# Hatred Stems from Ignorance! Distillation of the Persuasion Modes in
Countering Conversational Hate Speech

Ghadi Alyahya, Abeer Aldayel

###### Abstract

Examining the factors that the counter-speech uses is at the core of
understanding the optimal methods for confronting hate speech online. Various
studies assess the emotional base factor used in counter speech, such as
emotion-empathy, offensiveness, and level of hostility. To better understand
the counter-speech used in conversational interactions, this study distills
persuasion modes into reason, emotion, and credibility and then evaluates
their use in two types of conversation interactions: closed (multi-turn) and
open (single-turn) conversation interactions concerning racism, sexism, and
religion. The evaluation covers the distinct behaviors of human versus
generated counter-speech. We also assess the interplay between the replies’
stance and each mode of persuasion in the counter-speech. Notably, we observe
nuanced differences in the counter-speech persuasion modes for open and closed
interactions—especially on the topic level—with a general tendency to use
reason as a persuasion mode to express the counterpoint to hate comments. The
generated counter-speech tends to exhibit an emotional persuasion mode, while
human counters lean towards using reasoning. Furthermore, our study shows that
reason as a persuasion mode tends to obtain more supportive replies than do
other persuasion types. The findings highlight the potential of incorporating
persuasion modes into studies about countering hate speech, as these modes can
serve as an optimal means of explainability and paves the way for the further
adoption of the reply’s stance and the role it plays in assessing what
comprises the optimal counter-speech.

##  1 Introduction

Counter-speech is a strategy that is commonly adopted to combat online hate
speech (Parker and Ruths 2023). The dependency on counter-speech as a main
component to confront hate has been further intensified by the prevalence of
online conversation-based platforms (Caldevilla-Domínguez, Barrientos-Báez,
and Padilla-Castillo 2023), specifically either multi-turn conversation
platforms (chat systems that provide closed interactions between two users,
closed (multi-turn)) or single-turn conversation platforms (e.g., social media
platforms, where many users can be involved in a conversation in an open
interaction setting, open (single-turn)). Multiple studies have begun
directing attention to adopting counter-speech strategies to halt the spread
of hate and alleviate the effects of blocking or deleting the content on
freedom of speech (Gagliardone et al. 2015; Myers West 2018; Garland et al.
2020).

However, counter-speech still faces multifaceted challenges from a dependency
on the content dynamics and the toll of the interactions’ type closed or open
(Catherine 2022). The challenges intensify with the recent adoption of
machine-generated counter-speech to limit human moderation costs (Ashida and
Komachi 2022). Most previous studies address the dynamics of the counter-
speech contents by analyzing emotion-based aspects of words’ connotations,
such as sentiment (Brassard-Gourdeau and Khoury 2019; Garland et al. 2020) or
empathy (Hangartner et al. 2021; Lahnala et al. 2022). Similarly, current
studies have begun investigating content dynamics in the context of machine-
generated and human-based narratives. For instance, (Bonaldi et al. 2022; Sen
et al. 2023) compare human-based generated and machine-generated conter-
speech. Still, current attempts to distill the non-emotional aspects of
counter-speech are minimal. They typically analyze one target type of argument
strategy, such as stereotype-targeted strategies (Mun et al. 2023), with a
limited focus on one interaction type and ignore the replies to the counter-
speech (Mathew et al. 2019).

The persuasion mode is a communication method involving multiple parties who
exchange various arguments to persuade the other participants of the merits of
a different perspective in an attempt to strengthen their convictions (Barden
and Petty 2012; Dutta, Das, and Chakraborty 2020). To define the set of
persuasion modes for the analysis, we use the conceptual aspects of a
persuasive argument based on the rhetorical appeals of persuasion theory,
which categorizes argumentative building blocks into logos (reason), pathos
(emotion), and ethos (credibility) (Lukin et al. 2017; Cope and Sandys 2010).
Counter-speech is a direct response to hate speech and a form of intervention
that confronts the hateful content and disproves the argument to prove the
counterpoint (Schmuck 2024; Gagliardone et al. 2015). We hypothesize that
persuasion mode may appear in counter- and hate-speech contexts, as people
confronting hateful arguments tend to use persuasive appeals to convince
others with their counterpoints.

In this study, we distill the persuasion modes used in counter-speech,
focusing on two main dimensions: interaction type and context-based behaviors
(human-based or machine-generated contexts). Particularly, this study analyzes
the persuasion modes used in counter-speech and analyzes their interplay with
hate speech in conversations.

Our research questions are as follows:

  * •

(RQ1): How effective are the persuasion modes in identifying counter-speech
across domains (topics) and interaction types?

  * •

(RQ2): Does the persuasion mode used in counter-speech vary by interaction
type and the behavior of the machine-generated and human-based conversation
content?

  * •

(RQ3): To what extent do counter-speech persuasion modes drive the follow-up
replies in conversations?

We perform experiments using a subset of two multi-turn, conversation-based
counter-speech datasets (DialogConan, Bonaldi et al. 2022), which contains
approximately 8,000 multi-turn (closed interaction) conversations between two
users and 1,500 single-turn (ContextCounter, Albanyan, Hassan, and Blanco
2023) X posts demonstrating open interactions among many users 111Throughout
the paper we use the terminology closed (multi-turn) to indicate the closed
conversations’ interactions that contain multi-turns between two users, such
as chat online systems. We use open (single-turn) to indicate open
conversations’ interactions that contain a single turn with many user
interactions, such as social media platforms. . The persuasion mode
annotations were conducted for each turn in the conversation for both hate-
and counter-speech. We extend the experiment by detailing the type of
persuasion mode used to express counter-speech as narrated by humans compared
to the generative model. We also investigate the context of the replies to the
counter-speech as another discourse level that might serve as a proxy for
understanding the persuasion mode’s dimensionality and inferring the
reflection of the counter-narratives confronting the hate in these
conversations.

This paper’s specific contributions include the following:

  * •

We provide a coarse-grain examination of the persuasion modes for hate- and
counter-speech, based on emotion, reason, and credibility.

  * •

We empirically demonstrate the efficacy of incorporating the persuasion mode
in detecting hate- and counter-speech for closed (multi-turn) and open
(single-turn) conversational interactions.

  * •

We assess the persuasion modes in machine-generated counter-speech.
Considering the current tendency of current studies to adopt machine-generated
counter-speech to mitigate the hate in the conversation systems (Ashida and
Komachi 2022), we further analyze the persuasion modes by comparing human-
based and machine-generated counter-speech to highlight effective counter-
speech strategies for various domains.

  * •

We leverage the distinct features of single-turn conversations (open
interactions) created by allowing multiple users to interact with counter-
speech to extend the analyses and evaluate the extent to which replies support
counter-speech. We do so by disentangling the following four types of follow-
up replies: supporting the counter-speech, opposing the counter-speech,
supporting the counter-speech with additional context, and opposing the
counter-speech with hateful content.

This work provides general insights into how the three persuasion modes
(emotion, reason, and credibility) function as proxies to mitigate hate using
counter-speech, with the aim of increasing the focus on persuasion mode
computational modeling and its potential to disentangle counter-speech’s
effectiveness in hateful conversations .

##  2 Previous Work

Counter-speech in hateful conversations is an argumentative appeal that
attempts to challenge and contrast the hateful point (Gagliardone et al.
2015). There have been attempts to study counter-speech by aspects of the
content or conversation behaviors (human-based versus machine-generated). In
this section, we detail related works by the type of content aspect they
explored. Aspect refers to the core component of the counter-speech that the
study investigated, which can be further categorized as emotion-based and non-
emotion-based. Next, we discuss recent persuasion identification work and list
the common techniques used in the field.

###  2.1 Counter-speech Aspects

Previous studies have thoroughly investigated the emotion-based aspects of
counter speech. This is partly due to the ubiquity of the connotation-based
tool that facilitates the word connotation analysis. The study by (Hangartner
et al. 2021) shows that using empathy as a shaming technique in counter-speech
causes hateful commenters to delete their posts. This work analyzes
approximately 1,350 X (formerly Twitter) users and shows that empathy-based
counter-speech increases the retrospective deletion of hateful comments.
Similarly, the work by (Lahnala et al. 2022) thoroughly examines using empathy
as a proxy for mitigating toxicity and shows a distinction between emotional
and cognitive empathy. This finding shows that empathy’s cognitive components
significantly outperform its emotional components in toxicity mitigation.
Sentiment is widely employed as a proxy for identifying hateful comments. For
instance, the work by (Brassard-Gourdeau and Khoury 2019) uses sentiment
detection to recognize toxic and subversive online comments. Another work by
(Yu, Blanco, and Hong 2022) studies contents’ sentiments and sarcasm as
figurative language using six pairs of parent and target Reddit comments. The
author then evaluates the text polarity’s effectiveness and figurative
language in identifying hate- and counter-speech. In their work, they find
that counter-speech contains fewer negative and disgusting words than hateful
comments.

Conversely, there have also been attempts to study the non-emotion-based
aspects of counter- speech. In this setting, the content’s behavior and
social-linguistic features are the main aspects of the counter-speech
analysis. For instance, the work by (Mun et al. 2023) analyzes strategies used
in the counter speech to dispel the underlying inaccurate stereotypes implied
by hateful comments. Their work analyzed the following six psychological
strategies: alternate groups, external factors, alternate qualities,
counterexamples, broadening, and general denouncing. They used 1.8 posts from
the CONAN dataset and approximately 6,000 Reddit posts to evaluate human
preferences for these strategies in human-written and machine-generated
settings. Their findings show that human-written countering strategies are
specific to the implied stereotype, whereas machine-generated counter-speech
uses general strategies. A study by (Mathew et al. 2019) defined counter-
speech types based on the strategy users employed: presenting fact, warning of
offline or online consequences, humor, and hostility. In their study they
analyzed 13,000 YouTube comments and found that ”hostile language” is the
major strategy in most instances of counter-speech .

###  2.2 Content Dynamics and Interaction Types

Recent works explored generative counter-speech using the instruction-based
model LLM to mitigate hate speech. For instance, the work by (Sen et al. 2023)
introduces a semi-automated generation mechanism using GPT2, GPT-3.5, and
flanT5 to produce counter-speech to combat hateful comments, specifically
targeting sexism and general hate. A work by (Agarwal, Chen, and Sastry 2023)
uses Llama2 and GPT-3.5 to rephrase hateful comments and mitigate the
messages’ offensiveness. However, researchers have also begun evaluating the
effectiveness of off-the-shelf, black-box hate speech detection models. For
instance, (Pozzobon et al. 2023)’s work describes the difficulties with
utilizing black-box APIs for the evaluation of toxicity in research. Black-box
APIs are software tools that provide access to pre-trained models without
revealing their internal operations. APIs facilitate advanced model use, but
they introduce significant challenges, including the lack of transparency in
the models’ decision-making processes. Validating a model’s result can be
difficult without a thorough understanding of how the model made its
predictions.

###  2.3 Persuasion Techniques in Conversations

Persuasion is a communication method that attempts to influence or convince
others of a specific action or conclusion on an issue (Dutta, Das, and
Chakraborty 2020). Persuasion has been incorporated into different
conversation-based processing and has been used in arguments with counter-
narrative speech to influence speakers (Wright et al. 2017). However,
persuasion is primarily used in fake news detection to identify conversation
amplifications (Orrù et al. 2022).

The work by (Pauli, Derczynski, and Assent 2022) models persuasion by using
rhetorical appeals to find undesired persuasion in the speed of misinformation
. Aristotle defined rhetoric as the study of persuading or influencing others
through speech or writing utilizing the three types of appeals: logos, ethos,
and pathos (Rapp 2023; Cope and Sandys 2010). Ethos is the process of
persuading people using a speaker’s credibility, while pathos persuades using
the listener’s emotion and logos persuades using an argument’s validity. The
study findings demonstrated that rhetorical appeal misuse is prevalent in
misinformation.

Persuasive language plays a key role in aspects of human conversation. Yet,
little empirical evidence about modeling persuasive language in hateful
conversations exists. This study assesses the incorporation of persuasion as a
factor for hate speech detection and examines the interplay with counter-
narratives.

Closed (multi-turn)
---
Topic | HS | CS | Total
Racism | 1,480 | 1,477 | 2,957
Sexism | 1,390 | 1,391 | 2,781
Religion | 1,437 | 1,437 | 2,874
| 8,612
Open (single-turn)
Topic | HS | CS | Total
Racism | 211 | 297 | 508
Sexism | 221 | 328 | 549
Religion | 125 | 154 | 279
Other | 108 | 143 | 251
| 1,587
Table 1: Distribution of hate speech (HS) and counter-speech (CS) for the
ContextCounter [Open (single-turn)] and DialogConan [Closed (multi-
turn)]datasets.

##  3 Experimental Setup

###  3.1 Counter-speech Baseline Datasets

| Model | Method | Sexism | Racism | Religion | Overall
---|---|---|---|---|---|---
P | R | F1 | P | R | F1 | P | R | F1 | F1
closed | Llama-2 | txt | 0.96 | 0.96 | 0.96 | 0.96 | 0.95 | 0.95 | 0.90 | 0.88 | 0.88 | 0.93
txt + pr | 0.95 | 0.95 | 0.95 | 0.95 | 0.95 | 0.95 | 0.94 | 0.94 | 0.94 |  0.95 ****
BERT | txt | 0.95 | 0.95 | 0.95 | 0.96 | 0.96 | 0.96 | 0.95 | 0.95 | 0.95 | 0.95
txt + pr | 0.95 | 0.95 | 0.95 | 0.97 | 0.96 | 0.96 | 0.95 | 0.95 | 0.95 | 0.95
Bilstm | txt | 0.87 | 0.87 | 0.86 | 0.90 | 0.90 | 0.90 | 0.88 | 0.88 | 0.88 | 0.88
txt + pr | 0.88 | 0.88 | 0.88 | 0.91 | 0.91 | 0.91 | 0.88 | 0.88 | 0.88 |  0.89 ****
SVM | txt | 0.92 | 0.92 | 0.92 | 0.94 | 0.94 | 0.94 | 0.90 | 0.90 | 0.90 | 0.92
txt + pr | 0.93 | 0.93 | 0.93 | 0.93 | 0.93 | 0.93 | 0.92 | 0.92 | 0.92 | 0.93
Open | Llama-2 | txt | 0.70 | 0.51 | 0.30 | 0.78 | 0.76 | 0.77 | 0.70 | 0.61 | 0.53 | 0.57
txt + pr | 0.70 | 0.71 | 0.69 | 0.30 | 0.29 | 0.37 | 0.22 | 0.48 | 0.31 |  0.59 ****
BERT | txt | 0.87 | 0.89 | 0.87 | 0.89 | 0.88 | 0.88 | 0.80 | 0.80 | 0.80 | 0.86
txt + pr | 0.91 | 0.91 | 0.91 | 0.81 | 0.81 | 0.81 | 0.72 | 0.71 | 0.69 | 0.83
Bilstm | txt | 0.79 | 0.78 | 0.78 | 0.77 | 0.70 | 0.71 | 0.80 | 0.71 | 0.70 | 0.76
txt + pr | 0.81 | 0.76 | 0.77 | 0.80 | 0.79 | 0.80 | 0.68 | 0.68 | 0.68 |  0.76 **
SVM | txt | 0.89 | 0.82 | 0.83 | 0.83 | 0.79 | 0.80 | 0.83 | 0.76 | 0.76 | 0.81
txt + pr | 0.90 | 0.86 | 0.87 | 0.87 | 0.85 | 0.85 | 0.87 | 0.84 | 0.84 |  0.86 ***

Table 2: Performance (Binary F1) of training four models on two datasets
closed (multi-turn) and open (single-turn). Using two settings, training on
text only (txt), and training on text along with persuasion category (txt+pr).
We report the precision (P) and recall (R) along with F1 scores per topic and
the averaged macro F1 overall the topics. The bold scores indicates the best
macro F1 average for each model set (txt and text$+$pr). McNemar’s significant
test, ** (p $<$ .01), *** (p $<$ .001), and **** (p $<$ .0001)

To conduct our experiment, we selected two recent and well-known counter-
speech datasets. For the closed (multi-turn) dataset, we used (DialogConan,
Bonaldi et al. 2022), which contains approximately 3,000 unique dialogues
between two users and nearly 16,000 turns of conversation covering different
topics spanning the following three domains: racism, sexism, and religion. For
open (single-turn) interactions, we selected the recently released dataset
(ContextCounter, Albanyan, Hassan, and Blanco 2023), which contains 2,000 X
(formerly Twitter) posts demonstrating interactions among many users (open
interactions). These datasets provide a solid dialogical data baseline and
support our experiment’s aim to investigate the interaction type and context
of the reply. ContextCounter is a context-based dataset collected from X, a
social media platform containing hate speech, counter-speech, and replies to
counter-speech. This dataset further focuses on identifying replies’ stances
towards and the type of context they add to counter-speech X posts. To manage
the gold annotations (labels by trained human annotators) costs, we selected
half of the dialogues from DialogConan in each topic. Figure 4 illustrates the
dataset distribution for each topic of the persuasion modes.

Figure 1: Proportion of persuasion modes (emotion, reason, and credibility) in
human hate speech (HS) and counter-speech (CS), and in generated counter-
speech (GC) using GPT3 and LLama-2.

Figure 2: Proportion of persuasion modes in human counter-speech over topic

Figure 3: Proportion of persuasion modes in generated counter-speech (GPT3)
over topic

Figure 4: Proportion of persuasion modes in generated counter-speech (LLama-2)
over topic

Adhering to the conversational structure observed in multi-turn conversations,
where the average length was seven or more turns, the final gold annotation
count for single-turn conversations reached 1,500. The multi-turn conversation
dataset has several topic categories. We merged the fine-grained topic labels
to unify the hate speech labeling process in the closed (multi-turn)
conversation dataset (Appendix A).

Meanwhile, the open (single-turn) conversation dataset lacks topic labels, so
human annotators labeled the data by conversation topic and persuasion modes
(for more detail, see Appendix A). Table 1 illustrates the distribution of the
two datasets that we used for our experiments concerning hate speech and
counter-speech, spanning the three topics.

#### Persuasion Mode Labeling

For each conversation set, two annotators used LabelBox
222https://labelbox.com/ to perform the annotations. The annotators are
trained on specific annotation guidelines to define persuasion labels for
multi-turn conversations and conversation topic identification for single-turn
ones (Appendix B provides further annotation process details). We further
examined the annotation quality by using consensus between annotators to
measure their level of agreement. The average agreement between the annotators
for each topic was 70%, demonstrating optimal annotation quality.  We used the
conceptual aspects of persuasive arguments, based on persuasion theory’s
rhetorical appeals of ethos, logos, and pathos, to define the persuasion mode
labels (Lukin et al. 2017; Cope and Sandys 2010). This type of labeling
provides a method for understanding the high-level modes of persuasion in
counter-speech and its interplay with hate speech. Since this study
concentrates on the persuasion modes used in counter-speech, we use the
coarse-grained persuasion labels rather than the fine-grain strategies.
Moreover, current research has begun grouping persuasion strategies by their
original conceptual definitions. For example, in the work done by (Pauli,
Derczynski, and Assent 2022), they grouped the fake news detection fallacy
types into faulty ethos, logos, and pathos appeals. In this study, we
implement a high-level persuasion distillation using the coarse-grain
persuasion modes to categorize these three persuasion modes as reason (which
indicates logos), credibility (which indicates ethos), and emotion (which
indicates pathos). In detail, each label is defined as follows:

  * •

Reason. This label indicates logos as a mode of persuasion. Appeals to the
utilization of logic or reasons, for instance, giving related examples or
supporting evidence.

  * •

Credibility, this label indicates ethos as a mode of persuasion. Relies on the
credibility or authority derived from the speaker’s personal experience.

  * •

Emotion, this label indicates pathos as a mode of persuasion. Evoke emotions
and feelings of the audience and put them in a certain mood that appeals to
emotions.

###  3.2 Classifying Hate- and Counter-speech Using the Persuasion Modes

There are few studies that tried to detect counter-speech, such as (Garland et
al. 2020), due to the complexity of the hate conversation and the thin line
between hate speech and counterspeech, as in some cases, hostile and offensive
language might be used in the counter-speech (Mathew et al. 2019; Yu, Blanco,
and Hong 2022). Thus, to answer RQ1, we design a task to classify a given turn
of a conversation as hate speech or counter speech given a persuasion mode. We
experimented with the use of various model types, including the baseline model
(SVM), (Bilstm), masked language model (BERT), and an instruction tuning model
(Llama), on multi-turn and single-turn conversation datasets. A stratified
train/validation/test split of 70%/10%/20% was performed for each experiment
(Appendix C provides a comprehensive overview of the dataset split details and
models’ main hyperparameters). To incorporate the persuasion modes in a binary
classification, the persuasion modes were integrated as a categorical feature.
For the baseline model (SVM), we use word N-gram and Char N-gram. In Char
N-gram, the representation was conducted at the character level, considering
character bigrams of up to 5-grams. In the transformer model, we used bert-
based-uncased and structured a multimodal model that combines the categorical
features with the textual data from the dataset. First, we created individual
multilayer perceptrons (MLPs) for the categorical features (persuasion modes)
and then concatenated them with the transformer output to be processed before
the final classifier layer. This method can be represented as $M=T||MLP(c)$,
where $M$ refers to the multimodal representation, $||$ is the concatenation
operator, $T$ is the transformer output, and c represents the categorical
features (persuasion mode). For the Llama model, we incorporated specific
settings into the instruction prompt to ensure the model’s awareness of
persuasion modes while classifying hate- and counter-speech (Appendix C). The
instruction (prompt) tuning and parameter configuration settings in the
training process for all models were the same as in the first task (please see
Appendix A). We used a Bilstm model with a 64-unit bidirectional LSTM layer.
This was followed by a 0.2 dropout rate, 0.2 recurrent dropout rate, and dense
layer with sigmoid activation for prediction. We used the BERT-base-uncased
model for BERT. The training involved 5 epochs, batch sizes of 16, and a 5e-5
learning rate. The Llama-2-7B training consisted of 20 epochs and a 2e-4
learning rate. The parameter efficient fine-tuning (PEFT) approach was
employed to fine-tune a limited number of trainable parameters, reducing
computational and storage expenses. For efficient fine-tuning, we used low-
rank adaptation (QLoRA) to reduce the LLM memory usage without compromising
performance. . The results of training on text only and incorporating
persuasion with the text are shown in table 2. Appendix C provides a detailed
description of McNemar’s significant text comparing the prediction of the
baseline model (txt) with (txt+pr).

Topic-Type | Human-GPT | Human-LLAMA
---|---|---
Religion-Closed(multi-turn) | 1.08e-101 **** | 3.55e-90 ****
Racism-Closed(multi-turn) | 1.22e-15 **** | 1.04e-37 ****
Sexism-Closed(multi-turn) | 5.95e-113 **** | 1.32e-124 ****
Religion-Open(single-turn) | 4.74e-22 **** | 1.72e-22 ****
Sexism-Open(single-turn) | 4.81e-40 **** | 1.88e-38 ****
Racism-Open(single-turn) | 1.08e-14 **** | 0.007 ***

Table 3: Comparison of generative and human counter-speech. All comparisons
are statistically significant (p $<$ 1.00), ** (p $<$ .01), *** (p $<$ .001),
and **** (p $<$ .0001)

###  3.3 Counter-speech Generation

In this study, we evaluate persuasion modes in generative counter-speech and
compare them to human persuasion modes to analyze language models’ ability to
generate counter-speech. We employed Chat GPT-3 and Llama2 (the most recent
instruction-based language models) to generate counter-speech for each hateful
comment in multi- and single-turn conversations. Primarily, we use the open-
source LLaMA2-chat13B (Touvron et al. 2023) model, which was fine-tuned on the
publicly available instruction datasets with 13 billion parameters using the
GGML format model (library for machine learning that allows for CPU
inferencing). We also used the GPT-3.5-turbo-0613 model accessed via the
OpenAI API 333https://openai.com/blog/gpt-3-5-turbo-fine-tuning-and-api-
updates. We instructed the models using the direct method without few-shot
fine-tuning in the counter-speech data since the aim is to assess the
persuasion modes those models use (i.e., zero-shot setting with no examples
provided). We used the same instructions for the two models to force counter-
speech generation. The model GPT-3.5 tends to refuse to generate counter-
speech for offensive posts when directly prompted. We used a task-specific
prompt that indicated that the model needed to generate a counter-argument and
explained the task in the instructions. We used both models’ standard system
prompt styles and the same instructions to indicate that the task is to
generate counter-speech 444Appendix D provides a sample of the instructions
for the zero-shot instruction model to generate counter-speech for hateful
posts.

#### Generated-counter Labeling

After generating counter-speech for the datasets, a multi-classification
approach was applied to the gold persuasion labels to populate the weakly
supervised labels of persuasion modes in the generated counter-speech models.
This weak labeling approach has been adopted to label the output of the
generated text to maintain the annotation cost, especially if the annotation
needs to be done for multiple generative model outputs (Sen et al. 2023). More
specifically, for persuasion labeling in the conversations, weak supervision
modeling of persuasion has been shown effective in previous studies (Chen and
Yang 2021). They used a weakly supervised model that utilizes partially
labeled data to predict associated persuasive strategies for each sentence.
Thus, to maintain the cost of annotation, in this study, we experimented with
different models: Baseline (SVM), Masked Language Model (BERT), and
Instruction-tuning model (Llama-2). We train these models to predict the
persuasion modes on the gold annotations from the two datasets, closed (multi-
turn) and open (single-turn), using the same split of training and testing
reported in Appendix C. The result of training the models to predict the
persuasion modes and the hyperparameters used for this task were consistent
with those employed in the binary classification task (the weak labeling of
the generative process has been detailed in Appendix D).

#### Validation of Counter Generative Persuasion Labels

We ran both benchmark and human verification validations. In the benchmark
validation, where the remaining Close(multi-turn) DialogConan dataset that has
not been included in our gold annotations has been used to verify the general
model behavior. First, we compared the results of predicting the binary
classes of hate/counter in the silver (weak label) and gold label benchmarks.
We detailed the process in Appendix D and show that the behavior of the models
trained on the silver labels is consistent with models trained on gold labels.
This consistent pattern across diverse models reinforces the weakly supervised
model’s efficacy in predicting the persuasion modes in the generated counters
set. In the human validation, we manually validated the first 50 examples for
each topic. To validate the result of weak supervised labels for the generated
counter, we selected 50 generated counters from each topic and generative
model and validated the persuasion label. Independent annotator (one of the
authors) carried out the validation process through the two datasets, closed
(multi-tur) and open(single-turn), to ensure consistency of the labels
moderation process. In general, the closed (multi-turn) dataset, the overall
error rate is relatively small, about 19% for GPT3 and 12% for Llama2. For the
open (single-turn) dataset, the overall error rate is about 22% for GPT3 and
16% for Llama2 (more details in Appendix D).

To conduct our analysis, we investigated the significance of the comparison
between the persuasion labels of humans and generated a counter using the chi-
squared $X^{2}$, as shown in table 3, which shows that all the comparisons are
statistically significant.

Figure 5: Flow of persuasion modes between hate speech (HS) and counter-
narratives (CNs) in single-turn conversations (open interactions) Figure 6:
Correlation based on the reply’s stance

Figure 7: Multi-turn Racism

Figure 8: Multi-turn Sexism

Figure 9: Multi-turn Religious

Entities identified by Riveter and power scores of personas across closed
(multi-turn) conversations for three topics

Figure 10: Single-turn Racism

Figure 11: Single-turn Sexism

Figure 12: Single-turn Religious

Entities identified by Riveter and power scores of personas across open
(single-turn) conversations for three topics

##  4 Results and Analysis

In this section, we showcase the results of the previous experiments and
interpret the findings for each research question. The interplay of persuasion
modes in hate- and counter-speech in each type of conversation interaction,
RQ1. We trained various models to examine the effectiveness of persuasion
modes in identifying hate- and counter-speech. Mainly, we evaluated the
general capability of the persuasion modes as proxies to predict counter-hate
using open (single-turn) and closed (multi-turn) conversation interactions.
Table 2 illustrates a slight enhancement in the models’ performance when the
persuasion modes are incorporated as categorical features. Some models (such
as the masked language model BERT) have more resistance and an overall stable
performance in closed (multi-turn) interactions, with a small increase in the
Racism topic’s precision score (approximately a 97% F1 score). The same model
behavior can be observed in open (single-turn) interactions, in which BERT
performs better when text-only input is used instead of text and persuasion on
the overall average macro (F1 score). However, for topics, such as racism, the
text and persuasion performance is better, with a 91% F1 score. The text-only
F1 score is approximately 87%. This might be partly due to the transformer-
based model’s data shift effect, as some topics perform better than others.
The same behavior can be observed with open (single-turn) interactions in
Bilstm, where the performances of the text-only and text+persuasions are
stable, with F1 scores of approximately 76%. There is relatively little open
interactions data at the topic level 1. For instance, religion has 279
samples. The prediction score on the religious topic has the lowest F1
performance in Bilstm. This model behavior also can be confirmed with the SVM
open interactions performance because it has a better F1 score than
instruction-tuning model Llama, with (86% F1). However, in the closed (multi-
turn), Bilstm slightly improves when persuasion is incorporated with text, for
an F1 score of 89% compared to an F1 score of 88% for using the text alone.
The instruction-tuning model’s (Llama-2) performance is enhanced when
persuasion modes are incorporated as a feature, as a few examples in the
instruction fine-tuning show (an F1 score of 95% in close (multi-turn)). This
can also be seen in the open (single-turn) dataset with Llama-2, which
improved when using text and persuasion (59% F1) compared to the text alone
(57% F1).

Persuasion mode variations expressed in generated and human counter-speech,
RQ2.

We analyzed the persuasion mode distribution by conversation interaction type
and generative- and human-based counter-speech (please see figure 4). We also
used Riveter (Antoniak et al. 2023) to assess the social dynamics of personae
in the conversations for each persuasion type (please see figures 9\- 12).
Overall, generative counter-speech persuades using emotions, while humans
persuade using reason. As shown in figure 4, for closed (multi-turn)
interactions, the reason persuasion mode comprises approximately 45% of human
counter-speech, compared to only 31% and 21% of generative counter-speech (GPT
and Llama-2, respectively). Similarly, in open interactions, 51% of human
counter-speech uses reason, compared with 33% and 19% in the GPT and Llama-2
generative models, respectively. Looking closely at the topic level, the
reason persuasion mode is more often used in racism than in religion and
sexism. Emotion, however, is more often used in counter-speech, especially in
closed (multi-turn) interactions figure 4. This finding can be further
reiterated by reviewing the persona dynamics in the racism topic using Riveter
(figure 9 and figure 12). Using reasoning statements that contain
”immigration” and ”statistics” as entities have more powerful personae scores
in this conversation when discussing rationales to support counterpoints in
closed (multi-turn) interactions (see figure 9). These personae tend to be
fact-based rationales (immigration rules, statistical studies), while
persuasion in open (single-turn) interactions tends to use personal
characteristics (e.g., skin color) . Conversely, in conversations that used
emotion-based persuasion, the pronoun ’you’ has the highest persona dynamic
power score this mode in conversation tends to reflect shameful feelings in
the hate speech poster. Interestingly, credibility has a slightly higher
presence in human (8%) than in generative (5%) counter-speech (see figure 4).
As in closed (multi-turn), human counter-speech supports their argument by
using personal stories. For instance, in the religion topic, conversations
using the credibility persuasion mode tends to use first-person perspective
(”I”) when telling personal stories (see figure 9). For Religious and sexist
topics, conversations often use credibility compared to racism topics, in both
human and generated counter-speech.

The reply’s stance toward counter-speech, RQ3. The open interaction
conversation data has the distinct feature of enabling the broader public
(many users) to engage with the post compared to closed interaction
conversations in which the dialogue is exclusively between two users (such as
on GPT and chat platforms). We use open (single-turn) interaction conversation
data to examine the following replies’ stances toward counter speech. In
contrast to previous studies, such as (Mathew et al. 2019), which considered
the presence of a reply and likes on a post as indicative of counter-speech
support, our study implements the nuance of assessing what in counter-speech
makes it likely to receive support by focusing on the persuasion mode. Figure
5 illustrates the conversation flow between hate- and counter-speech, as well
as the stance of the replies the counter-speech receives. The general tendency
is for counter-speech to receive replies with stances against it(about 56% of
replies). The supportive replies tend to add context to further justify the
counterpoint (22.5%). Most of the against replies (43%) do not directly attack
the counter-speech; instead, they express a rejection of the counterpoint. We
examine the distribution of support and rebuttal stances by persuasion mode in
figure 6. There is a noticeable tendency for replies to support counter-speech
that uses the reasonpersuasion mode (50.4%), compared to 74% of replies
expressing stances against the counter-speech. The replies to counter-speech
that used the emotion persuasion mode were 40.7% against, and 37.7% in
support.

##  5 Discussion and Implication

We interpret the results of our analysis by first discussing the interplay
between persuasion and counter-speech. Next, we clarify the multifaceted
impacts of interaction type and propose possible directions for future work.
The results show that the persuasion mode can provide an explainability method
to counter-speechstudies.
Conversation type and persuasion modes. Interaction types of the conversation
clearly have nuanced effects on counter-speech’s exhibited persuasion mode. As
shown in Figure 4, 51% of the counter-speech examples in open (single-turn)
use reason as persuasion mode, while 45% of counter-speech examples in closed
(multi-turn) use reason. This might be partly due to the cost of public
opinion and the need to appear knowledgeable, which might drive people’s
tendency to support their counterpoints with facts and reasoning. By studying
the persona’s social dynamic in hate conversations using Riveter (Antoniak et
al. 2023), as shown in ( Figure 9\- 12), we can see this behavior and its high
power score towards people of a targeted group (white or black people) and
lower power score when using self-reference (I). This persuasion mode tends to
focus the conversation on justifying counterpoints to the target group rather
than expressing the reason behind the hate and linking it to the self as a
valid reason to hate. We also show the effectiveness of using persuasion as a
proxy to differentiate counter-speech from hate speech (see Table 2), which
highlights the persuasion mode’s ability to serve as an explanation instrument
for hate detection model interpretability. Overall, these results offer an
overview of counter-speech nuances and the conversation interaction type’s
effect on how those confronting hate might incorporate modes of persuasion to
express their counterpoint and combat hate.

Generative counter-speech persuades using emotions, while humans persuade with
reason. Our results show a clear gap in current counter-speech works’ singular
focus on emotion-based hate-speech analyses, which further confirms other
work’s recent findings (Lahnala et al. 2022). For instance, the work by
(Lahnala et al. 2022) highlights the need to look beyond emotion, as it shows
that cognitive empathy better mitigates hate than emotion-based empathy.
Figure 4 shows human-based counter-speech’s clear tendency to use reason to
express a counterpoint (over 45%), compared to a mere 19% of generative
counter-speech that uses reason. Emotion is the main persuasion mode.
Generative counter-speech’s behavior highlights the vicious cycle of training
hate speech detection models on emotion-based data and the complexity of
incorporating reason as a factor in current generative content. A few recent
studies pointed out generative counter-speech’s limitations, such as (Mun et
al. 2023; Sen et al. 2023); their conclusion focused on a target-specific
strategy and showed a lack of persuasive power in generative counter-speech
when compared to human-based counter-speech. Our study’s findings intensify
the call for studying counter-speech’s non-emotional factors as a building
block of hate mitigation.

Conversation structures and reply stance. Open interaction conversations (such
as those on social media) are a means to observe how the public perceives
counter-speech from multiple users’ perspectives. Thus, we analyze the follow-
up replies to counter-speech and show the stances of the replies toward each
type of persuasion mode. The results in figure 5 show that the conversational
flow of alternates between counter-speech and reply. Approximately 59% of
counter-speech examples received replies with against stances, especially if
the examples used the emotional persuasion mode (representing about 40.7% of
the against stances, as in figure 6). A reply’s support stance is likely to
favor the reason persuasion mode, as represented in about 50.4% of supportive
replies. Since this study’s scope is on providing a high-level comparison of
the conversation interaction types and persuasion mode, possible future
research might incorporate a nuanced analysis of how users perceived counter-
speech by specific topic. Recent studies investigated the role of conversation
structure in studying hate detection. For instance, the work by (Yi and
Zubiaga 2023) surveys session-based cyberbullying and discusses the structure
of social media conversations to construct a cyberbullying dataset. In our
study shows an empirical investigation that paves the way for future
consideration of reply of counter-speech to enhance the interpretation of the
counter-speech effective factors. The size of the open (single-turn) has its
limitations in this study. We further justify the results using the
significant test of comparison to validate the interpretation of comparison
between the generated counter and human counter, figure 3. Further, the
consistency pattern with most of the multi-turn model behavior in the
classification of hate speech and counter-hate alleviates the cost of the
limited size of open (single-turn) data in this study.

##  6 Conclusion

In this study, we examined the persuasion modes in two types of conversations:
closed (multi-turn) and open (single-turn) interaction. First, we categorize
three types of persuasion in counter-speech: emotion, reason, and credibility.
Next, we assess the persuasion modes in generative counter-speech and compare
them to human-based counter-speech. Our findings mainly demonstrate that the
persuasion modes used in counter-speech are nuanced and noticeably dependent
on the conversation topic. Emotion as a persuasion mode is more prevalently
used in generative than in human-based counter-speech, which primarily uses
reason to defend counterpoints. Reason-based counter-speech tends to obtain
more support responses compared to emotion-based counter-speech. Our work
notes the potential in incorporating persuasion modes in studies on countering
hate speech to serve as explanatory proxies to further enhance the ability to
interpret hate speech detection. Current counter-speech studies lack a clear
method with which to evaluate what makes counter-speech optimal. The
conversation structure in hate speech detection need to consider including the
replies’ stances on counter-speech as an analysis factor. This feature has a
clear presence in open (single-turn) interactions, in which multiple users can
reply to counter-speech. Our work focused on hate speech as a general
category. Future research might further evaluate the persuasion modes using
more fine-grained base types of hate (e.g., offensiveness, toxicity, or
abusiveness). A narrower domain-level counter-speech evaluation could be
performed by studying the topic’s social aspects, such as racial
discrimination against individuals or groups.

##  7 Ethical Statement

In this study, we extend current research attempts to evaluate the capability
of speech in confronting hate. Yet we have cautiously handled the extra
annotation layer (persuasion mode) that we added to the existing two released
datasets by ensuring the anonymity of the posts using the ID of X posts data.
Additionally, we carefully handled the data annotation process by removing any
mention of the user account from the open interaction dataset, ContextCounter,
(Albanyan, Hassan, and Blanco 2023); we also ensure a fair wage for the
annotators by using the label box annotation platform fixed pricing for
annotation per hour (as explained in Appendix B). Moreover, we tried to limit
the direct report of hate messages as a sample for analysis by using Riveter
(Antoniak et al. 2023) to measure the power and dynamics between the social
entity of conversation and help with interpreting the narrative of hate and
counter-speech.

## References

  * Agarwal, Chen, and Sastry (2023) Agarwal, V.; Chen, Y.; and Sastry, N. 2023.  HateRephrase: Zero- and Few-Shot Reduction of Hate Intensity in Online Posts using Large Language Models.
  * Albanyan, Hassan, and Blanco (2023) Albanyan, A.; Hassan, A.; and Blanco, E. 2023.  Not All Counterhate Tweets Elicit the Same Replies: A Fine-Grained Analysis.  In _Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)_ , 71–88. Toronto, Canada: Association for Computational Linguistics.
  * Antoniak et al. (2023) Antoniak, M.; Field, A.; Mun, J.; Walsh, M.; Klein, L.; and Sap, M. 2023.  Riveter: Measuring Power and Social Dynamics Between Entities.  In Bollegala, D.; Huang, R.; and Ritter, A., eds., _Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)_ , 377–388. Toronto, Canada: Association for Computational Linguistics.
  * Ashida and Komachi (2022) Ashida, M.; and Komachi, M. 2022.  Towards Automatic Generation of Messages Countering Online Hate Speech and Microaggressions.  In _Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH)_ , 11–23. Seattle, Washington (Hybrid): Association for Computational Linguistics.
  * Barden and Petty (2012) Barden, J.; and Petty, R. E. 2012.  Persuasion.  In Ramachandran, V. S., ed., _Encyclopedia of Human Behavior (Second Edition)_ , 96–102. San Diego: Academic Press, second edition edition.
  * Bonaldi et al. (2022) Bonaldi, H.; Dellantonio, S.; Tekiroglu, S. S.; and Guerini, M. 2022.  Human-Machine Collaboration Approaches to Build a Dialogue Dataset for Hate Speech Countering.
  * Brassard-Gourdeau and Khoury (2019) Brassard-Gourdeau, E.; and Khoury, R. 2019.  Subversive Toxicity Detection using Sentiment Information.  In Roberts, S. T.; Tetreault, J.; Prabhakaran, V.; and Waseem, Z., eds., _Proceedings of the Third Workshop on Abusive Language Online_ , 1–10. Florence, Italy: Association for Computational Linguistics.
  * Caldevilla-Domínguez, Barrientos-Báez, and Padilla-Castillo (2023) Caldevilla-Domínguez, D.; Barrientos-Báez, A.; and Padilla-Castillo, G. 2023.  Dilemmas Between Freedom of Speech and Hate Speech: Russophobia on Facebook and Instagram in the Spanish Media.  _Politics and Governance_ , 11(2): 147–159.
  * Catherine (2022) Catherine, B. 2022.  Why They Do It: Counterspeech Theories of Change.  _SSRN_.
  * Chen and Yang (2021) Chen, J.; and Yang, D. 2021.  Weakly-Supervised Hierarchical Models for Predicting Persuasive Strategies in Good-faith Textual Requests.
  * Cope and Sandys (2010) Cope, E. M.; and Sandys, J. E. 2010.  _Aristotle: Rhetoric_.  Cambridge University Press.
  * Dutta, Das, and Chakraborty (2020) Dutta, S.; Das, D.; and Chakraborty, T. 2020.  Changing views: Persuasion modeling and argument extraction from online discussions.  _Inf. Process. Manag._ , 57(2): 102085.
  * Gagliardone et al. (2015) Gagliardone, I.; Gal, D.; Alves, T.; and Martinez, G. 2015.  _Countering online hate speech_.  UNESCO Publishing.
  * Garland et al. (2020) Garland, J.; Ghazi-Zahedi, K.; Young, J.-G.; Hébert-Dufresne, L.; and Galesic, M. 2020.  Countering hate on social media: Large scale classification of hate and counter speech.  In Akiwowo, S.; Vidgen, B.; Prabhakaran, V.; and Waseem, Z., eds., _Proceedings of the Fourth Workshop on Online Abuse and Harms_ , 102–112. Online: Association for Computational Linguistics.
  * Hangartner et al. (2021) Hangartner, D.; Gennaro, G.; Alasiri, S.; Bahrich, N.; Bornhoft, A.; Boucher, J.; Demirci, B. B.; Derksen, L.; Hall, A.; Jochum, M.; Munoz, M. M.; Richter, M.; Vogel, F.; Wittwer, S.; Wüthrich, F.; Gilardi, F.; and Donnay, K. 2021.  Empathy-based counterspeech can reduce racist hate speech in a social media field experiment.  _Proc. Natl. Acad. Sci. U. S. A._ , 118(50).
  * Lahnala et al. (2022) Lahnala, A.; Welch, C.; Neuendorf, B.; and Flek, L. 2022.  Mitigating Toxic Degeneration with Empathetic Data: Exploring the Relationship Between Toxicity and Empathy.  In Carpuat, M.; de Marneffe, M.-C.; and Meza Ruiz, I. V., eds., _Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies_ , 4926–4938. Seattle, United States: Association for Computational Linguistics.
  * Lukin et al. (2017) Lukin, S.; Anand, P.; Walker, M.; and Whittaker, S. 2017.  Argument strength is in the eye of the beholder: Audience effects in persuasion.  In _Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers_. Stroudsburg, PA, USA: Association for Computational Linguistics.
  * Mathew et al. (2019) Mathew, B.; Saha, P.; Tharad, H.; Rajgaria, S.; Singhania, P.; Maity, S. K.; Goyal, P.; and Mukherjee, A. 2019.  Thou Shalt Not Hate: Countering Online Hate Speech.  _ICWSM_ , 13: 369–380.
  * McNEMAR (1947) McNEMAR, Q. 1947.  Note on the sampling error of the difference between correlated proportions or percentages.  _Psychometrika_ , 12(2): 153–157.
  * Mun et al. (2023) Mun, J.; Allaway, E.; Yerukola, A.; Vianna, L.; Leslie, S.-J.; and Sap, M. 2023.  Beyond Denouncing Hate: Strategies for Countering Implied Biases and Stereotypes in Language.
  * Myers West (2018) Myers West, S. 2018.  Censored, suspended, shadowbanned: User interpretations of content moderation on social media platforms.  _New Media & Society_, 20(11): 4366–4383.
  * Orrù et al. (2022) Orrù, L.; Moro, C.; Cuccarini, M.; Paita, M.; Dalla Riva, M. S.; Bassi, D.; Da San Martino, G.; Navarin, N.; and Turchi, G. P. 2022.  Machine Learning and MADIT methodology for the fake news identification: the persuasion index.  In _4th International Conference on Advanced Research Methods and Analytics (CARMA 2022)_ , 165–172. riunet.upv.es.
  * Parker and Ruths (2023) Parker, S.; and Ruths, D. 2023.  Is hate speech detection the solution the world wants?  _Proc. Natl. Acad. Sci. U. S. A._ , 120(10): e2209384120.
  * Pauli, Derczynski, and Assent (2022) Pauli, A.; Derczynski, L.; and Assent, I. 2022.  Modelling Persuasion through Misuse of Rhetorical Appeals.  In Biester, L.; Demszky, D.; Jin, Z.; Sachan, M.; Tetreault, J.; Wilson, S.; Xiao, L.; and Zhao, J., eds., _Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI)_ , 89–100. Abu Dhabi, United Arab Emirates (Hybrid): Association for Computational Linguistics.
  * Pozzobon et al. (2023) Pozzobon, L.; Ermis, B.; Lewis, P.; and Hooker, S. 2023.  On the Challenges of Using Black-Box APIs for Toxicity Evaluation in Research.  In Bouamor, H.; Pino, J.; and Bali, K., eds., _Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing_ , 7595–7609. Singapore: Association for Computational Linguistics.
  * Rapp (2023) Rapp, C. 2023.  _Aristotle’s Rhetoric_.  Metaphysics Research Lab, Stanford University, winter 2023 edition.
  * Schmuck (2024) Schmuck, D. 2024.  ‘You Got My Back?’ Severity and Counter-Speech in Online Hate Speech Toward Minority Groups.  _Media Psychol._ , 0(0): 1–32.
  * Sen et al. (2023) Sen, I.; Assenmacher, D.; Samory, M.; Augenstein, I.; van der Aalst, W.; and Wagner, C. 2023.  People Make Better Edits: Measuring the Efficacy of LLM-Generated Counterfactually Augmented Data for Harmful Language Detection.
  * Thalken et al. (2023) Thalken, R.; Stiglitz, E.; Mimno, D.; and Wilkens, M. 2023.  Modeling Legal Reasoning: LM Annotation at the Edge of Human Agreement.  In Bouamor, H.; Pino, J.; and Bali, K., eds., _Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing_ , 9252–9265. Singapore: Association for Computational Linguistics.
  * Touvron et al. (2023) Touvron, H.; Martin, L.; Stone, K.; Albert, P.; Almahairi, A.; Babaei, Y.; Bashlykov, N.; Batra, S.; Bhargava, P.; Bhosale, S.; Bikel, D.; Blecher, L.; Ferrer, C. C.; Chen, M.; Cucurull, G.; Esiobu, D.; Fernandes, J.; Fu, J.; Fu, W.; Fuller, B.; Gao, C.; Goswami, V.; Goyal, N.; Hartshorn, A.; Hosseini, S.; Hou, R.; Inan, H.; Kardas, M.; Kerkez, V.; Khabsa, M.; Kloumann, I.; Korenev, A.; Koura, P. S.; Lachaux, M.-A.; Lavril, T.; Lee, J.; Liskovich, D.; Lu, Y.; Mao, Y.; Martinet, X.; Mihaylov, T.; Mishra, P.; Molybog, I.; Nie, Y.; Poulton, A.; Reizenstein, J.; Rungta, R.; Saladi, K.; Schelten, A.; Silva, R.; Smith, E. M.; Subramanian, R.; Tan, X. E.; Tang, B.; Taylor, R.; Williams, A.; Kuan, J. X.; Xu, P.; Yan, Z.; Zarov, I.; Zhang, Y.; Fan, A.; Kambadur, M.; Narang, S.; Rodriguez, A.; Stojnic, R.; Edunov, S.; and Scialom, T. 2023.  Llama 2: Open Foundation and Fine-Tuned Chat Models.
  * Wright et al. (2017) Wright, L.; Ruths, D.; Dillon, K. P.; Saleem, H. M.; and Benesch, S. 2017.  Vectors for Counterspeech on Twitter.  In Waseem, Z.; Chung, W. H. K.; Hovy, D.; and Tetreault, J., eds., _Proceedings of the First Workshop on Abusive Language Online_ , 57–62. Vancouver, BC, Canada: Association for Computational Linguistics.
  * Yi and Zubiaga (2023) Yi, P.; and Zubiaga, A. 2023.  Session-based cyberbullying detection in social media: A survey.  _Online Social Networks and Media_ , 36: 100250.
  * Yu, Blanco, and Hong (2022) Yu, X.; Blanco, E.; and Hong, L. 2022.  Hate Speech and Counter Speech Detection: Conversational Context Does Matter.  In _Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies_ , 5918–5930. Seattle, United States: Association for Computational Linguistics.

##  Appendix A Preparation of the baseline datasets

To prepare the dialogConan dataset, we first unife the Topic definitions and
map each target base topic into its main domain (Racism, Sexism, Religious) as
follows:
$WOMEN+(WOMEN,LGBT)+(WOMEN,POC)+LGBT\Rightarrow SEXISM$
$MIGRANTS+(MIGRANTS,MUSLIMS)+POC\Rightarrow RACISM$
$MUSLIMS+(MUSLIMS,WOMEN)+JEWS\Rightarrow RELIGIOUS$
Moreover, to maintain the cost of the annotation, we selected 50% of the
dialogues of each topic in DilogConan. More precisely, the dataset originally
contained 3059 unique dialogues spanning three topics, as shown in table 4.
This process results in our baseline dataset for the close interaction (multi-
turn) dataset, which contains about 8K turns of conversations.

Topic | Unique dialoiuges | our 50% set of dialoues
---|---|---
Racisim | 1055 | 527
Sexism | 1031 | 515
Religoius | 974 | 487
Table 4: Preparing DialogConan for annotation by selecting 50% of the unique
dialogues in each topic.

##  Appendix B Annotation Setup

We use LabelBox as a platform to recruit and run annotation tasks. This
platform enables a conversation annotation where the whole dialogue is shown
to the user with the possibility to label each conversation turn. We provide a
thorough, detailed guideline for each interaction type (open/close) to the
annotated, figure 13, and figure 14555link to a GitHub repository that
contains the guideline of annotation will be provided. At this submission, we
provide a link to the pdf share file of the annotation guideline
https://shorturl.at/bxBGS . A fair wage per hour has been provided to the team
of two annotators as part of the Boost Tier payment setting of LabelBox.
666https://labelbox.com/product/annotate/.

Figure 13: Annotation example for persuasion modes, where the whole turns are
shown to facilitate labeling each turn of the conversation. Figure 14: Topic
annotation for Open (single-turn)dataset.

##  Appendix C Classification of HS and CS Model Hyperparameter

Table 5, shows the major set of hyper parameters used for each model in the
experiment. We also provide details of the split of the datasets for
training/validation/testing where we used a stratified split from Scikit-Learn
and saved the splits files to be used through the experiment to unify
experiment settings and to further ensure reproducibility as shown in tables
6, 7.

Model | Hyperparameter | Customization
---|---|---
SVM | Regularization parameter(C) | 3.0
Kernal function | rbf
Bilstm | LSTM units (LSTM_DIM) | 64
Epochs | 10
Spatial dropout | 0.2
Dropout rate | 0.2
Recurrent dropout | 0.2
BERT | Epochs | 5
Train Batch size | 16
Eval Batch size | 20
Learning rate | 5e-5
Warm up steps | 100
Weight decay | 0.01
Llama-2-7B | Batch size | 1
Learning rate | 2e-4
Epochs | 20
Max length | 3
Warm up steps | 2
Table 5: Main models hyperparameter  Closed (Multi-Turn)
---
Topic | Training | Validation | Test | Total
SEXISM | 1946 | 250 | 585 | 2781
RACISM | 2069 | 266 | 622 | 2957
RELIGIOUS | 2011 | 258 | 605 | 2874
count | 8612
Table 6: Taring split for closed (multi-turn). Open (Single-Turn)
---
Topic | Training | Validation | Test | Total
SEXISM | 384 | 49 | 116 | 549
RACISM | 355 | 45 | 108 | 508
RELIGIOUS | 195 | 25 | 59 | 279
count | 1336
Table 7: Taring split for open (single-turn).

For Llama-2 instruction tuning to classify a turn as hate or counter. In the
modual (text+persuation) we use the following instructions:

> ”Below is an instruction that describes a task. Write a response that
> appropriately completes the request for the following persuasion setting.
> setting: [persuasion mode]
> Instruction: Classify the input text as Hate Speech or Counter Narrative
> Input: [Text of turn]”
>
> Significant test of the counter and hate predictions. We use McNemar’s
> significant test (McNEMAR 1947) to evaluate the significance of baseline
> comparison on the model level. Table 8 show the results of comparing the
> significance of model predication using baseline where the model is trained
> on text only (txt), and when using persuasion along with text (txt+pr).
>
> | Models | P value
> ---|---|---
> Closed | Llama-2(txt) and Llama-2 (txt+pr) | 2.02e-11 ****
> BERT (txt) and BERT (txt+pr) | 0.16
> BiLSTM (txt) and BiLSTM (txt+pr) | 3.64e-06 ****
> SVM (txt) and SVM(txt+pr) | 0.154
> Open | Llama-2(txt) and Llama-2 (txt+pr) | 1.11e-16 ****
> BERT (txt) and BERT (txt+pr) | 0.735
> BiLSTM (txt) and BiLSTM (txt+pr) | 0.011 **
> SVM (txt) and SVM(txt+pr) | 0.00051 ***
> Table 8: The significance of comparing the prediction pattern of the
> training model on the text as baseline (txt) with the training model on text
> and persuasion (txt+pr). All comparisons are statistically significant,
> alpha = 0.05 (p $<$ 1.00), ** (p $<$ .01), *** (p $<$ .001), and **** (p $<$
> .0001)
>
> ##  Appendix D Generated Counter Speech Settings
>
> Generating the counter speech. we use the task-specific prompts for
> LLama2-13B and GPT-3.5 turbo to generate counter speech for each hate
> message.
>
> For GPT3, we use the following prompt
>

>> ”role”: ”system”, ”content”: ”You will be provided with a message
(delimited with XML tags). Provide a counter-narrative to the arguments of
each message. ”, ”role”: ”user”, ”content”: ”$<message\>"+text+"</message>$”,

>
> For Llama2, we use the following prompt with input indicating a hate message
>

>> $[INST]$$<<SYS>>$ provide a Counterargument to the input text below
$<</SYS>>$ ${input}$ $[/INST]$

>
> Weak supervision training to classify the persuasion modes in counter-speech
> and hate-speech.  We train four models on gold annotation from close (multi-
> turn) and open (single) turn to classify the persuasion mode in input turn
> (text of hate speech). The classification output is three labels (emotion,
> reason, credibility), we report the macro F1 score in table 9. The goal is
> to identify and utilize the most effective model for assigning persuasion
> labels to the generated-counter set. The outcomes presented in Table 9 show
> that the BERT and SVM have the highest F1 score (0.50). On the contrary,
> Llama-2 has the lowest F1 score (0.20). This behavior of the instruct-tuning
> model can be due to the complexity of the persuasion as a task (multi-
> classification and unpopular labeling), which also has been confirmed in a
> recent study where they found that BERT provides an optimal result in
> comparison with another instruct-tuning model such as Llama (Thalken et al.
> 2023). By referring to the result in table 9, on topic level, BERT performs
> better on two topics out of the three (59% F1 Racism topic, and 44% F1 in
> the Religious topic). Therefore, BERT has been used to populate the
> persuasion labels for the generated counter in closed (multi-turn) and open
> (single-turn) datasets.
>
> Dataset | Topic |  | SEXISM | RACISM | RELIGIOUS | Overall
> ---|---|---|---|---|---|---
> P | R | F1 | P | R | F1 | P | R | F1 | F1
> Close and Open | Llama-2-7B | Text | 0.20 | 0.23 | 0.13 | 0.31 | 0.23 | 0.22 | 0.20 | 0.25 | 0.19 | 0.20
> BERT | Txt | 0.43 | 0.43 | 0.43 | 0.64 | 0.60 | 0.59 | 0.49 | 0.43 | 0.44 | 0.50
> Bilstm | Txt | 0.35 | 0.83 | 0.49 | 0.19 | 0.85 | 0.32 | 0.36 | 0.88 | 0.51 | 0.33
> SVM | Txt | 0.47 | 0.42 | 0.43 | 0.66 | 0.54 | 0.57 | 0.42 | 0.39 | 0.38 | 0.50
> Table 9: Binary F1 score of training the models to classify the persuasion labels (emotion, reason, credibility).  Dataset | Models |  | SEXISM | RACISM | RELIGIOUS | Overall
> ---|---|---|---|---|---|---
> P | R | F1 | P | R | F1 | P | R | F1 | F1
> Closed (multi-turn) silver | Llama-2-7B | Txt | 0.94 | 0.94 | 0.94 | 0.93 | 0.92 | 0.92 | 0.92 | 0.92 | 0.92 | 0.93
> Txt + Pr | 0.93 | 0.93 | 0.93 | 0.94 | 0.94 | 0.94 | 0.91 | 0.91 | 0.91 | 0.93
> BERT | Txt | 0.96 | 0.96 | 0.96 | 0.93 | 0.93 | 0.93 | 0.94 | 0.94 | 0.94 | 0.95
> Txt + Pr | 0.95 | 0.95 | 0.95 | 0.95 | 0.95 | 0.95 | 0.94 | 0.94 | 0.94 | 0.95
> Bilstm | Txt | 0.88 | 0.88 | 0.88 | 0.85 | 0.85 | 0.85 | 0.88 | 0.87 | 0.87 | 0.87
> Txt + Pr | 0.89 | 0.89 | 0.89 | 0.85 | 0.85 | 0.85 | 0.88 | 0.88 | 0.88 | 0.88
> SVM | Text-only | 0.90 | 0.90 | 0.90 | 0.88 | 0.88 | 0.88 | 0.90 | 0.90 | 0.90 | 0.89
> Txt + Pr | 0.90 | 0.90 | 0.90 | 0.88 | 0.88 | 0.88 | 0.91 | 0.91 | 0.91 | 0.90
> Table 10: Binary F1 score of training the models on silver labels of closed (multi-turn). Dataset | GModel | Topic | Error-persuasion
> ---|---|---|---
> Closed(multi-turn) | GPT3 | Racism | 22%
> Sexism | 24%
> Religious | 12%
> Overall | 19%
> LLAMA2 | Racism | 2%
> Sexism | 22%
> Religious | 12%
> Overall | 12%
> Open(single-turn) | GPT3 | Racism | 24%
> Sexism | 22%
> Religious | 22%
> Overall | 22%
> LLAMA2 | Racism | 12%
> Sexism | 20%
> Religious | 16%
> Overall | 16%
> Table 11: Error rate of weak persuasion labels in the generated counter by
> the two generative models GPT3, and Llama2 for two dataset closed (multi-
> turn) and open(single-turn).
>
> Validation of the weak supervision model (Benchmark method). We use the
> weakly supervised model BERT to populate the rest of the DialogConan dataset
> [closed (multi-interaction)] with persuasion modes labels (silver labels).
> Then, we investigate the performance of the binary classification of hate
> speech (HS) and counter-speech (CS) using the same classification task in
> the section but trained on silver labels (weak labels). Table 10 shows the
> averaged F1 score over the three topics trained on the silver persuasion
> label to predict HS/CN. For the instruction-tuning (Llama-2) model, the F1
> score remains the same at 0.93 for both Text-only and Text + Per scenarios.
> Similarly, the BERT model exhibits no change in the F1 score, maintaining a
> high accuracy of 0.95. The Bilstm model sees a notable increase from 0.87 to
> 0.88 when incorporating the persuasion modes. Furthermore, the Baseline
> (SVM) model displays a similar pattern, with the F1 score improving from
> 0.89 to 0.90. This consistent pattern across diverse models reinforces the
> weakly supervised model’s efficacy in predicting the persuasion modes in the
> generated counters set.
> Validation of the weak supervision model (human validation). To validate the
> result of weak supervised labels for the generated counter, we selected 50
> generated counters from each topic and generative model and validated the
> persuasion label. Independent annotator (one of the authors) carried out the
> validation process through the two datasets, closed (multi-tur) and
> open(single-turn), to ensure consistency of the labels moderation process.
> As shown in table 11, in the closed (multi-turn) dataset, the overall error
> rate is relatively small, about 19% for GPT3 and 12% for Llama2. For the
> open (single-turn) dataset, the overall error rate is about 22% for GPT3 and
> 16% for Llama2.