This paper examines the relationship between violent discourse and emotional intensity in the early revolutionary rhetoric of the People's Republic of China (PRC). Using two fine-tunbedert-base-chinese models one for detecting violent content in texts and another for assessing their afective charge we analyze over 185,000 articles published between 1956 and 1989 in thPeople's Liberation Army Daily (Jiefangjun Bao), the ofÏcial journal of China's armed forces. We find a statistically significant correlation between violent discourse and emotional expression throughout the analyzed period. This strong alignment between violence and afect in ofÏcial texts provides a valuable context for appreciating how other forms of writing, such as novels and poetry, can disentangle personal emotions from state power.
of literary inquiry. We build upon existing scholarship on the political signification of
sentiments to suggest a computational perspective on the entanglements between violence and
afect in Chinese revolutionary discourse. In particular, we focus on the texts published
between 1956 and 1989 in the People’s Liberation Army Daily (PLA Daily, orJiefangjun Bao
), one of the major PRC journals and the ofÏcial publication of China’s armed forces, to
analyze how such entanglements manifested in ofÏcially sanctioned documents.
2. Related Works
2.1. Violent Discourse and Hate Speech
“Violent discourse” refers to the use of language to inflict harm, perpetuate power structures,
and normalize physical violence27[
Automatic hate speech detection includes research related to sexism, racism, cyberbullying,
and toxicity in the public realm. The literature focused on these topics is extensive and we
refer the reader to multiple surveys for comprehensive overvie3w3,s 3[
Similar to hate speech detection, automatic sentiment analysis has seen significant
contributions and surveys during the last two decades. Notable studies include those by L2i4u],[
Wankhade et al. [
Related to this article are the numerous studies focused on political contexts, revealing the
nuanced ways public opinion is shaped and expressed and highlighting the role of social media
in political discourse5,[
This project used two training datasets: ⇒ Violent/Non-Violent Texts: The first dataset was constructed from texts sourced from the PLA Daily.1 Texts were classified as “violent” if they included language depicting physical and military violence, as described in our related work on the distribution of violent discourse in the journa8l][, which adopts a dictionary-based approach to detect violent texts for model training. “Non-violent” texts were characterized by the absence of such vocabulary. The dataset includes a total of 5,728 examples, split evenly between the two classes, with no more than 100 examples taken from each year for either class. ⇒ Strong/Weak Emotion: The second dataset was derived from theDouban Dushu Dataset [39], containing more than 3.7 million Chinese book reviews. As there is no large dataset containing labeled emotional intensity specific to military-related Chinese texts from the mid-20th century, which otherwise would be an ideal training corpus for this project, we searched for a dataset that would capture a broad range of emotional expressions independent of specific subject matter. The Douban Dushu Dataset meets this requirement, including reviews of a wide variety of books and thus preventing the trained model from focusing on any particular topic. We labeled 1-star and 5-star comments as representing “strong” emotions due to their clear expression of either negative or positive sentiment, while 3-star comments were considered “weak” emotions. Only the comments that were at least 200 characters long were included, and we selected 62,000 examples from each class (“strong” and “weak” emotion) for training.
For this study, we usedPyTorch to fine-tune two open-source modelsbert-base-chinese on the datasets described above: one for classifying violent versus non-violent texts, and the other for categorizing the strength of emotions within texts. Notice that the sentiment analysis task considered in this project difers from the usual NLP applications which distinguish positive 1The PLA Daily corpus has been acquired from the digitized version of the journal available through the East View library (https://dlib.eastview.com). from negative sentiments or categorize them into diferent classes (anger, surprise, happiness, etc). Here, we focus on the intensity of the expressed sentiments rather than their quality.
Bert-base-chinese is a lightweight model (with 102 million parameters) pre-trained on a large corpus of Chinese text, which makes it suitable for various natural language processing tasks. It requires relatively modest computational resources and enables fast training. For both tasks violence detection and emotional intensity assessment we fine-tuned bert-base-chinese using sequence classification with the respective dataset, a batch size of 16, a learning rate of 2e-5, and the Adam optimizer. We used validation loss to find the optimal number of training epochs. Texts were tokenized into input sequences using thebert-base-chinese tokenizer, which splits by Chinese character (there are no spaces in Chinese). We have achieved F1 score o0.f981 for violence analysis (600 test samples) and0.926 for sentiment analysis (12,000 test samples).
After training, the 185,472 articles from thePLA Daily published between 1956 and 1989 were segmented into non-overlapping chunks of 500 characters, yielding 629,734 texts in total. The period in question begins with the establishment of the journal in 1956 and ends in 1989, a year marked by nation-wide pro-democratic protests. Each text was evaluated by the two models for the probability of being classified as “violent” or “strong” (emotionally intense), respectively. We then computed the average monthly probability of the “violent” class and the “strong” class.
As illustrated in Figur1e, there is a clear alignment between violent discourse and emotional expression in the journal. Afectively-charged texts are often about violence, and violence is described in afective terms. Both lines demonstrate an increasing trend from the late 1950s, peaking around 1968, followed by a decline through the late 1970s and 1980s. This trend indicates heightened periods of violence-related, emotionally-charged content published in the journal during the Cultural Revolution (1966-1976) and a subsequent decrease as China moved towards more stabilized periods in the post-Mao era. A very strong Pearson correlation between the monthly averages (408 months;r: 0.8468, p-value: 2.4296e-113) and yearly averages (34 years; r: 0.9092, p-value: 1.0183e-13) of violent discourse and emotional intensity can be observed, demonstrating how thePeople’s Liberation Army Daily “emotionalized revolution and revolutionized emotions” in the early PR2CT. his relationship can be further illustrated by plotting the percentage of articles displaying both high-violence and high-emotion scores throughout the analyzed period (Figur2e). Between 1966 and 1968, the number of such articles rises to nearly 50% of the total published content.
Examples from the extrema of the distribution have been provided in Ta1blien the Appendix. In high-violence, high-emotion texts, the sentiments are channelled towards the Communist Party and the leader Mao Zedong in thyeiku-sitian (“remember the bitter past and think of the sweet present”) mode. In the high-violence, low-emotion texts, the focus is placed on military matters analyzed from a professional perspective. The low-violence, high-emotion passages convey gratitude to the Communist Party and its members, with little to no mention of military history. The low-violence, low-emotion texts focus on civilian matters. 2It is important to notice that the relative values (trendws)ithin models are more informative than absolute comparisonsbetween the models, as they have been trained on diferent amounts and types of data. For example, emotional intensity of 0.7 and violence score of 0.5 does not entail that a given text is “more emotional than violent.”
The above findings ofer additional evidence that emotional mobilization was one of the
crucial aspects of revolutionary violence in modern China, fostering a collective identity among
the populace confronted with state-designated enemies29[
This last point is particularly important given that the intended reader was supposed to not
only sympathize with the sufering of proletarian heroes (Patrick Hogan’s “complementary
emotions” [
Fire means a command, and the scene of the fire is a battlefield! Lu Bingyi and his comrades from the propaganda team were the first to arrive at the site. A raging ifre was engulfing a local alleyway’s plastic processing factory. Through the thick, acrid smoke, they could hear the desperate cries of women trapped inside. [...] The ifre, fanned by plastic products, raged ever higher. Thick black smoke, carrying a pungent odor, stung Lu’s nose, causing it to bleed. With the combined heat of the lfames and the sufocating smoke, Lu felt dizzy and gasped for breath. Over and over, he silently recited, Be resolute, fear no sacrifice, overcome all difÏculties to win victory. Chairman Mao’s teachings, heavy with meaning, strengthened Lu as he charged into the flames and fought bravely. Foam from the fire extinguishers sprayed into Lu’s left eye, causing sharp pain, yet he persisted, helping Master Zhou rescue five class sisters in quick succession.
In this and similar passages, the vicarious details are meant to invoke both positive and negative responses in the reader, embedding ideological instruction at an afective level. A binary understanding of emotions risks oversimplifying such emotional dynamics and missing the nuanced ways in which political power can be intertwined with afect.
Our paper thus suggests a special role that can be played by narrative arts. If literature has
the potential to “personalize revolution and revolutionize romantic adventures,” as Liu puts
it [
Several limitations of this project must be acknowledged. Our primary dataset consists of articles from thePLA Daily, a single source that does not represent the full spectrum of revolutionary discourse in the PRC. Furthermore, the binary classification of texts as either violent or non-violent and as conveying strong or weak emotions simplifies the complex nature of human language. More refined classification systems could be developed to capture such subtleties. [39] J. Zhao and Z. Ji. LSICC: A Large Scale Informal Chinese Corpus. 2018. arXiv:2403.18314 [cs.CL].
Excerpt The heavens and earth are not as great as the Party’s kindness; Chairman Mao is truly the most dear person to us poor and lower-middle peasants. In the wicked old society, eleven of my relatives were killed. When I was fourteen, my father was beaten to death by a heartless landlord while working for him. After my father’s death, my mother led us siblings to beg for food. [...] A few years after joining my husband’s family, four members of his family died from hunger and exhaustion. I gave birth to eight sons, but two of them starved to death. I begged for food from Shandong to Guandong, from a young child until I was over fifty years old. A landlord’s vicious dog bit of my right ear, leaving me covered in wounds and nearly dead.
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In recent years, the Soviet military has paid great attention to the synchronization of air defense weapons and combat units, studying issues such as the deployment, firing, and logistical support of air defense units during movement to ensure the success of their large-scale mobile operations.
However, Western analysts believe that the mobility of the Soviet field air defense is far from meeting the requirements of rapid army ofensives. When the troops begin to move, the efectiveness of the air defense drops sharply.
Combined with a low level of electronic warfare capabilities, significant technical and tactical improvements are still needed.
I was so emotional that I couldn’t speak. I lost my mother when I was very young, and my father was constantly running around to make ends meet, leaving no one to take care of me as I was tormented by illness. But today, in the revolutionary forces, my superiors care for and look after me with such meticulous attention, like my own parents. As I thought about this, tears welled up in my eyes. With trembling hands, I accepted the fruits and snacks brought by Section Chief Yang. These were not just fruits and snacks, but a symbol of the heartfelt care from revolutionary comrades to their fellow soldiers, continually warming my heart. Dear Section Chief Yang, you worked tirelessly for the revolutionary cause, exhausting yourself to the point of illness, and now you have left us forever! Systematically and in an organized manner, military oficers are being dispatched to civilian enterprises and local universities to gain life experience and learn from the strengths of these institutions. This initiative aims to enhance and improve the educational work of military academies. The main considerations for this new attempt by the Air Self-Defense Force Oficers School are: first, to broaden the horizons of young military oficers, helping them understand society, learn from the strengths of civilian enterprises and local institutions, and address their own shortcomings; second, to deepen the understanding of the military within local and civilian communities...
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