#developer note

im going to try not to fall into any sort of "fanon-terpritations" of the managers (ie misty being a baby who can do no wrong/whiny manipulative bitch or chip being woobified or prester being bigoted). im going to try and keep every one as close to canon as possible, especially during the beginning chapters where not a lot of original character development has a chance to blossom. key word being TRY, as im not the best writer, i can tend to quickly exaggerate traits for a joke but im really really REALLY going to try to not do that.

DO NOT PERCEIVE ME!!!! AAAAA!!! the anxiety is enough to make me want to quit development sometimes (or just only make it for myself and never release it) but thatd just be a waste id 100% regret immediately!!

Master Project Developer Notes

  Data Visualization of User Engagements

I want to visualize user engagements in terms of the numbers of comments, suggestions, posts, votes and page views for a selected number of stories from each category.

  Data Visualization of Author and Story-telling

I want to visualize the growth of content in each category, such as the number of stories and the number of words. The number of daily-published words is an important visualization to study the platform and the story-telling process of authors. The number of daily-generated word over a period of time can also explain the trend of digital publishing platform.

  Data Visualization of Content

Visualizing the number of monthly published chapters, stories and associated number of page views over a couple of months can give readers an overview of the content generating speed and their contribution to the overall page views. The change of ranking, particularly data from the red list and the black list, is another factor to evaluate the quality of content and their engagement.

   Profitability of the Platform

To show the advantage of this new publishing platform, it is important to study its profitability or growth of market shares. I want to visualize the number ads and profits generated by the platform. I also interested in studying the profitability of a selected number of authors. I want to visualize their monthly-produced words and their monthly-generated profits. I want to map these number of authors’ profitability to the visualization of monthly-produced words and the monthly-generated profits of the entire platform.

Since most stories on the platform are free of charge, there will be limited paid content and data available for this analysis.

  I can use Data-Driven Document (D3) JavaScript Library or Google Chart Tools to implement the above four visualizations. I might need to clean data and write them into appropriate format, such as JSON or CSV formats. The current data is in comma separated value (csv) format, as shown in figure 1.

Figure 1: Story statistic data format.

I can use Python or Java to read such txt files and reorganize them into formats that are recognizable by visualization libraries. To read and reorganize these data files using Python, I need to import the following libraries:

  import os

import glob

import string

import csv

  If there are any missing data or content, I might need to import the BeautifulSoup library to fetch additional data from the website. Most of visualization libraries require developers to have data in JSON or CSV formats.

I recently found a number of visualizations from the D3 example gallery that are suitable for comparing page views or number of comments of each category. Some examples are the D3 Show Reel, Multi-Series Line Chart, Difference Chart and Crossfilter. I decided to test the D3 Show Reel visualization and implement a Python app, as shown in figure 2, to read the page view data and output to format that is supported by the D3 Show Reel visualization, as shown in figure 3.

Figure 2: Python app for reading page view and output to csv format.

Figure 3: the output file of the D3 Show Reel visualization.

The D3 Show Reel visualization for page views can be found from the following figures. The visualization enables the transition between a numbers of visualizations.

Key Word Evolution and Frequency Analysis

I plan to study the evolution of frequently used words of the user-enhanced stories and user-generated comments using TF-IDF algorithm. These bags of words can explain the trend of the each category and the interests of audiences.

  I can use the Gensim topic-modeling tool to do the TF-IDF analyses. Gensim is a free Python framework designed to automatically extract semantic topics from documents. Gensim aims at processing raw, unstructured digital texts in English. The algorithms in gensim, such as Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA) or Term Frequency–Inverse Document Frequency (TF-IDF), discover semantic structure of documents, by examining word statistical co-occurrence patterns within a corpus of training documents.

  Since all content is in Chinese, I might need to use a topic-modeling tool that can parse Chinese vocabularies and phrases, such as jieba. Jieba is a Python Chinese word segmentation module. To test the tool, I downloaded opinion articles from the People’s Daily Online since 2010, and organized them to a monthly collection. I run the Jieba text segmentation tool using Python on the grouped article collections and performed a TF-IDF analysis on the result.  Here is the link to all opinion articles from People’s Daily since 2010: http://opinion.people.com.cn/GB/8213/49160/49179/index.html

  After downloading all articles from the site, I read all txt files into Python:

Figure 4: Reading People’s Daily opinion text file into Python.

  I implemented a TF-IDF analysis application using the jieba library:

Figure 5: TF-IDF analysis app using jieba.

  The TF-IDF results can be found at figure 6. The result shows the change of trend in official press.

Figure 6: Result of the TF-IDF analysis using jieba.

User-Enhanced Self-Publishing Platform: Audience Engagement and Challenges to Traditional Media Monopoly

News organizations have lost their monopoly distributing breaking news, investigative stories and analyses. Revenues of traditional print newspapers have decreased from $44.939 billion in 2003 to $20.692 billion in 2011, as shown in figure 1. Similar to the shift in news industries, traditional book publishing industries have been challenged by the innovations of Internet technologies. Revenue of traditional print books has decreased from $23.2 billion in 2004 to $20.5 billion in 2009, as shown in figure 2. The traditional publishing monopoly is losing its profitability. In order to maintain their profitability, news and publishing corporations needed to understand their customers on the digital side and practice a combination of smart business models on the web to effectively distribute narrative content among targeted groups on the Internet.  

Figure 1: Print and Online Ad Revenue from 2003 to 2011 in millions of dollars (Source: Newspaper Association of America)

Figure 2: Print and Online Books Revenue from 2004 to 2009 in millions of dollars (Source: U.S. Census)

Asian online publishing companies, such as Zong Heng, take unique approaches to interacting with audiences, and pricing serially published content. The company introduced a combination of smart business models to a new publishing platform that allows audiences to recommend plots and suggest content to authors. Authors can interact with audiences to brainstorm a novel or investigate some historical records. Audiences play a role in checking quoted historical events, and drive authors to develop future plots. By suggesting plots and recommending sources, narrative user-enhanced stories can have rich details and will be serially published online. Instead of finding traditional publishers and waiting for the book to come out, such digital platforms allow authors to immediately promote their work to targeted audience groups.

The fledging digital publisher Zong Heng has found a place in the Asian media market to serially publish bricks-and-mortar content to the Internet. The company has identified a mechanism where traditional publishing companies cannot comfortably compete in Asia. Such digital publishing platform might be an effective model to price bricks-and-mortar serially published content in the western society. Instead of putting emphasis on saving media industries, I want to argue that user engagements will break the traditional media monopoly and take the lead in story telling process. This master project will emphasize analyzing user engagements on this new publishing platform and studying audience driven story-telling process and user participated story-promoting process. This new platform has attracted a large number of digital users. As we can see from figure 3, the number of page views of zhongheng.com significantly increased after the site launched in September 2008. The site frequently has page view peaks when popular user-enhanced stories published on the platform.

Figure 3: Daily Page Views in millions (Source: China Webmaster and Alexa)

1. Data Visualization of Clustered Digital Users

To analyze audiences of this new digital user-enhanced self-publishing industry, it is important to study their age, education background, career and gender. These numerous audiences might have different tastes in content and dissimilar behaviors to engage with stories. As John Dewey explained in his book Democracy and Education, such social groups have “more numerous and more varied points of shared common interest” (96). These social groups can be clustered and visualized based on their tastes and behaviors over a couple of years or months. Audiences might subscribe to particular stories for a range of times and switch to new content. By clustering digital customers based on these various stories, I can map audiences to a number of stories and find the correlation of these content for a range of times. Such mapping might change over a couple of months. I want to visualize the variation of user subscriptions and to explain the trend of self-published content.

  2. Data Visualization of User Engagements

I want to visualize user engagements in terms of comments, suggestions, posts and votes for a selected number of stories (and for all digital content). If I can get comments, suggestions and votes for a selected number of stories, I can study the evolution of frequently used words using TF-IDF technique. These bags of words can explain the interests of audiences. The number of daily-generated user content (word) over a period of time can also explain the trend of digital users.

3. Data Visualization of Content

To analyze serially published content, it is important to study published stories in various categories. I want to visualize the growth of content in each category, such as the number of stories and the number of words. The number of daily-published words over a couple of months or a couple of years is another important visualization to study the platform. I can compare it with the daily-generated comments. Visualizing the number of monthly published chapters, stories and associated number of page views over a couple of months can give readers an overview of the content generating speed and their contribution to the overall page views. The change of ranking, particularly data from the red list and the black list, is another factor to evaluate the quality of content and their engagement. I also want to visualize the statistics of a number of subjects (fiction, history, love, fairy tale, etc.).

  4. Data Visualization of Authors

To analyze authors of this new digital user-enhanced self-publishing industry, it is important to study their age, education background, career and gender. It is important to visualize the number of their published stories, the number of page views and the number of engagements over a couple of years. By visualizing the number of words generated per day or per month, I can study their speed in generating content. I can also compare it with the number of comment words generated per day or per month.

  5. Profitability of the Platform