An NLP Analysis of the Sinocism Newsletter

Commentary Length

I first provide a sense of Bill’s verbosity over time. To do so, I tokenized the texts and took a count, grouping by month and each type of commentary. The figure below plots the results. As you can see, the introductory remarks feature appears in November 2017 and slowly grows in magnitude until May 2018, when it surpasses the in-text commentary.

From my daily reading, I can tell you that the in-text commentary continues to grow sparser and Bill more laconic therein. Some of the residual token counts in this category are due to spillover from my roughshod classification approach combined with the fluid nature of his formatting.

Another thing you will notice is that both types of commentary tend to trend together. Some months Bill is simply less verbose, and the newsletter acts more like a simple news aggregator. The correlation between counts is also due to Bill taking a hiatus for different personal reasons.

Top Words and Phrases

Next, I wanted to get a sense of whether and how closely the content of each type of commentary aligns with the entire text. I did this in two ways. The first way is by comparing a simple count of the top 50 terms by frequency faceted by the three categories. The results are below.

Unsurprisingly, terms like China, US, Trump, Xi, Beijing, PRC dominate the top 5 slots across all three categories. A quick scan of the in-text commentary suggests that it has mostly focused on insider knowledge/rumours on the trade talks and the Party Congress/other party meetings. The introductory commentary is almost exclusively dominated by the trade war, but only because this has so heavily colored bilateral relations.

Only one chinese character, 的 (de) a possesive particle, makes it on the list. If I had utilized a Mandarin dictionary, it probably would have been removed as a stopword. On a side note, an interesting future analysis would be to focus explicitly which Chinese articles Bill includes, their content, and the top news outlets he taps apart from the People’s Daily.

While the single tokens might make for a nice plot, they are not that informative. So the second way I explore the congruency between commentary and content is to use spaCy’s pretrained part-of-speech (POS) tagger and extract the top 50 noun phrases, dropping collateral damage caused by the Mandarin. The figure below presents the findings.

The top 5-10 phrases closely mirror the results from the unigram approach, but slightly more proper names and places making the cut, e.g. Wang Qishan, Guo Wengui, Liu He, etc. Moving down the list, things become more interesting. Even though overall counts tend to drop off, we can start to see a divergence in what issues Bill pays attention to vis-a-vis the overall content.

Looking at the entire newsletter, we see the phrases are well-distributed across multiple topics. For instance, local_government is probably a result of the PRC’s focus on addressing local government debt issues. This topic was especially pronounced throughout 2017, but has since taken a backseat to the trade war. Lower on the list are phrases like national_security, a political staple; artificial_intelligence which is increasingly in the spotlight; foreign_investment, a sticking point in the trade talks; peking_university which has undergone a significant change in leadership and been engrossd in a few scandals; new_zealand, an epicenter for news on China’s United Front influence operations; discipline_inspection in relation to Xi’s corruption campaign; and environmental_protection, a nagging problem in China.

Moving to the in-text comentary, we see Bill’s focus centers more on high-level Chinese party officials and other notable figures, the Trump administration and its members, and “insider knowledge,” so to speak. These comments aim to provide deeper context and Bill’s personal read on the private dealings of both governments. This is not evident from the phrase counts alone, but my personal experience tells me so. Phrases such as ‘another_reminder’ and ‘another_sign’ usually precede clauses like ‘of Xi’s increasing consolidation of power’ and ‘that neither side is likely to make any concessions soon.’ The tailored nature of this commentary is intuitive given that it is often in response to one or more news specific news articles.

Finishing with the introductory remarks, three topics dominate apart from administrativia: the trade war, US-DPRK nuclear negotiations, and Sino-US technology competition. The first and third topics could be nested under a broader topic of Sino-US competition. Bill and many in the Beltway have framed these two issues as part and parcel of a ‘New Cold War’ (Elsewhere, I dubbed this meme ‘Cold War 2’ or CW2 for short). However, I think they are worthy of a distinction. Bill usually conveys his personal opinion on trends in each of these areas and occasionally offers predictions or things to look for on the horizon.

Memes

News articles often bundle coverage of tech advances—especially in areas like machine and deep learning, cloud computing, UAVs, and the hardware necessary to power these algorithm—under the umbrella of artificial intelligence. Typical coverage of the tech industry in China is no exception. While I believe this oversimplication gives the misleading impression of AI as an monolith and further obfuscates important distinctions, it is the norm among non-specialist journalists. Thus, despite my personal convictions, I employ just the term “artificial intelligence” to search for instances of the AI meme in the newsletter. CW2 allusions tend to be slightly more nuanced, sometimes not referencing the term “Cold War”" at all. I therefore represent this meme with the phrases “cold war,” “containment,” “tech war,” and “arms race.” I then used glob wildcard matching to trace the presence of these memes in each issue of the newsletter.

Juxtaposing the two types of commentary with the entire newsletter, one immediate result stands out. Although I have felt Bill increasingly employs Cold War-esque frames in his commentary, the data does not support this sentiment. The last time the topic appears is in September 2018. One conjecture is that the CW2 meme has become more prevalent in the news articles Bill chooses to feature but not his commentary. Another, more plausible, conjecture is that my hasty parsing of the text coupled with an under-defined set of terms caused me to miss more recent appearances of the meme in his liner notes.

The second result is the rapid growth in the CW2 meme within the general media since the end of 2017. While AI has been a fixture in coverage of China, CW2 is a relatively newer beast. I consider Campbell and Ratner’s “The China Reckoning” piece in Foreign Affairs to be one of the defining events that touched off the so-called “China Consensus” in DC. The plot confers some descriptive legitimacy to my hypothesis. In the two months prior to this piece CW2 appears 10 times in the newsletter, and 17 times in the two months after.

I am not implying a causal relationship here. Their piece could easily be considered a symptom of the shift rather than a harbinger. I merely believe this publication is a useful heuristic to mark the structural shift in Sino-Us relations.

Emotional Sentiment

Ideally, one should tailor a sentiment dictionary to corpus under study, but I haven’t the time for that. As an alternative, I use quanteda’s canned sentiment dictionary. While I am generally skeptical of the validity of a sentiment analysis, especially when using a canned dictionary, it can provide some exploratory utility. Below I plot the Positive-Negative ratio (valence) of the newsletter over time. The difference is normalized by the total number of tokens in each category per issue. I again demarcate the Campbell and Ratner piece’s publication date.

Based on this dictionary, the entire newsletter usually sits in the 2-5 percent negative valence range, with occasional positive spikes. The in-text commenatary is even more negative, with some issues containing over 20% negative tokens. However, the in-text commentary does appear to have more positive content than either the entire newsletter or the introductory remarks. I explore this more below. The intro remarks display a similar negative lean.

Does this tell us anything we don’t already know about Bill’s editorializing? Not really. Bill named his newsletter Sinocism as a play on cynicism for a reason. That alone should have sufficiently conditioned our expectations.

What about in regards to the entire newsletter which I believe is a useful barometer for bilateral relations. Again, the sentiment analysis turns up few surprises. As an interlocutor in the DoD aptly stated the other day, “… we went from cooperating for cooperation’s sake to where we are now, competing for competition’s sake.” I’d be surprised if the relative valences looked any different.

The one finding that stands out is how much the in-text valence vacillates in recent months and, in particular, the sheer number of positive issues. To look into this further, I randomly sample four issues from after December 2018, two with negative valences and two positive. Let’s take a look.

These samples lend further credence to my skepticism of canned dictionaries. The sentences from the relatively positive samples do not evince positivity. If anything, there seems to be a fair amount of cynicism throughout the May 6, 2019 newsletter. What’s more, Chinese propaganda is peppered about the June 19, 2019 newsletter which is probably driving the relative positive valence. A sample size of 2 is not nearly large enough to be representative, but given my hasty data cleaning, I’m confident the sentiment analysis is mainly noise.