Due to the change in the publishing industry in China, modern writters generally have to establish themselve by publishing their novel online and gain sufficient reputation before a publishing company approach and offers a contract.

Simliar to celebrities, the writters are encourage to publish their work on a daily basis to engage with their readers. Often 2 to 3 chapters are posted a day, and sometimes even more if a fan donates a generous amount.

I have been following this particular novel for several years, however, recently I found the update to be much slower than before. There has been discussions on the possibility that the series may be discontinued due to the high level of stress.

So I decided to investigate the case, and hope not to find evidence that my favourite series is going to be discontinued.

To do this, I scrapped the web page and extracted the time stamp of each post. Then investigate the duration between sequetial posts to see if there is any changes in the pattern.

Let’s load the required libraries and define some helper functions.

## Loading required package: methods

## This function builds the url to a specific thread/novel
buildUrl = function(thread, page){
    paste0("http://ck101.com/thread-", thread, "-", page, "-1.html")

## This function extracts the time stamp from a given page
extractTimeStampsFromPage = function(url){
    failed = TRUE
        doc = htmlTreeParse(url, useInternalNodes = T)
        if(!inherits(doc, "try-error"))
            failed = FALSE
    time_stamp_text =
        xpathSApply(doc, '//span[@class="postDateLine"]/text()', xmlValue)

    time_stamps_char =
            lapply(time_stamp_text, FUN = function(x){
                str_match(x, "[0-9]{4}\\-[0-9]+\\-[0-9]+.[0-9]{2}:[0-9]{2}")
    time_stamps = as.POSIXct(time_stamps_char, format = "%Y-%m-%d %H:%M")

## This function gets the current latest page of the novel
getMaxPageNumber = function(url){
    failed = TRUE
        doc = try(htmlTreeParse(url, useInternalNodes = T))
        if(!inherits(doc, "try-error"))
            failed = FALSE
    last_page =
        xpathSApply(doc, '//a[@class="last"]/text()', xmlValue)
    as.numeric(gsub("[^0-9]", "", last_page[1]))

## This function extracts all the time stamp of the novel
extractNovelTimeStamps = function(thread){
    last_page = getMaxPageNumber(buildUrl(thread, "1"))
    lapply(1:last_page, FUN = function(x){
        current_page = buildUrl(thread, x)
        cat("Extracting page ", current_page, "\n")
    }) %>%
        do.call("c", .)

## Calculate the time differences in hour
calculate_hour_diff = function(time_stamps){

After defining all the necessary helper function, we can start scrapping the data for the analysis. There are three novels in this series, the first two do puo and wu dong has been completed, and the third one da zhu zai is in progress.

I have removed time difference that is less than 1 and greater than 168 hours which corresponds to a week. The reason to delete time stamps less than an hour is due to the infrequent update on the forum of the first novel where the author had yet to establish a firm reputation. While the cap at 168 is for special chapters that were written many months after the completion of the first two novels.

da_zhu_zai_time_stamp = extractNovelTimeStamps("2762483")
da_zhu_zai_time_diff = calculate_hour_diff(da_zhu_zai_time_stamp)
da_zhu_zai_time_diff = da_zhu_zai_time_diff[da_zhu_zai_time_diff < 168]

do_puo_time_stamp = extractNovelTimeStamps("1455308")
do_puo_time_diff = calculate_hour_diff(do_puo_time_stamp)
do_puo_time_diff = do_puo_time_diff[do_puo_time_diff < 168 & do_puo_time_diff > 1]

wu_dong_time_stamp = extractNovelTimeStamps("1979168")
wu_dong_time_diff = calculate_hour_diff(wu_dong_time_stamp)
wu_dong_time_diff = wu_dong_time_diff[wu_dong_time_diff < 168]

full_time_diff = c(do_puo_time_diff, wu_dong_time_diff, da_zhu_zai_time_diff)

Let’s take a look at the time series plot, it appears that the average interval for novel one and two were fairly consistent to be around 12 hours or 2 post a day, while the third novel has a steady increasing trend approaching 40 hours (A really bad sign!).

novel_time =
    data.frame(duration = full_time_diff,
               post = c(c(1:length(do_puo_time_diff)),
               novel = c(rep("1.do_puo", length(do_puo_time_diff)),
                         rep("2.wu_dong", length(wu_dong_time_diff)),
                         rep("3.da_zhu_zai", length(da_zhu_zai_time_diff))))

ggplot(data = novel_time, aes(x = post, y = duration)) +
    geom_line() +
    geom_smooth(col = "red", level = 0.99) + 
## Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.

plot of chunk unnamed-chunk-3

This simple analysis shows that there are some possibliies that my favourite novel will be discontinued. However, the third novel is also expected to be much longer than the first two. So I hope the author may be simply gathering and structuring his ideas, then regain his momentum and finish the novel.

Python codes are also available.