coded purple and blue. A good majority of these anime are unbiased scores. 6
Figure 8 - xinil's score 6 (Fine) anime A good portion of anime rated 6 are unbiased rated. Some of the anime seem quickly judged based on the red coloring. Of 374 anime, 38 or about 10% of the anime are scored fine. There is an anime that is still airing and is unbiased. This is legitimate scoring because some of the best-rated anime are ones that are still airing like Naruto, and One Piece. Some anime watchers think some ongoing series are overrated, which xinil could be showing here with the few outliers. Figure 9 - xinil's score 7 (Good) anime Xinil scores 70 out of 374 about 20% of his anime as Good. A good portion of the anime is watched fully. There are a few that probably should not be scored yet and is represented in the mid-range. 7
Figure 10 - xinil's score 8 (Very Good) anime The majority of Very Good anime are completed. There are a few biased anime, which he is not close to finishing. Figure 11 - xinil's score 9 (Great) anime Xinil scores 43 of 374 or about 11% of his anime to be great. Almost all the anime that are scored this highly have an unbiased value. It seems that xinil took the time to really score these anime. It would not be surprising if xinil actually has reviews for some of these anime. 8
Figure 12 - xinil's score 10 (Masterpiece) anime Anime that are given the highest score are pretty rare. Only 12 out of 374 or about 3% of anime get this prestigious rating. All of xinil s anime that are scored this high are unbiased. He has seen all of the episodes, and they are completed series because all the blue circles are right on the unbiased plane. Figure 13 - xinil's anime Watching The amount a person is tracking as watching should be small, which xinil shows here as 6. A few of these anime are almost completed, which he does score. 9
Figure 14 - xinil's anime Completed Anime that are completed should have unbiased scores. Xinil s scores portray this. It shows that for about all of the completed anime, he scores them at least a 3. There is one completed anime that he has not scored. He most likely forgot about it. Figure 15 - xinil's anime On-Hold The majority of anime on-hold do not have unbiased scores. They tend to have no score. A few of these anime are scored within the good unbiased threshold. It is possible that the anime that have unbiased scores will be put back on the watching status. 10
Figure 16 - xinil's anime Dropped A good portion of dropped anime has biased scores. This could represent the mentality that if something feels awful, one should not watch it anymore. They should have lower scores, but what we see is a dump on scores from 5 to 7. At least a portion of the anime has no score, showing that xinil rather not give a biased score, which some other user could judge. Rather, xinil is putting quick judgment on anime he liked the first few times around, but he does not necessary have enough time to watch them. Figure 17 - xinil's anime Plan To Watch 11
The Plan To Watch status is properly used. All these anime have no episodes watched. It is shocking to see that 52 out of 374 or about 14% of anime are being planned to watch. This seems to be a dump list of intriguing anime to watch, which should go down when xinil decides to watch some of them. Figure 18 - xinil's anime Currently Airing In this case, xinil does not have much anime that are currently airing on his list. This makes sense. A user only tracks a few new anime at a time. For the most part, currently airing anime do not have an established number of episodes because they are ongoing. However, it is strange to see the opposite with a few anime that have a defined number of total episodes. These anime tend to be scored because they probably are series that have been established for a while. 12
Figure 19 - xinil's anime Finished Airing The majority of anime that xinil has on his list are finished airing. It makes sense because the anime that he does not get to could be on air for a time, but be finished by the time he gets to them. The majority of these anime seem to hover around average scoring. Figure 20 - xinil's anime Not Yet Aired In this case, xinil does not have any special privileges to start tracking anime that have not yet aired. You would think that because he is the creator of MyAnimeList, he would get some special privileges. If this is the case, it is highly likely that no other user can add any anime that have not yet aired. The anime creators most likely update these anime in the database. 13
Figure 21 - xinil's anime of type TV Xinil most watches anime of the type TV, which is 271 out of 374 or about 72%. This makes sense because most anime strive to be a TV series. Figure 22 - xinil's anime of type OVA Interestingly, the anime with type OVA are right on the unbiased plane. Most likely, they are completed. A few are being planned to watch. 14
Figure 23 - xinil's anime of type Movie As would most of the movies that are completed would have an unbiased score. It is strange to see some of the movies unwatched when they are scored. This seems to be a misuse of the anime list from xinil. Only a small group of movies have no score and are most likely being planned to watch. Figure 24 - xinil's anime of type Special It seems that Special anime are not really tracked by xinil because he only has 6 of them. They tend to have biased scores. 15
Figure 25 - xinil's anime of type ONA Xinil simply does not watch any anime with the type ONA. Figure 26 - xinil's anime of type Music Specifically, this shows that xinil is not a big fan of anime of the type music. Based on all the different filtering of xinil s anime list data, it can be concluded that he does not tend to score his anime low. He tends to use an average score to quickly score things. He does take the time to score an anime that are 8 to 10 when the majority of them are completed. The majority of the anime on 16
the list have the type TV. A few completed anime are incorrectly categorized. The user xinil does not watch anime that are ONA or music. For the most part, xinil knows how to use his personally built tool. 6 Related Work There has been work trying to visualize data with WebGL such as PhiloGL [2]. Other than that, there are not many MyAnimeList visualizers to date. There are JavaScript InfoVis tools like d3 [6]. There are others like JavaScript InfoVis Toolkit [7]. 7 Conclusion This project helps a person understand MyAnimeList user anime list data to show how a visualization tool can be used to understand the behaviors of a user using MyAnimeList s anime list tracker. It gives a good understanding by having a 3 dimensional and 2 dimensional visualizations working together with a lot of data filtering activity. It seems that it makes sense why InfoVis tends to lean towards 2d because it easier to understand than the 3d visualizations when dimensions represents scalar data that are not commonly referenced in math. References [1] Nicolas Garcia Belmonte. The three.js toolkit can be available from https://github.com/mrdoob/three.js/, 2012. [2] Nicolas Garcia Belmonte. The PhiloGL toolkit can be available from http://www.senchalabs.org/philogl/ [3] Unofficial MyAnimeList. http://mal-api.com/ [4] John Resig. jquery library. The jquery library is available from http://jquery.com/, 2012. [5] Paul Bakaus. jquery UI library. The jquery UI library is available from http://jqueryui.com/, 2012. [6] Mike Bostock. d3 library. The d3 library is available from http://mbostock.github.com/d3/, 2012. [7] Nicolas Garcia Belmonte. The JavaScript Infovis Toolkit is available from http://thejit.org/, 2012. 17