Venue: NAACL 2025

# Through the Lens of History:

Methods for Analyzing Temporal Variation in Content and Framing

of State-run Chinese Newspapers

 Shijia Liu

Northeastern University

liu.shij@northeastern.edu &David A. Smith

Northeastern University

dasmith@ccs.neu.edu

###### Abstract

State-run Chinese newspapers are believed to strategically select and frame news articles to align with the shifting political tides of the country. This paper describes methods to quantify these changes in content and framing over time. Looking at more than 50 years of articles from the _People's Daily_ and _Reference News_, we analyze differences in name mentions and sentiment in news articles for politicians before and after their deaths, as well as during and not during certain political events. We find significant estimates of difference, reflecting the changes in various aspects of the political environment in China during different time periods. We also apply change point detection methods to identify turning points in time series data of name mentions and sentiment. The identified turning points show a high co-occurrence with crucial political events and deaths of politicians. Furthermore, we utilize topic modeling to analyze the framing choices for articles written in different decades. The changes in frequent topic words are more significant in _People's Daily_ than in _Reference News_, which is consistent with the focus shifts of the Chinese central government in history. Finally, by using pre-trained language models to predict masked names in news articles, we analyze the distinctiveness of the language used to report individuals.

## 1 Introduction

Throughout the history of the People's Republic of China, the rhetoric of state-run newspapers has reflected the ideologies and power dynamics of party and government leaders. Who appears on the front pages of the newspapers and how political figures are described and framed with regard to particular political events reflect shifts in the agenda of influential leaders [3]. Furthermore, it is widely believed that the Chinese government has utilized various methods to control the spread of information and influence public opinion in order to stabilize society [4]. Among these strategic controls, the existence of radical changes in reporting on politicians due to the occurrence of certain political events is an interesting phenomenon worthy of close examination. When and how such changes occur provide a unique perspective for studying the interaction of government action and societal changes in modern China.

In Chinese state-run newspapers, the language used to report political figures is often carefully

Figure 1: Excerpts of articles mentioning Peng Dehuai from _People’s Daily_, published in two different eras. English translations provided by ChatGPT.



chosen to show where the politicians stand in various power struggles. Figure 1 shows two excerpts from articles in the _People's Daily_ that mention Peng Dehuai, a well-known and controversial political and military figure of the PRC, published in two different time periods. The first was published during the Korean War, when Peng was the commander of the People's Volunteer Army and was highly regarded by Mao Zedong as a war hero. The second was published during the Cultural Revolution, when Peng was completely removed from power and labeled as a "counter-revolutionary revisionist." The word choices and the overall sentiments of these two excerpts reflect the changing political status of Peng during two political periods.

Given the example above, we ask: Is there a systematic strategy utilized by state-run Chinese newspapers that gives quantifiable evidence of difference in reporting for articles written during certain political periods? Moreover, does such evidence also manifest itself in the difference in reporting when certain political figures are alive or dead? If such evidence exists, it would strongly support the claim that Chinese newspapers select the ways in which news is reported according to changing political tides and the impact of political events in the central government. Furthermore, we would also like to understand the shifts in framing choices of articles from different decades in order to have a holistic picture of changes of focus in Chinese society that could be influenced by the government in different time periods of the regime. Lastly, we wonder whether the context used to report certain politicians is unique enough so that it can be easily identified by pre-trained language models when the actual names of the politicians are not given. In other words, can language models encode such context so that the separation of the embeddings for different politicians is significant? If so, it would imply that reporters in China take politicians' identities and power status into consideration when constructing language and rhetoric in their writings.

The contributions of this paper are as follows: (1) As far as we know, this is the first work to analyze the complete text of two widely-circulated, state-run PRC publications, _People's Daily_ and _Reference News_, for the effects of political events and the deaths of certain politicians on changes in news reporting. (2) We measure features of news coverage and employ change point detection methods to quantify such effects and discover their social meaning. (3) We use topic modeling to identify the framing choices of news articles from different decades, examining the implied change of focus of the Chinese government. (4) We use a pre-trained BERT model to predict masked names in news articles and quantify how unique the language used to report different politicians is.

## 2 Related Work

Previous work has focused on strategies Chinese state media use to shape the perspectives and opinions of a global audience Xiaoling (2010); Curtin (2012). Specifically, Sun (2009) discusses the vast difference in the coverage of current events between Chinese and international media outlets. Lee (2003) outlines the interplay between nationalism and globalism in Chinese media. In addition, Fan et al. (2024) examines the strategies used by Chinese state media on Twitter in order to reach foreign publics. Hou et al. (2021) explores the changing rhetoric of Chinese political discourse in different periods in the PRC era.

Although previous research has examined the corpus of the _People's Daily_, most of this work focuses on studying particular political issues, such as international relations Guan (2018); Guan and Liu (2019), gay portrayal Huang (2018) and public health policy Yang (2021). Liu and Chang (2020) study the changes in different aspects of citizenship rights through news articles in different eras.

Looking at changes in US news coverage, Card et al. (2016) introduce a model to analyze the framing of news articles related to immigration. Card et al. (2022) use an approach based on contextual embedding of text to analyze 140 years of US political speeches that reveal changing framing of immigration by different political parties.

## 3 Methods

In this section, we detail the main methods used to perform our experiments, including the extraction of descriptive statistics, change point detection, topic modeling and masked token prediction by a pre-trained language model.

### Descriptive Statistics and Change Point Detection

Firstly, we compute the descriptive statistics of the differences of the normalized count of articles mentioning a politician's name and the average sentiment of sentences mentioning the politician 

before and after their death, as well as during and not during certain political events.

We define the following variables in our model:

* \(Y\) denotes the response variable where we estimate the differences on. We experiment with two possible settings for \(Y\): the (normalized) count of articles mentioning a politician's name and the average sentiment of sentences mentioning the politician in articles.
* \(L\) is an independent variable that indicates whether the politician is alive or dead (\(L=0\) for alive and \(L=1\) for dead).
* \(E\) is another independent variable that indicates whether articles are written in the middle of particular political events (\(E=1\) for invent and \(E=0\) otherwise).

We then proceed to compute the average difference statistics (\(ADS\)). The two \(ADS\) quantities with respect to two independent variables \(L\) and \(E\) are defined as:

\[ADS_{L}=\mathbb{E}[Y\mid L=1]-\mathbb{E}[Y\mid L=0] \tag{1}\]

\[ADS_{E}=\mathbb{E}[Y\mid E=1]-\mathbb{E}[Y\mid E=0] \tag{2}\]

Secondly, we obtain yearly time series data on normalized name counts and average sentiment for different politicians. Then we perform change point detection (CPD) on the data. Change point detection aims to discover abrupt and significant changes in the behavior of a time series. In this case, we attempt to identify such changes in normalized name counts and average sentiment to see if they coincide with important political events and deaths of politicians.

Many algorithms have been proposed for change point detection Van den Burg and Williams (2020). In our analysis, we choose to use kernel change point detection Arlot et al. (2019), which maps data to a higher-dimensional space and finds change points that minimize a certain cost function. We evaluate two kernel functions in our experiments: linear and radial basis functions.

### Framing Analysis and Topic Modeling

We perform our main framing analysis on the agenda-setting mechanism of the _People's Daily_ and _Reference News_ using statistical topic modeling Blei et al. (2003a). Topic modeling has been widely used in automated framing analysis Roberts et al. (2013); Boydstun and Gross (2013); Nguyen et al. (2013); Tsur et al. (2015); Field et al. (2018). In our case, we fit Latent Dirichlet Allocation (LDA) topic models Blei et al. (2003b) to different decades of newspaper corpora and identify which topic words carry the highest cumulative weights among all topics extracted by the models. Those top topic words are considered to show aspects of news reporters' framing choices.

### Predicting Masked Names

We also run experiments on predicting the masked names of the politicians listed in Table 1 in order to measure how similar the discourse around different politicians is. A masked language model, such as BERT Devlin et al. (2019), is trained to predict masked tokens in sentences given the information of the context surrounding them. In order to answer the research question of how unique the context used to report a politician is, we will mask the full names of the politician and replace the names with the masking token, in addition to a general-purpose title. Then we will make a pre-trained Chinese BERT model Cui et al. (2021) predict the masked names based on the context in the given text.

## 4 Datasets and Results

In this section, we first report the details of the datasets used in our experiments. We then proceed to analyze our experimental results. Table 1 and Table 2 list the politicians and political events of interest in our analysis. These politicians and political events are subjectively selected based on our judgment of the importance and representativeness of people and events in the history of the PRC.

### Datasets

We perform our experiments on news articles from the _People's Daily_ and _Reference News_, two widely circulated state-run newspapers in China. _People's Daily_ is the official newspaper of the Central Committee of the Chinese Communist Party. _Reference News_ is published by the Xinhua News Agency and translates and republishes articles by foreign news agencies. The news articles are scraped from [https://www.laoziliao.net/rmrb](https://www.laoziliao.net/rmrb) and [https://www.laoziliao.net/ckxx](https://www.laoziliao.net/ckxx). The articles from _People's Daily_ date from May 1946 to December 2003 (1,315,525 articles in total) and the articles from _Reference News_ date from March 1957 to December 2002 (569,490 articles in 

al).1 During the scraping process, we extract the URLs, the publication dates, the titles and the text contents of news articles. The scraped data are available at [https://github.com/sliu126/chinese_newspapers.git](https://github.com/sliu126/chinese_newspapers.git).

Footnote 1: Reference News was circulated only among the highest government officials before 1957, when it became available to the public under the order of Mao Zedong.

### Results of Descriptive Statistics and Change Point Detection

In this section, we report the \(ADS\)s (eqs. 1-2) for our descriptive statistics analysis. We compute the \(ADS\)s on two quantities: (normalized) name count and sentiment scores, as stated in SS3.1.

We normalize name counts by dividing them by the total number of articles mentioning the particular name in the corpus; hence, the normalized name count is between 0 and 1, and the \(ADS\)s for name count would be between -1 and 1. Sentiment scores of sentences are computed using the Google Cloud API2 and are normalized between -1 (negative) and 1 (positive). We exclude politicians who lived beyond the latest articles in our dataset.

Footnote 2: [https://cloud.google.com/natural-language/docs/analyzing-sentiment](https://cloud.google.com/natural-language/docs/analyzing-sentiment)

Figure 2 and Figure 3 show \(ADS_{L}\) for normalized name count and sentiment scores for different politicians, computed on the corpus of People's Daily. As we can see in Figure 2, the majority of the politicians receive more reports while alive, except for Zhou Enlai and Liu Shaoqi. We can also see in Figure 3 that most people have articles about them written with more positive sentiment after their death, suggesting that Chinese news reporters tend to show more respect to deceased politicians. The fact that Zhou Enlai was mentioned both more in count and more positively in sentiment after his death corresponds to the common agreement that he was posthumously very revered by the Chinese public and by the higher leadership. Interestingly, Mao Zedong was described with more positive sentiment while alive, possibly due to the fact that he has held up as the leader and savior of China while he was in power. After his death, articles about him were written in a more objective manner.

Figure 4 and Figure 5 illustrate the \(ADS_{E}\) for Mao in different political events. Note that the invent and out-of-event name counts are computed as the average name counts per month in order to mitigate the fact that out-of-event time is much longer than in-event time for some events. Unsur

\begin{table}
\begin{tabular}{p{85.4pt}|p{113.8pt} p{113.8pt}}
**Politician** & **Highest Government Position Held** & **Birth and Death** \\ \hline Mao Zedong (§ 

prisingly, _Mao_ was mentioned much more during the Cultural Revolution, and articles about him were more positive during it. After the Reform and Opening-up, reporters tended to write less about him and in a more neutral manner.

Other results on different politicians and different corpus are included in Appendix A.

Table 3 and Table 4 show results for change point detection on _People's Daily_ data. We use Python's Ruptures package Truong et al. (2020) to detect change points in time series data. The minimum size between two change points is set to 1 year and the number of change points is set to 3. We want the algorithm to detect two political events (Establishment of PRC and Cultural Revolution) and the deaths of politicians. A change point identified within 1 year of the true occurrence time of an event is considered a successful detection. We choose politicians from Table 1 who have experienced the two events while in power and died in the domain of our time series data. We also provide the time series plots of normalized name count and sentiment of _Mao Zedong_ in _People's Daily_ in Figure 6 and Figure 7 in order to present a general picture of what the data for our analysis look like.

As we can see from the results, the algorithm is able to identify at least one expected change point for all politicians. For name count data, all three expected change points are identified with similar frequency. For average sentiment data, death (DEA) is the most commonly identified change point across all politicians, while the Establishment of the PRC (EPRC) and Cultural Revolution (CR) are almost tied in the second place. In general, we can conclude that the two major political events and death constitute major turning points in the way Chinese

Figure 5: ADS (95% CI included) with difference in sentiment for different events (politician: _Mao Zedong_, corpus: _People’s Daily_)

Figure 4: ADS (95% CI included) with difference in (normalized) name count for different events (politician: _Mao Zedong_, corpus: _People’s Daily_)



politicians are reported in state-run newspapers. Note that the change points detected that do not represent the two events and the death are turning points relevant to each individual politician. For example, the sentiment change points detected for _Deng Xiaoping_ correspond to the Establishment of PRC, the "Criticize Deng" campaign in 1975, Mao's death in 1976 and Deng's Southern Tour in early 1992.

Change point detection results on _Reference News_ are included in Appendix B.

### Results of Topic Modeling

We use spaCy's Honnibal and Montani (2017) zh_core_web_sm model to segment and parse news articles. Afterwards, we fit an LDA topic model using the Mallet package McCallum (2002) (version 2.0.8) with the number of topics set to 10. For each decade of our newspaper data, we extract the top five topic words with the most cumulative weights across all topics. We remove stopwords3 during the process.

Footnote 3: We use the Chinese stopwords list from [https://github.com/stopwords-iso/stopwords-zh](https://github.com/stopwords-iso/stopwords-zh)

Table 5 and Table 6 show the top 5 topic words identified in retrieved topics of different decade of news articles from _People's Daily_ and _Reference News_. As we can see, the distributional shifts in topics are more obvious in _People's Daily_ than in _Reference News_, with "Production" and "Revolution" in earlier decades and "Economy" and "Development" in later decades, which corresponds to a shift of focus from political (class) struggle to economy development of the Chinese government. However, the top topic words from _Reference News_ are always about different countries, reflecting the fact that the newspaper strategically selects articles from foreign news agencies that mainly focus on China's diplomacy.

Other results on selected politicians are shown in Appendix C. Note that we only show politicians who have significant coverage, with at least 100 articles mentioning them in all decades in the newspapers.

\begin{table}
\begin{tabular}{l|l|l}
**Politician** & **CP: linear** & **CP: rbf** \\ \hline Mao Zedong & CR, DEA & CR, DEA \\ Zhou Enlai & EPRC, DEA & DEA \\ Deng Xiaoping & EPRC & EPRC \\ Peng Dehuai & DEA & DEA \\ Liu Shaoqi & CR, DEA & CR, DEA \\ Zhu De & CR, DEA & CR, DEA \\ Chiang Kai-shek & EPRC, DEA & EPRC, DEA \\  & \multicolumn{1}{c}{} & \\ \end{tabular}
\end{table}
Table 4: Change points detected on average sentiment over time on _People’s Daily_

Figure 6: Time series plot for normalized name count for _Mao Zedong_, corpus: _People’s Daily_)

\begin{table}
\begin{tabular}{l|l|l}
**Politician** & **CP: linear** & **CP: rbf** \\ \hline Mao Zedong & ALL & ALL \\ Zhou Enlai & CR & EPRC, DEA \\ Deng Xiaoping & CR, DEA & CR, DEA \\ Peng Dehuai & EPRC, CR & EPRC, CR \\ Liu Shaoqi & DEA & CR \\ Zhu De & ALL & ALL \\ Chiang Kai-shek & EPRC & EPRC \\  & \multicolumn{1}{c}{} & \\ \end{tabular}
\end{table}
Table 3: Change points detected on normalized name count over time on _People’s Daily_, ALL means {EPRC, CR, DEA}

Figure 7: Time series plot for sentiment for _Mao Zedong_, corpus: _People’s Daily_)





### Results of Predicting Masked Names

In this section, we report the results of masking different politicians' names in articles from _People's Daily_ and having the Chinese BERT model Cui et al. (2021) predicting the masked politicians.4 From each decade of articles mentioning the politicians listed in Table 1, we randomly sample 100 articles and remove the full names of the politicians. Then we insert "[MASK]#\(\pm\)" at the places where the politicians' names are removed. We append "\(\%\)\(\pm\)" after the masking token to encourage the language model to predict a surname. We then map the predicted surnames to actual Chinese politicians based on our knowledge of Chinese history.

Footnote 4: We use Huggingface’s hfl/chinese-bert-wnm model in our experiments.

Table 7 and Table 8 show the top five politicians who could replace the masked surnames of _Mao_ and _Zhou_ with their average token probabilities per sentence, predicted by the Chinese BERT model. As we can see, the model is able to identify those who have worked closely with Mao and Zhou. It is unsurprising that Zhou appears in Mao's top replacements and Mao appears in Zhou's, since these two politicians are constantly paired up to perform government duties. Deng Xiaoping is identified as a top replacement for Mao, probably because they have both been the highest leader of the PRC. Li Xiannian and Chen Yun, who appear in both tables, have been in the central power since the establishment of the PRC and have assumed crucial roles in the government (Li was the Chairman of PRC and Chen was the Vice Premier).

We also calculate the top five replacements for different politicians during different political events listed in Table 2. The results for selected politicians are in Appendix E. In general, the predicted probability of the true names is higher when the masked politicians were either in power or in the center of a political turmoil. For example, _Peng Dehuai's_ replacements include his own name in top five names during the Korean War, the Anti-rightist Campaign and the Great Leap Forward. However, his name disappears during the Cultural Revolution when he was ostracized by the central government (see E.1). Similarly, _Liu Shaoqi's_own name ranks higher as a replacement for himself during the Anti-rightist Campaign and the Great Leap Forward, but lower before the establishment of PRC and during the cultural revolution when he was not acting as the chairman of PRC (see E.2).

Finally, we are also interested in the gaps be

\begin{table}
\begin{tabular}{l|p{39.8pt} p{56.9pt} p{56.9pt} p{56.9pt} p{56.9pt}} \hline
**Decade** & \multicolumn{5}{c}{**Top 5 Topic Words with Weights**} \\ \hline  & The Masses & Work & China & Production & Government \\
1940 & \(\nexists\!

tween the predicted probabilities of the true name and of other replacement names for different politicians, since they could give us information on how unique the language used to report those politicians is. Therefore, we define the uniqueness ratio to be the ratio between the predicted probability of the true name and the highest predicted probability of replacement names. Furthermore, we would like to know how certain the language model is when making predictions, which can be represented by the entropy of the predictions. Table 9 shows the uniqueness ratio and entropy (in bits) of different politicians. Interestingly, those with higher uniqueness ratios are the exact people who have been in power for a long period of time, with the highest ratio belonging to Mao himself. Those with lower uniqueness ratios are the ones who have been in power and then removed from power, reflecting the tragic fact that the language used to report politicians in the PRC could change drastically for the

\begin{table}
\begin{tabular}{l|p{34.1pt} p{28.5pt} p{60.7pt} p{60.7pt} p{60.7pt}} \hline \multicolumn{1}{c|}{**Decade**} & \multicolumn{5}{c}{**Top 5 Topic Words with Weights**} \\ \hline  & The U.S. & China & Government & Country & Soviet Union \\
1950 & \#\#\# & \#\#\# & \#\#\# & \#\#\% & \#\% \\  & 0.1182 & 0.0977 & 0.0739 & 0.0596 & 0.0560 \\ \hline  & The U.S. & China & Soviet Union & Government & Country \\
1960 & \#\#\# & \#\#\# & \#\#\# & \#\#\% & \#\% \\  & 0.1163 & 0.0920 & 0.0768 & 0.0620 & 0.0519 \\ \hline  & Soviet Union & The U.S. & China & Country & Government \\
1970 & \#\#\# & \#\#\# & \#\#\# & \#\% & \#\% \\  & 0.1156 & 0.1155 & 0.1029 & 0.0585 & 0.0537 \\ \hline  & The U.S. & China & Soviet Union & Government & Country \\
1980 & \#\#\# & \#\#\# & \#\#\# & \#\% & \#\% \\  & 0.0893 & 0.0857 & 0.0645 & 0.0407 & 0.0389 \\ \hline  & The U.S. & China & Country & Japan & Economy \\
1990 & \#\#\# & \#\#\# & \#\#\# & \#\% & \%\% \\  & 0.0776 & 0.0653 & 0.0404 & 0.0333 & 0.0318 \\ \hline  & The U.S. & China & Company & Government & Taiwan \\
2000 & \#\#\# & \#\#\# & \#\#\# & \#\#\# & \#\% \\  & 0.1027 & 0.0559 & 0.0393 & 0.0337 & 0.0331 \\ \hline \end{tabular}
\end{table}
Table 6: Top topic words identified in different decades of articles in _Reference News_

\begin{table}
\begin{tabular}{l|l} \multicolumn{1}{c|}{**Politician**} & **Avg Probability** \\ \hline Mao Zedong (\#\#\#) & 0.3112 \\ De Xiaoping (\#\#\#) & 0.0385 \\ Zhou Enlai (\#\#\#) & 0.0348 \\ Li Xiannian (\#\#\#\#) & 0.0318 \\ Chen Yun (\#\#\#) & 0.0252 \\ \hline \end{tabular} 
\begin{tabular}{l|l} \multicolumn{1}{c|}{**Politician**} & **Uniq. Ratio** & **Entropy** \\ \hline Mao Zedong (\#\#\#) & 8.0831 & 1.4606 \\ Zhou Enlai & 2.3913 & 1.8865 \\ Chiang Kai-shek & 1.2846 & 2.2631 \\ Deng Xiaoping & 1.2615 & 2.2132 \\ Zhu De & 1.1411 & 2.3053 \\ Hu Yaobang & 1.1140 & 2.2607 \\ Jiang Zemin & 0.9863 & 2.2735 \\ Liu Shaoqi & 0.8970 & 2.2807 \\ Peng Dehuai & 0.8158 & 2.3136 \\ Hua Guofeng & 0.7427 & 2.2456 \\ Zhao Ziyang & 0.4763 & 2.2221 \\ \hline \end{tabular}
\end{table}
Table 7: Top 5 politicians’ names generated by BERT to replace the masked _Mao Zedong’s_ name in articles from _People’s Daily_

\begin{table}
\begin{tabular}{l|l|l} \multicolumn{1}{c|}{**Politician**} & **Uniq. Ratio** & **Entropy** \\ \hline Mao Zedong (\#\#\#) & 8.0831 & 1.4606 \\ Zhou Enlai & 2.3913 & 1.8865 \\ Chiang Kai-shek & 1.2846 & 2.2631 \\ Deng Xiaoping & 1.2615 & 2.2132 \\ Zhu De & 1.1411 & 2.3053 \\ Hu Yaobang & 1.1140 & 2.2607 \\ Jiang Zemin & 0.9863 & 2.2735 \\ Liu Shaoqi & 0.8970 & 2.2807 \\ Peng Dehuai & 0.8158 & 2.3136 \\ Hua Guofeng & 0.7427 & 2.2456 \\ Zhao Ziyang & 0.4763 & 2.2221 \\ \hline \end{tabular}
\end{table}
Table 9: Uniqueness ratio and entropy calculated from predictions made by Chinese BERT for different politicians 

same people with different political status. The language model tends to be more certain when predicting the replacements for Mao and Zhou, corresponding to the fact that the language used to report these two is more individualized.

## 5 Conclusion

We extract statistics on coverage of selected politicians and employ change point detection methods to news articles from _People's Daily_ and _Reference News_ to discover the difference in the ways of reporting during different political events and when deaths of political figures occur. Results show that such differences exist and can be traced to changes in political tides and public opinion in different eras of the PRC. Furthermore, through topic modeling we discover the changes in framing choices on news articles in different decades, which reflect the evolving agenda and shifts of focus of the Chinese government. Finally, by predicting masked names of politicians using a BERT model, we identify the difference in the uniqueness of the context used to report different politicians. We discover that those who have held power for a longer period of time tend to have more uniqueness in the language used to report them in articles. We hope that the methods used in this work can be applied to news articles from other media outlets and political systems.

Future work may involve inferring power networks among politicians by examining the closeness of language used by reporters when discussing them during political events. It would also be interesting to compare the results with existing discoveries of "inner circles" among Chinese political elites. In addition, it would be informative to study the framing choices adopted by newspapers published under different regimes on certain Chinese political events, since they might reflect the links between rhetoric in news reporting and ideologies of news agencies. We believe that these are exciting directions that could add valuable knowledge to our understanding of how news reporting is done in the PRC.

## 6 Limitations

We believe that we have conducted a comprehensive study on the corpus of two Chinese state-run newspapers that reveals multiple aspects of Chinese politics in different eras of the PRC. However, we suggest the following limitations that might open up avenues for future work:

* The implications of the top replacement names given by BERT can be further examined. How these names correlate to the political careers of the masked people is an interesting question to explore. For example, we can extract personal networks of certain politicians from their Wikipedia pages or history textbooks and compare the information to the experimental results we have.
* Regarding framing, it would be interesting to compare the results of topic modeling on _People's Daily_ with corpora of newspapers published under different regimes, such as Taiwanese newspapers. We believe that newspapers that carry different ideologies would frame the same political events differently.
* It is also interesting to make large language models identify the top topic words in sets of articles and compare the results with the LDA analysis. This would inspect LLMs' abilities of framing analysis on news articles, which may provide useful information on how powerful LLMs currently are.

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Additional Descriptive Statistics

Results

Figure 8: ADS (95% CI included) with difference in (normalized) name count when people are alive and dead (corpus: _Reference News_)

Figure 10: ADS (95% CI included) with difference in (normalized) name count for different events (politician: _Zhou Enlai_, corpus: _People’s Daily_)

Figure 9: ADS (95% CI included) with difference in sentiment when people are alive and dead (corpus: _Reference News_)



Figure 14: ADS (95% CI included) with difference in _Zhu De_, corpus: _People’s Daily_)

Figure 13: ADS (95% CI included) with difference in (normalized) name count for different events (politician: _Liu Shaoqi_, corpus: _People’s Daily_)



Figure 21: ADS (95% CI included) with difference in _shek_, corpus: _People’s Daily_)

Figure 24: ADS (95% CI included) with difference in sentiment for different events (politician: _Zhu De_, corpus: _People’s Daily_)



Figure 27: ADS (95% CI included) with difference in corpus: _People’s Daily_)

Figure 28: ADS (95% CI included) with difference in sentiment for different events (politician: _Zhao Ziyang_, corpus: _People's Daily_)

Figure 26: ADS (95% CI included) with difference in sentiment for different events (politician: _Deng Xiaoping_, corpus: _People’s Daily_)

Figure 30: ADS (95% CI included) with difference in (normalized) name count for different events (politician: _Mao Zedong_, corpus: _Reference News_)



Figure 31: ADS (95% CI included) with difference in (normalized) name count for different events (politician: _Zhou Enlai_, corpus: _Reference News_)

Figure 32: ADS (95% CI included) with difference in (normalized) name count for different events (politician: _Chiang Kai-shek_, corpus: _Reference News_)



Figure 38: ADS (95% CI included) with difference in (normalized) name count for different events (politician: _Hu Yaobang_, corpus: _Reference News_)

Figure 42: ADS (95% CI included) with difference in sentiment for different events (politician: _Zhou Enlai_, corpus: _Reference News_)



Figure 43: ADS (95% CI included) with difference in sentiment for different events (politician: _Chiang Kai-shek_, corpus: _Reference News_)

Figure 47: ADS (95% CI included) with difference in sentiment for different events (politician: _Hua Guofeng_, corpus: _Reference News_)

Figure 48: ADS (95% CI included) with difference in sentiment for different events (politician: _Deng Xiaoping_, corpus: _Reference News_)



Figure 49: ADS (95% CI included) with difference in sentiment for different events (politician: _Hu Yaobang_, corpus: _Reference News_)

Figure 50: ADS (95% CI included) with difference in sentiment for different events (politician: _Zhao Ziyang_, corpus: _Reference News_)





## Appendix B Change Point Detection Results on _Reference News_

Note that _Reference News_ starts to publish after the establishment of PRC so no change point shall be detected for EPRC.

\begin{table}
\begin{tabular}{l|l|l}
**Politician** & **CP: linear** & **CP: rbf** \\ \hline Mao Zedong & CR, DEA & CR \\ Zhou Enlai & DEA & CR, DEA \\ Deng Xiaoping & CR & CR \\ Peng Dehuai & CR & CR, DEA \\ Liu Shaoi & DEA & CR \\ Zhu De & DEA & CR, DEA \\ Chiang Kai-shek & DEA & CR, DEA \\ \end{tabular}
\end{table}
Table 10: Change points detected on normalized name count over time on _Reference News_

\begin{table}
\begin{tabular}{l|l|l}
**Politician** & **CP: linear** & **CP: rbf** \\ \hline Mao Zedong & CR, DEA & CR \\ Zhou Enlai & DEA & DEA \\ Deng Xiaoping & CR & CR \\ Peng Dehuai & CR, DEA & CR, DEA \\ Liu Shaoqi & DEA & CR, DEA \\ Zhu De & DEA & DEA \\ Chiang Kai-shek & DEA & DCA \\ \end{tabular}
\end{table}
Table 11: Change points detected on average sentiment over time on _Reference News_

Additional Results of Topic Modeling

Additional topic modeling results are in following pages: 



\begin{table}
\begin{tabular}{l|p{42.7pt} p{56.9pt} p{31.3pt} p{56.9pt} p{56.9pt}} \hline
**Decade** & \multicolumn{5}{c}{Top 5 Topic Words with Weights} \\ \hline  & China & Delegate & Government & Meeting & KMT \\
1940 & \(\text{$\scalerel*[-]{\scale[t]{\scale[t]{\scale[t]{\scale[t]{\scale[t]{\scale[ t]{\scale[t]{\scale[t]{\scale[t]{\scale[t]{\scale[t]{\scale[t]{\scale[t]{\



\begin{table}
\begin{tabular}{l|p{56.9pt} p{42.7pt} p{31.3pt} p{42.7pt} p{42.7pt}} \hline
**Decade** & \multicolumn{5}{c}{Top 5 Topic Words with Weights} \\ \hline  & Liberation Army & China & Liberation & Government & CPC \\
1940 & \(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nexists\)\(\nex\)\(\nexists\)\(\nex\)\(\nexists\)\(\nex\)\(\nexists\)\(\nex\)\(\nexists\)\(\nex\)\(\nexists\)\(\nex\)\(\nex\)\(\nexists\)\(\nex\)\



\begin{table}
\begin{tabular}{l|p{42.7pt} p{56.9pt} p{85.3pt} p{108.1pt} p{85.3pt}} \hline
**Decade** & \multicolumn{5}{c}{Top 5 Topic Words with Weights} \\ \hline  & China & Work & Delegate & Workers & Revolution \\
1940 & H\(\boxplus\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) \\  & 0.1123 & 0.0576 & 0.0553 & 0.0436 & 0.0405 \\ \hline  & China & Work & Country & Party & Delegate \\
1950 & H\(\boxplus\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) \\  & 0.1106 & 0.0793 & 0.0553 & 0.0419 & 0.0366 \\ \hline  & Revolution & China & Chairman Mao & Thought & The Masses \\
1960 & H\(\boxplus\) & H\(\boxplus\) & E\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) \\  & 0.1617 & 0.0899 & 0.0876 & 0.0476 & 0.0445 \\ \hline  & Revolution & Chairman Mao & The Masses & Production & Struggle \\
1970 & H\(\boxplus\) & E\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) \\  & 0.1526 & 0.1091 & 0.0735 & 0.0682 & 0.0554 \\ \hline  & Work & China & Party & Comrade & Revolution \\
1980 & II\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) \\  & 0.0632 & 0.0506 & 0.0495 & 0.0481 & 0.0366 \\ \hline  & China & Work & Party & Comrade & Construction \\
1990 & H\(\boxplus\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) \\  & 0.0683 & 0.0468 & 0.0348 & 0.0319 & 0.0288 \\ \hline  & China & Party & Work & Development & Construction \\
2000 & H\(\boxplus\) & S\(\boxplus\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) \\  & 0.0783 & 0.0335 & 0.0327 & 0.0306 & 0.0292 \\ \hline \end{tabular}
\end{table}
Table 16: Top topic words identified in different decades of articles mentioning **Liu Shaoqi** in _People’s Daily_

\begin{table}
\begin{tabular}{l|p{42.7pt} p{56.9pt} p{85.3pt} p{85.3pt} p{85.3pt}} \hline
**Decade** & \multicolumn{5}{c}{**Top 5 Topic Words with Weights**} \\ \hline  & China & Chiang Kai-shek & The U.S. & The Masses & Government \\
1940 & H\(\boxplus\) & H\(\boxplus\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) \\  & 0.0949 & 0.0836 & 0.0506 & 0.0377 & 0.0303 \\ \hline  & China & The U.S. & Country & Government & Taiwan \\
1950 & H\(\boxplus\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) \\  & 0.1334 & 0.1189 & 0.0490 & 0.0464 & 0.0360 \\ \hline  & China & The U.S. & Revolution & US Imperialism & Struggle \\
1960 & H\(\boxplus\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) \\  & 0.1282 & 0.0908 & 0.0803 & 0.0545 & 0.0536 \\ \hline  & Revolution & China & Chairman Mao & Struggle & Country \\
1970 & H\(\boxplus\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) \\  & 0.1096 & 0.1057 & 0.0763 & 0.0504 & 0.0273 \\ \hline  & China & Revolution & Comrade & Work & Party \\
1980 & H\(\boxplus\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) \\  & 0.0644 & 0.0603 & 0.0520 & 0.0435 & 0.0336 \\ \hline  & China & Revolution & Work & Comrade & History \\
1990 & H\(\boxplus\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) \\  & 0.1031 & 0.0345 & 0.0340 & 0.0291 & 0.0235 \\ \hline  & China & Construction & Work & History & Revolution \\
2000 & H\(\boxplus\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) & I\(\not\!\!k\) \\  & 0.0789 & 0.0300 & 0.0268 & 0.0260 & 0.0230 \\ \hline \end{tabular}
\end{table}
Table 17: Top topic words identified in different decades of articles mentioning **Chiang Kai-shek** in _People’s Daily_



\begin{table}
\begin{tabular}{l|p{42.7pt} p{56.9pt} p{31.3pt} p{85.4pt} p{30.0pt}} \hline
**Decade** & \multicolumn{5}{c}{Top 5 Topic Words with Weights} \\ \hline  & China & Communist Party & The U.S. & Country & Soviet Union \\
1950 & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) \\  & 0.2894 & 0.1404 & 0.0686 & 0.0644 & 0.0513 \\ \hline  & China & Revolution & Communist Party & Struggle & Soviet Union \\
1960 & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{min}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) \\  & 0.3125 & 0.0939 & 0.0741 & 0.0518 & 0.0478 \\ \hline  & China & The U.S. & Beijing & Revolution & Country \\
1970 & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{min}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) \\  & 0.3283 & 0.0687 & 0.0664 & 0.0569 & 0.0568 \\ \hline  & China & Country & Economy & Beijing & The U.S. \\
1980 & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{min}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) \\  & 0.3062 & 0.0408 & 0.0354 & 0.0337 & 0.0296 \\ \hline  & China & The U.S. & Beijing & Economy & Country \\
1990 & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{min}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) \\  & 0.2581 & 0.0468 & 0.0352 & 0.0349 & 0.0336 \\ \hline  & China & The U.S. & Beijing & Country & Taiwan \\
2000 & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{min}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) \\  & 0.2247 & 0.0648 & 0.0349 & 0.0297 & 0.0271 \\ \hline \end{tabular}
\end{table}
Table 18: Top topic words identified in different decades of articles mentioning **Mao Zedong** in _Reference News_

\begin{table}
\begin{tabular}{l|p{42.7pt} p{56.9pt} p{56.9pt} p{56.9pt} p{56.9pt}} \hline
**Decade** & \multicolumn{5}{c}{**Top 5 Topic Words with Weights**} \\ \hline  & China & Communist Party & The U.S. & India & Government \\
1950 & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) \\  & 0.3424 & 0.1327 & 0.0923 & 0.0860 & 0.0673 \\ \hline  & China & Beijing & India & Communist Party & Soviet Union \\
1960 & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{min}}$}\) & \(\text{$\scalerel*[-]{L}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) \\  & 0.3813 & 0.0860 & 0.0841 & 0.0804 & 0.0633 \\ \hline  & China & Beijing & The U.S. & Japan & Visit \\
1970 & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{min}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) \\  & 0.3561 & 0.1096 & 0.0941 & 0.0630 & 0.0583 \\ \hline  & China & The U.S. & Country & Relationship & Japan \\
1980 & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{min}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) \\  & 0.2948 & 0.0516 & 0.0450 & 0.0390 & 0.0388 \\ \hline  & China & Japan & The U.S. & Beijing & Country \\
1990 & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{min}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) \\  & 0.2152 & 0.0536 & 0.0496 & 0.0374 & 0.0297 \\ \hline  & China & The U.S. & Country & Japan & Beijing \\
2000 & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{min}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) & \(\text{$\scalerel*[-]{H}{P}_{T}^{\text{max}}$}\) \\  & 0.1563 & 0.0571 & 0.0276 & 0.0230 & 0.0227 \\ \hline \end{tabular}
\end{table}
Table 19: Top topic words identified in different decades of articles mentioning **Zhou Enlai** in _Reference News_



\begin{table}
\begin{tabular}{l|c c c c c} \hline
**Decade** & \multicolumn{5}{c}{**Top 5 Topic Words with Weights**} \\ \hline  & China & The U.S. & KMT & Taiwan & Government \\
1950 & \(\textFrFr$\) & \(\textFrF$\) & \(\textFrF$\) & \(\textFrF$\) & \(\textFrF$\) \\  & 0.2227 & 0.2070 & 0.1171 & 0.0862 & 0.0752 \\ \hline  & China & The U.S. & Government & KMT & Taiwan \\
1960 & \(\textFrFr$\) & \(\textFrF$\) & \(\textFrF$\) & \(\textFrF$\) & \(\textFrF$\) \\  & 0.2707 & 0.1523 & 0.0729 & 0.0697 & 0.0666 \\ \hline  & China & The U.S. & Taiwan & Japan & Government \\
1970 & \(\textFrFr$\) & \(\textFrF$\) & \(\textFrF$\) & \(\textFrF$\) & \(\textFrF$\) \\  & 0.2586 & 0.1599 & 0.1269 & 0.0825 & 0.0688 \\ \hline  & China & Taiwan & KMT & The U.S. & Beijing \\
1980 & \(\textFrFr$\) & \(\textFrF$\) & \(\textFrF$\) & \(\textFrF$\) & \(\textFrF$\) \\  & 0.1342 & 0.1127 & 0.0539 & 0.0518 & 0.0324 \\ \hline  & China & Taiwan & The U.S. & Japan & KMT \\
1990 & \(\textFrFrFr$\) & \(\textFrFrF$\) & \(\textFrF$\) & \(\textFrF$\) & \(\textFrF$\) \\  & 0.1109 & 0.0934 & 0.0790 & 0.0467 & 0.0434 \\ \hline  & Taiwan & The U.S. & China & Japan & KMT \\
2000 & \(\textFrFrFr$\) & \(\textFrFrF$\) & \(\textFrF$\) & \(\textFrF$\) & \(\textFrF$\) \\  & 0.1256 & 0.0747 & 0.0727 & 0.0562 & 0.0386 \\ \hline \end{tabular}
\end{table}
Table 21: Top topic words identified in different decades of articles mentioning **Chiang Kai-shek** in _Reference News_

\begin{table}
\begin{tabular}{l|l l l l l} \hline
**Decade** & \multicolumn{5}{c}{**Top 5 Topic Words with Weights**} \\ \hline  & China & Communist Party & Beijing & Mao Zedong & Policy \\
1950 & \(\textFrFrFr$\) & \(\textFr

Additional Results of Predicting Masked Names

The following pages show results of masked name predictions for different politicians: 



\begin{table}
\begin{tabular}{l|c}
**Politician** & **Avg Probability** \\ \hline Li Xiannian (\%) & 0.0789 \\ Hua Guofeng (\%) & 0.0586 \\ Mao Zedong (\%) & 0.0506 \\ Chen Yun (\%) & 0.0342 \\ Zhou Enlai (\%) & 0.0337 \\ \end{tabular}
\end{table}
Table 26: Top 5 politicians’ names generated by BERT to replace the masked _Hua Guofeng ’s_ name in articles from _People's Daily_

\begin{table}
\begin{tabular}{l|l}
**Politician** & **Avg Probability** \\ \hline Hu Yaobang (\%) & 0.0625 \\ Li Peng (\%) & 0.0561 \\ Chen Yun (\%) & 0.0520 \\ Zhou Enlai (\%) & 0.0321 \\ Yang Shangkun (\%) & 0.0294 \\ \end{tabular}
\end{table}
Table 28: Top 5 politicians’ names generated by BERT to replace the masked _Hu Yoabang’s_ name in articles from _People’s Daily_

\begin{table}
\begin{tabular}{l|l}
**Politician** & **Avg Probability** \\ \hline Li Yiannan (\%) & 0.0789 \\ Hua Guofeng (\%) & 0.0586 \\ Mao Zedong (\%) & 0.0506 \\ Chen Yun (\%) & 0.0342 \\ Zhou Enlai (\%) & 0.0337 \\ \end{tabular}
\end{table}
Table 27: Top 5 politicians’ names generated by BERT to replace the masked _Deng Xiaoping’s_ name in articles from _People’s Daily_

\begin{table}
\begin{tabular}{l|l}
**Politician** & **Avg Probability** \\ \hline Ha Yaobang (\%) & 0.0625 \\ Li Peng (\%) & 0.0561 \\ Chen Yun (\%) & 0.0520 \\ Zhou Enlai (\%) & 0.0321 \\ Yang Shangkun (\%) & 0.0294 \\ \end{tabular}
\end{table}
Table 29: Top 5 politicians’ names generated by BERT to replace the masked _Zhao Ziyang’s_ name in articles from _People’s Daily_

\begin{table}
\begin{tabular}{l|l}
**Politician** & **Avg Probability** \\ \hline Li Peng (\%) & 0.0768 \\ Chen Yun (\%) & 0.0562 \\ Chen Yun (\%) & 0.0551 \\ Mao Zedong (\%) & 0.0512 \\ Zhou Enlai (\%) & 0.0512 \\ \end{tabular}
\end{table}
Table 25: Top 5 politicians’ names generated by BERT to replace the masked _Zhu De’s_ name in articles from _People’s Daily_

\begin{table}
\begin{tabular}{l|l}
**Politician** & **Avg Probability** \\ \hline Liu Yeoheng (\%) & 0.0789 \\ Hua Guofeng (\%) & 0.0586 \\ Mao Zedong (\%) & 0.0506 \\ Chen Yun (\%) & 0.0506 \\ Chen Yun (\%) & 0.0342 \\ Zhou Enlai (\%) & 0.0337 \\ \end{tabular}
\end{table}
Table 26: Top 5 politicians’ names generated by BERT to replace the masked _Hua Guofeng ’s_ name in articles from _People’s Daily_



\begin{table}
\begin{tabular}{l|c}
**Politician** & **Avg Probability** \\ \hline Li Peng (\(\maltese\)\(\maltese\)\(\maltese\)) & 0.0585 \\ Jiang Zemin (\(\maltese\)\(\maltese\)\(\maltese\)) & 0.0577 \\ Xi Jinping (\(\maltese\)\(\maltese\)\(\maltese\)\(\maltese\)) & 0.0565 \\ Hu Jintao (\(\maltese\)\(\maltese\)\(\maltese\)\(\maltese\)) & 0.0368 \\ Chen Yun (\(\maltese\)\(\maltese\)\(\maltese\)) & 0.0300 \\ \end{tabular}
\end{table}
Table 30: Top 5 politicians’ names generated by BERT to replace the masked _Jiang Zemin’s_ name in articles from _People’s Daily_



## Appendix E Results of Predicting Masked Names during Different Events

### Masking _Peng Dehuai_

\begin{table}
\begin{tabular}{l|c}
**Politician** & **Avg Probability** \\ \hline Li Xiannian ( ) & 0.0523 \\ Peng Dehuai ( ) & 0.0429 \\ Chen Yun ( ) & 0.0323 \\ Yang Shangkun ( ) & 0.0335 \\ Zhou Enli ( ) & 0.0304 \\ \end{tabular}
\end{table}
Table 37: Top 5 politicians’ names generated by BERT to replace the masked _Peng Dehuai’s_ name in articles from _People’s Daily_ during the Titananmen Square Protest

\begin{table}
\begin{tabular}{l|l}
**Politician** & **Avg Probability** \\ \hline Peng Dehuai ( ) & 0.0785 \\ Li Xiannian ( ) & 0.0564 \\ Chen Yun ( ) & 0.0553 \\ Zhou Enli ( ) & 0.0505 \\ Yang Shangkun ( ) & 0.0364 \\ \end{tabular}
\end{table}
Table 36: Top 5 politicians’ names generated by BERT to replace the masked _Peng Dehuai’s_ name in articles from _People’s Daily_ after the Reform and Opening-up

\begin{table}
\begin{tabular}{l|l}
**Politician** & **Avg Probability** \\ \hline Zhou Enli ( ) & 0.1166 \\ Lin Biao ( ) & 0.0998 \\ Li Xiannian ( ) & 0.0837 \\ Chen Yun ( ) & 0.0805 \\ Yang Shangkun ( ) & 0.0556 \\ \end{tabular}
\end{table}
Table 37: Top 5 politicians’ names generated by BERT to replace the masked _Peng Dehuai’s_ name in articles from _People’s Daily_ during the Titananmen Square Protest

\begin{table}
\begin{tabular}{l|l}
**Politician** & **Avg Probability** \\ \hline Hao Zedong ( ) & 0.0785 \\ Li Xiannian ( ) & 0.0448 \\ Lin Biao ( ) & 0.0426 \\ Chen Yun ( ) & 0.0372 \\ Liu Shaoqi ( ) & 0.0273 \\ \end{tabular}
\end{table}
Table 35: Top 5 politicians’ names generated by BERT to replace the masked _Peng Dehuai’s_ name in articles from _People’s Daily_ during the Cultural Revolution

\begin{table}
\begin{tabular}{l|l}
**Politician** & **Avg Probability** \\ \hline Zhou Enli ( ) & 0.1166 \\ Lin Biao ( ) & 0.0998 \\ Li Xiannian ( ) & 0.0837 \\ Chen Yun ( ) & 0.0805 \\ Yang Shangkun ( ) & 0.0556 \\ \end{tabular}
\end{table}
Table 37: Top 5 politicians’ names generated by BERT to replace the masked _Peng Dehuai’s_ name in articles from _People’s Daily_ during the Titananmen Square Protest

\begin{table}
\begin{tabular}{l|l}
**Politician** & **Avg Probability** \\ \hline Zhou Enli ( ) & 0.1166 \\ Lin Biao ( ) & 0.0998 \\ Li Xiannian ( ) & 0.0837 \\ Chen Yun ( ) & 0.0805 \\ Yang Shangkun ( ) & 0.0556 \\ \end{tabular}
\end{table}
Table 34: Top 5 politicians’ names generated by BERT to replace the masked _Peng Dehuai’s_ name in articles from _People’s Daily_ during the Great Leap Forward



### Masking _Liu Shaoqi_

**Politician** & **Avg Probability**

\begin{tabular}{l|c} \hline Zhou Enlai (), \\ Liu Shaoqi (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Chen Yun (), \\ Chen Yun () \\ \end{tabular}  & 0.0628 \\ Liu Shaoqi (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Chen Yun () \\ \end{tabular}  & 0.0523 \\ Li Xiannian (), \\ Mao Zedong (), \\ Chen Yun () \\ \end{tabular}  & 0.0501 \\ \cline{2-3} \end{table}
Table 40: Top 5 politicians’ names generated by BERT to replace the masked _Liu Shaoqi's_ name in articles from _People’s Daily_ after the Reform and Opening-up

\begin{table}
\begin{tabular}{l|c} \multicolumn{1}{r}{**Politician**} & \multicolumn{1}{r}{**Avg Probability**} \\ \hline Liu Shaoqi (), \\ Liu Shaoqi (), \\ Li Xiannian (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Lin Biao (), \\ Hu Yaobang (), \\ \end{tabular}  & 0.1026 \\ \cline{2-3} \end{tabular} \begin{tabular}{l|c} \multicolumn{1}{r}{**Volitician**} & \multicolumn{1}{r}{**Avg Probability**} \\ \hline Liu Shaoqi (), \\ Liu Shaoqi (), \\ Li Xiannian (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Lu Zhao (), \\ Chen Yun (), \\ Cheng Xiaoping (), \\ \end{tabular}  & 0.0378 \\ \cline{2-3} \end{tabular} 
\begin{tabular}{l|c} \multicolumn{1}{r}{**Volitician**} & \multicolumn{1}{r}{**Avg Probability**} \\ \hline Liu Shaoqi (), \\ Liu Shaoqi (), \\ Li Xiannian (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Men (), \\ Chen Yun (), \\ Chen Yun (), \\ \end{tabular}  & 0.0313 \\ \cline{2-3} \end{tabular}
\end{table}
Table 42: Top 5 politicians’ names generated by BERT to replace the masked _Liu Shaoqi’s_ name in articles from _People’s Daily_ during the Cultural Revolution

\begin{table}
\begin{tabular}{l|c} \multicolumn{1}{r}{**Politician**} & \multicolumn{1}{r}{**Avg Probability**} \\ \hline Zhou Enlai (), \\ Liu Shaoqi (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Hu Yaobang (), \\ \end{tabular}  & 0.0685 \\ \cline{2-3} \end{tabular} 
\begin{tabular}{l|c} \multicolumn{1}{r}{**Volitician**} & \multicolumn{1}{r}{**Avg Probability**} \\ \hline Liu Shaoqi (), \\ Liu Shaoqi (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Hu Yaobang (), \\ \end{tabular}  & 0.0685 \\ \cline{2-3} \end{tabular}
\end{table}
Table 41: Top 5 politicians’ names generated by BERT to replace the masked _Liu Shaoqi’s_ name in articles from _People’s Daily_ during the Great Leap Forward

\begin{table}
\begin{tabular}{l|c} \multicolumn{1}{r}{**Politician**} & \multicolumn{1}{r}{**Avg Probability**} \\ \hline Zhou Enlai (), \\ Liu Shaoqi (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Hao Yao (), \\ Chen Yun (), \\ Mao Zedong (), \\ Chen Yun (), \\ \end{tabular}  & 0.0685 \\ \cline{2-3} \end{tabular}
\end{table}
Table 39: Top 5 politicians’ names generated by BERT to replace the masked _Liu Shaoqi’s_ name in articles from _People’s Daily_ during the Korean War

\begin{table}
\begin{tabular}{l|c} \multicolumn{1}{r}{**Politician**} & \multicolumn{1}{r}{**Avg Probability**} \\ \hline Liu Shaoqi (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Chen Yun (), \\ Chao Zedong (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Mao Zedong (), \\ Li Xiannian (), \\ Li Xiannian (), \\ Li Xiannian (), \\ Li Xiannian (), \\ Li Xiannian (), \\ Li Xiannian (), \\ Li Xiannian (), \\ Hu Yaobang (), \\ \end{tabular}  & 0.0301 \\ \hline \end{tabular}
\end{table}
Table 38: Top 5 politicians’ names generated by BERT to replace the masked _Liu Shaoqi’s_ name in articles from _People’s Daily_ before the Establishment of PRC