Beyond Denouncing Hate: Strategies for Countering Implied Biases and Stereotypes in Language
Authors: Jimin Mun
Emily Allaway
Akhila Yerukola
Laura Vianna
Sarah-Jane Leslie
Maarten Sap
Venue: EMNLP 2023 Findings (2023)
Keywords: counterspeech
stereotypes

Abstract: Counterspeech, i.e., responses to counteract potential harms of hateful speech, has become an increasingly popular solution to address online hate speech without censorship. However, properly countering hateful language requires countering and dispelling the underlying inaccurate stereotypes implied by such language. In this work, we draw from psychology and philosophy literature to craft six psychologically inspired strategies to challenge the underlying stereotypical implications of hateful language. We first examine the convincingness of each of these strategies through a user study, and then compare their usages in both human- and machine-generated counterspeech datasets. Our results show that human-written counterspeech uses countering strategies that are more specific to the implied stereotype (e.g., counter examples to the stereotype, external factors about the stereotype’s origins), whereas machine-generated counterspeech uses less specific strategies (e.g., generally denouncing the hatefulness of speech). Furthermore, machine generated counterspeech often employs strategies that humans deem less convincing compared to human-produced counterspeech. Our findings point to the importance of accounting for the underlying stereotypical implications of speech when generating counterspeech and for better machine reasoning about anti-stereotypical examples.

Beyond Denouncing Hate:
Strategies for Countering Implied Biases and Stereotypes in Language
Warning: content this paper may be upsetting or offensive.
Jimin Mun ?
Emily Allaway
Akhila Yerukola
Laura Vianna
Sarah-Jane Leslie
Maarten Sap +
Carrnegie Mellon University
Columbia University
University of Washington
Princeton University
Allen Institute for Al
Abstract
whats gay guys favorite restaurant? five guys
Counterspeech, i.e. responses to counteract po- tential harms of hateful speech, has become an increasingly popular solution to address on- line hate speech without censorship. However, properly countering hateful language requires countering and dispelling the underlying inac- curate stereotypes implied by such language. In this work, we draw from psychology and philosophy literature to craft six psychologi- cally inspired strategies challenge the derlying stereotypical implications hateful language. We first examine the convincing- ness of each of these strategies through user study, and then compare their usages in both human- and machine-generated counterspeech datasets. Our results show that numan-Written counterspeech uses countering strategies that more specific to the implied stereotype (e.g.. counter examples to the stereotype, external factors about the stereotype's origins), whereas machine- generated counterspeech uses less spe- cific strategies (e.g., generally denouncing the hatefulness of speech). Furthermore, machine- generated counterspeech often employs strate- gies that humans deem less convincing com- pared to human-produced counterspeech. Our findings point to the importance of account- ing for the underlying stereotypical implica- tions of speech when generating counterspeech and for better machine reasoning about anti- stereotypical examples.
Stereotypical Imprication: goy men are promiscuous
underrtted
this jake purpetistes sterentooes that can be hurtful to community essential respect and be mindful of the diverse experiences within this community.
avoiding jokes
besed on harmful assumptions and making spaces
welcoming
This statement presents inacourate stereotypn; Ikoow hereroseoual friedd who this noint in thair lives seem ent enty diderereeree à lang term monogamous relationship, But the close gay friends have are "both" long torm monogamous relattonshipe ps. my expertences to refute your position, addition, you know gay people you know? went gay bar, rd probably yy find a lot of gay people who are just looking casual but that's true stralghht people who to bars tos... so watch out for confirmation
I ask how
Figure 1: Responses to same implication where humans able use appropriate countering strategies to target the implication by presenting alternate groups, comn- terexamples, and external factors while LLMS focus on denouncing.
machine-generated
such, NLP systems can provide a promising op- portunity to further understand counterspeech and assist content moderators and other stake holders in generating responses (Parker and Ruths, 2023). One major challenge towards effectively combat- ing hateful and prejudiced content that counter- speech should dispel the implied stereotypical be- liefs about social groups (Buerger, 2021b), beyond simply denouncing the speech. For instance, Figure 1, the human-written counterspeech targets the stereotype that "gay men are promiscuous" im- plied in the original post. If left unchallenged, such prejudiced implications can perpetuate and main- tain existing stereotypes (Beukeboom and Burgers, 2019) and exacerbate discrimination and intergroup conflict (Fiske, 1998; Macrae and Bodenhausen, 2000). However, exactly how to convincingly com- bat stereotypical implications with counterspeech
1 Introduction
Counterspeech, i.e., responses that counteract hate- ful or dangerous content (Benesch et al., 2016), has emerged as widely supported solution to address hateful online speech without censorship risks of deletion-based content moderation (Myers West, 2018; Sap et al., 2019). However, the scale of hateful content increases online (Leetaru, 2019; Buggs, 2021), effectively responding with counter- speech infeasible for humans to do alone. As

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remains an open question (Buerger, 2022).
that prejudiced language about social groups can often convey and exacerbate stereotypes through subile implicature (Fiske, 1998; Sap et al., 2020; Perez Gomez, 2021, $2.1). Properly combating such language requires convincingly dispelling and fact-checking the stereotypical implications beyond simple deflection norm-setting (Lepoutre, 2019, $2.2). Additionally, our investigations aim to shed light on how NLP systems can help humans better analyze and produce counterspeech ($2.3).
To investigate this question, we analyze the us- age and convincingness of psychologically inspired strategies for countering implied biases and stereo- types, in both human- and machine- machine-generated coun- terspeech. In contrast to prior work, which has ex- amined tone- or style-based effectiveness (Hangart- ner al., 2021; Han et al., 2018), we design and analyze six stereotype-targeting strategies drawing from literature in social psychology. social cogni- tion, and philosophy of language.
2.1 Language and Stereotypes
Our strategies aim to directly combat the stereotypical association between a group and an auribute (e.g.. "gay men", "promiscuous"): reduc- ing the association between the attribute and the group by suggesting alternate groups that exhibit similar attributes, or focusing on different attributes of the targeted group, providing counterexamples to the stereotype, reducing the intrinsic implication of the stereotype through external factors about stereotype origins, broadening the group bound- aries to emphasize individual variation, and gener- ally labeling the stereotype as negative.
Prejudiced language reflects and perpetuates social inequalities and stereotypical beliefs (Beukeboom and Burgers, 2019). As argued by Perez Gomez (2021), when accepting subtly prejudiced state- ments as true, we inherently accept the underlying stereotype true. Therefore, the harmful implica- tions of hate speech must be challenged to mitigate the spread of such beliefs.
These underlying stereotypes are often in the form of generics, i.e., generalizing statements such as "girls wear pink". (Rhodes et al., 2012) which are difficult to counter due to their unquantified structure (Leslic, 2014). Generies show especially strong association to the group when the charac- teristics are striking and negative (Leslie, 2017), and once formed, these beliefs become extremely difficult change (Scheffer al., 2022). Thus, countering hate speech is important yet challeng- ing because requires extensive reasoning about the underlying implications.
We first investigate how prevalent and convinc- counter-
ing these strategies are in speech. Specifically, we conduct a crowdsourced user study to examine which strategies workers prefer, and analyze two corpora of human-written counterspeech. Our results show that humans per- ceive countering strategies that present different qualities of the targeted group as most persuasive, and generally prefer strategies that require deeper reasoning about the implied stereotype (e.g., pre- senting external factors or counterexamples).
2.2 Countering Hate vs. Implied Stereotypes
Counterspeech can have many intended effects to a diverse audience including original speakers and bystanders (Buerger, 2021a, 2022). Therefore, counterspeech, and more broadly countering hate, has been studied from number of different angles. Observing Twitter interactions, Hangartner et al. (2021) found that empathy resulted in better out- comes (e.g.. deletion, decrease in number of hate tweets) compared to using humor or warnings of consequences. Benesch et al. (2016) suggests vari- ous strategies for countering hate such as empathy, shaming, humor, warning of consequences, and fact-checking. Moreover, studics show the conta- gion effect (Buerger, 2021a) where social feedback (Berry and Taylor, 2017) and the quality of the comments (e.g., civility) impact subsequent com- ments and discussions (Friess et al., 2021; Han et al., 2018). These works provide insights into
Furthermore, when comparing and machine-generated counterspeech, we find that machines often generate nonspecific strategies (e.g., general denouncing) whereas humans use more nu- anced and specific strategies, like counterexamples. Our findings highlights the potential for understand- ing counterspeech strategies that persuasively chal- lenge stereotypical beliefs and reveal the inherent limitations of current NLP models in generating effective stereotype tarrgein counterspeech.
2 Background: Language, Stereotypes, and Counterspeech
Our goal is investigate stereotype-targeting coun- terspeech strategies that most convincingly chal- lenge the underlying implications of hate speech. Our analyses and strategies are based on the fact

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preferences for strategies to counter the explicit content of hate speech. In contrast, our work stud- ies strategies for persuasive arguments against hate conveyed through implied steretoypes.
Post: girls think guys flex muscles n stroke their c* before taking pics Be that's basically what pathetic girls do in every pic
Implied Stereotype: Women are shallow.
ALT-GRE This statement implies inaccurate stereolype because many men also be shallow.
To specifically target stereotypes and group per- ception, previous works in psychology have stud- ied humanization of targeted groups through re- minding people that individuals belong in multiple categories (Prati et al., 2016), exposure to posi- tive outgroup examples (Cernat, 2011), and anti- stereotypes (Finnegan et al., 2015) and have shown promising results. Moreover, the positive im- pacts on belief change of fact-checking (Porter and Wood, 2021) and counter narratives (Lewandowsky et al., 2012) call for counterspeech beyond just re- jecting the original speech.
ALT-QUAL. This statement implies inaccurate stereo- type because many women are intelligent, profound, and multi-dimensional
CNTR-ENT Exs This statement implics an inaccurate stereo- type because a lot of women are profound, including women philosophers, women scientists, and women writ- crs.
EXTERNAL This statement implies an inaccurate stereo- type; historical contexts such as gender incquality and scxism have perpetuated this false stereotype that women are shallow.
BROAD This statement implies inaccurate stereotype because women have all sorts of personality types and characteristics, just like all groups of people; we cannot characterize them all as shallow.
GEN-DEN This statement's 's implication inaccurate and unnecessarily hurtful generalization about women that they shallow.
2.3
NLP for Counterspeech
With the ability to analyze and generate text at a large scale, NLP is an invaluable asset for counter- speech and number of works in NLP have collected and generated counterspeech datasets (Mathew et al., 2019; Garland et al., 2020; Qian et al., 2019; Chung et al., 2019; Bonaldi et al., 2022; Fanton et al., 2021) for the detection, analysis, and gener- ation of counterspeech. Yu al. (2022) observed that question marks and words related to awareness and problem solving are frequently used in coun- terspeech. Similarly, Lasser et al. (2023) noted that responding with simple opinions or sarcasm reduced extreme hate. Generative approaches to counterspeech have incorporate stylistic strategies (e.g.. politeness, joyfulness, detoxification; Saha al. 2022) and relevance (Zhu and Bhat, 2021). Al- though previous works have made progress towards observing and generating using different counter- speech strategies, the focus has been on stylistic differences and while promising, do not explcitly target the underlying stereotypes in hate speech.
Example statements of each countering strategy. These statements were also used as counter statements for user study in $4.
Table I:
humans and machines to understand these strate- gies and improve counterspeech. Building on these prior works, we aim to provide insights into how language models can help counter stereotypes and mitigate the harmful effects of hate specch.
3 serretyyee-Targtting Strategies
To investigate how counterspeech can address stereotypical speech, we examine stereotypes through the lens of generics about social groups (Rhodes et al., 2012; Sap et al., 2020), i.c., short un- quantified statements that link a social group with quality:
GROUP + relation + QUALITY.
For example, the stereotype in Table I "Women are shallow" can be separated into three components: "Women" (GROUP), "are" (relation) and "shallow" (QUALITY). The GROUP typically refers to demo- graphic groups such as black people, gay men, etc. The relation is typically stative verb like "are" or a verb indicating a lack of ability, such as "can't". A QUALITY usually has negative meaning or conno- tation (c.g., shallow, terrorist), but can sometimes be positive (e.g., for positive stereotypes; Cheryan and Bodenhausen, 2000).
Fraser et al. (2021) and Allaway et al. (2023)
have more closely investigated computational meth- ods to counter stereotypes. Fraser et al. (2021) investigated stereotypes and counter stereotypes using warmth and competence of the groups and associated words and noted the difficulty in auto- matically generating anti-stereotypes. Moreover, Allaway et al. (2023) have studied automatic gen- eration of counterstatements to implied stereotypes and provides a proof-of-coneed strategies. Our work, on the other hand, adds upon the proposed strategies and analyzes their usage by
for countering
Through this formulation we introduce six coun- tering strategies that aim to challenge the accept- ability, validity, or truthfulness of stereotypes ex-

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pressed as generics, crafted based on prior work on combating stereotypes and essentialism in social psychology and finding exceptions to generies in philosophy of language.
External Factors (EXTERNAL) When readers are reminded that stereotypical characteristics of a group G exist due to external conditions (i.e 'G relation q is due to external factor f"), they have been shown to exhibit more flexible and dispens- able thinking towards the group G (Vasilyeva and Lombrozo, 2020). This style of unconfounding is referred to as structual construal (Berkowitz, 1984) in social psychology.
Alternate Groups (ALT-GRP) People often ac- cept generics of the form 'G relation q' true based on relatively weak evidence (Cimpian et al.. 2010). However, for unfamiliar groups, we often attribute qualities more assertively, claiming that (almost) 'all G relation q' even with scarce sup- porting evidence (Rooij and Schulz, 2020). To counter such inferences, we can remind readers that alternate groups' relation to q is often higher than commonly recognized.
Thus, we propose the EXTERNAL strategy, in which counterspeech employs external causes or factors demonstrate why a stereotype might have been formed (e.g., historical context such as gender inequality). Note that it differs from CNTR-EXS by focusing on external factors or details for refutation, rather than specific instances within the group.
We define the ALT-GRP strategy as follows:
Alr(GROUP) + relation + QUALITY
Here, Alt(G) represents a relevant set of alter- native groups to G with the same set of separat- ing characteristic (e.g., gender). For instance, if G = ="women", Alt(G) would consist of group like "men" rather than set of unrelated groups.
Broadening (BROaD) The broadening strategy questions the validity of GROUP in a generic or stereotype by employing humanizing tech- niques. Essentially, broadening emphasizes either the uniqueness of a certain quality to the group or highlights the complexity of individual group members, suggesting that they cannot defined by merely one quality.
Alternate Qualities (ALT-QUAL) Similarly, we construct strategy that highlights alternate distinc- tive or defining qualities of G. We consider values of (l)(g) to promote more nuanced understand- ing and challenge preconceived notions about the group. Thus, formulate the ALT-QUAL QUAL strategy the following:
An example of broadening can be formalized
AUIQQUAITY)
All people + relation
QUALITY
General Denouncing (GEN-DEN) Another com- monly observed countering strategy in NLP is denouncing (Mathew et al., 2019; Qian et al., 2019). Denouncing involves expressing general disapproval for the hate speech (c.g.. "This is just wrong"). This approach has the potential to es- tablish discursive norms and preemptively dismiss similar instances of hate speech (Lepoutre, 2019) 9).. Here, counterspeech assigns general negative val- ues to the stereotypical statement, using terms like "hurtful", "unhellful" or "not okay" to express dis- approval and challenge the stereotype.
GROUP relation + AIr(QUALITY)
In this case, Alt(q) represents alternate contex- tually relevant qualities. For example, if S Women are shallow., Alt(q) would include qual- ities like "intelligent" and "profound" but not posi- tive and unrelated quality (e.g., fun). This aims to combat the definitional aspect of a stereotype.
Counterexamples (CNTR-EXS) Counterexam- ples aim to point out exceptions to the stereo- type and thereby challenge the implication that the stereotype holds for all members of the GROUP.
relation Alt Alt(QUALITY)
4 Human Evaluation of Sterrotype-TTargeingg Strategies
x relation + QUALITY
where is specific individual member of GROUP subgroup of GROUP?.
In order to determine which strategies persua- sive in countering implied stereotypical beliefs, we first conduct a user study on Amazon Mechanical Turk (MTurk) to measure the convincingness of expert-crafted counter-statee statements, i.c., short sen- tences that embody a specific countering strategy.
'This strategy follows similar formulation as the direct exception statements Allaway et al. (2023).
'A subgroup must be precisely defined; for example, "my gay friends" is specific, while "most Muslims" or "gay people 30 years ago" are not.

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ALT-QUAL, BROAD, CNTR-EXS, and EXTERNAL. These results are mostly consistent with findings from social psychology that highlight the value of strategies similar to ALT-QUAL (Leslie, 2008, 2017), BROAD (Foster-Hansee et al., 2022), and EXTERNAL (Vasilyeva and Lombrozo, 2020).
At-Qual
20%
22%
Courteting Stralegiie
Broad
25%
14%
20%
20%
GenDen 10%
16%
We also observe that less complex strategies not necessarily the most convincing In particu- lar, while GEN-DE. and BROAD require the least amount of reasoning about the stereotypical quality or group to understand, they have very different lev- els of convincingness: BROAD is the most preferred first choice while GEN-DEN is the least preferred. Furthermore, strategies that require a similar level of involved reasoning can have drastically different levels of convincingness (c.g., ALT- QUAL is cho- convincing twice frequently ALT-GRP, cumulatively). This suggests that differences in strategy complexity are not sufficient to account for convincingness.
Aist Choike
At-Grp
14%
7%
Second Choice
0.0%
5.0%
10.0%
16.0% 20.0% 25.0% 30.0%
35.0% 40.0%
Figure 2: First and second choices of countering strate- gies sorted by the sum of the two responses. Percentage is calculated separately for first and second choice.
4.1 Experimental Setup
We first collected social media posts with their corresponding implied stereotype for 10 demo- graphic groups from the Social Bias Inference Cor- pus (SBIC; Sap et al., 2020). The groups were selected from the top 25 most targeted groups in SBIC', and the stereotypes were selected from the top 10 most frequent stereotypes for each group. The selected groups and stereotypes are shown in Figure 7.
There are also clear differences in strategy pref- erences depending on the group and stereotype (see Figure 7). For example, BROAD is not chosen at all for the stereotype about feminists (Fig. 7, fourth from the bottom), despite its popularity in counter- ing other stereotypes such "women shallow".
Counter-statements For each stereotype, six counter-statements (one for each strategy) were crafted collaboratively by the authors, to ensure maximal faithfulness to the strategy definitions. This resulted in total of 60 counter-stalements; see Table for examples.
Countering Strategies in Human vs. Machine-generated Data
The results from the previous section suggest that some strategies are more convincing than others. To further investigate this, we examine the usage of these six strategies in human-written counter- speech. Additionally, we compare human and counterspeech to understand whether machines use these strategies. In this sec- tion, discuss the methods and datasets used for this analysis, while our findings are presented in $6.
Task Description Each participant is presented with post and its implied stereotype, along with six counter-statements in random order. Annotators are asked to select the first and second most con- vincing counter-statements according to them (i.c., following a descriptive data labeling paradigm; Röttger et al., 2022). We recruited annotators from a pool of pre-qualified participants who were paid 0.27 USD for each text and stercotype pair. Please refer Appendix G for further details.
5.1 Datasets
We consider two datasets that contain posts paired with counterspeech comments: Multitarget- CONAN (Fanton et al., 2021), a dataset of hate speech and counterspeech pairs generated using human-machine collaboration, and the Winning Arguments (ChangeMy View) Corpus (Tan et al., 2016) extended with more recent posts from the same subreddit, r/ChangcMy View (hence forth CMV) for the period 2016-2022. Appendix A
4.2 Results
Our results in Figure 2 suggest that there is clear distinction and ordering of convincing countering strategies. Although the order of preference for the first choice and the second choices differ- ent, the cumulative top choices remain consistent:
'The groups were aggregated by a rough regex matching identity terms, for example, "asian [folksipcopicpperoon]]] or "asians" for Asian folks, within the target group identified hy the SBIC annotators.
hhttps://www.ret reddit.ddd....e wtw.:/itttcoooo/oe/eeeeeeeeeee wwtpo///////aaggnnii/ee.e

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shows details on datasets and their collection and filtering process.
with the strategy definition and two in-context ex- amples (one using the strategy and one without it). and instruct them to provide a binary label that indi- cates the presence of the strategy. We also prompt them to output the text span that contains it. Please refer to Appendix D.2 for detailed prompts used for analysis.
These two datasets offer insights into how coun- tering strategies are employed in two distinct types of online interactions: in CONAN, users share short comment replies without necessarily seek- ing change, while in CMV, users actively engage via long comment posts to challenge or potentially alter their opinions.
Test Set Creation To validate the reliability of using LLMs for detecting these nuanced counter- ing strategies, we randomly sampled 100 examples from each dataset. These samples were assigned gold labels through expert annotation conducted by three of the authors. The authors manually reviewed the examples independently, resolving disagreements through discussions. This process led to the creation of a test set comprising of 200 expert- hepert-annotated samples.
5.2 Selecting Data
For both MTCONAN and CMV datasets, we are interested in analyzing the implicd stereotype of the original post as well as the strategies used in the counter-statements (counterspeech in MTCO- NAN; A and non-A comments in CMV). We use the process outlined below filter and select data that contain specific implied stereotypes.
Evaluating Strategy Detection We utilize these gold labels to assess the performance of each LLM in detecting strategies, as shown in Table 2. Al- though performance is only moderate, both models are outperform the majority class classifiers, which include both the 0 and majority classes. This find- ing is particularly notable given that the majority class of the entire dataset is unknown, suggests that these models have recognized some meaning- ful signals within the data. GPT-4 shows similar or improved performance on strategy detection com- pared to GPT-3.5 for all strategies and is thus used in subsequent experiments for strategy labeling. However, akin to expert-annotators, determining whether a reply contains specific strategy is not trivial for LLMs. For example, we observed during qualitative inspection that models occasionally confuse simple mention of alternative qualities or groups for the presence of ALT-QUAL or ALT-GRP, respectively. See Appendix FF for further analysis.
Implied Stereotype Extraction To analyze strategies for combating stereotypical implications, we filter our two datasets to include only posts that target or reference specific stereotype about demographic group. Based on evidence of its label- ing abilities (Ziems et al., 2023), we use GPT-4 to identify such posts and generate the target group and implied stereotype, by prompting it with three in- on-context examples (see Appendix B for details). Table 3 shows the number of unique harmful posts in each dataset.
5.3 Counterspeech Generation
To investigate machine-gencratee counterspeech, we prompt two large language models (LLMs) to respond to posts with counterspeech: GPT-4
(OpenAI, 2023) and Alpaca (Taori et al., 2023).
We instruct the LLMs to assist user in respond- ing to harmful posts without providing any spe- cific counter strategy definitions, to avoid bias- ing the model's output towards predetermined ap- proaches.5
Usage and Convincingness Results
In this section, discuss the results of our investi- gations regarding the usage and persuasive power of our six stereotype-targeting strategies, for both human and machine-writted counterspeech.
5.4 Counter Strategy Labeling
To detect the usage of any our six stereotype- targeting strategies within a reply to post, we leverage the labeling abilities of two LLMs, namely GPT-3.5 and GPT-4. We prompt these models
6.1
CMV: Persuasive vs. Non-persuasive Comments
"We provide LLM with prompt, "How should I respond to a post that says (POST]." along with the system message, "You are helping people respond to harmful posts online. Reply directly to the post." " See Appendix D for forther details.
The comparison of strategy usage between A and non-A comments in CMV dataset shown in Fig- ure 3. Overall, the most frequently used strategies were ALT-QUAL (84.8%) followed by EXTERNAL
"gpt-3.5 "spt-3.5-turbo S-turbo-0314

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Methol
GPT4
GPT-3.5
Majoriy Class (1)
Majadly Class (1)
Straiegs
Precision
Rozall
Pricision
Recall
Fi
Pricicon
Recall
Fi
Precision
Recall
Fi
AL ALV-GRP
0.44
0.75
0.60
0,62
0.71
01.59
0.42
10.
0.43
0,05
0.5
0.15
ALT-QUAL
0.68
0.66
0.61
0.66
0.64
01.58
0.31
0.5
0.58
0.30
0.5
0.25
UNTR:
0.74
0.99
0.36
EXTERNAL
0.72
0.34
GEX-DEN
0.40
Table GPT-4 and GPT-3.5 strategy labeling performance datasets using expert-annotations shown with majority class classifiers. While performance is moderate, both models significantly outperform majority class classifiers, and GPT-4 outperforms or shows similar performance to GPT-3.5 strategies.
Dataset
Harmful Posts
Counterspeech
CMV-delta
GPT-1
Alpaca
1.2
CMV MTCONAN
1586
5191
876
1000
1.0
2666
Table 3: Number of unique harmful posts and counter- speech for each dataset.
Strategy Usage
0.8
TI5
non
1.0
0.8
n15
Strategy Usage
0.0
0.6
At--Gr
Alt-Qual
Cntrexs
External
Broad
GenDen
Strategy
Figure 4: Delta comments and machine- gentrrtted coun- terspeech comparisons of strategy usage annotated with independent f-test P values. The legends indicate ns (p < 1.00), (p .01), and **** .0001). Error bars indicate standard error.
0.2
AttGrp
Alt-Qual
A comments, the highest proportion of targeted de- mographic types were related to gender identity, accounting for 42.5% of the total. This was fol- lowed by race/ethnicity (10.9%), sexual orientation (8.0%), appearance (7.7%), socio-economic sta- tus (4.8%), and religion (4.8%). We notice that the most distinguishing strategies for addressing various demographic types were EXTERNAL and GEN-DEN. Further, GPT-4 showed higher usage of Gen-DEn when countering stercotypes concern- ing race/ethnicity, appearance, and religion.
Strategy
Figure 3: Delta and non-delta comments comparisons of strategy usage annotated with independent t-test values. The legends used in this graph are ns (p < 1.00), (p < .05), ** (p < .01), and *** (p < .001). Error bars indicate standard error.
(66.7%) BROAD (66.5%), ALT-GRP (61.1%), CNTR-ExS (42.3%), and GEN-DEN DEN (34.3%). On average, each comment used 3.56 strategies. Using an independent i-test, we find that the most significant difference in strategy usage is for the BROAD strategy, with 68.8% and 61.1% for A and non-A comments respectively (p 0.001). We also see that GEN-DES strategy usage is less persuasive (32.6% in A comments vs 36.1% in non-A comments; < 0.01). Additionally, CNTR- ExS and BROAD were found to be more persuasive.
6.2
CMV: Persuasive Comments vs. Machine-generated Counterstatements
The comparison between persuasive human re- sponses (A comments) and machine-gent -eeeinneegerrttedd re- sponses in CMV dataset is shown in Figure 4, Both GPT-4 and Alpaca generated counterspeech showed significant differences in strategy usage compared to persuasive human responses. Alpaca- generated counterspeech contained average of
Strategies by Demographic Identity Types The analysis of results categorized by target group de- mographic types is shown in Figure The key- words corresponding to each demographic group can be found at Appendix H. Among the persuasive
"Some identities are mentioned intersoctionally, but in order to holistically evaluate strategy usage associated with identities, we allowed overlaps in our comparison data.

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type CMV-delta
type GPT-4
Strategy Usagg
demographic, __ype
0.8
CMV
0.6
gender identity
sexual orientation
0.4
ethnicity
appearance
religion
0.0
Alt-Grp
Ar-Qual
Extemal
Broad
Dan
Alt-Grp
Alt- DraQual
External Strategy
Broad
Strategy
Figure 5: Delta comments and machine-generatee counterspeech strategy usage categorized by target group demographic identity types. GPT-4 shows much higher denouncing for certain demographic groups than others.
MTCONAN
tives in MTCONAN indicate higher eral denoucing and lower usage of all other strate- gies. The most frequent strategies are ALT-QUAL (51.0%) and GEN-DEN (18.3%), while the least frequent ones are EXTERNAL (16.9%), and CNTR- Exs (12.6%). This analysis reveals that uncivil stereotypical speech is countered differently from civil engagements.
of gen-
Strategy Usage
0.8
0.6
0.4
In contrast, machine-gencrater generated responses to MT- CONAN posts show high usage of GEN-DEN and ALT-QUAL at 96.9% and 91.4% respectively, sig- nificantly differing from MTCONAN counter nar- ratives using 48.3% and 5.1% respectively. GPT-4- generated responses demonstrate higher usage all the strategies averaging 4.24 strategies per com- ment, followed by alpaca (2.91) and MTCONAN counter narratives (1.87).
0.0
External
Broad
GenDen
Strategy
Figure 6: Delta comments and CONAN counterspeech comparisons strategy usage annotated with indepen- dent t-test P values. The legends indicate ns (p < 1.00), (p < 001), and (p < .0001).
2.30 strategies per counterstatement, while GPT- 4 generated ones used the most out of the three variations, averaging 4.05 per counterstatement. Most notably, GPT--4-enerated counterspeech contained GEN-DEN more frequently (55.6%) compared to persuasive human (32.6%) and Alpaca-generated (32.3%) responses. Further, both GPT-4 and Alpaca generated counterspeech used CNTR-EXS significantly lesser than human responses (43.6% vs. 31.5% and 11.4% for GPT-4 and Alpaca respectively). Also, GPT-4- generated counterspeech used lower ALT-GRP (56.6%) than human responses (61.4%). The comparison between non-persuasive human re- sponses (non-A comments) and machine-generated responses shown in Appendix C show similar pattern where GPT-4 uses more GEN-DEN and ma- chines use less CNTR-EXS.
7 Conclusion & Discussion
In this work, investigated diereotyee-argeting strategies that convineingly counter the underly- ing implication of harmful speech. We crafted six different strategies ($3) and observed human pref- erences under a controlled setting ($4) and in two datasets ($5). We compared the strategy usage persuasive vs. unpersuasive human generated coun- terspeech and in human vs. machine generated counterspeech ($6).
machine-generated
Humans prefer more specific strategies. In both our user study ($4) and data study ($5), ALT- QUAL was the most preferred and used method. Similarly, the delta award difference and the pref- erence ranking both showed EXTERNAL, BRoAd, and CNTR-EXS as more convincing. These top strategies, with the exception of broadening, are methods that require specific reasoning about the implication. Morcover, correctly identifying exter- nal factors and devise counter examples requires up-to-date world knowledge. In this work, we did not focus on the correctness of the usage of these strategies by machines, but - given their persua- siveness to humans further study is required to
6.3 MTCONAN: Counter Narratives vs Machine-generated Counterstatements
The usage of countering strategies in the MTCO- NAN dataset, compared to machine responses, presented in Figure 6. In contrast to CMV A comments, the observed trends of counter narra-

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ensure their quality.
evaluating diverse of stereotypes and counter- statements with human evaluators, there are still variations in human judgement that we need fur- ther investigation. We also only test relatively short counter-statements and limited variations in how the strategics are presented. Therefore, it would be an important step in future works to understand these human variations and account for diversity in both counter-statements and participant pool.
LLMs use less anti-stereotypical examples. Across the two models, GPT-4 and Alpaca, we see that LLMs do not use anti-stereotypical exam- ples often humans, and instead more gen- eral strategies such as denouncing. However, we see from our results and previous work (Finnegan et al., 2015) that humans prefer countering strate- gies with specific examples. These findings suggest that LLMs may lack exposure to anti- stereottrrlll examples and reveals an interesting challenge for improving muchine-generatee counterspeech.
Human Strategy Evaluation Instructions To intrinsically motivate and empower annotators, which can increase annotation quality (August et al., 2018), we instructed annotators to select the most convincing statement "content mod- erator". However, this framing could limit the context of convincingness of counter-statements one group of users whose values and decisions can sometimes disagree with the community members (Weld et al., 2022). Therefore, future works should expand this framing and perform experiments un- der multiple contexts with varying audiences and intentions, especially focusing on counterspeaker motivations (Buerger, 2022), to understand how these variations interact with strategy effects and preferences.
Stereotypes about each target group are han- dled differently. From both our human evalua- tions and data analysis, we see different usage of strategies for each stereotype. For example, we observe increased use of EXTERNAL challenge socio-economic stereotypes (e.g.. poor folks, home- less). Similarly, stereotypes about race-ethnicity (e.g., black, person of color, Asian) and appearance (e.g, fat, overweight, slim) also use more EXTER- NAL. These results suggest that there are similari- ties in strategy usage between identity groups that might be associated with stereotypes with similar characteristics. These results show that there is no one correct strategy for counterspeech and deeper examination is necessary to select strategies with an understanding of the stereotype and group.
Limitations in Strategy Labeling While we ex- plored different prompts and settings for strategy labeling to improve performance, we did not ex- plore different methods models. We have also made effort create high quality annotations among the authors but were only able annotate a total of 200 samples across two datasets. This points to a possible further direction into investigat- ing strategy labeling and extraction methods.
Different intended effeets result in different strategy usage. Our results ($6.1, $6.3) also show that different intended effects (c.g., chang- ing a viewpoint) result different strategy usage. Although most hate speech datasets are limited to short social media comments (Mathew et al., 2019; Yu et al., 2022), as our Reddit data shows, there are diverse forms of harmful speech that requires countering. Therefore, our findings highlight the importance of further investigation into contextual differences in countering hate speech.
Limitations in Counterspeech Generation In this work. we only explore two LLMs for counter- speech generation: GPT-4, a closed model which we have limited information about, and Alpaca. Es- pecially with GPT-4, our lack of understanding of training data and procedure leads to unanswered questions about the results shown in this work. We also used naive prompting where we ask the model to "help respond" to the original post sim- ulate a non-expert interaction with these models. Therefore, future work should explore different set- tings and prompts including various definitions of counterspeech and countering strategics to improve counterspeech generation.
Limitations & Ethical Considerations
While we attempt a comprehensive understanding of countering strategies through user study and coun-
analysis of human and terspeech, we face several limitations as well as unanswered ethical considerations, which are dis- cussed below.
Human Judgement Variations in Counter- statements Despite our efforts at creating and

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Limited Definition of Harm While counter- speech avoids censoring, it can still have some biases and backfiring effects (Lasser et al., 2023). Additionally, due to differences in perceptions of toxicity (Sap et al., 2022), deciding which posts to respond to could introduce biases, and exces- sive machine responses could limit conversations. Therefore, future works should strive to understand when these systems should be used generate counterspeech and how they should be integrated into applications.
Use Communication about Categorized Individu- als. Review of Communication Research, 7:1-37.
Helena Bonaldi, Sara Dellantonio, Serra Sinem Tekiroğlu, and Marco Guerini. 2022. Human- machine collaboration approaches to build dialogue dataset for hate speech countering. In Proceedings of the 2022 Conference on Empirical Methods in Nat- ural Language Processing, pages 8031 ---049, Abu Dhabi, United Arab Emirates. Association for Com- putational Linguisties.
Catherine Buerger. 2021a. Counterspeech: A Literature Review.
Machine-generated Persuasive Content Auto- matic language generation systems have potential to affect the way users think (Jakesch et al., 2023). Since main research question asks strategies to persuade readers against harmful beliefs, there is a greater risk of it being used in unintended appli- cations to do the opposite. Additionally, automati- cally generated counterspeech can be incorrect and therefore can spread false information especially about social groups Considering these risks, it is important to also investigate into systems and regu- lations that can protect stakeholders from dangers of malfunctioning misused systems (Crawford, 2021).
Catherine Buerger: 2021b. Speech as Driver of Inter- group Violence: A Literature Review.
Catherine Buerger. 2022. Why They Do It: Counter- speech Theories of Change.
Vasile Cernat. 2011. Extended Contact Effects: Expo- sure to Positive Outgroup Exemplars Sufficient or Is Interaction With Ingroup Members Necessary? The Journal of Social Psychology. 151(6):737-753
Cheryan and G V Bodenhausen. 2000. When positive stereotypes threaten intellectual performance: the psychological hazards of "model minority" status. Psychological science, 11(5):399-402.
Yi-Ling Chung. Elizaveta Kuzmenko, Serra Sinem Tekiroglu, and Marco Guerini. 2019. CONAN COunter NArratives through nichesourcing: a mul- tilingual dataset of responses fight online hate speech. In Proceedings of the 57th Anmual Meet- ing of the Association for Computational Linguistics, pages 2819-2829, Florence, Italy. Association for Computational Linguisties.
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A Dataset Details
Below sections describe the datasets used in the experiments in detail.
Maarten Sap, Swabha Swayamdipta, Laura Vianna, Xuhui Zhou, Yejin Choi, and Noah A. Smith. 2022. Annotators with attitudes: How annotator beliefs and identities bias toxic language detection. In Pro- ceedings of the 2022 Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Technologies. pages 5884-5906 Seattle, United States, Association for Computational Linguistics.
A.1 Multitarget-CNNNN (MTCONAN)
Starting with seed dataset of human generated hate speech and counterspeech pairs (N=880) from NGO workers, Fanton et al. (2021) generate a to- tal of 5, 003 hate speech and counterspeech pairs using human-in -n-tn----tth--oopp methods. In particular, they used a machine author and human reviewer paradigm where data was generated iteratively us- ing edits made by NGO workers.
Marten Scheffer, Denny Borsboom, Sander Nieuwen- huis, and Frances Westley. 2022. Belief traps: Tackling the inertia of harmful beliefs. Pro- ceedings of the National Academy of Sciences, 119(32):c2203149119
A.2 Change My View (CMV)
The CMV community on Reddit is "dedicated to civil discourse"* "8 and utilizes the delta system explicitly mark view-changing posts. When com- ment changes a user's view, either the original poster or other community members can respond with a "A" symbol, marking the comment as per- suasive. This unique interaction among community members highlights the persuasiveness of various countering strategies and offers valuable insights into their usage and impact on readers.
Chenhao Tan, Vlad Niculae, Cristian Danescu- Niculescu-Mizil, and Lillian Lee. 2016. Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussions. Pro- ceedings of the 25th International Conference on World Wide Web, pages 613-624. International World Wide Web Conferences Steering Committee.
Rohan Taori, Ishaan Guirajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023. Stanford alpaca: An instruction- followiin Ilama model. https:// gi thub. com/ tatsu-lttuuuatt Lanford_al _aaapa
TTaken from CMV wiki

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Woman are shallww
demographic group perrfful_t__gruup (2)
Musiit Tolks don't respect women.
Mexccan
if harmful_to_grouu which demographic group the post is targeting Starget_group and (3) if harmful_to_group the stereotypes implied by the post in the form of a generic.
Storedlpae
slupid
Jew an tolks are greedy
B124
Bay men sew acusos.
Feminists hale men. Fotifliis
Black bazy.
Asian tolks can't drive. B12d
Based on evidence of its labeling abilities (Ziems et al., 2023), posts (two harmful and one non-harmful) to gen- crate these annotations. Following this, we anno- tate the hate speech posts from the MTCONAN dataset and the titles from the CMV dataset." !2 The results were then validated by three human cspert-annotators and the percentage of those marked correct were 99.0%, 98.5%. and 98.5% for harmful_to. to_goup, target_groud and the gen- erated generics respectively. The detailed settings and prompts are listed in the following sections.
20%
40%
60%
External
prompt GPT-4 with 3 example
Figure The most convincing strategies by stereotypes.
(a) Winning Argument Corpus The Winning Argument Corpus is dataset of delta and non-delta awarded threads. For each Reddit thread, we con- sider only the root comment (i.e., direct replies to the original post) to evaluate the arguments used for countering the original post, rather than subsequent interactions. The corpus covers data collected from 2013 to 2015, and to supplement this dataset, we gather additional comments from 2016 to 2022.
B.1 GPT-4 Stereotype Extraction Settings
We use the following setting for stereotype extraction mentioned above: max_tokens=256. tempearture=0. and top_p=1.
(b) Additional Reddit Data To select the most relevant subset the scraped data (2016-2022), we follow these steps:
We also use the following system message "You are evaluating and anlyzing posts found online hate and how they express potentially harmful beliefs. Give all answers in JSON format." and the user message shown below filled with the original post.
1. Extract posts (longer than 50 words) with demographic-relate words in the title sim- ilar Tan al.
whether they contain
2. For these posts, retrieve only the (root, A- comment) pairs, where a delta has been awarded to the comment!!
3. Augment this subset with (root, non-A com- ment) pairs, where non-A comments are di- rect replies to the root post that did not receive delta. For each root post, we select a non-A comment based on the highest sentence-BERT similarity (Reimers and Gurevych, 2019) and at least 30% word count overlap with the A comment.
User message: "Is the following post harmful towards a demographic group? If so, which groups is the post targeting? Give the implication of the statement in generic format (e.g., fat folks don't exercise, women are shallow, asian folks can't drive). An\nPost: "()"An\nGive an answer in the following format: ("is_hate_spedtt "harmful_to_groupp bool, "target_group". str, sempliee_generic.. str)"
bool,
Finally, we result in a dataset (referred to as CMV) of 5, 191 comments across the Winning Ar- gument Corpus (Tan et al., 2016) and the addition- ally scraped Reddit data.
Few-shot Examples We use the following posts as few-shot examples along with the correct labels:
B Implied Stereotype Extraction
"CMV: Black people are biologically more prone to violence."
Our goal is to label posts from both the MTCO- NAN and CMV datasets with the following infor- mation: (1) whether the post is harmful towards
"Did you hear the one about the jewish philan- thropist ? Me neither."
DDtt source (last accessed 06/23/2023)
10 ''The of words is given Appendix
Delta awarded when "Idelta" or "A" is in the comment, according to the Dolta System wiki
We opt for CMV post titles, rather than root posts, as they are more concise and provide a clearer focus for analysis.

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CMVinan-deta
Alpaca
repsonse characterized by longer and frequent denouncing statements and lack of countering ex- amples. Moreover, the broadening method used and labelled by GPT-4 does not actually counter the implication but rather sets the tone (e.g.. "let's promote understanding [...] for everyone's individ- ual choices").
1.2
Strategy Usage
0.8
0.6
Across number of samples, observe common patterns in the usage of countering strate- gies. First, GEN-DEL DEN has higher occurrence in the beginning or end of the countering posts and of- ten supplemented with CNTR-EE: or EXTERNAL descriptions as supporting evidence. Additionally, many CNTR- EXS are from first person experiences (e.g., "my gay friends") or the use of second person pronouns (c.g., "you'd 'd get made fun of")). These observations point future directions in generat- ing counterstatements, including tone setting and directly addressing the reader.
0.4
Grp
Cntrexs External
GenDen
Strategy
Figure 8: Non-delta comments and machine- -eeeerrteee counterspeech comparisons of strategy usage annotated with independent f-test P values. The legends indicate ns (p < 1.00), (p < .01), and **** (p < .0001). Error bars indicate standard enror.
"Why? So you can get angry and pretend that all women make false rape claims and having sex with woman is the biggest risk in the history of mankind?"
D GPT-4 Generation Settings & Prompts
For all generation, we max_ tokens to 256 and use tempearture=0.. 7, and top_p=1.
The fist two examples are used as harmful posts and the last example is used as a non-harme hon-harmful post.
D.1 Counterspeech Generation
We provide the following system message to GPT-4: "You are helping people respond to harmful posts online. Reply directly to the post. along with the following user message where we (post) replaced with the original post.
Extended Analysis
In addition to our analysis in $6, here we pro- vide comparison of strategy usage between persuasive comments and machine-generated coun- terspeech and a qualitative analysis human and machine-generater counterspeech.
User message: "How should to post that says: "(post)"?"
respond
C.1 CMV: Non-persuasive Comments vs Machine-generated Counterstatements
D.2 Strategy Labeling
We use the following system message: "You analyzing countering strategies used in counter ar- guments of stereotypes expressed as generies. Give all answers JSON format." along with user message and few-shot examples that differ by strat- egy.
As seen in Figure 8, machine-generated counter- speech shows similar discrepancies mentioned car- lier in $6.2. Compared to persuasive (A) com- ments, nenpeesuasive (non-A) comments used more GEN-DEN, but still, GPT-4 used the strat- egy significantly than humans whereas Al- paca used it slightly less. CNTR-EXS used significantly less for both Alpaca and GPT-4.
The prompts are shown in Table 5, We fill each prompt with counterspeech and implication. More- over,
the examples shown in Table 6 and their respective labeling few shot examples the model. We use two examples for each strategy labeling task.
C.2 Qualitative Analysis
In Table we show example of a harmful post from Reddit dataset. The original post, which claims that "gay men are incapable of a committed relationship". and its implication that, "gay men are promiscuous", is countered by community mem- bers with counter examples. However, GPT-4's
E Alpaca Generation Setting & Prompts
For counterspeech alpaca-7b model from
generation, we used huggingface

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Title: Gay men are incapable of committed loving, monogamous relationship. CMV Implied Stereotype: Gay men cannot have committed, loving, monogamous relationships Post: This is from my personal anecdotal experiences. There are exceptions, but 98% of gay men prefer lifestyle casual, multiple sexual relationships. A partial cause of this is trauma of being marginalized and demonized by society; but given choice and freedom choose, a gay man will overwhelmingly choose lifestyle of multiple partners over monogamous committed relationship. support gay marriage, but our portrayal of a gay family unit a rarity from what is actually happening, statistically
A I'll just chime the anecdotal experiences of others. I've met lots of gay people, but most of them I honestly don't know what their sexual lifestyle is. But the two close gay friends have are "both in long-ten seng-trrmme monogamous relationships, Mcanwhile, know lots of heterosexual friends who at least this point in their lives seem entirely disinterested in long term monogamous relationship. So my experiences seem to refute your position.Inln lnlaIn addition, may I ask how you know the gay people you know? If went to gay bar, I'd probably find a lot of gay people who are just looking for casual sex, but that's true of straight people who go hars too... so watch out for confirmation bias.
similar
others. you're re view is hased anecdotal experience, you should swayed by
V Of course my evidence is only anecdotal, but am gay and have been in a committed monogamous relationship for 17 years. That is the longest term relationship in my family, including immediate family who have all heen married at least there times. am also part of several gay themes meetups and am,active in several gay organizations, men are partnered and most of those relationships are decade if not decades Innn.,n,,,Sso, of course you admitted this. It is wildly unfair to judge gay people by criteria that they
instances, majority of
permitted to engage namely marriage.
While understand that you'ne re sharing your personal ancedotal experiences, it's important not generalize the experiences and preferences an entire group based on individual cases. The preferences and choices of individuals within the LGBTQ+ community are as diverse as those in any other community, It's crucial to recognize that people's choices and lifestyles influenced by various factors, and it's not fair assume that one's sexual orientation is the primary determinant of their relationship preferences, in Morcove, using a percentage like "98%" without any supporting data or research can be misleading and perpetuate harmful stereolypes, It's essential to remember that people's 's choices and preferences are personal and should not used judge or define an entire community. Instead, let's promote understanding. acceptance, and respect for everyone's 's individual choices and lifestyles, irrespective of their sexual orientation.
Table 4: Examples of responses for comparison where A denotes delta awarded comment, V denotes non-delta comment, and 2 denotes GPT-4 generated counterspeech. Highlights were added to example quotes chosen by GPT-4 during each counter strategy labeling task.
chavinlo/alpaca-natee with max_new_tok set to 512.
for strategy ALT-GRP since the comment mentions another group "an amputee"; however, GPT-4 did not label this strategy as present.
We used the following prompt template where {input} is replaced by the post.
Moreover, the commenter provides an EXTER- NAL reason for misunderstanding of the original poster by pointing out that "it's typically not much the male/female roles they're 'r refuting, but that the way their brain sees their body." This strat- egy, however, was also predicted to be not present by GPT-4. Such errors highlight the difficulty of the task and model shortcomings.
Prompt: "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
G User Study Details
You are helping people respond to harmful posts online. Generate a response to the following input.
We use the following settings for user study experi- ment in $4.
#H# Input: (input)
G.1 Qualification Process & Attention Check
The qualification process included three questions:
#*# Response:"
Humans are mammals. How true do you think is the above statement?
F Strategy Labeling Error Analysis
2. People are right handed. What percentage of people do you think are right handed?
An example of post with its predictions and labels are shown in Table 7. Our research team labelled 1

---

Strategy
User Message
ALT-GRP
Alternate groups are used to challenge generic stereotypes (e.g., women emotional) and emphasize the presence of various other groups that share similar characteristics (e.g., can also be emotional). Does the following post include alternate groups to counter the statement that (implied stereotype)?\n'nn Post:(counterspeecchh grroult" bool, "alt_groups__uoor : [str]]
ALT-QUAL
Alternate qualities
used to challenge stereotypical generic statements (e.g., positive inclusive, respectful, and supportive
feminists hate men) presenting additional qualities that emphasize
or accurate characteristics (e.g., feminists of equality for both men and wonen). Does the following post include alternate qualities counter the statement that (implied stereotype)?/nn'n Post:(counterspeechh)) quuttes" [str]]
"uf"alt_qualities"" bool, calt_qualities quotes":
CNTR-EXS
Counter examples challenge stereotypical generic statements (e.g., women are domestic) by highlighting the existence numerous instances that contradict such statements (e.g., many women are very career-driven such as women in the medical field, women CEOs, and women professors). Does the following post include counter examples to counter the statement that (implied stereotype)?\n's Post: ccst:eccontrspeecchh bool, External justifications are used to refute stereotypical generic statements (e.g., fat individuals don't engage exercise) by highlighting external factors that have contributed to the perpetuation of such stereatypes (e.g., media portrayals and beauty standards have reinforced this stereotype, despite the fact that fat individuals do exercise). Does the following post include external reasons to counter the statement that (implied stereotype) \n''n Post: P (counterspeech)Mn \n("external":; bool, "external_quotes"" [str]]
"[""ounter_examlee"
[str]]
EXTERNAL
BROAD
Broadening is used to counter stereotypical generic statements (e.g., women are shallow) by questioning the boundaries imposed on the group to demonstrate either the multitude of variations within the group that render the boundary meaningless or the shared characteristics among all humans (e.g., women are complex individuals like everyone else, and some may be shallow but that cannot be general ized to all women). Does the following post use broadening to counter the statement that (implied stereotype)?\n\n Post: pust::(oonterspeechhh speech)nn \nf"broadening": bool, bbroadening.qquter General denouncing is used to challenge stereotypical generic statements (e.g., feminists hate men) by showing broad disapproval (e.g., this is an unnecessary and hurtful generalization about feminists). Does the following post use general denouncing to counter the statement that (implied stereotype)?Nnn'n Post: (couutrrcntrs "[""genera__deouncinng" bool, "general wewpaaldddnnnunnnnuuue quuuttss"" [str]]
[str]]
GEN-DEN
Table Prompts used for strategy labeling, We fill (implied stereotype) and (counterspeech) with respective values.
3. Penguins can't fly. What percentage of penguins do you think can't fly?
The exact wording in the instruction was the fol- lowing: "Pretend you are playing the role of an online content modcrator and fact-checker, where your job is to provide counter-statements or correc- tions when people say things that are stereotypical, generalizations, or blatant biases against certain demographic groups."
The workers answer using slider with percent- age ranging 0 to 100 11 point likert scale. We accept the answer to correct for each question they answer 1) 10, 2) greater than or equal to 50%, and 3) 10. We consider a worker qualified if they score 3 on this task. The workers were paid 0.22 USD for the qualification task. We had I 110 partici- pants out of which 95 passed both attention check and choosing different options for most convincing and second most convincing option.
Additionally, we provided the following details:
Determine which of the 6 statements is most effective in counteracting the stereotype by selecting one of 6 options.
Note: plcase seleet the counter-statements that you think are best to counter the stereotype. There is no right answer.
G.2 Task Instructions
In our task instructions, we asked participants to choose the counter-staten statement that they found most effective in countering the stereotype.
G.3 Example UI
UI used for human evaluation is shown in Figure 9. We use attention check, which asks the annotator to

---

Implication
counterspeech
lesbians are butch
"That stereotype is prevalent cause it's the casiest to There tons of lesbians that do not look butch and you wouldn't 't know without asking their sexuality. To expand this think of the stereotype of gay feminine men. The bears normally aren't I outted because they don't look stereotypical most of the time." "
"What should done with this information Sww.aatiioonnnSSSuuuuluel police target more black people and put them jail with less serutiny? Recause that what cops in US already do." mean you are taking into account
black folks are violent
things that affect testosterone production. Eg smoking more prevalent among black people than is with white people Smoking increases testosterone fyi) Also talking about blacks as an ethnic group fucking idiotic since african people are genetically by far more varied than people european descent."
feminists are not willing
"No one is saying women never do anything wrong you re pretty much completely strawmanning the position. They're 'r saying false accusations are rare and unpunished sexual assault is common. None of this has anything to do with accountability, either."
to acknowledge false ac-
cusations and women's 's
capacity for wrongdo- ing
Table 6: Counterspeech and implications used for few-shot prompting.
Implied Generic Comment
"Alternative gender identities are invalid and not to be acknowledged"
"How do you feel ahout transgenderism? From people who I've spoken about being transgender, it's typically not so much the male/female roles they're refuting, but that the way their brain sees their body and how it ought to feel different from what it actually is; almost like the way amputee has phantom limbs, someone who transgender feels the different parts of their body."
Strategies Predictions
ALT-GRP
ALTT-QUAL.
CNTR-EXS
EXTERNAL.
BROAD
GEN-DEN
0
0
Labels
Table 7: An example of a comment from CMV with GPT-4 predictions and labels for each strategy.
select the number between 1 to 5 that is randomly populated.
H Demographic Identity Related Words
The demographic related words used for data filter- ing and analysis of countering strategies are shown in Table 8.

---

Task
Statement:
8(post)
This statement impiles the following stereotype: $(stereo)
not be politically correct say, but how much do agree that this stenescype trus?
Siruraly dauggem at dhagres
Nusther chaggee agries
Sammrofur agras
Strungly agras
Counter Statements:
Statement
5/esample1)
Counter Statement
Sjermmple2)
Counter Scatement: 3:
Sjezamplicl
Counter Statement 4:
5jscompled)
Counter Statemeent S Sjesamples)
Counter Statoment & sjorompled)
Now choose most corvincing counter-itatement our the previous staemants
Siscmpplc)
Sprsmpiec)
Statement Steuampled)
Statement Sinusmpled)
Statement 6 Niscmplle)
Pasce option
you're do atterttvely.
New thoose the second-moct convincing counter counter-statemennt statement out of the remaining statements texcluding the one you selected abovet:
(pesmpin))
fiescmpee)
Statement Sposmpied)
Statement 5: ((scrppee))
Statement & Stescrppedi
Figure 9: UT used for human evaluation in $4.

---

Demographic Dimension
Terms
gender identity
women, men, guys, girls, nonbinary, transgender, cisgender, agender, trans, non-binary, cis, sex, gender
intersectional
white men, black women, black men, white women, straight white, queer of color, straight white men, queer white men, queer white women. queer men of color. queer women of color, queer men, queer women, trans of color, cishet white, cisgender heterosexual white, transgender of color, non-disabled white, disahled of color
sexual orientation
straight, heterosexual, gay, lesbians, bisexuals, queer, asexual, homosexual, Igbiq, Igbt, monoga- mous, polyamorous, gay men, lesbian women, butch, bear, femme, feminine teenagers, older, millennials, elderly, old, young, middle aged, younger
age race ethnicity
person of color, people of color, asian americans, main--americass, asians, asian, african ameri- cans, affricn-ammercans, black, white-americans, white americans, white, caucasians, hispanic, latinx, latinos, latinas, latin americans, native americans, native, indigenous, native american/first nation, arabs, american indians, alaska native, native hawaiians, pacific islanders Chinese, Japanese, American, Canadian, Indian, Middle Eastern, Luropean, Alrican, Korean, Mexican, Russian, Cuban, Italian, German, French, Jamaican, Filipino. non-American, foreign, foreigners, Americans
nationality
religion
buddhists, hindus, christians, jewish, jews. agnostic, muslims, mormons, orthodox, atheists, tavists. protestants, christian, catholics, sikhs, amish, non-religious, satanists. muslim able-bodied, non- -in-disabled, disabled, paralyzed, vision impaired, blind, hearing impaired, deaf, hard of hearing, differently abled, hearing impairment. visual impairment, vision impairment, disability, paraplegia, quadriplegia, short, cognitive disability, intellectual disability, learning disability. ADHID, a brain injury, autism, autistic, depression. bipolar disorder, psychosocial disability. a mental health condition, mentally disabled, mental illness, mental disorder tall, short, fat, thin, slim, overweight, bald, ugly, beautiful, light skinned, dark skinned, attractive, unattractive, obese, obesity
disability
appearance
education
highly educated, less educated, smart, less smart, dumh, stupid
personality
introverts, extroverts
politics
democrats, libertarians, liberals, republicans, conservatives, feminists
socio
rich, wealthy. poor, homeless, lower class, welfare, middle class, working class, upper class, formerly incarcerated, first generation, immigrants, refugees, US citizens
country
China, India, the United States. Indonesia. Pakistan, Brazil, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, Philippines. Egypt, Vietnam, DR Congo, Turkey, Iran. Germany, Thailand, the United Kingdom, France, Italy, South Africa, Myanmar, Kenya, South Korea, Colombia, Spain, Uganda, Argentina, Algeria, Sudan, Ukraine, Iraq. Afghanistan, Poland, Canada, Morocco, Saudi Arabia. Uzbekistan. Peru, Angola, Malaysia, Mozambique, Ghana, Yemen, Nepal, Venezucla, Madagascar, Cameroon, North Korea, Australia, Relgium, Cuba, Greece, Portugal, Sweden, Austria, Israel, Switzerland, Singapore, Norway, Finland, Denmark, New Zealand, Cambodia, Ethiopian, Arabic, Polish, Italians Africa, Asia, the Middle-East, Europe, North America, Central America, South America,
continents
Oceania
other
racism, sexism
Table 8: Demographic dimensions and related terms.


Reviews:
[{"review_id": "7urrqdJpU2", "reviewer": "Reviewer_L28W", "summary": "", "questions": "How many samples are used in Section 4? It sounds like a cumbersome task to craft six counter-statements for many samples by all the authors.\n\nLine 329: How did you select 10 groups among the top 25 and what were these groups? This section needs more clarifications.\n\n\"the role of a content moderator\" does not imply that the selected counter speech would be the most convincing. I disagree that one can accurately evaluate the effectiveness of a counterspeech from the provider point of view. Why didn't you use the role 'bystander', who is the most critical group to convince and represents the receiver of counterspeech?\n\nIt would be fairer if the LLMs were given actual instructions to respond to harmful posts. Suppressing the real power of an LLM and comparing it with human generated text do not yield a proper conclusion. The authors did not even employ implied biases in the generation process. It is not clear what was tried to be proven by comparing:\nHuman-written, their convincingness already proven replies with machine-written no specific instruction-provided replies. There are certain behaviours of these models, regarding biases inherently coming from their training data. If there was a comparison among non-persuasive CMV comments and machine generated counterspeech, then we most probably would see a different picture. If it is a wrong assumption, I would like to read the reasoning of the authors.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thoughtful response and for considering our \"experimentations on convincingness and effectiveness of the counter speech [as] very valuable.\" Here is our response to your questions.\n\n1.  _Counterspeech generation process_: \\\n\\\nWe will clarify how we instructed the LLMs: we used the system message / instruction “You are helping people respond to harmful posts online. Reply directly to the post.” and user message / input “How should I respond to a post that says: [POST]?” (Appendix D1). By instructing it as such, we wanted to avoid biasing the models' responses (e.g., prompting models to focus on implied biases which could lead them to not respond to the post's content, or prompting models to only use one specific strategy).\n\\\n\\\nWe made this instruction somewhat general not to suppress LLMs but rather to generate a sample that is unbiased by instructions. The goal of this paper was not to study prompting approaches, which can be an interesting future work (see Limitations in Counterspeech Generation under Limitation & Ethical Consideration). Instead, our goal was to understand how the models approach responding to harmful posts, especially in strategy usage patterns in their responses. \n\\\n\\\nWe will move more specific prompting instructions into the main part of the paper in our final version.\n\\\n&nbsp;\n2. _Comparing machine-generated responses to human-written ones_: \\\n\\\nWe chose to compare the machine generated output with human generated content with “proven convincingness” in order to directly address the main goal of the paper: investigating effective strategies for countering implied bias and stereotypes. To do this, we first compared convincing and unconvincing human-written responses (delta to non-delta CMV reddit posts), in order to identify patterns in how humans used different strategies to convince. Then, to understand the strategy choices of LLMs, along with the convincingness of LLM generated counterspeech, we compared directly to only the convincing human-written counterspeech. In this way, we wanted to gain an understanding of how effective LLMs might be as tools for counterspeaking or when interacting with users with stereotypical beliefs with suggested directions of improvements. \n\\\n\\\nWhile comparing machine generated results to non-persuasive comments may not directly address the main goal of our paper, we agree that it would yield an interesting comparison and will add it to the Appendix section in the final version. \n\\\n&nbsp;\n3.  _Human study instructions_: \\\n\\\nIt is a great point that the framing of the human convincingness evaluation task is an important factor. In our task instructions, we used the wording “the role of a content moderator” so that participants would be more intrinsically motivated and empowered to action (which increases annotation quality [6]) and by doing so, focus on choosing convincing answers that the participants might have written themselves as an agentic and active community member. \n\\\n\\\nWhile we do agree that counterspeakers often consider bystanders as the most critical group to convince [1], we did not want to specifically direct the question of convincingness to bystanders when the goal of counterspeech and the meaning of effective counterspeech are still open questions [2]. Bystanders are also less likely to engage [3], and for our study, we wanted the participants to have greater agency when considering both community well-being and the poster. This gives us a better understanding of the intent of the counterspeaker, the participants in this case, when making their choices. \n\\\n\\\nMoreover, content-moderators, especially in communities such as Reddit and Wikipedia, are volunteer community members that take on multiple community roles and use many community moderation approaches beyond just flagging and removing posts, much like community leaders [4]. Therefore, we think that by asking the participants to assume the role of a content moderator, our results can both help with explanations of moderation decisions [5] and engagement as a community member, i.e., bystander. \n\\\n\\\nNevertheless, we agree that the question of audience selection and strategy variation is an interesting future work, which we will expand on in our discussion and limitations section in the final version.\n\\\n\\\n[1] Catherine Buerger. 2022. [Why They Do It: Counterspeech Theories of Change](https://dx.doi.org/10.2139/ssrn.4245211).\\\n[2] Susan Benesch, Derek Ruths, Haji Mohammad Saleem, Kelly P. Dillon, and Lucas Wright. 2016. [Considerations for Successful Counterspeech](https://dangerousspeech.org/wp-content/uploads/2016/10/Considerations-for-Successful-Counterspeech.pdf).\\\n[3] Samuel Hardman Taylor, Dominic DiFranzo, Yoon Hyung Choi, Shruti Sannon, and Natalya N. Bazarova. 2019. [Accountability and Empathy by Design: * Encouraging Bystander Intervention to Cyberbullying on Social Media](https://doi.org/10.1145/3359220). Proc. ACM Hum.-Comput. Interact. 3, CSCW, Article 118 (November 2019), 26 pages.\\\n[4] Joseph Seering. 2020. [Reconsidering Community Self-Moderation: the Role of Research in Supporting CommunityBased Models for Online Content Moderation](https://doi.org/10.1145/3415178). Proc. ACM Hum.-Comput. Interact. 4, CSCW2, Article 107 (October 2020), 28 pages.\\\n[5] Shagun Jhaver, Amy Bruckman, and Eric Gilbert. 2019. [Does Transparency in Moderation Really Matter?: User Behavior After Content Removal Explanations on Reddit](https://doi.org/10.1145/3359252). Proc. ACM Hum.-Comput. Interact. 3, CSCW, Article 150 (November 2019), 27 pages.\\\n[6] Tal August, Nigini Oliveira, Chenhao Tan, Noah Smith, and Katharina Reinecke. 2018. [Framing Effects: Choice of Slogans Used to Advertise Online Experiments Can Boost Recruitment and Lead to Sample Biases](https://doi.org/10.1145/3274291). Proc. ACM Hum.-Comput. Interact. 2, CSCW, Article 22 (November 2018), 19 pages.\\\n&nbsp;\n4. _Missing numbers_: \\\n\\\nThank you for pointing out the missing numbers! We will include them in the final version of our paper. \n\\\n\\\nIn section 4, our research team crafted six counter-statements for each of the 10 toxic posts that targeted different groups, a total of 60 counter-statements. This process took our research team multiple revisions and pilot experiments with different wordings.\n\\\n\\\nThe top 25 groups were ranked by frequency, i.e., the number of mentions in the field “target group” in the original dataset (SBIC; [1]), and were aggregated by a rough regex matching identity terms such as “asian [folks|people|person]” or “asians” within the target group identified by the SBIC annotators. \n\\\n\\\n[1] Maarten Sap, Saadia Gabriel, Lianhui Qin, Dan Jurafsky, Noah A Smith, and Yejin Choi. 2020. [Social bias frames: Reasoning about social and power implications of language](https://aclanthology.org/2020.acl-main.486). In ACL.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "The experimentations on convincingness and effectiveness of the counter speech is very valuable. The paper is well-written and easy to follow. Extensive discussion and in-depth analyses of the results are provided.", "reasons_to_reject": "The role of stereotype or implied bias in the final results, especially when the generation task results are discussed, is not clear. A few very important numbers are missing: e.g., the number of samples that the authors wrote a counter-speech for each 6 strategies.", "questions_to_authors": "How many samples are used in Section 4? It sounds like a cumbersome task to craft six counter-statements for many samples by all the authors.\n\nLine 329: How did you select 10 groups among the top 25 and what were these groups? This section needs more clarifications.\n\n\"the role of a content moderator\" does not imply that the selected counter speech would be the most convincing. I disagree that one can accurately evaluate the effectiveness of a counterspeech from the provider point of view. Why didn't you use the role 'bystander', who is the most critical group to convince and represents the receiver of counterspeech?\n\nIt would be fairer if the LLMs were given actual instructions to respond to harmful posts. Suppressing the real power of an LLM and comparing it with human generated text do not yield a proper conclusion. The authors did not even employ implied biases in the generation process. It is not clear what was tried to be proven by comparing:\nHuman-written, their convincingness already proven replies with machine-written no specific instruction-provided replies. There are certain behaviours of these models, regarding biases inherently coming from their training data. If there was a comparison among non-persuasive CMV comments and machine generated counterspeech, then we most probably would see a different picture. If it is a wrong assumption, I would like to read the reasoning of the authors.", "paper_topic_and_main_contributions": "The paper proposes countering hateful content by considering its implied stereotypes and biases towards the target group. 6 strategies, i.e., alternate groups, alternate qualities, counterexamples, external factors, broadening, and general denouncing, are composed to do so, and human and machine generated counter speech datasets are analysed in terms of strategy usages. The results show that while automatically generated counter speeches are more generic and denouncing, mostly towards the hatred itself, the human-written ones are mostly targeting the implied statements and more specific. In addition, machine generated counter speech is found less convincing in comparison to the human-written replies.", "excitement": 4, "reproducibility": 2, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "Line 87 - humna-written"}, "extra_text": {}}, {"review_id": "5YLe60rNF5", "reviewer": "Reviewer_PBEi", "summary": "", "questions": "Q1- Why these six strategies? (line 237: \"crafted based on prior work on combating stereotypes\" ). Which prior work do these strategies come from? \n", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your positive review. We are excited that you found the topic of our work an “interesting problem related to counter argument”.\n\n1.  _Strategy selection_: \\\n\\\nOur six strategies were selected based on preliminary investigations on existing hate speech datasets, as well as by drawing from related work in NLP [1] and psychology and philosophy. As discussed in Section 2.1, Language and Stereotype, generics, i.e., generalizing statements such as “girls wear pink” [2], often convey stereotypes about social groups and lead to default assumptions and generalization [3]. With this in mind, we constructed the strategies that challenge specific qualities of generics such as typicality (generalizability), definitional quality [4], and striking quality [5] (see Section 3 for further details). Additionally, we included general denouncing, which is more commonly seen in counterspeech literature [6]. We acknowledge that there may be other strategies that could be useful in countering social stereotypes, which we hope future work will explore.\n\\\n\\\n[1] Emily Allaway, Nina Taneja, Sarah-Jane Leslie, and Maarten Sap. 2023. [Towards countering essentialism through social bias reasoning](https://doi.org/10.48550/arXiv.2303.16173).\\\n[2] Marjorie Rhodes, Sarah-Jane Leslie, and Christina M. Tworek. 2012. [Cultural transmission of social essentialism](https://doi.org/10.1073/pnas.1208951109). Proceedings of the National Academy of Sciences, 109(34):13526–13531.\\\n[3] Sarah-Jane Leslie. 2014. [Carving up the social world with generics](https://philpapers.org/rec/LESCUT). Oxford studies in experimental philosophy, 1.\\\n[4] Robert Rooij and Katrin Schulz. 2020. [Generics and typicality: A bounded rationality approach](https://doi.org/10.1007/s10988-019-09265-8). 43(1):83–117.\\\n[5] Sarah-Jane Leslie. 2017. [The Original Sin of Cognition: Fear, Prejudice, and Generalization](https://doi.org/10.5840/jphil2017114828). 114(8):393–421.\\\n[6] Maxime Charles Lepoutre. 2019. [Can ’More Speech’ Counter Ignorant Speech? Tackling the Stickiness of Verbal Ignorance](https://doi.org/10.26556/jesp.v16i3.682). 16(3).", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- Analyze an interesting problem related to counterarguments\n- Extensive analysis\n- Well-written paper and easy to follow", "reasons_to_reject": "- No substantial reason to reject.", "questions_to_authors": "Q1- Why these six strategies? (line 237: \"crafted based on prior work on combating stereotypes\" ). Which prior work do these strategies come from? \n", "paper_topic_and_main_contributions": "This paper focuses on the stereotypical implications of hateful language. They found that naturally written counterarguments use countering strategies more specific to the implied stereotype than machine-generated counterarguments; machine-generated counterspeech uses less specific strategies. They compared the countering strategies used by humans versus the ones generated by machines (GPT-4 and Alpaca). This was done to understand whether machines use countering strategies as humans. They found that less complex countering strategies are not necessarily the most convincing counterarguments.\n", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "- Line 362: missing period. \n"}, "extra_text": {}}, {"review_id": "iOhNFFLzkC", "reviewer": "Reviewer_ds7j", "summary": "", "questions": "As mentioned above, I have two questions that may need the authors to answer:\nA. Why do you form your tasks as assuming the annotators as content moderators? What standards or rules do content moderators follow in this task?\n\nB. How do you define convincingness and explain it to annotators?", "limitations": "", "rating": 3, "confidence": 5, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your thoughtful comments on our work. We appreciate your positive comment about “sound and well-grounded” strategies and your enthusiasm that future studies can build on our work to counter hatred. \n\n1.  _Human study instructions_: \\\n\\\nThank you for providing insightful comments about the human study instructions. We used the wording “role of a content moderator” to motivate our participants [1] to select convincingness in general in a community-agnostic way. As you pointed out, there can be disagreements between moderators and community [2]. However, asking annotators to assume the role of content moderator, someone who is often a volunteer community leader, over the role of a community member encourages the annotators to think directly about what they might write to the poster while also keeping community well-being in mind. There may be certain strategies that are more effective in reducing disagreements between community members or that are successful in certain communities (e.g., subreddits have their own social and communication norms [2]). We will add a discussion of this in the paper to be explored by future work. See response to Reviewer L28W for further details.\n\\\n\\\n[1] Tal August, Nigini Oliveira, Chenhao Tan, Noah Smith, and Katharina Reinecke. 2018. [Framing Effects: Choice of Slogans Used to Advertise Online Experiments Can Boost Recruitment and Lead to Sample Biases](https://doi.org/10.1145/3274291). Proc. ACM Hum.-Comput. Interact. 2, CSCW, Article 22 (November 2018), 19 pages.\\\n[2] Weld, G., Zhang, A. X., & Althoff, T. (2022, May). [What makes online communities ‘better’? Measuring values, consensus, and conflict across thousands of subreddits](https://doi.org/10.48550/arXiv.2111.05835). In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 16, pp. 1121-1132).\n\\\n&nbsp;\n2.  _Classification performance_: \\\n\\\nWe do agree that the results seem low at first glance. To clarify our strategy labeling performance by GPT-4, we would like to highlight that our performance is comparable to previous works in various argumentative strategies detection: persuasive strategy detection in meme text (0.593 micro F1) [1],  persuasive strategies in English news (0.376 micro F1) [2], propaganda metaphor detection (single label) in news (0.387 F1 for loaded language and 0.287 F1 for name calling) [3], logical fallacy detection (0.587 F1 score for fine-tuned and 0.136 for GPT-2 zero shot) [4]. Collectively these results show that argumentative strategy labeling is a hard task, even for fine-tuned models, and that our performance is within range of these models despite not fine-tuning (instead using few-shot prompting). \\\n\\\nSince argument strategy labeling is a hard task, as also shown by previous works mentioned above, we needed expert annotations. Since we wanted to measure the quality of our GPT-4-based classifier, we focused on obtaining a high-quality and high-agreement test set (by resolving disagreements through deliberation between expert annotators for all samples, which were often long posts) rather than trying to obtain more annotations. Test sets of randomly sampled 100-200 examples are not uncommon for validation [5], and since our objective is to validate the usability of the current model rather than to train a new model, it is unlikely that increasing our test set size would impact our results. \\\n\\\nNevertheless, we do acknowledge that there is a room for improvement in strategy labeling, which will be an interesting future work (see Limitations in Strategy Labeling under Section Limitations & Ethical Considerations). To provide insights into how future work can improve upon this classification task, we will add other baseline models' performance: ChatGPT (`gpt-3.5-turbo-0314`) and majority class classifiers. As shown in the table below, GPT-4 outperforms other methods in strategy labeling in all but two strategies, ALT-QUAL and BROAD. Additionally, we will include error analysis of GPT-4’s classification performance in the final version of our paper. \\\n&nbsp;\n|      Method    |          GPT-4\t\t|||ChatGPT           |||          Majority Classifier (0)\t\t|||          Majority Classifier (1)\t\t|||\n| -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- | -------- |\n| Strategy | Precision | Recall | F1 | Precision | Recall   | F1   |Precision | Recall   | F1   |Precision | Recall   | F1   |\n| ALT-GRP  | **0.59**  | **0.65** | **0.56** | 0.56     | 0.61   | 0.55     | 0.42 | 0.5 | 0.45 | 0.09 | 0.5 | 0.15 |\n| ALT-QUAL | **0.63**  | **0.61** | 0.55     | 0.60     | 0.61   | **0.61** | 0.31 | 0.5 | 0.38 |0.20| 0.5 | 0.28 |\n| CNTR-EXS | **0.70**  | **0.72** | **0.70** | 0.54     | 0.54   | 0.54     | 0.36 | 0.5 | 0.42 | 0.14 | 0.5 | 0.22 |\n| EXTERNAL | **0.69**  | **0.71** | **0.69** | 0.62     | 0.63   | 0.63     | 0.34 | 0.5 | 0.40 | 0.16 | 0.5 | 0.25 |\n| BROAD    | 0.63      | **0.70** | 0.57     | **0.64** | 0.68   | **0.65** | 0.41 | 0.5 | 0.45 | 0.10 | 0.5 | 0.16 |\n| GEN-DEN  | **0.61**  | **0.66** | **0.59** | 0.56     | 0.59   | 0.56     | 0.40 | 0.5 | 0.44 | 0.11 | 0.5 | 0.17 |\n\n      [1] Dimitar Dimitrov, Bishr Bin Ali, Shaden Shaar, Firoj Alam, Fabrizio Silvestri, Hamed Firooz, Preslav Nakov, and Giovanni Da San Martino. 2021. [SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Images. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)](https://aclanthology.org/2021.semeval-1.7), pages 70–98, Online. Association for Computational Linguistics.\\\n      [2] Jakub Piskorski, Nicolas Stefanovitch, Giovanni Da San Martino, and Preslav Nakov. 2023. [SemEval-2023 Task 3: Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multi-lingual Setup](https://aclanthology.org/2023.semeval-1.317). In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 2343–2361, Toronto, Canada. Association for Computational Linguistics.\\\n      [3] Daniel Baleato Rodríguez, Verna Dankers, Preslav Nakov, and Ekaterina Shutova. 2023. [Paper Bullets: Modeling Propaganda with the Help of Metaphor](https://aclanthology.org/2023.findings-eacl.35). In Findings of the Association for Computational Linguistics: EACL 2023, pages 472–489, Dubrovnik, Croatia. Association for Computational Linguistics.\\\n      [4] Zhijing Jin, Abhinav Lalwani, Tejas Vaidhya, Xiaoyu Shen, Yiwen Ding, Zhiheng Lyu, Mrinmaya Sachan, Rada Mihalcea, and Bernhard Schoelkopf. 2022. [Logical Fallacy Detection](https://doi.org/10.48550/arXiv.2202.13758). In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 7180–7198, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.\\\n      [5] Nikita Nangia, Clara Vania, Rasika Bhalerao, and Samuel R. Bowman. 2020. [CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](https://aclanthology.org/2020.emnlp-main.154). In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1953–1967, Online. Association for Computational Linguistics.\\\n      &nbsp;\n3. _Definition of convincingness_: \\\n\\\nThank you for asking a question about an important point of clarification: the definition of convincingness provided to human study participants. In our detailed instructions, we asked participants to choose the counter-statement that they found most effective in countering the stereotype.\\\n\\\nThe exact wording in the instruction was the following: \n\n   - Determine which of the 6 statements is most effective in counteracting the stereotype by selecting one of 6 options.\n   - Note: please select the counter-statements that you think are best to counter the stereotype. There is no right answer.  \n\n&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;We will add more details about the specific instructions in the Appendix of the final version.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1.This paper is well-written overall. \n2.The strategies are sound and well-grounded.\n3.Future studies can build on these strategies to develop language technologies to counter hatred.\n4.It compares the use of strategies in human and machine generated language.", "reasons_to_reject": "1. The dataset annotated and validated by humans is small as stated in limitations: only 200. \nIt is hard for future work to build models upon it. The performance on this dataset is not ideal: for binary classification, f1-scores are between 0.55-0.70. The results for analysis in section 6 are mainly based on the modest classification results, thus lacks convincingness.\n \n2. For the user study based on 200 data, the choice and rationale of settings are not fully justified. \nIn section 4.1 \"annotators are assumed the role of content moderator\", while seems intuitive, this may need further justification. What's the rationale to make this assumption for this specific task? What is the standard or guideline that the role of content moderators use or follow? Conflicts are common between moderators and non-moderators (Weld et al., 2022), I believe it is important to consider more individual perspectives. In addition, different communities vary in moderation rules, these have not been considered in this paper too. \n\nReference: Weld, G., Zhang, A. X., & Althoff, T. (2022, May). What makes online communities ‘better’? Measuring values, consensus, and conflict across thousands of subreddits. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 16, pp. 1121-1132).", "questions_to_authors": "As mentioned above, I have two questions that may need the authors to answer:\nA. Why do you form your tasks as assuming the annotators as content moderators? What standards or rules do content moderators follow in this task?\n\nB. How do you define convincingness and explain it to annotators?", "paper_topic_and_main_contributions": "This paper proposed a list of strategies to challenge the underlying stereotypical implications of hateful language.\nThey further conducted a user study to examine the convincingness of each strategy.\nFinally, they compare the usage in both human and machine-generated CS datasets.\n\nThe main contribution is the six strategies that they propose to challenge the stereotypical implications of hate speech.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "Overall this paper is well-structured and reads well. \n\nThere are a few typos:\n\nLine 154: multiple \".\"\n\nLine 362: missing \".\""}, "extra_text": {}}]

Author rebuttal:

Meta-review: Reviewers thought that this paper was interesting and covered an important topic. They thought that the taxonomy of proposed strategies was well-motivated, and could be useful to build on in future work; some also found the annotation and LLM-enabled analyses valuable as well. Taken together, my sense is that the paper makes an interesting contribution to the area of NLP-enabled counterspeech, but could be substantially improved. I’ve highlighted some concerns from the reviews, and I encourage the authors to attend to these, as well as to the rest of the reviewers’ comments, as they revise their paper.
* The notion of “convincingness” is key to the paper, and reviewers had doubts about whether the authors operationalized it in a sound way. I’d further add that “convincingness” means entirely different things in the context of CMV and in the instructions to annotators. This merits clarification: it is find for the authors to examine multiple notions of convincingness, but they should be more explicit that their operationalization is narrow, and perhaps tailored to specific purposes that they could elaborate on (e.g., certain forms of moderation).
* The classifier performance is low. We realize this is because the task is hard, but the low performance, barring any further explanation, seems to undermine much of the analyses in section 6. For instance, if classification errors, as the authors say, often come from “mere mentions of alternative qualities/groups” that do not have a countering nature, then this seems like a systematic overestimation. Here, the authors could either improve their classifier — perhaps by increasing their data size, as reviewers suggest, or more critically discuss the results of their analysis.
Decision: Accept-Findings