Uncovering the Root of Hate Speech: A Dataset for Identifying Hate Instigating Speech
Authors: Hyoungjun Park
Ho Sung Shim
Kyuhan Lee
Venue: EMNLP 2023 Findings (2023)
Keywords: hate speech
hate instigating speech
machine learning
natural language processing

Abstract: While many prior studies have applied computational approaches, such as machine learning, to detect and moderate hate speech, only scant attention has been paid to the task of identifying the underlying cause of hate speech. In this study, we introduce the concept of hate instigating speech, which refers to a specific type of textual posts on online platforms that stimulate or provoke others to engage in hate speech. The identification of hate instigating speech carries substantial practical implications for effective hate speech moderation. Rather than targeting individual instances of hate speech, by focusing on their roots, i.e., hate instigating speech, it becomes possible to significantly reduce the volume of content that requires review for moderation. Additionally, targeting hate instigating speech enables early prevention of the spread and propagation of hate speech, further enhancing the effectiveness of moderation efforts. However, several challenges hinder researchers from addressing the identification of hate instigating speech. First, there is a lack of comprehensive datasets specifically annotated for hate instigation, making it difficult to train and evaluate computational models effectively. Second, the subtle and nuanced nature of hate instigating speech (e.g., seemingly non-offensive texts serve as catalysts for triggering hate speech) makes it difficult to apply off-the-shelf machine learning models to the problem. To address these challenges, in this study, we have developed and released a multilingual dataset specifically designed for the task of identifying hate instigating speech. Specifically, it encompasses both English and Korean, allowing for a comprehensive examination of hate instigating speech across different linguistic contexts. We have applied existing machine learning models to our dataset and the results demonstrate that the extant models alone are insufficient for effectively detecting hate instigating speech. This finding highlights the need for further attention from the academic community to address this specific challenge. We expect our study and dataset to inspire researchers to explore innovative methods that can enhance the accuracy of hate instigating speech detection, ultimately contributing to more effective moderation and prevention of hate speech propagation online.

Uncovering the Root of Hate Speech:
A Dataset for Identifying Hate Instigating Speech
Hyoungjun Park
Ho Sung Shim
Kyuhan Lee
Department of Information Systems, Korea University Business School, Seoul, Republic of Korea
(parkhj1111, hsshim9702, kyuhanlee akorea.ac. kr
Abstract
linguistic contexts. We have applied existing machine learning models our dataset and the results demonstrate that the extant models alone are insufficient for effectively detecting hate instigating speech. This finding highlights the need for further attention from the academic community to address this specific challenge. We expect our study and dataset to inspire researchers to explore innovative methods that can enhance the accuracy hate instigating speech detection, ultimately contributing to more effective moderation and prevention of hate speech propagation online.
While many prior studies have applied computational approaches, such as machine learning, to detect and moderate hate speech, only scant attention has been paid to the task of identifying the underlying cause of hate speech. In this study, we introduce the concept of hate instigating speech, which refers to specific type of textual posts on online platforms that stimulate or provoke others to engage in hate speech. The identification of hate instigating speech carries substantial practical implications for effective hate speech moderation. Rather than targeting individual instances of hate speech, by focusing their roots, i.e., hate instigating speech, becomes possible to significantly reduce the volume of content that requires review for moderation. Additionally, targeting hate instigating speech enables early prevention of the spread and propagation of hate speech, further enhancing the effectiveness of moderation efforts. However, several challenges hinder rescarchers from addressing the identification of hate instigating speech. First, there lack of comprehensive datasets specifically annotated for hate instigation, making difficult train and evaluate computational models effectively. Second, the subtle and nuanced nature of hate instigating speech (e.g., seemingly non-offensive texts serve catalysts for triggering hate speech) makes difficult to apply off-the-shell' machine learning models the problem. To address these challenges, in this study, we have developed and released a multilingual dataset specifically designed for the task of identifying hate instigating speech. Specifically, it encompasses both English and Korean, allowing for comprehensive examination of hate instigating speech across different
Introduction
Hate speech is pressing issue in our society that requires significant attention. To this end, machine learning (ML) has gained a large amount of attention from both academia and industry as a valuable means to address hate speech (Davidson et al., 2017). In general, ML methods use features extracted from textual content, such document embeddings and POS tags, with other behavioral information, such social interactions, to enhance hate speech classification performance. Previous research (Burnap and Williams, 2015; Waseem and Hovy, 2016) has demonstrated the promising results of these approaches in identifying instances of hate speech.
Although the identification and moderation of hate speech hold significance per se, the paramount objective should lie in eliminating the underlying cause that gives rise to hate speech. For instance, within the context of online interactions, various forms of discourse exist that may not explicitly embody hatred itself but instead contribute the provocation of hostility, aggression, and toxicity. These include behaviors like trolling. this study, we refer to these behaviors as hate instigating behaviors. While is crucial to identify and control hate instigating behaviors (or more specifically

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creating multilingual dataset', in both English and Korea, which can facilitate the development of computational algorithms for mitigating the spread of hate speech. Specifically, comments and replies on the videos from multiple YouTube channels operated by major US and Korean news media companies, i.e., CNN, Fox News, JTBC, and TVChosun. Then, human annotators manually labeled the data as hate speech, some of which are further classified as hate instigating speech. By incorporating hate instigator detection alongside traditional hate speech detection, we aim to gain more nuanced understanding of the dynamics and triggers that contribute to the propagation of hate speech within online communities. We expect this to promote the development of targeted interventions and proactive measures to disrupt the spread of harmful content. In the upcoming sections, we will examine the existing literature on hate speech detection and provide a comprehensive overview of our dataset generation process. Subsequently, we will apply various ML techniques to our dataset and discuss the practical implications of our findings.
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Figure I: Comparison of hate speech and hate instigating speech detection social media.
hate instigating speech) as an early preventive measure against the proliferation of hate speech, this issue has often received limited attention, particularly from a computational perspective. Figure I-A illustrates the traditional hate speech detection process, which evaluates texts, regardless of their conversational levels (i.e., comments and replies) within online platforms to determine the presence of hate speech. This approach focuses solely on identifying instances of hate speech without explicitly considering whether those instances incite provoke hatred in others. On the other hand, in Figure I-B, we introduce the concept of hate instigating comments. That is, in addition to the identification of hate speech, it highlights the examination of the likelihood of each comment provoking hatred of others. In Figure I-B, although the comment itself does not exhibit any direct expressions of hatred (as in Figure I-A), it serves a comment that elicits hate-filled replies from others, which makes a hate instigating comment. The identification of hate instigating comments holds significant practical implications as serves as a crucial deterrent in mitigating the spread of hate speech. As an example, the content moderation workforce can strategically adjust their focus by allocating more resources to monitoring and addressing comment threads' that involve hate instigating aspects. By doing so, they can enhance hate moderation efficiency by reducing attention given to non-hate-instigating comments that have a lesser impact on the spread of hate speech.
Related literature
2.1 Hate speech detection datasets
In prior literature, many hate speech datasets have been suggested. These existing datasets have played pivotal role in advancing research on hate speech detection by providing valuable resources for training and testing ML models. In large sense, the types of hate speech covered by extant datasets can be classified into two categories: target- specific and target-generic hate speech. First, target-specific hate speech has deliberate target for hatred, including religion (Zannettou et al., 2020), race (Waseem and Hovy, 2016; Basile et al., 2019) and gender (Fersini et al., 2020). On the contrary, targer-generic hate speech encompasses a broad range of hatred without a specific target group, such as bullying or abusing random people (Chatzakou et al., 2017).
The vast majority of hate speech datasets are developed in English (Poletto et al., 2021). However, there is growing interest in increasing
In this study, we aimed to address the issue of hate instigating comments in online platforms by
In this context, comment thread refers to discussion consisting of main or top-level comment along with multiple replies or responses to that comment.
We use the term hate instigator a text level. not at human level. That is, it refers to hate instigating comment, not human who incite hatred of others.
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the linguistic diversity of hate speech datasets (Chiril et al., 2022). For instance, Van Hee et al. (2015) have suggested Dutch dataset for detecting cyberbullying
detection of hate instigation at the individual level introduces additional concerns including issues
related to privacy and ethics. For example, in the
social media site. Ross et al. (2017) have collected German tweets representing hatred towards refugees. Sanguinetti et al. (2018) have developed dataset comprising Italian tweets labeled with the different levels of offensiveness and aggressiveness. Moon et al. (2020) have created Korean dataset consisting of comments exhibiting social bias and hatred.
development of a hate instigation detection model aimed at individuals, it becomes necessary gather user-specific data, potentially raising privacy concerns. Also, directing moderation efforts towards individuals (e.g., blocking user accounts), rather than hate instigation speech (e.g., removing comments), may give rise to ethical concerns. Therefore, in this study, we consider hate instigation at a behavior level (i.e., text level) rather than at an individual level.
The main data source of existing hate-speech datasets has been Twitter (Wassem and Hovy, 2016 6 Davidson et al., 2017). On top of that, other social media sites, including Facebook (Rodriguez et al., 2019), Yahoo! (Nobata et al., 2016), Reddit (Qian et al., 2019), Instagram (Corazza et al., 2019) have been used as important sources to collect data from. In addition, video sharing websites, such as YouTube, have been recognized as valuable data sources that prior researchers have extensively explored (Sharma et al., 2018; Pavlopoulos et al., 2017). While these studies have contributed to a comprehensive collection of datasets for the development of automated hate speech detectors, as stated earlier, there has been limited emphasis on creating datasets specifically tailored for identifying hate instigating speech.
Similar to our focus, there have been several recent studies examining hate instigation detection at a behavioral level. For example, Sahnan et al. (2021) and Dahiya et al. (2021) have provided datasets that can be used to predict the hate intensity of replies given a tweet. However, for the development of the datasets, both studies have computational approaches for estimating hate intensity scores whose results can diverge from the actual ground truth. On the other hand, our dataset is meticulously curated through manual labeling, which yields more accurate results of labeling. Furthermore, our dataset is more diverse in that it is multilingual (i.e., English and Korean) and its source encompasses You'Tube channels biased towards both ends of the political spectrum (i.e., Democrats and Republicans). In the next section, describe the details of our dataset and its collection process.
2.2 Hate instigating speech
Hate speech has a higher spreading velocity and tends to spread farther and wider compared to normal posts (Mathew et al., 2019). Hate speech infects other people to spread even more hatred in the society, thriving like disease (Ahammed al., 2019). However, prior studies, particularly those that use ML as a moderation tool, have only considered hate speech itself rather than its possibility of influencing others (Mondal et al.,
3
Method
3.1
Data collection and annotation
YouTube has emerged as a valuable source for numerous datasets developed in the field of hate speech detection. This is primarily due to the presence political discussions and conflicts that frequently occur on the platform. Hence, order to construct a comprehensive dataset of hate instigating speech, we collected comments data from YouTube channels operated by major news media companics the US and South Korea. Specifically, our list includes CNN, Fox News, JTBC, and TV Chosun, which allows us to capture a diverse range of political perspectives in both English (left-leaning: CNN; right-leaning: Fox News) and Korcan (left-lcaning: JTBC; right- leaning: TV Chosun). The development of our dataset primarily centers around the identification
2018). A few studies, not necessarily in the field of ML-based hate speech detection, have made exceptions to this trend. For instance, ElSherief et al. (2018) suggested a concept of hate instigators to delve into the underlying causes of hate behaviors. They defined hate instigators as individuals who actively engage in expressing explicit and targeted hatred, thereby triggering the occurrence of hate behaviors by others. However, their definition may have limitations since it assumes that individuals who are classified as hate instigators always present hatred behaviors, which might not always be the case. Furthermore, the

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of the impact of a comment on its corresponding replies. That is, we aim to investigate how comment-level texts, illustrated in Figure 1-B, impact the responses and reactions of individuals who engage in replying to the original comment. Depending the level of hatred presented in these replies, may be able understand whether the comment instigates hatred responses or not (i.e., whether the comment is hate instigating speech or not).
speech that is aggressive but does not have specific target also falls under this category. (b) Non-Hate Speech: The speech that does not fall under the above categories. Criticism and sarcasm do not qualify hate speech.
With these definitions in place, three graduate- level students with expertise in the domain of hate speech detection participated in the annotation process. To ensure consistency among annotators, have developed detailed guidelines that provide clear and comprehensive definition of hate speech. These guidelines outline specific criteria and examples to help annotators accurately identify instances of hate speech. They cover various aspects, such as explicit and implicit forms of hate speech, targeted groups or individuals, offensive language, discriminatory content, and other relevant factors. The guidelines aim to provide a common understanding and interpretation of hate speech, ensuring that all annotators follow a consistent approach when labeling comments and replies in the dataset.
We collected data using YouTube Data API', the comments and replies of the videos posted on the aforementioned list of YouTube channels during timeframe between January 2022 and April 2022.
As a result, we could collect in total 4165 comments and 11310 replies on 210 videos. Specifically, CNN had 46 vidcos, 935 comments, and 2499 replies; FoxNews had 44 videos, 980 comments, and 2592 replies; JTBC had 45 videos, 1018 comments, and 3496 replies; and TVChosun had 75 videos, 1232 comments and 2723 replies.
As previously mentioned, our main objective is to create a dataset that includes annotations indicating whether comments are hate instigating speech or not. The annotation process for our dataset involves two key steps. First, we label the replies associated with each comment as either hate speech or non-hate speech. This step helps us understand the nature of the responses generated by users in relation the original comment. Secondly, we measure the level of hate speech instigation at the comment level by examining the ratio of hate replies associated with each comment (i.e., the proportion of hateful replies received in relation to the total number of replies). This metric allows us gauge the extent which a comment may incite or provoke hate speech from others.
In order to refine and validate our annotation guidelines, we conducted a pilot-annotation process. During this phase, a subset of the dataset (i.e., 100 samples) was randomly selected, and a team of annotators applied the provided guidelines to label given data. The pilot annotation allowed us to assess the clarity and effectiveness of the guidelines and identify any potential issues ambiguities. After completing the pilot annotation, conducted thorough review and analysis of the annotated data ensure consistency and agreement among the annotators. Any areas of uncertainty were through further refinement of the guidelines.
discrepancies discussed and addressed
For a consistent annotation, we draw upon existing literature (Assimakopoulos et al., 2020;
Based on the refined annotation guidelines, our team of annotators proceeded label the entire dataset. To statistically measure the level of agreements among the annotators, we calculated Fliess' kappa for each subset of data: CNN = 0.586; FoxNews 0.425; JTBC 0.340; and TVChosun 0.610. We argue that these values indicate our annotation process has demonstrated a good level of agreement (Gisev et al., 2013).
Mulki al., 2019; Pavlopoulos al., 2017:
Davidson et al., 2017; Mathur et al., 2018:
Sigurbergsson and Derezynski, 2019) to define hate speech and non-hate speech as follows:
(a) Hate Speech: The speech that deliberately targets a specific group or individual for condemmation. This includes, but is not limited to, sexism, homophobia, and hate towards certain political groups. Additionally,
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3.2 Data description
English
Korcan
To gain initial insights into the characteristics of hate instigating speech, we conduct an explorative analysis, offering an overview of our annotated data and shedding light on various patterns of hate instigating speech. First, in Table 1, we show descriptive statistics at a comment level for each language in our dataset. Note that, as mentioned earlier, we gauge the level of hate instigated by comment in continuous manner (i.e., the ratio of hate replies to total replies). However, in this section, we apply a binary classification approach where a comment is classified HIS if it has at least one reply associated with it.
HIS
NHIS
HIS
NHIS
Total
Number of
976
939
861
1389
Comments Average Number of Words in Each
40.26 (88.75)
41.07
18.57
22.02
(81.99)
(18.63)
(34.64)
Comment
Average Vocabulary
27.04
27.79
14.18 (13.33)
17.14
Size of
(36.49)
(33.26)
(22.26)
Each
Comment
In the case of English, total of 976 instances of hate instigating speech (henceforth, referred as HIS) and 939 instances speech (henceforth, referred to NHIS). The average number of words in each comment was 40.26 for HIS and 41.07 for NHIS, On top of that, we also calculated the average number of unique words in a comment (i.e., vocabulary size of comment). We noticed that slightly larger number of unique words were used in NHIS comments compared to HIS comments (i.e., 27.79 and 27.04, respectively). In addition, we observed that HIS received a higher level of attention. That is, on average, 3.03 replies were associated with one HIS comment while only 2.77 replies were associated with one NHIS comment. Among the replies to HIS comments, approximately 63.0% contained hatred (i.e., hate replies).
dataset consists of
Average
3.03
2.77
3.89
2.08
Number of Replies*
(4.27)
(8.65)
(5.47)
(3.03)
Average Number of Hate
of non-hate-instigating
1.91
1.88
N/A
N/A
(2.26)
(1.77)
Replies*
Standard deviations noted in parentheses.
Table I: Descriptive Statistics HIS and NHIS by Language.
publisher). One of the interesting findings is that, in both US and Korea, the politically right-leaning media channels (i.e., FoxNews and TVChosun) had a proportion of HIS to NHIS. In addition, the proportion of hate replics to total replics associated with HIS was also higher in the right-leaning channels.
One interesting aspect of our data is that, unlike prior studies, we focus on the nature of speech that can provoke hate, regardless of whether the speech itself includes hatred. That is, while prior studies in general have only emphasized the propagation of hatred through a chain of hate speech, we claim that even non-hate speech can be the cause of hatred (Anderson and Barnes, 2022; Khurana et al., 2022). For instance, in Table 3, we further categorize HIS into hate HIS and non-hate HIS. The former indicates HIS comments that contain expressions of hatred while the latter refers to HIS comments but do not necessarily contain expressions of hatred. According to Table 3, identify that large proportion of HIS comments in fact non-hate HIS. In addition, they induce higher level of attention in both languages (i.e., 3.07 replies compared to 2.82 replies in English; 4.01 replies compared to 2.76 replies in Korean). This notable
On the other hand, our Korean dataset consists of total of 861 instances of HIS and 1,389 instances of NHIS. The average number of words in each comment was 18.57 for HIS and 22.02 for NHIS. In addition, for HIS, on average, 14.18 unique words were used while, for NHIS, much larger number of unique words (i.e., 17.14) were used, confirming the pattern of vocabulary usage that we identified from the English dataset. On top of that, another similar trend could be found regarding the amount of attention given HIS and NHIS. That is, HIS comments received much larger attention than NHIS comments, with an average of 3.89 replies compared 2.08 replies. Among the replies to HIS comments, approximately 48.3% contained hatred (i.e., hate replies)
In Table 2, we further break down the information in Table 1 by media channel (i.e,, news

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CNN
Fox News
JTBC
TV Chosun
HIS
NHIS
HIS
NHIS
HIS
NHIS
HIS
NHIS
Total Number of
418
517
558
422
209
809
652
580
Comments
Average
Number of Words in
53.10 (128.52)
49.30
30.65
30.99 (32.30)
18.94
22.95 (28.12)
18.45 (19.67)
20.74
(105.86)
(34.49)
(14.96)
(42.06)
Each
Comment
Average
Vocabulary Size of Each
32.88
32.13
22.66
22.47 (19.47)
15.17 (11.47)
18.24
13.86
15.60
(49.88)
(40.74)
(20.58)
(19.41)
(13,87)
(25.67)
Comment
Average Number of Replies*
2.99
3.12
3.07
2.25
6.97
2.52
2.88
1.48
(4.51)
(10.67)
(4.08)
(4.52)
(8.42)
(3.79)
(3.54)
(1.15)
Average Number of Hate
1.74 (2.12)
2.03
1.52
2.03 (1.85)
N/
N/A
N/A
(2.35)
(1.23)
Replies*
* Standard deviations are noted parentheses.
Table 2: Descriptive Statistics of HIS and NHIS by Media Channel.
highly likely that they may fail to effectively manage non-hate HIS which could significantly contribute to the propagation of hatred. Therefore, claim that it is particularly important shift the paradigm of hate speech moderation to hate instigating speech moderation, and our study is of the pioneering efforts in this direction. In the next section, we show the practical application of our dataset in combination with extant ML methods for effectively managing hate instigating speech.
English
Korean
Hate
Non- hate
Hate
Non-
HIS
HIS
hate
HIS
HIS
Total
Number
255
721
111
750
Comments
Average
Number of Words in
39.10
40.67
19.46
18.43
(64.61)
(95.85)
(18.40)
(18.67)
Each
Comment
Average
4 Data application
Vocabulary Size of Each
26.56
27.21 (38.59)
15.24
14.02
4.1 Experimental setting
(29.82)
(13.39)
(13.32)
Comment
In this section, we present how our dataset can be applied in real-world setting. We develop and evaluate a computational model for detecting hate instigation speech using multiple off-the-shelf ML methods. Specifically, our problem setting aims to develop ML models that accurately predict the level of hate instigation based on text data. Formally, it is to identify an optimal function, (x, = [0.1], where x; is an element of the potential HIS set X function fs closed interval between 0 and 1, which signifies the likelihood of a given text being classified as HIS higher value indicates a higher probability of the text being classified as HIS).
Average
2.82
3.07
2.76
4.01
Number of Replies
(4.06)
(4.32)
(3.64)
(5.65)
Average
Number of
2.05 (2.69)
1.85
1.36
1.83
Hate
(2.08)
(0.81)
(1.98)
Replies*
Standard deviations noted in parentheses.
Table 3: Descriptive Statisties of Hate HIS and Non-hate HIS by Language.
"M, The range of
result highlights the limitation of existing hate speech moderation approaches, which mostly focus on hate speech itself but not its root. It is

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As mentioned earlier, we tested ML methods that are largely adopted in practice, including linear regression (i.e., Lasso regression), tree-based models (i.c., random forest, extra tree, and gradient boosting; Davidson et al., 2017), and multiple variations of neural networks (i.e., CNN, LSTM, and BERT Regressor; Wolf et al., 2019), 9), to develop computational models for the HIS detection. For preprocessing of texts and their projection to vector space, we used Spacy an open-source software library for natural language processing. For each baseline, we trained and tested based on five-fold cross validation. It important to note that our primary objective in this study is to showcase the applicability result, we did not engage in an extensive process of hyperparameter tuning. The focus was on demonstrating the feasibility and potential our dataset, rather than achieving optimal performance through fine-tuning of hyperparameters.
identifying HIS. There are several interesting points to be made from it. First, it appears that the difficulty level of detecting HIS varies depending on the language being analyzed. That is, in general, the task of detecting HIS in English produces higher level of error rate than in Korean, This result remains the same even when pretrained language model, which predominantly leverages English corpora over Korean corpora", is used as baseline (i.e., BERT Regressor). To find explanation for the discrepancy in difficully levels between the two languages, we conducted manual exploration of subsets of English and Korean HIS comments. English, HIS comments encompassed subtle cases, such as sarcastic and satirical expressions, which may require nuanced understanding to identify as instances of hate speech. In contrast, Korean HIS comments tended to be more direct, often containing offensive or explicit language that was readily recognizable as offensive. The variation hanguage-specifes difficulties underscores the importance of considering language-specific factors when developing detection methods or models for HIS.
our dataset. As
To assess the performance of the baselines, we adopted the Mean Absolute Error (MAE) as the primary evaluation metric, calculating the absolute difference between the predicted probability scores and true scores. This aligns with our objective of hate instigator detection, where accurately estimating the probability of texts instigating hatred is significant.).
On top of that, except for the JTBC dataset, off- the-shelf ML methods were not effective in identifying HIS comments. That is, the results regarding CNN, FoxNews, and TVChosun yielded high level of MAF, deviating from our initial expectation. The difficulty of predicting hate instigation speech based on extant ML methods aligns with the results of the previous studies within similar context (Dahiya et al., 2021). This
4.2 Results
Table 4 serves as a summary of the results of the aforementioned experiment, offering insights into the effectiveness of different ML methods in
(1)
(2)
English (1+2)
(3)
(4)
Korean (3+4)
CNN
FoxNews
JTBC
TVChosun
Lasso Regression
0.416
0.426
0.428
0.179
0.415
0.347
Gradient Boosting
0.413
0.430
0.421
0.188
0.417
0.342
Extra Trees
0.414
0.419
0.425
0.193
0.417
0.343
Random Forest
0.416
0.424
0.421
0.192
0.420
0.348
SVM
0.401
0.431
0.430
0.210
0.412
0.315
CNN
0.364
0.491
0.467
0.131
0.428
0.252
LSTM
0.364
0.458
0.469
0.131
0.400
0.258
BERT Regressor
0.362
0.434
0.469
0.128
0.410
0.245
Table 4: Performance Comparison of HIS Detection Models Media Channel.
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This discrepancy in attention and resources allocated to different languages has led relative imbalance in the availability quality of pre-trained language models for non- mon-English languages.
With respect to the development of pre-trained language models for difference purposes, English corpora have been the main source of data while other languages, including Korean, have been paid less attention (Kim et al., 2022).

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Burnap, P., Williams, M. L. (2015). Cyber hate indicates that there are a higher level of challenges and complexities associated with identifying HIS than initially expected. This calls for future research to delve into the specific linguistic nuances of HIS and to develop ML methods that effectively capture and address these nuances.
classifying texts as either HIS or NHIS, we may consider the multifaccted nature of hate instigating speech. This will involve categorizing HIS into various subcategories that correspond to different types of hatred, such racism instigating speech, sexism instigating speech, religious HIS, motiveless HIS, and so on. Lastly, it is worth nothing that our data in this study solely comprises textual information. However, incorporating a different modality of data, such as images, may provide a different angle to our problem setting.
5 Discussion
In this study, we provide a novel concept of hate instigation speech (HIS) and develop a dataset that can be a catalyst for future research on hate speech moderation, particularly those based on ML-based approaches. Compared to prior studies, our approach of highlighting HIS, which identifies and manages the root of hate speech, is more effective way of dealing with the propagation of online hate speech. That is, by targeting the root causes of hatred, we can potentially have greater impact on reducing the propagation and harmful effects of hate speech in online environments. Furthermore, our approach is capable of addressing broader forms of discourse, which may contribute to the propagation of hate speech but were not the primary focus of moderation in previous studies. That is, while non-hate HIS does not explicitly manifest hatred by itself but still significantly contributes to the provocation of hostility, aggression, and toxicity, prior studies have not paid proper attention to it. By introducing such a broader concept of harmful discourse, we expect more comprehensive moderation strategies which will foster healthier and more inclusive online environments.
References
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Anderson, L., & Barnes, M. (2023). Hate speech. In N. 7alta & U. Nodelman (Eds.), The Stanford Encyclopedia of Philosophy (Fall 2023).
https://plato.. https://latto.saanfrd..c www.f//dwccaaaccciie////ll/2222. hate-speech/
Assimakopoulos, S., Muskat, R. V., Van Der Plas, L., & Gatt, A. (2020). Annotating for hate speech: The MaNeCo corpus and some input from critical discourse analysis. arXiv preprint 2rxi::208 202.020........
Basile, V., Bosco, C., Fersini, E., Nozza, D., Patti, V., Pardo, F. M. R., & Sanguinetti, M. (2019, June). Semeval-2019 task 5: Multilingual detection of hate speech against immigrants and women in twitter. In Proceedings of the 13th international workshop semantic evaluation (pp. 54-63).
6 Limitations
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Reviews:
[{"review_id": "jgpo3qN0zv", "reviewer": "Reviewer_9nDB", "summary": "", "questions": "", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your insightful comments. We appreciate the opportunity to clarify and strengthen our work in light of your feedback. Below, we provide our point-by-point responses regarding your comments.\n\nOur response to your comment #1: We acknowledge the importance of situating our work within the context of existing literature. The papers you mentioned, are indeed relevant to the broader context of our study. However, while there are certain similarities between the studies you mentioned and our work, it's important to note that there exist notable distinctions as well. \n\nFirst, the studies you mentioned (Sahnan et al. [2021] and Dahiya et al. [2021]), both using the same dataset, primarily relied on machine-learning-based and lexicon-based computational techniques for estimating hate intensity scores, and hence their results can diverge from the actual ground truth. In contrast, our dataset is meticulously curated through manual labeling, which tends to yield more accurate results of labeling.\n\nFurthermore, while Sahnan et al. (2021) and Dahiya et al. (2021) focused solely on political events, our dataset encompasses a diverse range of news domains, including politics, economics, and various social subjects. This broader scope provides a comprehensive perspective that distinguishes our work.\n\nMoreover, our dataset stands out as bilingual, incorporating both English and Korean, whereas the other studies are confined to English only. This linguistic inclusivity contributes to the richness and applicability of our dataset.\n\nThe unique collection platform is another aspect that differentiates our study. Our data is sourced from YouTube, which introduces distinctive environmental factors that can influence hate-instigating behaviors, setting it apart from the platforms that other studies have collected data.\n\nLastly, it's important to highlight that the research objectives of Hada et al. (2021) and Masud et al. (2022) – the two other studies you mentioned – diverge from our focus on hate instigation. Specifically, Masud et al. (2022) introduce a method for capturing specific hate-related parts within a text, while Hada et al. (2021) propose a novel approach for annotating levels of hate speech. These divergent objectives underscore the unique contribution of our study, centered on hate instigation.\n\nIn our revised manuscript, we will ensure to incorporate these references to provide a more comprehensive overview of the existing literature, further underscoring the distinct value and complementarity of our research in the domain of hate-instigating speech prediction.\n\nOur responses to your comment #2:\n2a: Our dataset includes content from diverse news channels such as CNN, FOX News, JTBC, and Chosun. While it is true that these platforms often focus on political discussions, our dataset is not limited to political content as mentioned in our response to your comment #1. This ensures that our dataset captures a wide range of hate-instigating speech across diverse domains.\n2b: We did not apply any text preprocessing to the crawled data before the annotation process. The data was used in its raw form to maintain the original context and nuances, which we believe is crucial for the quality of the annotations.\n2c: We acknowledge the importance of providing a detailed and systematic explanation of our annotation guidelines, definitions, and processes. In the manuscript, we did include our annotation process in general but we found out that there was a lack of definitions regarding hate and non-hate speech. We provide them below.\n\n<Definitions of hate speech and non-hate speech>\n\nHate Speech: Speech that deliberately targets a specific group or individual for condemnation. This includes, but is not limited to, sexism, homophobia, and hate towards certain political groups. Additionally, speech that is aggressive but does not have a specific target also falls under this category.\n\n- Examples:\n- Explicit hatred towards a particular group: \"INDIA + CHINA = DISGUSTING RAT EATING 3RD WORLD COUNTRIES!\"\n- Hate towards a political group: \"Biden Is the biggest Disaster in American History 😫\"\n- Sexism: \"She is a woman; it's hard for women to understand things like this.\"\n- General aggression: \"YouTube comments are such a cesspit.\"\n\nNon-Hate Speech: Speech that does not fall under the above categories. Criticism and sarcasm do not qualify as hate speech in our dataset. \n- Examples: \n- Sarcasm: \"Wow, what a surprise, American.\"\n- Criticism: \"I'm part Russian and absolutely disappointed in this whole war!\"\n\nThe definitions and examples are based on prior literature (Assimakopoulos et al., 2020; Mulki et al., 2019; Pavlopoulos et al., 2017; Davidson et al., 2017; Mathur et al., 2018; Sigurbergsson and Derczynski, 2019).\n\n2d: The annotations were performed by three graduate-level students. Disagreements among annotators were resolved through discussions and by achieving a high Fleiss-alpha score, as indicated in line 336 of our manuscript.\n\n2e: As described in line 287 and line 353 of our manuscript, our annotation process involves two key steps. Initially, replies are labeled as either hate speech or non-hate speech. Subsequently, we measure the level of hate instigation at the comment level based on the proportion of hateful replies. Comments are classified as hate-instigating speech if they have at least one associated hateful reply. \n\nOur response to your comment #3: To clarify, we observed that hate-instigating speech (HIS) in our dataset received more comments overall, while non-hate-instigating speech (NHIS) received fewer comments. Additionally, by definition, comments categorized as NHIS received no hateful replies. Our intention in stating these observations was not to argue statistical differences between the two groups of comments but to provide a descriptive analysis of the observed trends in our dataset. We will make this point explicit in the revised manuscript to avoid any potential confusion.\n\nOur response to your comment #4: While we acknowledge that the dataset does not delve into the underlying social-physiological aspects of hate instigation, the focus of this paper is rather methodological, aimed at presenting the dataset itself. The term \"hate-instigating speech\" was chosen to emphasize the dataset's unique focus on speech that is more likely to elicit hateful responses. However, we appreciate your suggestion to consider alternative terminology like \"hate prone\" or \"hate provoking.\" We will take this into account in the revision process.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. Introduce a new dataset in the hate speech literature.\n2. Bring back the focus on dataset that is not only about hate speech detection but rather its propagation (aka instigation)", "reasons_to_reject": "1. While the attempts to present a new dataset on hate instigation speech, the information presented is not complete and accurate because:\nExisting literature needs to be appropriately cited and compared. There are existing datasets in English that attempt to determine the instigation level of a post, work: Better Prevent than React: Deep Stratified Learning to Predict Hate Intensity of Twitter Reply Chains\", \"Would your tweet invoke hate on the fly? Forecasting hate intensity of reply threads on Twitter\", \"Ruddit: Norms of Offensiveness for English Reddit Comments.\", \"Proactively Reducing the Hate Intensity of Online Posts via Hate Speech Normalization\" needs to be examined in this study.\n2. Being a dataset paper, it needs to provide the following details.\n2a: Did the authors only look at political content on the news channels?\n2b: Was any text preprocessing applied before and after the annotation process?\n2c: Information on the annotation guidelines, definitions used and finalized, and the annotation process must be included in a detailed and systematic manner.\n2d: Demographic details about the annotators and how the disagreements were handled should also be included.\n2e:The authors should also highlight if, at first, the annotations were for only hate and non-hate and then mapped to HIS and NHIS. How many posts from each category map to the other should also be enlisted in a table.\n3. In the dataset description section, authors ought to substantiate their analysis with significance testing when they state that HIS speech received more comments and more hateful comments (line 386).  \n4. Calling this dataset a hate instigation dataset may not be correct, as the dataset is not looking at instigation from an underlying social-physio perspective but instead just looking at posts that are more likely to receive hateful posts (hate prone/hate provoking rather than hate instigation would be better terminology.)", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper aims to establish the importance of studying root posts that may not be classified as hate speech via text classification systems but are nevertheless hate instigating in nature. The authors curate such a dataset from Youtube in English and Korean and then compare the modelling of the said dataset via ML-based regressors.", "excitement": 2, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "Enlisted the papers above.", "Typos_Grammar_Style_and_Presentation_Improvements": "1. Line 387 typo: incomplete sentence.\n"}, "extra_text": {}}, {"review_id": "5c55O0VRSk", "reviewer": "Reviewer_eite", "summary": "", "questions": "1. Were samples to create this dataset scraped from news websites?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your feedback. Below, we provide our point-by-point responses regarding your comments.\n\nOur response to your comment #1. Sorry for the confusion. In Figure 1, our intention was to underscore the core emphasis of our study. While previous investigations (1-(A)) focus on identifying hate speech without considering the hierarchical structure of comments and replies, our research (1-[B]) centers on comments, particularly their potential to incite or provoke hate speech at the reply level. This distinction holds significance because identifying hate-instigating speech can prove more effective in curbing the proliferation of hate speech compared to the blanket detection of all instances. \n\nIn addition, our definition of hate speech is based on prior studies. For instance, Davidson et al. (2017) defined hate speech in a broad sense as \"language that is used to express hatred towards a targeted group or is intended to be derogatory, to humiliate, or to insult the members of the group.\" As you have mentioned, at the word level, we cannot determine whether a comment contains hate or not. Therefore, we centered our definition of hate speech at the comment (or reply) level and employed the human annotation process instead of the machine annotation process since the latter, often based on lexicon-based models, could fail in accurately identifying hate speech.\n\nOur response to your comment #2. We concur that various factors, including individual characteristics, can indeed impact the likelihood of producing hate-instigating speech. While it would be ideal to possess such information, the practical reality is that identifying personal characteristics from a mere snippet of information is nearly impossible. This is precisely why our focus rests on behavioral aspects, particularly linguistic characteristics associated with hate speech.\n\nOur response to your comment #3. We agree with your observation that the models employed in this study are not best-in-class. However, the primary focus of this study is on the novelty of the dataset provided rather than the methods employed. In addition, there is empirical evidence from prior literature showing that even state-of-the-art language models often fail to solve similar tasks, highlighting the difficulties of identifying hate-instigating speech (Dahiya et al., 2021). We assert that our dataset has the potential to serve as a stepping stone for future studies aimed at overcoming these difficulties.\n\nDahiya, S., Sharma, S., Sahnan, D., Goel, V., Chouzenoux, E., Elvira, V., ... & Chakraborty, T. (2021, August). Would your tweet invoke hate on the fly? forecasting hate intensity of reply threads on twitter. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 2732-2742).\nDavidson, T., Warmsley, D., Macy, M., & Weber, I. (2017, May). Automated hate speech detection and the problem of offensive language. In Proceedings of the international AAAI conference on web and social media (Vol. 11, No. 1, pp. 512-515).", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. The paper looks at an overlooked angle of dialogs for hate speech, hence dealing with hate speech instigation.", "reasons_to_reject": "1.\tIt is unclear by which definition of hate speech (fig1) the replies are hate speech. Offensive words may be toxic, but may not constitute hate speech.\n2.\t(L217) Behaviour level is also not always the case. One can argue anything influential can be a hate instigator.\n3. Models used are not the best-in-class, yet the conclusion of existing models being insufficient to detect hate speech instigation is claimed.", "questions_to_authors": "1. Were samples to create this dataset scraped from news websites?", "paper_topic_and_main_contributions": "This paper looks at the problem of hate speech instigation to determine the underlying cause of hate speech. The authors collect data from multiple news websites in English and Korean to establish this problem.", "excitement": 2, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "zsKREwBul7", "reviewer": "Reviewer_wEu5", "summary": "", "questions": "N/A", "limitations": "", "rating": 1, "confidence": 4, "soundness": 1, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your feedback. Below, we provided point-by-point responses to your comments.\n\nOur response to your comment #1: We concur with your feedback that our definition of hate-instigating speech has some limitations such as not considering the dynamics between replies. For instance, a reply, instead of a comment, can be the cause of hate presented in another reply. Nonetheless, given that our work represents the initial attempt to establish a human-annotated dataset for identifying hate-instigating speech, we contend that our study serves as a stepping stone for future research, which may potentially present a more nuanced and precise definition of hate-instigating speech.\n\nOur response to your comment #2: In the manuscript (Section 3.1.), we did include our annotation process in general but we found out that there was a lack of definitions regarding hate and non-hate speech. We provide them below.\n\n<Definitions of hate speech and non-hate speech>\nHate Speech: Speech that deliberately targets a specific group or individual for condemnation. This includes, but is not limited to, sexism, homophobia, and hate towards certain political groups. Additionally, speech that is aggressive but does not have a specific target also falls under this category.\n\nExamples:\nExplicit hatred towards a particular group: \"INDIA + CHINA = DISGUSTING RAT EATING 3RD WORLD COUNTRIES!\"\nHate towards a political group: \"Biden Is the biggest Disaster in American History 😫\"\nSexism: \"She is a woman; it's hard for women to understand things like this.\"\nGeneral aggression: \"YouTube comments are such a cesspit.\"\nNon-Hate Speech: Speech that does not fall under the above categories. Criticism and sarcasm do not qualify as hate speech in our dataset.\n\nExamples:\nSarcasm: \"Wow, what a surprise, American.\"\nCriticism: \"I'm part Russian and absolutely disappointed in this whole war!\"\n\nThe definitions and examples are based on prior literature (Assimakopoulos et al., 2020; Davidson et al., 2017; Mulki et al., 2019; Pavlopoulos et al., 2017).\n\nAssimakopoulos, S., Muskat, R. V., Van Der Plas, L., & Gatt, A. (2020). Annotating for hate speech: The MaNeCo corpus and some input from critical discourse analysis. arXiv preprint arXiv:2008.06222.\n\nDavidson, T., Warmsley, D., Macy, M., & Weber, I. (2017, May). Automated hate speech detection and the problem of offensive language. In Proceedings of the international AAAI conference on web and social media (Vol. 11, No. 1, pp. 512-515).\n\nMulki, H., Haddad, H., Ali, C. B., & Alshabani, H. (2019, August). L-hsab: A levantine twitter dataset for hate speech and abusive language. In Proceedings of the third workshop on abusive language online (pp. 111-118).\n\nPavlopoulos, J., Malakasiotis, P., & Androutsopoulos, I. (2017, September). Deeper attention to abusive user content moderation. In Proceedings of the 2017 conference on empirical methods in natural language processing (pp. 1125-1135).", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "While urging interest in hate instigating speech, the authors have constructed a multilingual dataset for this purpose. ", "reasons_to_reject": "1. The criteria for Hate Instigating Speech are not clear. Merely categorizing a post as Hate Instigating Speech in the comments on that post lacks persuasiveness. \n2. As the key contribution of this paper lies in dataset construction, it is essential to provide a more detailed explanation of the criteria and guidelines for annotation. \n", "questions_to_authors": "N/A", "paper_topic_and_main_contributions": "The authors introduce the concept of Hate Instigating Speech (HIS), a specific type of textual posts on online platforms (Youtube) that stimulate or provoke others to engage in hate speech. The paper highlights the importance of targeting the root cause of hate speech rather than focusing solely on individual instances of hate speech. The authors provide a multilingual (English and Korean) dataset designed for HIS. ", "excitement": 1, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Missing_References": "N/A", "Typos_Grammar_Style_and_Presentation_Improvements": "N/A"}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper introduces new dataset for hate speech instigation. Reviewers raised concern about the missing literature, annotations, and modeling. Authors in their rebuttal have provided reasonable explanation for these points and how these could be addressed.
Decision: Accept-Findings