# Chinese Offensive Language Detection:
Current Status and Future Directions

Yunze Xiao 1, Houda Bouamor 1∗ and Wajdi Zaghouani 2∗

###### Abstract

## 1

Despite the considerable efforts being made to monitor and regulate user-
generated content on social media platforms, the pervasiveness of offensive
language, such as hate speech or cyberbullying, in the digital space remains a
significant challenge. Given the importance of maintaining a civilized and
respectful online environment, there is an urgent and growing need for
automatic systems capable of detecting offensive speech in real time. However,
developing effective systems for processing languages such as Chinese presents
a significant challenge, owing to the language’s complex and nuanced nature,
which makes it difficult to process automatically. This paper provides a
comprehensive overview of offensive language detection in Chinese, examining
current benchmarks and approaches and highlighting specific models and tools
for addressing the unique challenges of detecting offensive language in this
complex language. The primary objective of this survey is to explore the
existing techniques and identify potential avenues for further research that
can address the cultural and linguistic complexities of Chinese.

##  2 Keywords:

Chinese Offensive Language, Chinese Benchmarks, Toxic Content Online,Chinese
social media,Cultural and linguistic complexities

##  3 Introduction

Disclaimer: This paper contains some examples of hateful content. This is
strictly for the purposes of enabling this research, and we have sought to
minimize the number of examples where possible. Please be aware that this
content could be offensive and cause you distress.

Hate speech detection represents a critical frontier in the fight against
online toxicity. Detecting offensive language, in general, is a crucial task
for maintaining a civilized, safe, and respectful online environment and
ensuring the well-being of users in the digital space Deng et al. (2022a).
With the rapid growth and increasing use of social media platforms, offensive
language has become pervasive, taking many forms, such as hate speech,
cyberbullying, and adult-oriented content T et al. (2020). Through machine
learning and linguistic analysis, the goal has always been: to build robust
solutions that protect users from hate speech and its damaging impact.
Offensive language detection has been studied for various languages, such as
English Davidson et al. (2017); Pitsilis et al. (2018); Wei et al. (2021),
Arabic Fatemah and Ozlem (2021), French Battistelli et al. (2020), Turkish
Beyhan et al. (2022) with a recent interest in Asian languages Dhanya and
Balakrishnan (2021) and Chinese Deng et al. (2022a); Zhou et al. (2023). But,
despite the increasing interest in detecting offensive language in Chinese,
many studies fail to fully acknowledge the unique challenges posed by the
language and culture. The Chinese language’s contextual-based nature, its wide
range of dialects and regional variations, and the prevalence of subversive
expressions make it difficult for automatic systems to detect offensive
language accurately. Moreover, Chinese is heavily influenced by various
cultural references, adding further complexity to the task Zhou et al. (2023).
For instance, the expression ”ham ga fo gui”(咸家富贵/全家富贵) is used to mean ”may
your whole family die” in Cantonese-speaking areas and ”may your whole family
get rich” in Mandarin context. The expression ”grass mud horse”(草泥马) is an
example of subversive language, which is used to represent the highly
offensive phrase ”f**k your mother”(操你妈). Furthermore, A Chinese character
appearing in an offensive word may also appear in an inoffensive one. For
example, although the character ”干” means ”fxxk” in some contexts, it also has
meanings of ”do” and ”work”, as its appearances in the words “干活” (do works)
Su et al. (2017). These kinds of phrases are not easy to detect by current
offensive language detection systems underscoring the necessity for
researchers to conduct in-depth investigations into their recognition and
handling to develop more effective systems for detecting offensive language in
Chinese.

In this paper, we present a comprehensive survey that reviews prior research
on offensive language detection in the Chinese language. We summarize the
literature covering state-of-the-art benchmarks and techniques, with a focus
on identifying open challenges and research gaps. By addressing these issues
and highlighting opportunities for further investigation, our objective is to
advance the development of effective offensive language detection systems for
Chinese. Our work involves a detailed examination of existing benchmarks,
approaches, and state-of-the-art models, emphasizing the unique focus on the
Chinese language. We explore cross-cultural transfer learning, translational
techniques, and advanced models while also identifying intriguing research
ideas and challenges, including subversive expression disambiguation, the
incorporation of cultural context T et al. (2020), and data labeling concerns.
The importance and relevance of detecting offensive language in Chinese lie in
the fact that it is one of the most widely spoken languages in the world, and
understanding the nuances of offensive language in Chinese and its variants
can contribute to the development of more effective and culturally sensitive
systems Chuang (2019).

The paper is structured as follows. Section 4 defines offensive language and
introduces the background and unique challenges that occur in the Chinese
language. Section 5 presents the current benchmarks used for offensive
language detection in Chinese. Section 6 explores the state-of-the-art models
and approaches developed for this task. Section 7 highlights existing research
gaps. Section 8 presents the challenges that researchers in this task. The
conclusion is given in Section 10.

##  4 Background

Identifying offensive text is a complex task, as it relies on subjective
interpretations and the definition of offensiveness. Hence, we start by
providing definitions of offensive language in the context of Chinese and
categorizing the common types of offensive expressions. We also discuss the
specific challenges posed by the Chinese language.

###  4.1 Offensive Language

Offensive language refers to words or expressions that are considered
inappropriate, insulting, or disrespectful towards a particular individual or
group, often on the basis of their race, ethnicity, gender, religion, sexual
orientation, or other personal characteristics Jay and Janschewitz (2008);
Davidson et al. (2019). Such language may include slurs, insults, profanity,
or other forms of derogatory language. The use of offensive language can have
a harmful impact on the targeted individuals or groups, contributing to
discrimination, prejudice, and hate speech.

#### Hate Speech

is defined as a speech targeted at a specific group of people with the
intention of causing harm, violence, or social disruption Waseem et al.
(2017); Sigurbergsson and Derczynski (2020); Ajvazi and Hardmeier (2022),
regardless of the language used. Common examples of hate speech include the
use of racial and homophobic slurs. Waseem et al. established a list of rules
to identify hate speech, including negative statements or stereotypes about
minority groups, disparaging or racial terms, and support for entities that
encourage hate speech. Hate speech is usually done based on characteristics
such as race, color, ethnicity, gender, sexual orientation, nationality, or
religion. For instance, in Chinese, the term ”婚驴” /the married donkey/ is
commonly used, particularly by radical feminists to describe married women,
which can be seen as a form of derogatory language targeting married women.

#### Adult Content

Adult content, intended for 18 and older audience, encompasses various
materials, from sexual content to violence and explicit language Mubarak et
al. (2021). Not all adult content is inherently offensive; its offensiveness
often depends on context and cultural norms. In the Chinese context, adult
content is conveyed implicitly due to strict regulations. This has led to the
widespread use of subversive phrases in offensive language Zidani (2018). For
example, terms like “萝卜”(carrot) are used to reference male genitals, and
“奶子”(milk) is employed to refer to female breasts. These metaphorical
expressions pose several challenges to the automatic detection of adult
content.

#### Sarcasm and Dark Humor

Sarcasm and dark humor are two linguistic phenomena that have been studied
extensively in linguistics Skalicky and Crossley (2018), psychology Kreuz and
Glucksberg (1989), and NLP Joshi et al. (2018). While these forms of speech
can serve as sources of entertainment in non-offensive contexts, they are more
frequently encountered in potentially offensive situations. Sarcasm presents a
controversial aspect of offensive language, largely due to its varying
interpretations. In this paper, we adopt the definition from merriam-webster:
”a sharp and often satirical or ironic utterance designed to cut or give
pain”.111https://www.merriam-webster.com/dictionary/sarcasm Using this
definition, we categorize sarcasm that fits this description as offensive
language, given its intent to cause harm. While previous research hasn’t
explicitly included sarcasm as a type of offensive language, several studies
link sarcasm to negative sentiments like anger or jealousy Riloff et al.
(2013); Abu Farha and Magdy (2020). Failing to classify sarcasm as offensive
language can overlook its potential harm Frenda et al. (2022a, b). Hence, we
include sarcasm as a form of offensiveness.

Conversely, dark humor entails employing humor to address taboo or morbid
subjects, as noted in a study by Mittal et al. (2021) . However, dark humor
can cross into offensiveness when it turns cruel or targets marginalized or
vulnerable groups, including those with disabilities, specific genders, races,
sexualities, religions, or cultural identities Kumari et al. (2023). Social
media platforms frequently feature dark humor, with memes incorporating such
humor being widely shared Kiela et al. (2021).

Dark humor in Chinese can come in different forms, majority of them come from
a historical perspective. Prominent ones such as ”headless Louis XVI” and
”farming equipment” (which refers to slavery in the 17th century) are used in
a variety of contexts. However, this dark humor is heavily influenced by
Western culture and has only become pervasive within a few years. Sarcasm, on
the other hand, is more pervasive than dark humor in a Chinese context. For
example, comments in Tieba often allude to the overweighted female as
”健达奇趣蛋”(kinder egg).

###  4.2 Chinese Language: Characteristics and Challenges

Approximately 1.3 billion people around the world are using Chinese as their
primary language. The Chinese language can be divided into many dialects, the
most common being Mandarin, which is the official language of China and
Singapore, and Cantonese, which is spoken in Singapore, Hong Kong, and
Guangdong Province. Other dialects include Shanghainese, Hokkien, and Hakka,
among others. The dialectal and regional variations coupled with the highly
context- and culture-based nature of the language have made several NLP tasks
specifically challenging. Offensive content detection in Chinese presents
several unique challenges. For instance, the Chinese language includes a vast
number of characters, which can be combined to form new words and phrases.
This leads to a high degree of flexibility in constructing hate speech, making
it difficult to build comprehensive detection systems. Also, hate speech can
be deeply embedded in cultural and historical contexts. Understanding the
cultural nuances is crucial for accurate detection. In addition to text,
online content in China often includes images, videos, and audio. Effective
hate speech detection systems need to encompass these different modalities. To
address these challenges, hate speech detection in the Chinese language
requires sophisticated NLP models, cultural understanding, and continuous
adaptation to the evolving linguistic landscape.

##  5 Datasets for Chinese Offensive Language

In order to effectively address the issue of offensive language detection, it
is essential to establish a dependable and adaptable benchmark as the
foundational cornerstone for advancing in-depth research in this field.
Quality datasets are vital for training machine learning models to detect
offensive language in Chinese, as they provide labeled data for model training
and evaluation. These datasets are usually collected from various sources like
social media, comments, and forums, forming the foundation for accurate and
effective offensive language detection systems, given the language complexity.
In this section, we present a few key datasets and benchmarks for this task,
in Chinese.

###  5.1 General Offensive Language Benchmarks

#### The Chinese Offensive Language Dataset (COLD)

is among the first benchmarks designed to facilitate Chinese offensive
language detection and evaluation Deng et al. (2022b). It includes a dataset
and a baseline detector that are used to explore the task of offensive
language detection in Chinese. The dataset contains 37,480 sentences labeled
with four main categories: attacking individuals, attacking groups, anti-bias,
and other non-offensive. In addition to addressing the challenge of Chinese
offensive language detection, COLD investigates the factors that influence
offensive generations and finds that anti-bias content and keywords referring
to certain groups or revealing negative attitudes trigger offensive outputs
more easily. Table 1 summarizes the state-of-the-art results for this task.

Model | Train Set | Test F1 | Test ACC
---|---|---|---
XLM-R${}_{\text{large}}^{*}$ | COLD+KOLD | 0.798 | 0.825
COLDET | COLD | 0.810 | 0.810
TJIGDET | Jigsaw dataset | 0.620 | 0.600
BAIDUTC | Unknown | 0.540 | 0.630
PSELFDET | BERT | 0.580 | 0.590
Table 1: State-of-the-art offensive language detection system’s performance
using the COLD benchmark

Even though this dataset, was the first of its kind, it suffers from a lack of
diversity as it only covers the topics of race, gender, and region and it does
not adequately represent the full spectrum of offensive language in Chinese.
Also, labeling offensive language can be highly subjective, and it may vary
based on cultural context and personal interpretation. Furthermore, as
offensive language evolves over time, the COLD dataset may not reflect the
current landscape of offensive language on the internet.

#### TOCP and TOCAB

TOCP Yang and Lin (2020) is a dataset of Chinese profanity created for the
purpose of detection and rephrasing. The dataset includes over 16,000
sentences from social media, with over 17,000 profane expressions, extracted
from PTT, a famous BBS site in Taiwan.222https://www.ptt.cc/bbs/index.html A
system that takes a profane expression and its preceding word as input and
generates its possible reformulations was built using this dataset. In 2021,
TOCABChung and Lin (2021) was introduced as an extension to TOCP. The TOCAB
dataset contains 1,000 posts collected from PTT, with 121,344 comment
sentences, 17,836 of which are labeled as abusive and classified into six pre-
defined abusive classes. Table LABEL:table:TOCAP summarizes the state-of-the-
art abusive language detection systems built using this dataset.

Model | Precision | Recall | Test ACC
---|---|---|---
BERT | 0.886 | 0.885 | 0.886
BiGRUx3 | 0.900 | 0.830 | 0.806
SVM | 0.910 | 0.762 | 0.829
Table 2: Offensive language detection system’s performance using the TOCAB
benchmark

While TOCP and TOCAB are valuable language resources, it’s important to
acknowledge that their exclusive sourcing from Taiwan presents limitations.
These datasets may not fully represent offensive language usage in mainland
China, where cultural differences can significantly impact language dynamics.
Moreover, the prevalence of traditional Chinese text in these datasets may
pose distinctive challenges when adapting and fine-tuning pre-trained models
for broader applications in mainland China. Therefore, while these datasets
provide a valuable foundation, their effectiveness in addressing major tasks
in mainland China may be compromised by cultural differences and the unique
encoding of text, warranting careful consideration when applying them in such
contexts.

#### Sina Weibo Sexism Review (SWSR)

dataset Jiang et al. (2021), the first Chinese dataset focused on sexism. It
is a comprehensive dataset created to investigate the diverse behaviors,
beliefs, and attitudes expressed toward women in Chinese social media. The
dataset was created by collecting sexism-related posts and comments from Sina
Weibo, a Chinese microblogging website, and then annotated with three labels:
(i) sexism or non-sexism, (ii) sexism category, and (iii) target type. SWSR is
also accompanied by a large lexicon called SexHateLex, which is the first
lexicon in Chinese made of abusive and gender-related terms. This dataset was
used to train models for sexist speech detection and provides a benchmark for
sexism detection in Chinese. It also highlights the difficulty of identifying
sexist targets in posts and the potential over-dependence of models on sexist
words, which underscores the importance of developing comprehensive and
diverse datasets for researching hate. Table 3 summarizes the state-of-the-art
results for this task.

While the SWSR dataset and the SexHateLex lexicon are valuable resources for
researching online sexism in Chinese social media, they face challenges in
comprehensively capturing the evolving nature of sexist speech. The reliance
on specific keywords may limit their ability to identify nuanced expressions
of sexism. Moreover, the efficacy of models trained on such datasets can be
affected by the constantly changing dynamics of online discourse.

Model | Test F1 | Test ACC
---|---|---
BERT | 0.776 | 0.806
Bert-wwm | 0.762 | 0.792
RoBerta | 0.764 | 0.792
SVM + ngram | 0.739 | 0.739
Table 3: State-of-the-art offensive language detection system’s performance
using the SWSR benchmark

#### Categorizing Offensive Language (COLA)

Tang and Shen (2020), introduced as the first Chinese offensive language
classification dataset, is a large-scale dataset of offensive texts in Chinese
built by crawling comments from YouTube and Weibo, resulting in a total of
18.7k comments. Three annotators categorized the texts into four classes:
neutral, insulting, antisocial, and illegal. This dataset is the first of its
kind and includes user-generated comments from different social media
platforms. Using this dataset, four automatic classification systems were
built and evaluated.

###  5.2 Chinese Sarcasm Datasets

There were several datasets introduced to detect sarcasm in Chinese. Among
them, the Chinese Sarcasm Dataset Gong et al. (2020) is a large and high-
quality dataset of sarcastic and non-sarcastic texts in the Chinese language.
The dataset contains 2,486 manually annotated sarcastic texts and 89,296 non-
sarcastic texts. More recently, Zhu (2022) introduced the balanced open
Chinese Internet sarcasm corpus. The corpus consists of 2,000 texts that were
selected from larger datasets labeled to indicate whether they are sarcastic
or not. Similarly, Zhang et al. (2022) introduced a dataset extracted from
Bilibili, a Youtube-like website with a variety of sarcastic information on
specific topics.333https://www.bilibili.com/ While these datasets were used to
make significant strides in sarcasm detection and data collection from various
online platforms, a critical aspect has been largely overlooked: the
connection between sarcasm and offensiveness. Sarcasm is a complex form of
communication that often relies on irony and humor, making its interpretation
context-dependent. What may be intended as playful sarcasm in one setting can
easily be perceived as offensive in another.

###  5.3 Toxic Content Dataset

In the realm of Chinese offensive language detection, the TOXICN dataset
represents a significant milestone Lu et al. (2023). This dataset was
meticulously constructed by collecting data from prominent online platforms
such as Zhihu and Tieba, followed by a series of filtering and labeling
measures.444https://www.zhihu.com/, http://c.tieba.baidu.com/ The resulting
dataset features a sophisticated multi-level labeling system, encompassing
categories like offensive language, hate speech, targeted groups, and
expression types. This comprehensive approach offers researchers valuable
insights into the complexities and nuances of offensive language in the
Chinese context. TOXICN was used to introduce a benchmark known as ”Toxic
Knowledge Enhancement (TKE)” designed to enhance the detection of toxic
language using lexical knowledge. TKE involves enriching the representation of
sentences by incorporating lexical features. This is achieved by embedding
each token of a sentence in a vector space and introducing toxic knowledge
through a specific embedding, which categorizes tokens as related to insults.
The enhanced token representation combines the toxic embedding with the
original word embedding, enabling a more effective integration of toxic and
linguistic information. TKE can be applied to any pre-trained large language
models to enhance the detection of toxic language.

The TOXICN dataset undeniably marks a significant advancement in the field of
Chinese offensive language detection. However, it’s essential to critically
examine certain aspects of it. One notable concern is that the percentage in
offensive language in the platform tieba is significantly higher than other
platform. While the dataset creators may have had valid reasons for this
labeling, such categorization raises questions about potential biases and the
discriminatory effects it might have on the platform’s users. Additionally,
while the TKE benchmark proposes an intriguing approach to enhancing toxic
language detection, its effectiveness across various offensive language
contexts and its potential biases should be subject to rigorous evaluation to
ensure it aligns with the goal of reducing harm without unjustly censoring
legitimate discourse.

##  6 Current Approaches and Models

In this section, we present an overview of the state-of-the-art approaches and
models introduced to tackle the task of offensive language detection in
Chinese.

#### Lexicon-based Models

These models have proven to be effective in offensive language detection Zhang
et al. (2010). One common technique employed in this field is keyword matching
Deng et al. (2022b) using a predefined lexicon. Su et al. (2020), for
instance, presented a method that leverages BERT and LDA for identifying
abusive language in Chinese text. However, it had limitations, including the
fact that a predefined lexicon may not encompass all potentially offensive
terms, and LDA modeling may fail to capture the nuanced nature of offensive
language. Liu et al. (2018) proposed a similar approach, but their strict
keyword-matching approach could potentially miss implicit offensive language
and informal expressions.

However, it is important to note that most of the lexicon-based detection
models are impacted by the dynamic nature of Chinese, with new words and
phrases emerging rapidly. These models may fail in detecting hate speech that
can take advantage of the language evolution to escape detection. Also, these
models contribute to the propagation of subversive expressions in alternative
forms, which can make the task of offensive language detection significantly
more challenging. Nevertheless, despite its limitations, lexicon-based models
remain widely used in the realm of Chinese offensive language.

#### Machine Learning Based Models

These models are built not only using supervised Jiang et al. (2021) and semi-
supervised Wu et al. (2019) approaches but also exploring the use of
adversarial learning to solve this task Liu et al. (2020a). Most of the
supervised approaches such as those presented by Yuan et al. (2019), provide a
more adapted approach to identifying offensive language in a text. However,
they too have their limitations. For example,Yuan et al. (2019)’s model
trained on WeiboHate, can suffer from imbalanced class distribution and the
variability of annotator judgments, potentially impacting the quality of the
training data. Several other models were presented in the literature that made
use of ML techniques for classifying Chinese text as offensive or not.

#### knowledge-based Models

These models, like the one discussed by Liu et al. (2020b), seek to combat
negative stereotypes and implicit biases in language. While this approach has
potential, obtaining comprehensive common sense knowledge can be challenging.
The success of such models depends on the availability and quality of the
knowledge they are built upon, which can sometimes be incomplete or biased in
itself, leading to limitations in performance.

#### Multimodal Approaches

Zhang et al. (2020)’s study on HurtMePlenty showcases the use of multimodal
approaches for nuanced hate speech detection in Chinese. Nevertheless, this
approach faces challenges related to class imbalance within the dataset and
the potential for platform-specific biases to affect the findings. Multimodal
approaches, while promising, need careful consideration to account for these
limitations and ensure robust results.

#### Pretrained Language Models

In Chinese offensive language detection, pre-trained models such as BERTDevlin
et al. (2019) are commonly used in a supervised learning framework. In this
approach, the model is initially trained on a large dataset containing labeled
examples of offensive and non-offensive language. The training procedure
involves fine-tuning the pre-trained language models using the task-specific
dataset, which helps the model learn the specific features and characteristics
of the Chinese language. Once this fine-tuning process is complete, the BERT-
based model is used to classify new text as offensive or non-offensive with a
high degree of accuracy. bert-base-chinese555https://huggingface.co/bert-base-
chinese and roberta-base-chinese666https://huggingface.co/roberta-base-chinese
are the most commonly used ones for Chinese benchmarks Deng et al. (2022a).

#### Cross-Cultural Transfer Learning

Work from Zhou et al. (2023) explores the impact of offensive language data
from different cultural backgrounds on Chinese offensive language detection.
It shows that Language Models are sensitive to cultural differences in
offensive language, learning culture-specific biases that negatively impact
their transfer ability. Additionally, the model they build quickly adapts to
the target culture in the few-shot scenario, even with very limited Chinese
examples. Such findings suggest that there exists a need to address the cross-
cultural aspects of offensive language detection.

The experiments of translating Chinese and Korean datasets(COLD and KOLD
respectively) into English showed similar trends, demonstrating that cross-
cultural transfer is not only due to linguistic similarities. The data also
displays experimental results indicating that the correlation between the
ability to detect offensive language and target cultural knowledge follows a
pattern similar to that of an increasing logarithmic function. These findings
offer promising opportunities for low-resource offensive language detection
systems.

#### User Behavior Encoding

Zhang et al. (2019) showed that taking user information such as past comments
and number of followers into account can significantly increase the accuracy
of offensive language detection, especially when considering recall and F1
score metrics. This work was the first to propose an estimation of the
potential aggressive score of a user and dynamically update this score. The
model is trained using a corpus compiled from the Douban Chinese film reviews
Wang et al. (2021).777Douban (https://douban.com/)is a Chinese movie
recommendation website like IMDB where users can rate each movie with their
comments. The authors suggested updating their work in the future with more
data and SOTA techniques, such as mocking detection and larger neural networks
that consider more user details, such as interpersonal relationships, hobbies,
and interests. However, such a method might raise ethical considerations such
as violating people’s privacy and providing negative labels that would cause
discrimination.

##  7 Research Gaps in Chinese Offensive Language Detection

Challenges | Example |  Explanation
---|---|---
Mislabeled data | Women often get paid less |  Facts are often considered offensive
Variety of cultural context | 咸家富贵 |  The phrase means differently under different contexts
Subversive expressions | 草泥马/操你妈 |  Such phrases can be undetectable
Table 4: Examples of Challenges in Detecting Offensive Language in Chinese

Despite the decent amount of research on Chinese offensive language detection,
there are still several important research gaps that need to be addressed in
order to further advance the performance of the systems.

#### Context-aware Offensive Language Detection

Most of the existing models for offensive language detection only consider the
text of a comment, tweet, or post without taking into account its broader
context. In many cases, the same words or phrases can be used in a neutral or
even positive way, or in an offensive way, depending on the surrounding words
and the topic being discussed. This can lead to false positives and false
negatives in offensive language detection.

For example, ”业务水平太高了！牛逼！”(The skills (possessed by someone) is so awesome) is
not offensive if we neglect the context from where it exists. However, if such
a comment appears under a news title that depicts some failures, such as a
medical accident, the phrase mentioned above can be offensive to the doctor.
Thus, textual context is important in detecting offensive language in Chinese
as they often contain essential information. Therefore, there is a growing
interest in developing approaches that take into account the textual context
of the comments or tweets. One of the potential solutions is to incorporate
topic modeling techniques to identify the topic of the comment or tweet and
use this information as an additional input to the offensive language
detection model.

This would allow the model to better understand the intended meaning of the
comment and distinguish between offensive and non-offensive uses of certain
words or phrases. Such models could be particularly useful in cases where the
topic itself may be inflammatory or controversial, which can affect the
interpretation of the comment text. Addressing this research gap could help
improve the accuracy and effectiveness of offensive language detection systems
in Chinese social media.

#### Limited offensive language variety and unclear severity boundaries

Offensive language is a complex and multi-dimensional phenomenon that can take
many forms, ranging from direct hate speech and discriminatory language to
more subtle forms of sarcasm. In the Chinese language context, there is a lack
of unified research on the varieties of offensive language types and the clear
boundaries of the level of offensive language, including hate speech, sarcasm,
adult content, and cyberbullying. Additionally, there is a need to establish
clear boundaries between biased attitudes, acts of bias, discrimination, and
bias-motivated violence.

Despite the increasing awareness of the negative effects of offensive
language, current research on Chinese offensive language detection tends to
focus on a narrow range of categories and fails to capture the full complexity
of this phenomenon. For example, some studies have focused solely on sexist
language Jiang et al. (2021). The lack of differentiation may lead to
ineffective detection and prevention strategies, as different types of
offensive language require different approaches.

Furthermore, there still exists a research gap in terms of defining clear
boundaries of the level of offensiveness. While some studies have attempted to
address the issue of detecting hate speech and discriminatory language, there
lacks a clear agreement and distinction on what constitutes biased attitudes
and behaviors in different contexts. This creates additional challenges to
developing effective and trustworthy models for detecting offensive languages
that are based on the complexity of the Chinese language and culture. Thus,
there exists a need for a more comprehensive dataset that includes a broader
range of offensive language, including various levels of biased attitudes and
behaviors, to support the development of accurate and contextually relevant
detection models.

##  8 Offensive Language Detection in Chinese: Challenges

Offensive Language Detection presents several challenges that must be
addressed in order to overcome potential issues and achieve meaningful and
effective results. Table 4 presents a few examples of these challenges.

###  8.1 Data Labeling Problems and Subjectivity

One of the major challenges in Chinese offensive language detection is the
existence of mislabelled data, which can result from various factors such as
the contested definition of offensiveness, which can vary depending on
cultural and social context. This can lead to inconsistent annotations of
offensive language in different datasets, as well as disagreement among
annotators.

Another factor that can contribute to obtaining mislabeled data is annotator
subjectivity. Annotators tend to misunderstand the nature and scope of
offensive language. In fact, annotators may have different levels of
sensitivity and tolerance towards certain types of language and may interpret
the same text differently based on their personal beliefs and values. This can
lead to inconsistencies in labeling and may result in a lower recall rate and
precision. Hence, spending time on training and educating annotators about the
subject and providing clear guidelines and definitions for labeling offensive
language is crucial in obtaining accurate and reliable data. Additionally,
regular quality checks and measures to ensure an inter-annotator agreement can
also help mitigate the impact of potential misunderstandings and
inconsistencies.

###  8.2 Cultural Context Variety

One of the biggest challenges in Chinese offensive language detection is
accounting for the variety of cultural contexts that exist within the
language. Due to the vastness and diversity of Chinese culture, language use
can vary widely depending on the context and the cultural background of the
speaker or writer. This presents a significant challenge for Chinese offensive
language detection models, as what might be considered offensive in one
cultural context may not be considered offensive in another. As the example
mentioned above, the phrase “ham ga fo gui ”’(咸家富贵/全家富贵) means the same thing
as the phrase ”ham ga can”(咸家铲/全家铲), where the prior one in a Mandarin context
being “hope your family gets rich”, and the later being “hope you whole family
dies”. The phrase ”fo gui”, in such context refers to the paper money that
dead people “receive” when they are dead, rather than a sincere wish for the
individual to become rich. Such a phrase in this particular context acts as a
way of sarcasm, rather than a sincere compliment, which adds another layer of
complexity to detecting offensive language in Chinese. To address this
challenge, offensive language detection models must take into account the
cultural context in which the language is being used. This requires a deep
understanding of Chinese culture and language use, as well as the ability to
identify and analyze the cultural factors that influence language use. A
qualitative analysis of the social media content matters could be suggested.

###  8.3 Neologism in Chinese

Neologism presents a unique challenge for detecting offensive language in
Chinese. These are linguistic expressions, words, or phrases that are often
used to circumvent censorship or to express taboo or sensitive topics in a
covert manner Wiener (2011). Even worse, people would embed implicit offense
inside a seemingly polite and acceptable phrase. These phrases are often
deeply embedded in specific cultural contexts and may be difficult to detect
without an understanding of the relevant cultural references and language
nuances. An example of subversive expressions from a post in Baidu Tieba, a
popular Chinese online forum platform where users can discuss various topics
and share information, is given in Figure
LABEL:figure:tieba.888https://tieba.baidu.com/

Figure 1: An example of the use of subversive phrases on Tieba

This kind of alternative expressions can not be detected by standard offensive
language detection systems, which rely on detecting specific keywords or
patterns of language. Therefore, it is crucial to define a model that takes
into account the cultural context and the use of alternative symbolic
infrastructures when developing offensive language detection algorithms in
Chinese. However, to the best of our knowledge, there is no research work that
explores how potentially harmful neologism can be detected using existing NLP
technology. Tackling the issue of harmful Neologism can help preserve the
original meaning of words and lessen the effect of cyberbullying and thus be
beneficial to the development of the Chinese language.

##  9 Addressing the current issue

To address the challenges in detecting offensive language in Chinese text,
several strategies can be employed: (1) Incorporate context-aware detection
using topic modeling and discourse-level analysis, and consider dialogue
models for contextual cues; (2) Develop more diverse datasets covering various
offensive language types, including sarcasm and regional variations; (3)
Enhance annotation quality with clear guidelines, hierarchical classification,
and better annotator training; (4) Capture cultural references and creative
expressions in knowledge bases for covert offensive language detection; (5)
Train models jointly on sarcasm and offensive language datasets to recognize
connections; (6) Generate realistic adversarial examples and employ semi-
supervised approaches for model resilience; (7) Conduct cross-geography
transfer learning studies to adapt models to different cultures while avoiding
bias with techniques like gradient reversal; (8) Foster collaboration through
shared offensive language resources and collaborative annotation projects; (9)
Engage users and utilize participatory design approaches to improve model
cultural awareness.

##  10 Conclusion

In this paper, we explored the current benchmark and approaches for offensive
language detection for Chinese and presented the models and tools built to
address this task. We also identified the challenges and research gaps for
this task posed mainly by the inherently complex nature of the Chinese
language. Our study highlights the need to address the unique difficulties due
to cultural ambiguity. Future research should consider cultural context when
detecting offensive language, and explore ways to detect subversive
expressions and sarcasm.

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