Context: China's Image Abroad and WWD

The PRC pays sustained attention to how China is perceived abroad.Footnote ¹⁸ Even prior to taking power, the CCP was keen to manage its image to persuade foreign sympathizers.Footnote ¹⁹ PRC image crafting has proactive and reactive dimensions.Footnote ²⁰ Proactive efforts include showing a peaceful and non-threatening China while highlighting its achievements.Footnote ²¹ Reactive efforts include responding to foreign criticism of PRC policies.Footnote ²² Messages are communicated via party-state media like Xinhua or CGTN, statements from diplomats, PRC-friendly think tanks or institutes, and/or foreign public relations firms. At times, the “message” is coercive, as when the PRC attempts to sanction, intimidate, deny access to or otherwise silence those who criticize its policies.Footnote ²³ In recent years, Western social media has become important in China's external communication strategies as the PRC fears ceding the public opinion battle in platforms controlled from the West.Footnote ²⁴

WWD emerged in this context. In 2019, Zhao Lijian 赵立坚, a PRC diplomat in Pakistan who had previously served in the US, began to be noticed for his Twitter account.Footnote ²⁵ His tweets played up his love of Pakistan, defended PRC policy and challenged American hypocrisy. Zhao and others who took similar rhetorical approaches were promoted, and “diplomats across the [foreign] ministry noticed that the shift in tone was being rewarded – just as they too began opening their own Twitter accounts” around 2019.Footnote ²⁶ The confluence of Chinese diplomats and state media bolstering their global social media profiles, declining US–China relations, increased foreign attention to China's repression of Uyghurs, the PRC's focus on “discourse power” to shape international narratives, domestic nationalism, Xi's stated preference for assertiveness, visible career progression for diplomats like Zhao, and China's growing material power all combined to set the context for the birth of WWD.Footnote ²⁷ Did the 1 June report on the collective study session change the approach in noticeable ways? It is that question we try to answer in the next section.

Data, Methods and Results

The messages that diplomats and embassies post on Twitter are indicative of the PRC's communication strategy. Relying on a list of Chinese diplomatic accounts (ambassadors, embassies, consulates, Ministry of Foreign Affairs accounts, or high-level staffers) and state-backed media accounts, we retrieved the tweets of all accounts that posted at least once between 1 January 2021 and 7 October 2021.Footnote ²⁸ We limited the analysis to English language tweets and excluded retweets. Next, we extracted a random sample of 1,000 tweets. We manually coded each tweet for whether it is “friendly,” “WWD” or “neutral.” Based on this hand-coding, we identified “seed words” that represent two ends of a unidimensional scale ranging from “friendly” to “WWD.” Afterwards, we used Latent Semantic Scaling (LSS) to score the text of all 200,000 tweets. Generally, LSS assigns a score to each term in the text corpus based on the semantic similarity with the seed words and then allows a predicted text score based on frequencies of these terms in each document.Footnote ²⁹

Table 1 reports the 100 terms with the lowest and 100 terms with the highest scores. Many of these terms, identified through the small set of 23–25 seed words (see Table A1 in the online Appendix), make intuitive sense and speak to the method's validity. Terms like “accusations,” “fabricated,” “disinformation,” “smear” and “oppression” appear in the category of the most unfriendly terms, highlighting the WWD tone. Words such as “gratitude,” “brotherly,” “donation,” “thank” and names of countries with amicable relations with the PRC received very high scores.

Table 1: 100 Terms with the Lowest and 100 Terms with the Highest Word Scores, Based on the Full Sample of Tweets

Moving from keywords to texts, Tables A2–A3 list the “friendliest” and most “WWD” tweets based on the LSS scores. Again, higher values imply a “friendlier” tone. For instance, the following tweet, portraying foreign critics as liars, has one of the lowest LSS scores:

Conversely, one of the highest-scoring tweets portrays China as a friend and partner:

Using this data, we exploit the timing of the Xinhua announcement to identify whether the study session report led to meaningful changes in the tone of PRC public diplomacy. We considered several methodological options. We opted not to use a regression discontinuity in time (RDiT) approachFootnote ³⁰ or a simple two-period difference-in-difference design using the date of the study session summary as a cut-off. Since there are no comparison (untreated) units, we cannot recover an estimate of an average treatment effect (ATE) or an average treatment effect of the treated (ATT). In other words, a simple pre-post comparison is very likely to be spurious to any other temporal factors that might influence tweeting.

Rather, given the aforementioned mixed and complex signals in the May 2021 collective study session announcement, and because there is no true “non-treated” comparison group (such as a group of accounts that would not have seen the announcement but continued to tweet), we compare two groups of accounts that we think should have been differentially influenced by the announcement. Consistent with the notion that the PRC's external communication advances different messages for different audiences, we evaluate if there is a differential effect of the announcement on tweets from accounts based in the OECD (mostly wealthy democracies) versus those from non-OECD countries (broadly, states in the Global South and state-affiliated media accounts based in China). PRC diplomacy has a long history of building friendly relations with countries in the developing worldFootnote ³¹ and at times it portrays itself as a leader of this group vis-à-vis the West.Footnote ³² In an August 2013 national propaganda work conference, Xi himself stressed the need to spread PRC viewpoints more effectively among “developing states.”Footnote ³³ Underlying the WWD approach is an anti-imperialist disposition that is more critical of Western states and allies and more sympathetic to states perceived to not be in this category. We use the OECD/non-OECD distinction as a proxy to capture these categories to see if diplomats in these countries reacted differently to the collective study session.

Our approach is therefore based on an expectation of heterogeneous treatment effects of two “treated” groups, rather than on a “treated” group and a control group. We expect this heterogeneity based on the underlying assumption that PRC Twitter accounts will react to stimuli with the understanding that the PRC crafts its external diplomacy with different messages for different audiences. This comparison necessitates a parallel trends assumption between tweets from OECD and non-OECD accounts, which we substantiate below and in the online Appendix.

Using tweets as the unit of analysis, we assign an indicator status based on the account's location. We generate a binary variable and assign the value of “1” to tweets from accounts in OECD countries. We then create a binary temporal variable where we assign a value of “1” to the time period after the collective study session report. Our data contain a total of 10,066 tweets from accounts in OECD countries, 5,904 prior to the 1 June announcement and 4,162 after. Likewise, we identify 190,542 tweets from accounts in non-OECD countries and state-backed media accounts, with 79,997 of these prior to the 1 June announcement and 110,545 after. These data allow us to employ the classic difference-in-difference equation to evaluate the differential effect before and after the summary report comparing the OECD and non-OECD accounts.Footnote ³⁴ Note that we train the LSS models on all tweets, while the difference-in-difference design is limited to a symmetric time window ranging from ±1 day to ±115 days.

Starting with a descriptive overview of temporal developments, Figure 1 shows the daily average scores for all accounts by diplomats, ambassadors, institutions and staff. Vertical bars show 95 per cent confidence intervals. Overall, we do not observe a consistent change in WWD scores around the announcement in June. Yet the plot reveals considerable and meaningful variation over time. Late March 2021 is a clear outlier, which directly corresponds with a highly contentions meeting on 19 March in Alaska between top US and Chinese diplomats. During the meeting, PRC diplomats accused the US of encouraging countries “to attack China,” while US representatives listed criticisms of several PRC policies.Footnote ³⁵ Many PRC accounts tweeted about the meeting, with the WWD scores during this time lending credence to the coding and validation.

Figure 1: Daily WWD Scores for All Diplomatic Twitter Accounts

Moving forward, in the main models we do not include any covariates owing to the potential of post-treatment bias. We standardize our measure by account, which should absorb much of the unobserved heterogeneity at the account level in our main models and ease comparative interpretation. In other words, if some accounts have a high amount of variation in their tone, and others have low variation, the same absolute difference would equate to a larger proportion of the standard deviation for the low variance account vis-à-vis the high variance account. Standardizing the measure ensures we are comparing the degree of variance relative to the account. When comparing OECD and non-OECD diplomat accounts, we see that the standard deviation of the score is almost identical, 1.403 to 1.396. As such, it is no surprise that we find results in the Appendix that are substantively consistent when evaluating models that use the non-standardized scores (see Table A7).

The main results are presented in Figure 2 and in Table A4 (for ±100 days) in the Appendix. Each point estimate and error bar shows the DiD estimate for varying specific time windows. Running models on a window increasing from ±1 to ±115 days allows for a visual inspection of the robustness of our findings. Values above 0 mean that OECD accounts are friendlier than non-OECD accounts. The vertical lines show 90 per cent and 95 per cent confidence intervals.

Figure 2: Difference-in-Difference Estimates for Varying, Symmetric Time Windows around the Announcement

The results show that OECD accounts seemed to become more positive immediately after the announcement (±1–±4 days). Owing to the small sample size of tweets, these large estimates are not immediately statistically significant. However, a clear pattern emerges and when the window is increased, where after about 1.5 months we observe consistently positive DiD estimates. The coefficients usually range between 0.1 and 0.15 standard deviations. This small but relatively consistent change is also visible when considering distinct classes of accounts. We make comparisons for tweets from all accounts (diplomatic and state-backed media, where state-backed media accounts are considered in the non-OECD group), tweets only from diplomatic accounts, tweets from institutional accounts (embassies or consulates), and tweets only from ambassadorial and staffer accounts (Figure A1). Based on these results, we conclude that accounts based in the OECD became somewhat more positive after the study session, relative to accounts in non-OECD countries. The time lag may be owing to the amount of tweets needing to become large enough to reliably recognize shifts, infrequent tweeters taking time to build up a measurable corpus after the announcement, or perhaps more detailed internal guidance that followed in subsequent weeks after the session. However, the data also do not reveal a dramatic strategy change resulting from the session.

What does a difference of 0.1–0.15 standard deviations mean in practice? A comparison of two tweets that are 0.12 standard deviations apart in their WWD score, the estimated difference for ambassadors in OECD versus non-OECD countries, illustrates this difference. A tweet at the mean is not aggressive but hints at some hypocrisy by foreigners:

A tweet 0.12 standard deviations more friendly (less WWD) retains the positive tone about China but sheds the implicit criticism:

The shift is subtle but real. In our first set of robustness checks (online Appendix Section B), we introduce both country and account fixed effects to deal with variation not already absorbed by standardization of the score by account. Our second set of robustness checks adds tweet-level and account-level control variables, including the number of words in the tweet, the number of followers of the account on the day of the announcement, the number of retweets and the number of “likes” received. Finally, we test models with a non-standardized WDD score. The results do not depend on the inclusion or exclusion of controls and fixed effects, and they are substantively consistent when we use unstandardized measures of our textual measure.

Returning to the parallel trends assumption, recall that this is a potential threat to our inferential strategy, as post-statement differences between OECD and non-OECD accounts could be driven by other, external factors which have a differential effect. We examine the parallel trends assumption by considering only tweets between 30 days before and after the announcement. For each comparison, we plot the raw data (with LOESS pre- and post-treatment trends) in Figure A2. For some comparator groups, it is conceivable (but potentially unknowable) that the assumption of parallel pre-treatment trends is violated. For example, collective study sessions feature expert input in advance, so perhaps there was awareness of the announcement and a corresponding change of behaviour among some tweeters before it happened; however, we cannot say with certainty. If anticipation effects are in the same direction as the treatment effect, this pre-trend could lead to unadjusted difference-in-difference results understating the magnitude of the treatment.Footnote ³⁶ This would mean that the true treatment effect of the announcement is actually larger than what we report. On the other hand, if the pre-trend was endogenous, it would mean that the study session made its announcement in response to tweets that were becoming less WWD; however, we find this unlikely given that the Party leads and the ministry and media follow.Footnote ³⁷ Finally, any pre-trend could also suggest that our treatment effect is simply picking up some spurious correlation. We cannot completely discount that any potential pre-trend we see for OECD accounts is partially driven by some unobserved event but, overall, we think the parallel trends assumption holds reasonably well.