# Understanding Counterspeech for
Online Harm Mitigation

Yi-Ling Chung1 Gavin Abercrombie2 Florence Enock1
Jonathan Bright1 Verena Rieser2

1The Alan Turing Institute 2The Interaction Lab, Heriot-Watt University
{ychung, fenock<EMAIL_ADDRESS>
{g.abercrombie<EMAIL_ADDRESS>Now at Google DeepMind

###### Abstract

Counterspeech offers direct rebuttals to hateful speech by challenging
perpetrators of hate and showing support to targets of abuse. It provides a
promising alternative to more contentious measures, such as content moderation
and deplatforming, by contributing a greater amount of positive online speech
rather than attempting to mitigate harmful content through removal. Advances
in the development of large language models mean that the process of producing
counterspeech could be made more efficient by automating its generation, which
would enable large-scale online campaigns. However, we currently lack a
systematic understanding of several important factors relating to the efficacy
of counterspeech for hate mitigation, such as which types of counterspeech are
most effective, what are the optimal conditions for implementation, and which
specific effects of hate it can best ameliorate. This paper aims to fill this
gap by systematically reviewing counterspeech research in the social sciences
and comparing methodologies and findings with computer science efforts in
automatic counterspeech generation. By taking this multi-disciplinary view, we
identify promising future directions in both fields.

##  1 Introduction

The exposure of social media users to online hate and abuse continues to be a
cause for public concern. Volumes of abuse on social media continue to be
significant in absolute terms (Vidgen et al.,, 2019), and some claim they are
rising on platforms such as Twitter where at the same time content moderation
appears to be becoming less of a priority (Frenkel and Conger,, 2022).
Receiving abuse can have negative effects on the mental health of targets, and
also on others witnessing it (Siegel,, 2020; Saha et al.,, 2019). In the
context of public figures the impact on the witnesses (bystanders) is arguably
even more important, as the abuse is potentially witnessed by a large volume
of people. In addition, politicians and other prominent actors are driven out
of the public sphere precisely because of the vitriol they receive on a daily
basis (News,, 2018), raising concerns for the overall health of democracy.

Within this context, research on mechanisms for combating online abuse is
becoming ever more important. One such research angle is the area of
“counterspeech” (or counter-narratives): content that is designed to resist or
contradict abusive or hateful content (Benesch, 2014a, ; Saltman and Russell,,
2014; Bartlett and Krasodomski-Jones,, 2015), also see Figure 1. Such
counterspeech (as we will elaborate more fully below) is an important
potential tool in the fight against online hate and abuse as it does not
require any interventions from the platform or from law enforcement, and may
contribute to mitigating the effects of abuse (Munger,, 2017; Buerger, 2021b,
; Hangartner et al.,, 2021; Bilewicz et al.,, 2021) without impinging on free
speech. Several civil organisations have used counterspeech to directly
challenge hate, and Facebook has launched campaigns with local communities and
policymakers to promote accessibility to counterspeech
tools.111https://counterspeech.fb.com/en/ Similarly, Moonshot and Jigsaw
implemented The Redirect Method, presenting alternative counterspeech or
counter videos when users search queries that may suggest an inclination
towards extremist content or groups.222https://moonshotteam.com/the-redirect-
method/

The detection and generation of counterspeech is important because it
underpins the promise of AI-powered assistive tools for hate mitigation.
Identifying counterspeech is vital also for analytical research in the area:
for instance, to disentangle the dynamics of perpetrators, victims and
bystanders (Mathew et al.,, 2018; Garland et al.,, 2020, 2022), as well as
determining which responses are most effective in combating hate speech
(Mathew et al.,, 2018, 2019; Chung et al., 2021a, ).

Automatically producing counterspeech is a timely and important task for two
reasons. First, composing counterspeech is time-consuming and requires
considerable expertise to be effective (Chung et al., 2021c, ). Recently,
large language models have been able to produce fluent and personalised
arguments tailored to user expectations addressing various topics and tasks.
Thus, developing counterspeech tools is feasible and can provide support to
civil organisations, practitioners and stakeholders in hate intervention at
scale. Second, by partially automating counterspeech writing, such assistive
tools can lessen practitioners’ psychological strain resulting from prolonged
exposure to harmful content (Riedl et al.,, 2020; Chung et al., 2021c, ).

However, despite the potential for counterspeech, and the growing body of work
in this area, the research agenda remains a relatively new one, which also
suffers from the fact that it is divided into a number of disciplinary silos.
In methodological terms, meanwhile, social scientists studying the dynamics
and impacts of counterspeech (e.g. Munger,, 2017; Buerger, 2021b, ; Hangartner
et al.,, 2021; Bilewicz et al.,, 2021) often do not engage with computer
scientists developing models to detect and generate such speech (e.g. Chung et
al., 2021b, ; Saha et al.,, 2022) (or vice versa).

The aim of this review article is to fill this gap, by providing a
comprehensive, multi-disciplinary overview of the field of counterspeech
covering computer science and the social sciences over the last ten years. We
make a number of contributions in particular. Firstly, we outline a definition
of counterspeech and a framework for understanding its use and impact, as well
as a detailed taxonomy. We review research on the effectiveness of
counterspeech, bringing together perspectives on the impact it makes when it
is experienced. We also analyse technical work on counterspeech, looking
specifically at the task of counterspeech generation, scalability, and the
availability and methodology behind different datasets. Importantly, across
all studies, we focus on commonalities and differences between computer
science and the social sciences, including how the impact of counterspeech is
evaluated and which specific effect of hate speech it best ameliorates.

We draw on our findings to discuss the challenges and directions of open
science (and safe AI) for online hate mitigation. We provide evidence-based
recommendations for automatic approaches to counterspeech tools using Natural
Language Processing (NLP). Similarly, for social scientists, we set out future
perspectives on interdisciplinary collaborations with AI researchers on
mitigating online harms, including conducting large-scale analyses and
evaluating the impact of automated interventions. Taken together, our work
offers researchers, policy-makers and practitioners the tools to further
understand the potentials of automated counterspeech for online hate
mitigation.

Figure 1: Counterspeech dynamics. (1) Perpetrator(s) generate Hate Speech.
This may be witnessed by either targets and/or bystanders. (2)
Counterspeaker(s) respond with counterspeech, which may be directed at the
perpetrator(s), bystanders (e.g. to provide alternative perspectives), or
other targets (e.g. in support). Counterspeakers may themselves be targets or
bystanders, or could be members of organised counterspeech groups. They can
have _in-_ or _out-_ group identities with respect to either the
perpetrator(s) or the target(s). Counterspeech is directed at recipients, who
can be one or more of (a) the perpetrator(s), (b) the target(s), or (c) other
bystanders. Both counterspeakers and targets can be individual or multiple
(one-to-one, one-to-many and so on).

##  2 Background

Interest in investigating the social and computational aspects of
counterspeech has grown considerably in the past five years. However, while
extant work reviews the impact of counterspeech on hate mitigation (Saltman
and Russell,, 2014; Carthy et al.,, 2020; Buerger, 2021a, ), none have
systematically addressed this issue in combination with computational studies
in order to synthesise social scientific insights and discuss the potential
role of automated methods in reducing harms. Carthy et al., (2020) present a
focused (2016-2018) systematic review of research into the impact of counter-
narratives on prevention of violent radicalisation. They categorise the
techniques employed in counter-narratives into four groups: (1) counter-
stereotypical exemplars (challenging stereotypes, social schema or moral
exemplars), (2) persuasion (e.g., through role-playing and emotion
inducement), (3) inoculation (proactively reinforcing resistance to attitude
change or persuasion), and (4) alternative accounts (disrupting false beliefs
by offering different perspectives of events). The measurements of counter-
narrative interventions are based on (1) intent of violent behaviour, (2)
perceived symbolic/realistic group threat (e.g., perception of an out-group as
dangerous), and (3) in-group favouritism/out-group hostility (e.g., level of
trust, confidence, discomfort and forgiveness towards out-groups). They argue
that counter-narratives show promise in reducing violent radicalisation, while
its effects vary across techniques, with counter-stereotypical exemplars,
inoculation and alternative accounts demonstrating the most noticeable
outcomes. Buerger, 2021a  reviews the research into the effectiveness of
counterspeech, attempting to categorise different forms of counterspeech,
summarise the source of influences in abusive/positive behaviour change, and
elucidate the reasons which drive strangers to intervene in cyberbullying.
Here, the impact of counterspeech is mostly evaluated by the people involved
in hateful discussions, including hateful speakers, audiences, and
counterspeakers. In comparison, we focus on what makes counterspeech effective
by comprehensively examining its use based on aspects such as strategies,
audience and evaluation.

On the computational side, some work reviews the use of counterspeech in
social media using natural language processing, including work outlining
counterspeech datasets (Adak et al.,, 2022; Alsagheer et al.,, 2022),
discussing automated approaches to counterspeech classification (Alsagheer et
al.,, 2022) and generation (Chaudhary et al.,, 2021; Alsagheer et al.,, 2022),
and work focusing on system evaluation (Alsagheer et al.,, 2022). However,
work from computer sciences is not typically informed by important insights
from the social sciences, including the key roles of intergroup dynamics, the
social context in which counterspeech is employed, and the mode of persuasion
by which counterspeech operates. Taking an interdisciplinary approach, we join
work from the computer and social sciences.

##  3 Review Methodology

Taking a multi-disciplinary perspective, we systematically review work on
counterspeech from computer science and the social sciences published in the
past ten years. To ensure broad coverage and to conduct a reproducible review,
we follow the systematic methodology of Moher et al., (2009). The search and
inclusion process is shown in Figure 2.

Figure 2: Flow diagram showing the identification, eligibility screening, and
inclusion phases of the selection of items analysed in this review.

We used keyword terms related to counterspeech to search three key databases
(ACL Anthology, ArXiv, and Scopus) that together offer a broad coverage of our
target literature. We included the search terms ‘counter-speech’, ‘counter-
narratives’, ‘counter-terrorism’, ‘counter-aggression’, ‘counter-hate’,
‘counter speech’, ‘counter narrative’, ‘countering online hate speech’,
‘counter hate speech’, and ‘counter-hate speech’. We also included 34
publications that we had identified previously from other sources, but that
were not returned by keyword search due to not including relevant keywords or
not being indexed in the target search repositories. The search was conducted
in December 2022. Of the returned results, we include all publications that
concern (1) analysis of the use and effectiveness of interventions against
hateful or abusive language online, (2) characteristics of counterspeech users
or recipients, or (3) data and/or implementation designed for counterspeech
(e.g., counterspeech classification or generation). These inclusion criteria
were applied by two of the authors. Following this process, we include 90
papers for analysis in this review. Each of the papers was read by at least
one of the co-authors of the article.

Our review is divided into several sections (the results of which are
presented sequentially below). First, we examine definitional characteristics
of counterspeech, looking at how the term itself is defined, how different
taxonomies have been created to classify different types of counterspeech, and
the different potential purposes attributed to it. Then, we examine studies
that have looked at the impact of counterspeech, discussing the different
analytical designs employed and analysing evidence of the results. Following
this, we discuss computational approaches to counterspeech, focusing in
particular on both detection and generation. Finally, we examine ethical
issues in the domain of counterspeech, and also speculate about future
perspectives and directions in the field.

##  4 Defining counterspeech

Counterspeech is multifaceted and can be characterised in several different
ways. In Table 1 we outline a framework for describing and designing
counterspeech, covering who (speaker) sends what kinds of messages
(strategies) to whom (recipients), and for what purpose (purpose). Using this
structure, we summarise how counterspeech has typically been categorised in
past studies.

Aspects |  Description
---|---
Speaker |  Who is the counterspeaker? What is the social identity and status of the counterspeaker?
Strategy |  Which linguistic and rhetorical methods are used in the counterspeech? Which emotions or attitudes are expressed towards the hateful content?
Recipient |  Who is the target audience? Are they hate speakers, targets of hate, or bystanders?
Purpose |  What is the aim of disseminating counterspeech?
Table 1: Framework for describing and designing counterspeech.

Most studies in the field use one of three main terms: counterspeech, counter-
narratives (Reynolds and Tuck,, 2016; Carthy and Sarma,, 2021; Tuck and
Silverman,, 2016; Iqbal et al.,, 2019) and hope speech (Snyder et al.,, 2018).
These three terms broadly refer to a similar concept: content that challenges
and rebuts hateful discourse and propaganda (Saltman and Russell,, 2014;
Bartlett and Krasodomski-Jones,, 2015; Benesch et al.,, 2016; Saltman et al.,,
2021; Garland et al.,, 2022) using non-aggressive speech (Benesch et al.,,
2016; Reynolds and Tuck,, 2016; Schieb and Preuss,, 2016). There are some
differences between the terms. Ferguson, (2016) considers counter-narratives
as intentional strategic communication within a political, policy, or military
context. Additionally, the term counter-narrative also refers to narratives
that challenge a much broader view or category such as forms of education,
propaganda, and public information (Benesch et al.,, 2016). Such counter-
narratives are often discussed in the context of the prevention of violent
extremism. Hope speech, meanwhile, could be seen as a particular type of
counterspeech: it promotes positive engagement in online discourse to lessen
the consequences of abuse, and places a particular emphasis on delivering
optimism, resilience, and the values of equality, diversity and inclusion
(Chakravarthi,, 2022). In this paper, we review work that relates to all of
these three concepts, and largely make use of the catch-all term
counterspeech, while acknowledging the slight differences between the
concepts.

###  4.1 Classifying counterspeech

Researchers have identified a variety of different types of counterspeech.
Here, we outline four main ways in which counterspeech can vary, in terms of
the identity of the counterspeaker, the strategies employed, the recipient of
the counterspeech and the purpose of counterspeech.

#### Counterspeakers (who)

Psychological studies show that the identity of a speaker plays a key role in
how large an audience their message reaches and how persuasive the message is.
Common crucial factors include group identity (such as race, religion, and
nationality), level of influence, and socioeconomic status. For instance,
counterspeech provided by users with large numbers of followers and from an
in-group member is more likely to lead to changes in the behaviour of
perpetrators of hate (Munger,, 2017).

Some studies characterise individuals who use counterspeech and suggest that
these users exhibit different characteristics and interests than users who
spread hate (Mathew et al.,, 2018, 2019; Buerger, 2021b, ). Through lexical,
linguistic and psycholinguistic analysis of users who generate hate speech or
counterspeech on Twitter, Mathew et al., (2018) find that counterspeakers are
higher in agreeableness, displaying traits such as altruism, modesty, and
sympathy, and display higher levels of self-discipline and conscientiousness.
Possibly driven by a motive to help combat hate speech, counterspeakers tend
to use words related to government, law, leadership, pride, and religion.
Regarding the impact of being a counterspeaker, in an ethnographic study,
members of a counterspeech campaign reported feeling more courageous and keen
to engage in challenging discussions after expressing opinions publicly
(Buerger, 2021b, ).

#### Strategies (how)

Counterspeech can take many forms. Benesch et al., (2016) first identify eight
types of counterspeech used on Twitter: (1) _presentation of facts_ , (2)
_pointing out hypocrisy or contradiction_ , (3) _warning of consequences_ ,
(4) _affiliation_ [i.e. establishing an emotional bond with the perpetrators
or targets of hate], (5) _denouncing_ , (6) _humour/sarcasm_ , (7) _tone_ [a
tendency or style adopted for communication, e.g., empathetic and hostile],
and (8) _use of media_. Based on this taxonomy, follow-up studies on
counterspeech make minor modifications to cover strategies in a broader scope.
Mathew et al., (2018) analyzed and classified counterspeech on Twitter, taking
Benesch et al., (2016)’s taxonomy but dropping _the use of media_ and adding
hostile language and positive tone, which replaces general strategy _tone_.
Similarly, Mathew et al., (2019) collected and annotated counterspeech
comments from Youtube, adopting Benesch et al., (2016)’s taxonomy but
excluding tone and adding positive tone, hostile language and miscellaneous.
Chung et al., (2019) collaborated with NGOs to collect manually written
counterspeech. For data annotation, they followed the taxonomies provided by
Benesch et al., (2016) and Mathew et al., (2019), while adding counter
question and discarding the use of media. Counterspeech examples for each
strategy are provided in Table 2.

Strategy |  Example
---|---
Facts |  Actually, studies show that on the whole migrants contribute more to public finances than they take out, see this article for example.
Hypocrisy |  Immigrants stealing British resources? A bit rich given how much was stolen from colonies by the British Empire.
Consequences |  Spreading hateful content is illegal. Police will knock on your door.
Affiliation |  As a British national, I know life is hard here right now. But I assure you that your unemployment is not the fault of immigrants.
Denouncing |  Stop with the racist and derogatory slurs. It’s unacceptable to talk this way.
Counter questions |  Do you have a problem with all immigrants or only ones from lower income countries? Are you suggesting we have enough qualified and willing British born workers to fill all the jobs?
Humour |  You should think about how the Spanish feel next time you go on holiday to Costa Del Sol (laughing emoji)?
Positive tone |  Immigrants strengthen UK society in so many ways - greater diversity, skillsets and innovation to name a few! And no way our NHS could function without the immigrant workforce.
Table 2: Synthetic examples of different counterspeech strategies in response
to an example of abuse against immigrants. Here the abuse example is:
‘Immigrants are invading and stealing our resources’.

#### Counterspeech recipients (whom)

Depending on the purpose of the counterspeech, the target audience may be
perpetrators, victims or bystanders (see Figure 1). Identifying the
appropriate target audience or ‘Movable Middle’ is crucial to maximise the
efficacy of counterspeech. Movable middle refers to individuals who do not yet
hold firm opinions on a topic and can hence be potentially open to persuasion.
They are also receptive to arguments and more willing to listen. These
individuals often serve as ideal recipients of messages addressing social
issues such as vaccination hesitancy (Litaker et al.,, 2022). In the context
of counterspeech, previous studies show that a small group of counterspeakers
can shape online discussion when the audience holds moderate views (Schieb and
Preuss,, 2016; Buerger, 2021b, ).

Wright et al., (2017) group counterspeech acts into four categories based on
the number of people involved in the discussion: _one-to-one_ , _one-to-many_
, _many-to-one_ , or _many-to-many_. Some successful cases where counterspeech
induces favourable changes in the discourse happen in a one-to-one discussion.
This allows for dedicated opinion exchange over an ideology, which in some
cases even yields long-lasting changes in beliefs. The use of hashtags is a
good example of one-to-many and many-to-many interaction where conversations
surge quickly (Benesch et al.,, 2016; Wright et al.,, 2017). For instance,
Twitter users often include hashtags to express support (e.g.,
#BlackLivesMatter) or disagreement with haters (e.g., #StopHate) to
demonstrate their perspective.

#### The purpose of counterspeech

Hateful language online can serve to reinforce prejudice (Citron and Norton,,
2011), encourage further division, promote power of the ingroup, sway
political votes, provoke or justify offline violence, and psychologically
damage targets of hate (Jay,, 2009). Just as the effects of hate are wide-
ranging, counterspeech may be used to fulfil a variety of purposes.

$\bullet$ Changing the attitudes and behaviours of perpetrators In directly
challenging hateful language, one key aim of counterspeech can be to change
the attitudes of the perpetrators of hate themselves. The strategy here is
often to persuade the perpetrator that their attitudes are mistaken or
unacceptable, and to deconstruct, discredit or delegitimise extremist
narratives and propaganda (Reynolds and Tuck,, 2016). Counterspeech aimed at
changing the attitudes of spreaders of hate may address the hate speaker
directly, countering claims with facts or by employing empathy and
affiliation. Challenging attitudes is often seen as a stepping stone to
altering behaviours (Stroebe,, 2008). In attempting to change the minds of
perpetrators, counterspeakers ultimately hope to discourage associated
behaviours such as sharing such content again in the future or showing support
for other hateful content (i.e., stopping the spread of hate). In changing the
minds of perpetrators, counterspeakers may also hope to prevent them from
engaging in more extreme behaviours such as offline violence.

$\bullet$ Changing the attitudes and behaviours of bystanders More commonly,
counterspeech is initiated with the intention of reaching the wider audience
of bystanders rather than perpetrators of hate themselves (Buerger,, 2022).
These bystanders are not (at least yet) generating hateful language
themselves, but rather are people exposed to hateful content either
incidentally or by active engagement. Here, counterspeakers hope to persuade
bystanders that the hateful content is wrong or unacceptable, again by
deconstructing and delegitimising the hateful narrative. The strategy here may
be to offer facts, point out hypocrisy, denounce the content, or use humour to
discredit the speaker. Additionally, counterspeakers will often invoke empathy
for targets of hate. In preventing bystanders from forming attitudes and
opinions in line with the hateful narrative, counterspeakers hope to mitigate
further intergroup division and related behaviours such as support for or
engagement with additional abuse or physical violence. Counterspeakers may
also hope to encourage others to generate rebutals and rally support for
victims (Benesch, 2014a, ), bringing positive changes in online discourse.

$\bullet$ Showing support for targets of hate A third key way in which
counterspeech functions is to show support directly to targets of hate. Online
abuse can psychologically damage the wellbeing of targets and leave them
feeling fearful, threatened, and even in doubt of their physical safety
(Benesch, 2014b, ; Leader Maynard and Benesch,, 2016; Saha et al.,, 2019;
Siegel,, 2020). By challenging such abuse, counterspeakers can offer support
to targets and encourage bystanders to do the same (Buerger, 2021b, ). This
support aims to alleviate negative emotion brought on by hate by demonstrating
to targets that they are not alone and that many people do not hold the
attitudes of the perpetrator. Here the particular strategies may be to
denounce the hate and express positive sentiment towards the target group.
Intergroup solidarity may in turn reduce retaliated antagonism.

##  5 The Impact of Counterspeech

The concrete effects of using counterspeech remain debated. The methods
applied for evaluating the effectiveness of counterspeech vary considerably
across studies in the field. In this section, we outline eight aspects that
can help to better understand the impact of counterspeech.

#### Research design

A wide range of methodologies have been adopted to assess the impact of
counterspeech on hate mitigation, including observational studies (Ernst et
al.,, 2017; Stroud and Cox,, 2018; Garland et al.,, 2022), experimental
(Munger,, 2017; Obermaier et al.,, 2021; Hangartner et al.,, 2021) and quasi-
experimental designs (Bilewicz et al.,, 2021). In observational studies,
investigators typically assess the relationship between exposure to
counterspeech and outcome variables of interest without any experimental
manipulation. For instance, a longitudinal study of German political
conversations on Twitter examined the interplay between organized hate and
counterspeech groups (Garland et al.,, 2022). There is also an ethnographic
study interviewing counterspeakers on Facebook to understand external and
internal practices for collectively intervening in hateful comments, such as
how to build effective counterspeech action and keep counterspeakers engaged
(Buerger, 2021b, ). For experimental and quasi-experimental designs, both aim
at estimating the causal effects of exposure to different kinds of
counterspeech on outcome variables in comparison with controls (no exposure to
counterspeech).

#### Languages and countries

In the reviewed work, the impact of counterspeech is investigated in five
different languages across nine countries. Notably, experiments are focused on
counterspeech used in Indo-European languages such as English (USA, UK, Canada
and Ireland), German (Germany), Urdu (Pakistan) and Swedish (Sweden). Only two
studies are dedicated to Afro-Asiatic languages, Arabic (Egypt and Iraq). We
did not find research dedicated to other language families, suggesting that
the language coverage of counterspeech studies is still low.

#### Platforms

Most experiments were conducted on text-based social media platforms, such as
eight on Twitter (Benesch et al.,, 2016; Reynolds and Tuck,, 2016; Silverman
et al.,, 2016; Stroud and Cox,, 2018; Munger,, 2017; Hangartner et al.,, 2021;
Poole et al.,, 2021; Garland et al.,, 2022), six on Facebook (Reynolds and
Tuck,, 2016; Silverman et al.,, 2016; Schieb and Preuss,, 2016; Leonhard et
al.,, 2018; Saltman et al.,, 2021; Buerger, 2021b, ), and one on Reddit
(Bilewicz et al.,, 2021), as well as image-based online spaces, such as three
on Youtube (Reynolds and Tuck,, 2016; Silverman et al.,, 2016; Ernst et al.,,
2017) and one on Instagram (Stroud and Cox,, 2018). Often, the counterspeech
interventions are directly monitored on such platforms, but in some cases,
fictitious platforms are created in order to mimic online social activity
under a controlled environment (Obermaier et al.,, 2021; Carthy and Sarma,,
2021; Bélanger et al.,, 2020). There are three studies analysing the impact of
counterspeech across multiple platforms (Reynolds and Tuck,, 2016; Silverman
et al.,, 2016; Stroud and Cox,, 2018).

Twitter and Facebook are widely used for measuring the effects of
counterspeech, with eight and six experiments respectively. For Twitter, this
can be explained by its easily accessible API (even if at the time of writing
continued research access to the API was in doubt). Similarly, because of
difficulties in gathering data, Schieb and Preuss, (2016) resort to developing
an agent-based computational model for simulating hate mitigation with
counterspeech on Facebook. It is worth highlighting that none of the studies
we reviewed had investigated recently popular mainstream platforms, such as
Tiktok, Weibo, Telegram, and Discord.

#### The target of hate speech

Abusive speech can be addressed towards many different potential targets, and
each individual hate phenomenon may require different response strategies for
maximum effectiveness. Existing studies have evaluated the effectiveness of
counterspeech on several hate phenomena, with Islamophobia, Islamic extremism,
and racism being the most commonly addressed, while hate against LGBTQ+
community and immigrants being the least studied. In these studies, abusive
content is typically identified based on two strategies - hateful keyword
matches (Hangartner et al.,, 2021; Bilewicz et al.,, 2021), or user accounts
(e.g., content produced by known hate speakers) (Garland et al.,, 2022).

#### Types of interventions

A wide range of methods are exploited to design and surface counterspeech
messages to a target audience. We broadly categorise these methods based on
modality and approach to creation. Counter speech is generally conveyed in
text (Bélanger et al.,, 2020; Hangartner et al.,, 2021; Poole et al.,, 2021)
or video mode (Ernst et al.,, 2017; Saltman et al.,, 2021; Carthy and Sarma,,
2021). In both cases, counterspeech materials can be created in three
different ways: written by experimenters as stimuli (Obermaier et al.,, 2021;
Carthy and Sarma,, 2021), as well as written by individuals or campaigns that
are collected from social media platforms (Benesch et al.,, 2016; Garland et
al.,, 2022; Buerger, 2021b, ). We also found one study integrating
counterspeech messages in media such as films, TV dramas and movies (Iqbal et
al.,, 2019).

#### Counterspeech strategies

Following the strategies summarised in Section 4.1, commonly used
counterspeech strategies include facts (Buerger, 2021b, ; Obermaier et al.,,
2021), denouncing (Stroud and Cox,, 2018; Saltman et al.,, 2021), counter-
questions (Silverman et al.,, 2016; Reynolds and Tuck,, 2016; Saltman et al.,,
2021), and a specific tone (humour or empathy) (Reynolds and Tuck,, 2016;
Munger,, 2017; Hangartner et al.,, 2021; Saltman et al.,, 2021). There are
more fine-grained tactics for designing counterspeech in social science
experiments. According to psychological studies, the use of social norms can
reduce aggression and is closely related to legal regulation in society
(Bilewicz et al.,, 2021). This tactic was tested in an intervention study
where participants were exposed to counterspeech with one of the inducements
of empathy, descriptive norms (e.g., Let’s try to express our points without
hurtful language) and prescriptive norms (e.g., Hey, this discussion could be
more enjoyable for all if we would treat each other with respect.) (Bilewicz
et al.,, 2021). Bélanger et al., (2020) designed counterspeech based on
substances rather than tactics, varying three different narratives: (1) social
(seeking to establish a better society), (2) political (bringing a new world
order through a global caliphate), and (3) religious (legitimising violence
based on religious purposes). Considering broader counterspeech components, a
few organisations further focus on challenging ideology (e.g., far-right and
Islamist extremist recruitment narratives), rather than deradicalising
individuals (Silverman et al.,, 2016; Saltman et al.,, 2021). Counterspeech
drawing from personal stories in a reflective or sentimental tone is also
considered as it can resonate better with target audiences (Silverman et al.,,
2016). In addition to neutral or positive counterspeech, radical approaches
are taken by counter-objecting, degrading or shaming perpetrators in public
for unsolicited harmful content (Stroud and Cox,, 2018; Obermaier et al.,,
2021).

#### Types of evaluation metrics

Based on Reynolds and Tuck, (2016)’s counterspeech _Handbook_ , we identified
the following three types of metrics used by the authors of the papers to
evaluate the effectiveness of counterspeech interventions: social impact,
behavioural change, and attitude change measures.

$\bullet$ Social impact metrics are (usually automated) measurements of how
subjects interact with counterspeech online. Such measures include, bounce
rate, exit rate,333Bounce rate is the number of users who leave a website
without clicking past the landing page; exit rate measures how many people
leave the site from a given section (Reynolds and Tuck,, 2016). geo-location
analysis and the numbers of likes, views, and shares that posts receive
(Garland et al.,, 2020; Hangartner et al.,, 2021; Poole et al.,, 2021;
Reynolds and Tuck,, 2016; Leonhard et al.,, 2018; Saltman et al.,, 2021;
Silverman et al.,, 2016). For example, for one of their experiments, Saltman
et al., (2021) measure the ‘click-through rates’ of Facebook users redirected
from hateful to counterspeech materials, while Hangartner et al., (2021)
measure retweets and deletions (in addition to behavioural change measures).

Social impact measures are also applied to synthetic data by Schieb and
Preuss, (2016), who measure the ‘likes’ of their (simulated) participants as
hate and counterspeech propagate through a network (as well as applying
behavioural metrics). Taking a more distant, long-term view, Iqbal et al.,
(2019) cite Egypt’s overall success at countering radicalisation with
counterspeech campaigns by comparing its position on the Global Terrorism
Index with that of Pakistan.

While the majority of these measurements are automated, Leonhard et al.,
(2018) use survey questions to examine participants willingness to intervene
against hate speech depending on the severity of the hate, the number of
bystanders, and the reactions of others. Unlike the survey-based approaches
described below, they do not consider _changes_ in attitude. In addition,
Buerger, 2021b  assess the success of the #jagärhär counterspeech campaign
(#iamhere in English, a Sweden-based collective effort that has been applied
in more than 16 countries) based on the extent to which it has facilitated the
emergence of alternative perspectives.

$\bullet$ Behavioural change measures reveal whether subjects change their
observable behaviour towards victims before and after exposure to
counterspeech, for example in the tone of their language as measured with
sentiment analysis.

For instance, Hangartner et al., (2021) conduct sentiment analysis to
determine the behaviour of previously xenophobic accounts after treatment with
counterspeech, Bilewicz et al., (2021) measure levels of verbal aggression
before and after interventions, and Garland et al., (2020) assess the
proportion of hate speech in online discourse before and after the
intervention of an organised counterspeech group. Other such measures are
those of Saltman et al., (2021), who compare the number of times users violate
Facebook policies before and after exposure to counterspeech, and Munger,
(2017), who examine the likelihood of Twitter users continuing to use racial
slurs following sanctions by counterspeakers of varying status and
demographics. And in a network simulation experiment, Schieb and Preuss,
(2016) measure the effect of positive or negative (synthetic) posts on
(synthetic) user behaviour.

$\bullet$ Attitude change measures are used to assess whether people
(hate/counter speakers or bystanders) change their underlying attitudes or
intentions through non-automated methods such as interviews, surveys, focus
groups, or qualitative content analysis.

For potential hate speech perpetrators, Carthy and Sarma, (2021) use
psychological testing to measure the extent to which participants legitimized
violence after exposure to differing counterspeech strategies, Bélanger et
al., (2020) compare support for ISIS and other factors using in participants
exposed to differing counterspeech strategies and a control group, Ernst et
al., (2017) code user comments on hate and counterspeech videos to perform
qualitative content analysis of users’ attitudes.

For bystanders that may be potential counterspeakers, Obermaier et al., (2021)
use a survey to examine whether counterspeech leads to increased intentions to
intervene. And for those already engaged in counterspeech, Buerger, 2021b
conduct interviews with members of an organised group to reveal their
perceptions of the efficacy of their interventions.

#### Effectiveness

Owing to the variation in experimental setups, aims, and evaluation methods of
the counterspeech efforts we review, it is not straightforward to compare
their levels of success. Indeed, several of the studies concern broad long-
term goals that cannot be easily evaluated at all (e.g. Reynolds and Tuck,,
2016; Silverman et al.,, 2016) or provide only anecdotal evidence (e.g.
Benesch et al.,, 2016; Stroud and Cox,, 2018; Buerger, 2021b, ).

Beyond this, evidence of successful counterspeech forms a complex picture. For
example, Garland et al., (2022) show that organised counterspeech is
effective, but can produce backfire effects and actually attract more hate
speech in some circumstances. They also show that these dynamics can alter
surrounding societal events—although they do not make causal claims for this.
Similarly, Ernst et al., (2017) find mixed results, with counterspeech
encouraging discussion about hate phenomena and targets in some cases, but
also leading to increases in hateful comments. However, Silverman et al.,
(2016) suggest that even such confrontational exchanges can be viewed as
positive signs of engagement.

There is some evidence for the comparative efficacy of different counterspeech
strategies. Bilewicz et al., (2021) find that three of their intervention
types (‘disapproval’, ‘abstract norm’, ‘empathy’) are effective in reducing
verbal violence when compared with no intervention at all. Here, empathy had
the weakest effect, which they put down to the empathetic messages being
specific to particular behaviours, limiting their capacity to modify
aggression towards wider targets. Hangartner et al., (2021) also found that
empathy-based counterspeech can consistently reduce hate speech, although this
effect is small. And Carthy and Sarma, (2021) found that counterspeech that
seeks to correct false information in the hate speech actually leads to higher
levels of violence legitimisation, while having participants actively counter
terrorist rhetoric themselves (‘Tailored Counter-Narrative’) was the most
effective strategy to reduce this. They found counterspeech to be more
effective on participants that are already predisposed to cognitive
reflection. However, focusing on the effect of factual correction on the
victims rather than perpetrators of hate speech, Obermaier et al., (2021)
found it to be effective in providing support and preventing them from hating
back and therefore widening the gap between groups.

There is also some evidence that the numbers of the different actors involved
in a counterspeech exchange can affect an intervention’s success. Schieb and
Preuss, (2016) find that counterspeech can impact the online behaviour of
(simulated) bystanders, with the effectiveness strongly influenced by the
proportions of hate and counter speakers and neutral bystanders. According to
their model, a small number of counterspeakers can be effective against
smaller numbers of hate speakers in the presence of larger numbers of people
lacking strong opinions. Saltman et al., (2021) found their counterspeech
strategies to be effective only for higher risk individuals within the target
populations, although they did not see any of the potential negative effects
of counterspeech (such as increased radicalisation) reported elsewhere.

Focusing on who in particular delivers counterspeech, Munger, (2017) finds
that success of counterspeech depends on the identity and status of the
speaker. However, with only a small positive effect, Bélanger et al., (2020)
found that the content of counterspeech was more important than the source.
And Garland et al., (2022) found that, while organised counterspeech can be
effective, the efforts of individuals can lead to increases in hate speech. In
Buerger, 2021b , members of #jagärhär claim that their counterspeech
interventions were successful in making space for alternative viewpoints to
hate speech.

##  6 Computational Approaches to Counterspeech

In this section, we switch the focus to look at literature on counterspeech
emerging from the field of computer science. We tackle three subjects in
particular: the datasets being used in these studies, approaches to
counterspeech detection, and approaches to counterspeech generation.

###  6.1 Counterspeech Datasets

Approaches for counterspeech collection focus on gathering two different kinds
of datasets: spontaneously produced comments crawled from social media
platforms, and deliberately created responses aiming to contrast hate speech.
In the first case, content is retrieved based on keywords/hashtags related to
targets of interest (Mathew et al.,, 2018; Vidgen et al.,, 2020; He et al.,,
2022; Vidgen et al.,, 2021) or from pre-defined counterspeech accounts
(Garland et al.,, 2020). In principle, due to the easily accessible API
required for data retrieval, the majority of datasets are collected from
social media platforms including Twitter (Mathew et al.,, 2018; Procter et
al.,, 2019; Garland et al.,, 2020; Kennedy et al.,, 2020; Vidgen et al.,,
2020; He et al.,, 2022; Goffredo et al.,, 2022; Toliyat et al.,, 2022; Lin et
al.,, 2022), and only a few are retrieved from Youtube (Mathew et al.,, 2019;
Kennedy et al.,, 2020; Priyadharshini et al.,, 2022) and Reddit (Kennedy et
al.,, 2020; Vidgen et al.,, 2021; Lee et al.,, 2022; Yu et al.,, 2022),
respectively (though again it is worth noting that at the time of writing the
Twitter API was starting to become a lot less accessible).

In the second category, counterspeech is written by crowd workers (Qian et
al.,, 2019) or operators expert in counterspeech writing (Chung et al.,, 2019;
Chung et al., 2021b, ). While such an approach is expected to offer relatively
controlled and tailored responses, writing counterspeech from scratch is time-
consuming and requires human effort. To address this issue, advanced
generative language models are adopted to automatically produce counterspeech
(Tekiroğlu et al.,, 2020; Fanton et al.,, 2021; Bonaldi et al.,, 2022), as we
will discuss further below.

Regarding granularity of taxonomies, most existing datasets provide binary
annotation (counterspeech/non-counterspeech) (Garland et al.,, 2020; Vidgen et
al.,, 2020; He et al.,, 2022; Vidgen et al.,, 2021), while three datasets
feature annotations of the types of counterspeech (Mathew et al.,, 2018, 2019;
Chung et al.,, 2019). In terms of hate incidents, datasets are available for
several hate phenomena such as islamophobia (Chung et al.,, 2019) and East
Asian prejudice during COVID-19 pandemic (Vidgen et al.,, 2020; He et al.,,
2022). The aforementioned datasets are mostly collected and analyzed at the
level of individual text, not at discourse or conversations (e.g., multi-turn
dialogues (Bonaldi et al.,, 2022)). Most of the datasets are in English, while
only a few target multilinguality, including Italian (Chung et al.,, 2019;
Goffredo et al.,, 2022), French (Chung et al.,, 2019), German (Garland et
al.,, 2020), and Tamil (Priyadharshini et al.,, 2022).

###  6.2 Approaches to Counterspeech Detection

Previous work on counterspeech detection has focused on binary classification
(i.e. whether a text is counterspeech or not) (Vidgen et al.,, 2020; Garland
et al.,, 2022; He et al.,, 2022) or identifying the types of counterspeech as
a multi-label task (Mathew et al.,, 2018; Garland et al.,, 2020; Chung et al.,
2021a, ; Goffredo et al.,, 2022). Automated classifiers are developed to
analyse large-scale social interactions of abuse and counterspeech addressing
topics such as political discourse (Garland et al.,, 2022) and multi-hate
targets (Mathew et al.,, 2018). Moving beyond monolingual study, Chung et al.,
2021a  evaluate the performance of pre-trained language models for
categorising counterspeech strategy for English, Italian and French in
monolingual, multilingual and cross-lingual scenarios.

###  6.3 Approaches to Counterspeech Generation

Various methodologies have been put forward for the automation of
counterspeech generation (Qian et al.,, 2019), addressing various aspects
including the efficacy of a hate countering platform (Chung et al., 2021c, ),
informativeness (Chung et al., 2021b, ), multilinguality (Chung et al.,,
2020), politeness (Saha et al.,, 2022), and grammaticality and diversity (Zhu
and Bhat,, 2021). These methods are generally centred on transformer-based
large language models (e.g., GPT-2 (Radford et al.,, 2019)). By testing
various decoding mechanisms using multiple language models, Tekiroğlu et al.,
(2022) find that autoregressive models combined with stochastic decoding yield
the optimal counterspeech generation. In addition to tackling hate speech,
there are studies investigating automatic counterspeech generation to respond
to trolls (Lee et al.,, 2022) and microagressions (Ashida and Komachi,, 2022).

#### Evaluation of counterspeech generation

Assessing counter speech generation is complex and challenging due to the lack
of clear evaluation criteria and robust evaluation techniques.

Previous work evaluates the performance of counterspeech systems via two
aspects: automatic metrics and human evaluation. Automatic metrics, generally,
evaluate the generation quality based on criteria such as linguistic surface
(Papineni et al.,, 2002; Lin,, 2004), novelty (Wang and Wan,, 2018), and
repetitiveness (Bertoldi et al.,, 2013; Cettolo et al.,, 2014). Despite being
scalable, these metrics are uninterpretable and can only infer model
performance according to references provided (e.g., dependent heavily on exact
word usage and word order) and gathering an exhaustive list of all appropriate
counterspeech is not feasible. For this reason, such metrics cannot properly
capture model performance, particularly for open-ended tasks (Liu et al.,,
2016; Novikova et al.,, 2017) including counterspeech generation. As a result,
human evaluation is heavily employed based on aspects such as suitableness,
grammatical accuracy and relevance (Chung et al., 2021b, ; Zhu and Bhat,,
2021). Despite being trusted and high-performing, human evaluation has
inherent limitations such as being costly, difficult (e.g., evaluator biases
and question formatting), and time-consuming (both in terms of evaluation and
moderator training), and can be inconsistent and inflict psychological harm on
the moderators. The effectiveness of counterspeech generations should be also
carefully investigated ‘in-the-wild’ to understand its social media impact,
reach of content, and the dynamics of hateful content and counterspeech (see
Section 5). To our knowledge, no work has examined this line of research yet.

#### Potentials and limits of existing generative models

We believe that in some circumstances counterspeech may be a more appropriate
tool than content moderation in fighting hate speech as it can depolarise
discourse and show support to victims. However, automatic counterspeech
generation is a relatively new research area. Recent progress in natural
language processing has made large language models a popular vehicle for
generating fluent counterspeech. However, counterspeech generation currently
faces several challenges that may constrain the development of efficient
models and hinder the deployment of hate intervention tools. Similar to the
use of machine translation and email writing tools, we advocate that
counterspeech generation tools should be deployed as suggestion tools to
assist in hate countering activity (Chung et al., 2021b, ; Chung et al.,
2021c, ).

$\bullet$ Faithfulness/Factuality in generation Language models are repeatedly
reported to produce plausible and convincing but not necessarily
faithful/factual statements (Solaiman et al.,, 2019; Zellers et al.,, 2019;
Chung et al., 2021b, ). We refer to faithfulness as being consistent and
truthful in adherence to the given source (i.e. model inputs) (Ji et al.,,
2023). Many attempts have been made to mitigate this issue (Ji et al.,, 2023),
including correcting unfaithful data (Nie et al.,, 2019), augmenting inputs
with additional knowledge sources (Chung et al., 2021b, ), and measuring
faithfulness of generated outputs (Dušek and Kasner,, 2020; Zhou et al.,,
2021). We encourage reporting the faithfulness/factuality of models.

$\bullet$ Toxic degeneration Language models can also induce unintendedly
biased and/or toxic content, regardless of whether explicit prompts are used
(Dinan et al.,, 2022). In the use case of counterspeech generation, this can
result in harm to victims and bystanders as well as risking provoking
perpetrators into further abusive behaviour. This issue has been mitigated by
two approaches: data and modelling. The data approach aims at creating proper
datasets for fairness by removing undesired and biased content (Blodgett et
al.,, 2020; Raffel et al.,, 2020). The modelling approach focuses on
controllable generation techniques that, for instance, employ humans for post-
editing (Tekiroğlu et al.,, 2020) and detoxification techniques (Gehman et
al.,, 2020).

$\bullet$ Generalisation vs. Specialisation With the rise of online hate,
models that can generalize across domains would be helpful for producing
counterspeech involving new topics and events. Generalisable methods can also
ameliorate the time and manual effort required for collecting and annotating
data. However, as discussed in Section 5, counterspeech is multifaceted and
contextualised. There may not be a one-size-fits-all solution. For instance,
abuse against women can often be expressed in a more subtle form as
microaggressions. It may, therefore, be difficult to implement an easy yet
effective counterspeech strategy in one model. Moreover, model
generalisability is challenging (Fortuna et al.,, 2021; Yin and Zubiaga,,
2021), and can have potential limitations (Conneau et al.,, 2020; Berend,,
2022). Finding the right trade-off between generalisation and specialisation
is key.

##  7 Future Perspectives

Of the many promising abuse intervention experiments that we review, results
are not always consistent, demonstrating weak claims or limited success
(applicable only to certain settings). Possible reasons include short-term
experiments, small sample sizes and non-standardised experimental designs. To
improve this, effective interventions should come with the characteristics of
scalability, durability, reliability, and specificity. In this section, we
highlight key distinctions and overlaps across areas that have and have not
been explored in social sciences and computer science, discuss ethical issues
related to evaluating counterspeech in real-life settings and automating the
task of counterspeech generation, and identify best practices for future
research.

#### Distinctions and overlaps across areas

By recognizing the commonalities and differences between social sciences and
computer science, we pinpoint the unique contributions of each discipline and
encourage interdisciplinary collaborations to address complex societal
challenges and better understand human behaviour with the help of
computational systems.

$\bullet$ Terminological clarity. Throughout the counterspeech literature,
terminology is used inconsistently. Terms such as counterspeech and counter-
narratives are often used interchangeably or used to refer to similar
concepts. In social science, counterspeech is used to refer to content that
disagrees with abusive discourses and counter-narratives often entail
criticism of an ideology with logical reasoning. As a result, counter-
narrative stimuli designed in social experiments are generally long form
(Bélanger et al.,, 2020). In computer science on the other hand, the
distinctions between counterspeech and counter-narratives have been vague, and
training data is generally short form (while this may be bound by character
limit on social media platforms). For instance, short and generic responses
such as ‘How can you say that about a faith of 1.6 billion people?’ can be
commonly found in counter-narrative datasets (Chung et al.,, 2019).

$\bullet$ The focus of evaluation. Social scientists and counterspeech
practitioners generally attempt to understand and assess the impact of
counterspeech on reducing harms (e.g., which strategies are effective and
public perception towards counterspeech), whereas computer scientists focus
more on technical exploration of automated systems and testing their
performance in producing counterspeech (e.g., comparing system outputs with a
pre-established ground truth or supposedly ideal output). One commonality
between the social science and computer science studies is that most findings
are drawn from controlled and small-scale studies. Applying interventions to
real-world scenarios is a critical next step.

$\bullet$ Datasets. Dataset creation is an important component in computer
science for developing machine learning models for generating counterspeech,
while such contributions are less commonly considered in social sciences which
rely on experiments using hand-crafted stimuli and one-time analyses of their
effectiveness.

$\bullet$ Scope of research. We observe that, while computer scientists have
focused on responses to abusive language and hate speech, social science
studies address a wider range of phenomena, in particular radicalisation and
terrorist extremism. It can be difficult to measure the effectiveness of
counterspeech in challenging these over the short term, leading to some of the
differences in evaluation metrics across disciplines.

$\bullet$ Lack of standardised methodologies. A variety of methodologies have
been adopted in the literature, making comparisons across studies difficult.
Without standardised evaluations, it is difficult to situate the results and
draw robust findings.

#### Ethical Issues, Risks and Challenges of Conducting Counterspeech Studies

Effective evaluation of counterspeech not only identifies users who may need
help, but also safeguards human rights and reinforces a stronger sense of
responsibility in the community. This discussion is based on the authors’
opinion and not stemming from the review.

$\bullet$ Evaluating counterspeech in real-life settings Conducting the
evaluation of counterspeech in real-world scenarios appears to provide a
proactive and quick overview of its performance on hate mitigation. However,
from an ethical perspective, the debate surrounding it is ongoing and reaching
an agreement can be difficult. For instance, one side argues about the
morality of exposing participants to harm, while another points to the
importance of internet safety. Exercising counterspeech can offer mitigation
of online abuse in good faith and may be exempt from liability based on
several legal groundings. As an example, Good Samaritan laws provide indemnity
to people who assist others in danger (Smits,, 2000). In 2017 the EU
Commission released a communication on tackling illegal content online,
stating that ‘This Communication … aims to provide clarifications to platforms
on their liability when they take proactive steps to detect, remove or disable
access to illegal content (the so-called “Good Samaritan” actions)’
(Commission,, 2017). Section 230(c)(2) of Title 47 of the United States Code
extents this protection to the good faith removal or moderation of third-party
material they deem “obscene, lewd, lascivious, filthy, excessively violent,
harassing, or otherwise objectionable, whether or not such material is
constitutionally protected.” and stresses liability towards online hate
speech. It protects online computer services from liability for moderating
third-party materials that are harmful (Ardia,, 2009; Goldman,, 2018). The aim
of these safeguards is to ensure that individuals are not hesitant to help
others in distress due to the fear of facing legal consequences in case of
unintentionally making errors in their efforts to provide support.

Responsible open-source research can facilitate reproducibility and
transparency of science. Recently, reproducible research has been deemed
critical in both social sciences (Stroebe et al.,, 2012; Derksen and
Morawski,, 2022) and computer science, and low replication success is found
despite using materials provided in the original papers (Belz et al.,, 2023;
Collaboration,, 2015). To tackle this issue, a few initiatives for transparent
research have been proposed, advocating researchers to state succinctly in
papers how experiments are conducted (e.g., stimuli, mechanisms for data
selection) and evaluated, including A 21 Word Solution (Simmons et al.,, 2012)
and Open Science Framework.444https://osf.io/ Furthermore, practising data
sharing encourages researchers to be responsible for fair and transparent
experimental designs, and to avoid subtle selection biases that might affect
substantive research questions under investigation (Dennis et al.,, 2019). At
the same time, when handling sensitive or personal information, data sharing
should adhere to research ethics and privacy standards (Dennis et al.,, 2019;
de la Cueva and Méndez,, 2022). For instance, in the case of hate speech,
using synthetic examples or de-identification techniques is considered a good
general practice for ensuring the safety of individuals (Kirk et al.,, 2022).

$\bullet$ Automating counterspeech generation There are several ethical
challenges related to automating the task of counterspeech generation. First
of all, there is the danger of dual-use: the same methodology could also be
used to silence other voices.

Furthermore, effective and ethical counterspeech relies on the accuracy and
robustness of detecting online hate speech: an innocent speaker may be
publicly targeted and shamed if an utterance is falsely classified as hate
speech – either directly or indirectly as in end-to-end response generation.
For example, Google’s Jigsaw API (Google Jigsaw,, 2022), a widely used tool
for detecting toxic language, makes predictions that are aligned with racist
beliefs and biases—for example it is less likely to rate anti-Black language
as toxic, but more likely to mark African American English as toxic (Sap et
al.,, 2022). It is thus important to make sure that the underlying tool is not
biased and well-calibrated to the likelihood that an utterance was indeed
intended as hate speech. For example, the ‘tone’ of counterspeech could be
used to reflect the model’s confidence.

A related question is free speech: what counts as acceptable online behaviour,
what sort of speech is deemed inappropriate, in which contexts, and should be
targeted by counterspeech? A promising direction for answering this complex
question is participatory design to empower the voices of those who are
targeted (Birhane et al.,, 2022).

In sum, there is a trade-off between risks and benefits of counterspeech
generation. Following the ‘Good Samaritan’ law: automating counterspeech
provides timely help to victims in an emergency which is protected against
prosecution (even if it goes wrong). Similar legislation is adopted by other
countries, including the European Union, Australia and the UK. Under this
interpretation, well-intentional counterspeech (by humans and machines) is
better than doing nothing at all.

#### Best practices

We provide best practices for developing successful intervention tools.

  1. 1.

Bear in mind practical use cases and scenarios of hate-countering tools. A
single intervention strategy is unlikely to diminish online harm. To design
successful counterspeech tools, it is important to consider the purposes of
counter messages (e.g., support victims and debunk stereotypes), the speakers
(e.g., practitioners, authorities and high-profile people), recipients (e.g.,
ingroup/outgroup, political background and education level), the content
(e.g., strategy, style, and tones), intensity (e.g., one message per
week/month), and the communication medium (e.g., videos, text, and platforms).

  2. 2.

Look beyond automated metrics and consider deployment settings for evaluating
the performance of generation systems. Generation systems are generally
evaluated on test sets in a controlled environment using accuracy-based
metrics (e.g., ROUGE and BLEU) that cannot address social implications of a
system. Drawn from social science studies, metrics assessing social impact
(e.g., user engagement), behavioural change (e.g., measure abuse reduction in
online discourse) and attitude change (e.g., through self-description
questionnaires) can be considered. A good intervention system is expected to
pertain long-lasting effects.

  3. 3.

Be clear about the methodology employed in experiments, open-source
experimental materials (e.g., stimuli, questionnaires and codebook), and
describe the desirable criteria for evaluating counterspeech intervention. As
standardised procedures are not yet established for the assessment of
counterspeech interventions, examining the impact of interventions becomes
difficult. A meaningful description of experimental design would therefore
enhance reproducible research and help capture the limitation of existing
research.

  4. 4.

Establish interdisciplinary collaboration across areas such as counter-
terrorism, political science, psychology and computer science. AI researchers
can help guide policymakers and practitioners to, for instance, identify long-
term interventions by performing large-scale data analysis using standardized
procedures on representative and longitudinal samples. With expertise in
theories of human behaviour change and experimental design, social science
researchers can conduct qualitative evaluations of AI intervention tools in
real-life scenarios to understand their social impact.

##  8 Conclusion

Online hate speech is a pressing global issue, prompting scientists and
practitioners to examine potential solutions. Counterspeech, content that
directly rebuts hateful content, is one promising avenue. While AI researchers
are already beginning to explore opportunities to automate the generation of
counterspeech for the mitigation of hate at scale, research from the social
sciences points to many nuances that need to be considered regarding the
impact of counterspeech before this intervention is deployed. Taking an
interdisciplinary approach, we have attempted to synthesize the growing body
of work in the field. Through our analysis of extant work, we suggest that
findings regarding the efficacy of counterspeech are highly dependent on
several factors, including methodological ones such as study design and
outcome measures, and features of counterspeech such as the speaker, target of
hate, and strategy employed. While some work finds counterspeech to be
effective in lowering further hate generation from the perpetrator and raising
feelings of empowerment in bystanders and targets, others find that
counterspeech can backfire and encourage more hate. To understand the
advantages and disadvantages of counterspeech more deeply, we suggest that
empirical research should focus on testing counterspeech interventions in
real-world settings which are scalable, durable, reliable, and specific.
Researchers should agree on key outcome variables of interest in order to
understand the optimal social conditions for producing counterspeech at scale
by automating its generation. We hope that this review helps make sense of the
variety of types of counterspeech that have been studied to date and prompts
future collaborations between social and computer scientists working to
ameliorate the negative effects of online hate.

## Acknowledgements

We thank Bertie Vidgen for the valuable feedback on the initial structure of
this manuscript and Hannah Rose Kirk for her help with the collection of
target literature.

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