# Promoting Constructive Deliberation: Reframing for Receptiveness

Gauri Kambhatla  Matthew Lease∗ Ashwin Rajadesingan
The University of Texas at Austin
{gkambhat, ml<EMAIL_ADDRESS>Denotes equal author contributions

###### Abstract

To promote constructive discussion of controversial topics online, we propose
automatic reframing of disagreeing responses to signal receptiveness while
preserving meaning. Drawing on research from psychology, communications, and
linguistics, we identify six strategies for reframing. We automatically
reframe replies according to each strategy, using a dataset of Reddit comments
and replies. Through human-centered experiments, we find that the replies
generated with our framework are perceived to be significantly more receptive
than the original replies, as well as a generic receptiveness baseline. We
analyze and discuss the implications of our results and highlight applications
to content moderation. Overall, we illustrate how transforming receptiveness,
a particular social science construct, into a computational framework, can
make LLM generations more aligned with human perceptions.

Promoting Constructive Deliberation: Reframing for Receptiveness

Gauri Kambhatla and Matthew Lease∗ and Ashwin Rajadesingan††thanks: Denotes
equal author contributions The University of Texas at Austin {gkambhat, ml,
<EMAIL_ADDRESS>

##  1 Introduction

Constructive deliberation and debate amongst disagreeing views promotes
sharing ideas and collective intelligence for innovation, decision-making, and
governance Porter and Schumann (2018). Such exchanges also promote empathy and
tolerance, rather than isolation and polarization Mutz (2002). However, online
discussion today is often hostile and confrontational, where exposure to
opposing views in this setting can instead provoke even greater polarization
Bail et al. (2018).

Figure 1: Example of generated receptiveness reframes. Given a post, comment,
and reply from Reddit Pougué-Biyong et al. (2021), we generate a reframe for
each of our six receptiveness-signaling strategies.

Current methods for promoting pro-social discussion and debate online are
limited. For example, community guidelines may principally focus on preventing
harms rather than promoting pro-social norms. In addition, moderator controls
are often fairly coarse: simply blocking users or posts is scalable but rigid,
while crafting open-ended messages to guide behavior is extremely flexible but
can be difficult to scale Jhaver et al. (2019).

NLP research for assisting scalable content moderation has also largely
focused on anti-social tasks such as removing toxic speech Pamungkas et al.
(2020); Ranasinghe and Zampieri (2021), reducing offensive language with
style-transfer Atwell et al. (2022), and removing content that violates
community rules Ye et al. (2023). While some studies have begun to pursue
content moderation through the lens of promoting pro-social content Ashida and
Komachi (2022); Yu et al. (2022); Madaan et al. (2020), promoting constructive
deliberation has received relatively little attention. This is a difficult
task, involving nuanced and subtle linguistic cues that underlie productive
discussions.

Research from communications, psychology and linguistics suggests how we might
promote open-mindedness to opposing views. In particular, Yeomans et al.
(2020)’s conceptualization of conversational receptiveness focuses on
conveying openness while still disagreeing. Others have studied how to measure
receptiveness towards disagreeing perspectives Minson et al. (2020); Minson
and Chen (2022). However, there has been limited success in translating such
theory into practice.

In this work, we seek to bridge NLP research on textual reframing Sharma et
al. (2022); Ziems et al. (2022); Madaan et al. (2020); Chakrabarty et al.
(2021) with social science theories on receptiveness. In so doing, we aim to
promote pro-social deliberation online, particularly in the context of
disagreement on contentious issues. We develop a computational framework to
signal receptiveness while preserving the original meaning of reframed
content, exploring the capabilities of LLMs for this task. We illustrate the
value of translating social science theory to a computational framework by
showing how model generations created with our framework better align with
human notions of receptiveness.

We operationalize receptiveness through a framework of six lower-level
strategies to make messages more receptive to opposing viewpoints (Section 3).
We ground this conceptual work in a concrete study of Reddit posts,
illustrated in Figure 1. Using an in-context framework, we compile a new
corpus of automatically reframed Reddit replies. In Section 4, we assess (via
automatic measures and human evaluation) the validity of the generated
replies.

Section 5 then compares the reframes generated with our framework to the
original replies and baselines, finding they are significantly more aligned
with what humans find receptive. Reframed replies elicit relatively low
negative emotional reactions, high curiosity to engage with opposing views,
and reduced belief that the user is trying to shutdown discussion. We also
explore the interaction between toxicity and receptiveness reframing. We find
that more toxic content gets greater benefit from reframing, suggesting
greater flexibility for moderation. We create a corpus of receptive-reframed
replies, baseline reframes, and human annotations of receptiveness (16.6k
reframes and over 9.2k total annotations)111If interested, contact the authors
for the dataset..

##  2 Related Work

###  2.1 Receptiveness in political discussions

Constructive discussions across differences require that individuals not only
voice their own arguments, but that they also meaningfully engage with
arguments presented by others Bächtiger and Parkinson (2019). Researchers
typically draw on the related concepts of reciprocity, listening and
receptiveness to study such reciprocal engagement.

Graham and Witschge (2003) view reciprocity as the first step in this process
and define it as “the giving and taking of validity claims, arguments, and
critiques among participants.” They argue that reciprocity followed by
reflexivity and empathy is required for deliberations to result in mutual
understanding. Esau and Friess (2022) further distinguish reciprocity from
replying, defining “a reciprocal comment” as one “which is on topic,
respectful in tone and reasoned.” Scudder (2020) instead argues for democratic
listening, a communicative act beyond simply listening that is performed “for
the sake of considering what others have to say”.

We draw on Yeomans et al. (2020)’s concept of conversational receptiveness as
it more directly focuses on receptiveness when expressing disagreement: the
“extent to which participants in disagreement communicate their willingness to
engage with each other’s views.” Their study identified numerous linguistic
markers of receptiveness during disagreement, such as hedging and
acknowledgement.

Minson et al. (2020) developed a scale for dispositional receptiveness
(receptiveness with regard to beliefs or personality, rather than through
communication). Items in the scale correspond to four factors: negative
emotions, curiosity, bias, and belief of lack of openness. We use these
factors to develop our receptiveness index (Section 5.2).

###  2.2 Reframing

Other studies ground reframing (style transfer while preserving meaning)
techniques in social science theory, with applications to various domains.
Ziems et al. (2022) study positive reframing: reframing pessimistic tweets to
be more optimistic using strategies from psychology literature. They introduce
a conditional generation task for this. Sharma et al. (2023) likewise look at
reframing negative thoughts, but draw from cognitive reframing techniques.
They use a retrieval-based in-context learning approach to reframing.
Chakrabarty et al. (2021) study reframing arguments to be more trustworthy
(defined as non-partisan and without appeals to fear) with arguments on the
subreddit ChangeMyView. Madaan et al. (2020) introduce politeness transfer, or
converting to polite text using a two-stage tag and transfer approach to
reframing.

Closest to our work, Argyle et al. (2023) seek to improve political
discussions using GPT-3 to reframe conversations with opposing views in terms
of restatement, validation, and politeness. They find that participants in
chats with GPT-reframing assistance reported higher conversational quality and
democratic reciprocity than those without assistance. Our work also pursues
reframing, but we focus on grounding and translating a specific theoretical
construct – conversational receptiveness – into practice. Furthermore, we
extend our analyses to better understand individual factors that influence
perception of receptiveness, and evaluate contexts in which reframing is most
effective.

##  3 Approach

Our broad goal is to foster pro-social online discussions in which
participants constructively engage with others having opposing viewpoints. To
this end, we pursue automated techniques to reframe online discussions, i.e.,
rephrasing messages in a manner that would preserve a message’s original
meaning while modifying its tone to convey greater openness to opposing
viewpoints.

In Section 3.1, we introduce and motivate the six specific methods for
conveying receptiveness curated from social science literature that we
transform into reframing strategies. These are also illustrated in Figure 1.
Following this, we describe our specific task formulation and data used
(Section 3.2). Finally, we describe our method for automating this reframing
(Section 3.3).

###  3.1 Reframing strategies for receptiveness

Brown and Levinson (1987)’s politeness theory suggests that people desire in
social interactions to be appreciated and maintain a positive self image
(“positive face”) and to not be curtailed or imposed upon by others (“negative
face”). Disagreements may cause disrespect or offense, threatening positive
face, and/or may intrude on the receiver’s autonomy, threatening negative
face. Face threatening disagreements are especially evident when discussing
politics on social media where partisan distrust Iyengar et al. (2019) coupled
with the limited affordances available to gauge perceptions Bail (2022) may
derail very well-intended interactions. Numerous strategies have been
identified to mitigate perceptions of threats to face and continue the
conversation while still expressing disagreement Minson and Chen (2022);
Yeomans et al. (2020).

In this study, we focus on six key strategies (described below) to signal
receptiveness, which we pursue in our automated reframing work.

#### Hedges

are words or phrases that add ambiguity in a way that softens the force of a
statement, thus potentially saving negative face of the receiver. They signal
possibility in arguments, allowing writers to present “their claims with
caution, and enter into dialogue with their audiences” Hyland (1996). Although
hedges may weaken the claim, they tend to strengthen the argument as hedged
claims are harder to falsify Meyer (1997). Prior work identifies hedges as a
potential signal for receptiveness Minson and Chen (2022); Yeomans et al.
(2020). Examples: "may", "perhaps" and "I think".

#### Acknowledgements

convey understanding and validate another’s feelings, appealing to their
positive face. Such affirming speech acts signify to the receiver that the
speaker is engaging in good faith, likely resulting in reciprocation
Rajadesingan et al. (2021). Examples: "I understand what you mean", "I see
where you’re coming from".

#### Elaboration

includes asking for further details, clarifying understanding, and repeating
what was heard. Seeking more details may be viewed as delaying expressing
disagreement, potentially softening threats to negative face Pomerantz (2021).
Further asking for elaboration signals expressed interest in the receiver’s
view which is known to increase perceptions of receptiveness Chen et al.
(2010). Examples: "what I heard you say was that…", "what did you mean by…".

#### Common Ground

is often an essential precondition for conflict resolution van Bijnen (2020).
Highlighting and making explicit shared beliefs, assumptions and knowledge is
known to increase conversational receptiveness in civic-minded workshops to
reduce polarization Oliver-Blackburn and Chatham-Carpenter (2023); ListenFirst
(2018). Examples: "we both believe…", "we both think…".

#### Expressing Gratitude

often yields more positive perceptions and increased trustworthiness of the
speaker Percival and Pulford (2020); Dunn and Schweitzer (2005). Examples:
"thank you for your comment", "I appreciate you bringing this up".

#### Agreement

is also called “token agreement” Pomerantz (2021) and typically takes the
‘yes,.. but’ form. This mitigation strategy allows individuals to delay and
minimize the force of disagreement with a nominal token of agreement, before
stating their alternate view. Examples: "I agree about …but", "I also think
…yet".

###  3.2 Task formulation and data

Task. To ground our study, we investigate automated reframing of Reddit
discussions. These discussions begin with a post in which someone typically
shares a link to news or other content. Next, users comment on the post, after
which other users reply to these comments. This initial post-comment-reply
plays an important role in establishing the tone and basis for any ensuing
discussion, so we focus our reframing efforts here. In particular, could the
reply be reframed to convey greater openness to the opposing viewpoint in the
comment while preserving its original meaning?

Data. English Reddit data is drawn from the Debagreement dataset Pougué-Biyong
et al. (2021) for disagreement detection. It consists of comments and replies
from controversial subreddits: r/Brexit, r/democrats, r/Republicans,
r/climate, and r/BlackLivesMatters. We exclude r/Brexit due to our US-based
annotators being less familiar with Brexit-related people and issues. We
select examples that are labeled as disagreement and where the comment and
reply are at most 30 words each since preliminary pilots revealed that longer
text increased the cognitive burden on annotators.

We run all replies through the Perspective API222https://perspectiveapi.com/
and exclude any reply having predicted toxicity above 0.9, as these were
typically too toxic to be reframed without significantly altering their
original meaning. Further discussion of the interaction between toxicity and
reframing is explored in Section 5).

###  3.3 Generating reframes

To automatically generate reframes for each reply, we use gpt-4 OpenAI (2023).
To improve generation quality, we use in-context learning. Specifically, we
provide five example comment-reply-reframe triples with each instruction
prompt. The five comment-reply pairs are drawn from the Reddit data and held
constant across the six reframing strategies. For each strategy and for each
of the comment-reply pairs, we write a reframe of the reply for the strategy.
This requires a total of 30 reframes over the six strategies and five comment-
reply pairs.

Baseline reframes. We consider two zero-shot prompts with gpt-4 as baselines.
For the paraphrase baseline, we prompt to simply paraphrase the reply for each
comment-reply pair. This baseline serves to distinguish whether observed
differences stem from our specific reframing strategies or any language model
generation. For the receptiveness baseline, we directly prompt for
receptiveness, without reference to any of our six strategies. This baseline
allows us to evaluate our social science-informed reframing framework against
a generic prompt to reframe for receptiveness. In particular, this allows us
to discover whether model generations using our framework are more aligned
with human perceptions of receptiveness, compared to the generic prompt.
Appendix A lists full prompts and few-shot examples.

In total, we generate six reframes (plus two baseline reframes) for each of
$\sim$2k comment-reply pairs in the Debagreement dataset, yielding 16.6k
generations to be evaluated. We choose 450 for human validation.

##  4 Validation Experiments

We next present analyses conducted to validate our reframe generation methods
(Section 3). We assess whether our reframes satisfy three key properties: 1)
demonstrate the six distinct strategies for receptiveness; 2) preserve the
meaning of original replies being reframed; and 3) remain responsive to the
original comment.

###  4.1 Do reframes reflect the six strategies?

We inspect differences in the most common trigrams found in generated reframes
compared to the original replies. We study two probability distributions: (a)
$P(t|s)$, the probability of trigram $t$ being generated by strategy $s$, and
(b) $P(s|t)$, given observed trigram $t$, the probability of it having been
generated by strategy $s$. The former tells us whether the model is adding
appropriate linguistic phrases for each strategy, while the latter measures
how distinct the generations are for each strategy.

The top trigrams and probabilities are listed in Table 1. For each strategy,
trigrams are shown ordered by $P(t|s)$. Regarding (a), we see that the most
common trigrams being added do appear to be consistent with each strategy. In
regard to (b), some strategies appear to be more unique than others. For
example, Gratitude and Elaboration seem to be the most unique. While
agreement, grounding, and acknowledgement share some of the most common
phrases added, we note that the amount of overlap of the trigrams between the
different strategies is still relatively low (Table 5 in Appendix B).

Strategy |  $\Delta$ Trigrams |  $P(t|s)$ |  $P(s|t)$
---|---|---|---
Hedging |  it might be |  0.062 |  0.703
|  might not be |  0.037 |  0.393
|  it seems like |  0.027 |  0.271
Acknowledg. |  I understand your |  0.061 |  0.397
|  your point about |  0.023 |  0.371
|  I see your |  0.022 |  0.382
Elaboration |  it sounds like |  0.086 |  0.807
|  sounds like you’re |  0.036 |  0.940
|  are you suggesting |  0.022 |  0.816
Grounding |  I also think |  0.058 |  0.955
|  I agree that |  0.025 |  0.237
|  I also believe |  0.020 |  0.977
Gratitude |  thank you for |  0.170 |  0.986
|  you for your |  0.068 |  0.985
|  for sharing your |  0.043 |  0.992
Agreement |  I agree that |  0.080 |  0.748
|  I understand your |  0.067 |  0.381
|  I see your |  0.029 |  0.450
Table 1: Most common trigrams added to reframes vs. original replies. Trigrams
are ordered by $P(t|s)$. $P(t|s)$ denotes probability of the trigram given the
strategy, and $P(s|t)$ the probability of the strategy given the trigram.

###  4.2 Do the reframes preserve meaning?

We assess the degree to which generated reframes preserve the meaning of
original replies with three measures. First, we use form similarity to measure
the average number of distinct n-grams for n={1, 2, 3, 4} per prior work
(Tevet and Berant, 2021). Second, we consider semantic similarity via SBERT
scores (Reimers and Gurevych, 2019). Third, we measure the ratio of
contradictions to non-contradictions between the original and reframed reply
using an out-of-the-box DeBERTa model trained on MNLI and
SNLI333https://huggingface.co/cross-encoder/nli-deberta-v3-large. Following
Yerukola et al. (2023), we perform automatic evaluations for form and semantic
similarities between the reframe and concatenation of the original reply and
the original comment, which provides context for the reply. For
contradictions, we only seek to determine whether the generated reply
contradicts the original, so we exclude the original comment.

Table 2 shows that our reframes exceed both baselines in both form and
semantic similarity with the original reply + context. When we compare our
reframes to the original reply without the context, both baselines’ form and
semantic similarity scores are higher than our reframes. This indicates that
our strategies incorporate context, which has been shown to be greatly
preferred (Yerukola et al., 2023). While the paraphrase baseline exhibits the
lowest ratio of contradictions, all strategies are very low (< 3%); generated
replies rarely contradict the original, as desired. We note that these methods
do not consider potential added hallucinated content; we are only checking if
existing content is retained.

Strategy | Distinct | SBERT | PC ($\downarrow$)
---|---|---|---
|  Ngrams ($\downarrow$) |  Score ($\uparrow$) |
Paraphrase (B1) | 0.54 | 0.59 | 0.010
Receptive (B2) | 0.56 | 0.56 | 0.050
Hedging | 0.46 | 0.61 | 0.013
Acknowledge | 0.47 | 0.71 | 0.024
Elaboration | 0.44 | 0.76 | 0.016
Grounding | 0.45 | 0.70 | 0.019
Gratitude | 0.47 | 0.64 | 0.028
Agreement | 0.43 | 0.75 | 0.025
Table 2: Automatic validation of meaning preservation of model generations.
Distinct ngrams and SBERT score are taken between original dialogue (comment +
reply) and reframed reply. PC indicates the proportion of contradictions
between original and reframed replies. Values for all columns are on a [0,1]
scale. $\downarrow$ / $\uparrow$ arrows indicate that smaller/larger values
are better. “B” indicates a baseline.

###  4.3 Are the reframes contextually relevant?

We evaluate whether our reframing framework produces replies that actually
reply to the comment, (i.e., that are contextually relevant). For this
analysis, we compare our reframes with the original reply from Reddit. We ask
annotators whether each reply shown constitutes a reasonable response to the
original comment using a 5-point Likert scale (0 = reply doesn’t make sense, 4
= the reply is very reasonable) in order to determine whether annotators think
a reply is relevant in context of the comment. The interface is shown in
Appendix Figure 4.

Annotations are collected via Amazon Mechanical Turk (MTurk). To ensure
quality annotation, we apply two strategies to curate our annotator pool: (1)
sourcing workers already pre-qualified by Amazon for its Ground Truth
workforce444https://aws.amazon.com/sagemaker/groundtruth/, and (2) filtering
other workers ourselves, requiring at least 97% accuracy and 5000 completed
hits. We also require workers to pass a qualification test consisting of four
examples similar to our main task. Finally, given the US-centric nature of our
data, we require workers to be US-based. We pay workers $10/hr. We give
annotators 420 examples in a multiple batches, and we assign 3-7 workers per
example (3.7 on average). Reasonability scores are averaged across annotators
and examples within a strategy. Table 3 shows results.

Strategy | Reasonability
---|---
Original | 2.32 $\pm$0.150
Hedging | 2.77 $\pm$0.238
Acknowledgement | 3.33 $\pm$0.256
Elaboration | 3.04 $\pm$0.255
Grounding | 3.24 $\pm$0.500
Gratitude | 3.05 $\pm$0.285
Agreement | 3.17 $\pm$0.268
Table 3: Mean reasonability scores for each strategy, with 95% CI in
parentheses. Table 10 shows paired t-test for differences between each
strategy and the original.

We conduct a paired t-test between the examples for each strategy and the
original to determine if the difference is statistically significant. We find
that the mean reasonability score for each of the strategies is significantly
higher than the reasonability of the original reply (see Table 10 in Appendix
E for mean differences, test statistics, and p-values). This indicates that
our reframes are perceived to be reasonable replies to the original comments.

Surprisingly, the original, human-written replies are perceived to be
significantly less reasonable, likely because some of the original replies do
not directly respond to the comment (as is common for social media dialogue),
while the generated replies almost always take the context of the original
comment into account. See Table 8 in Appendix C for examples. Another possible
reason is that annotators may have interpreted reasonable response to mean
more receptive which may have affected their ratings.

##  5 Receptiveness Experiment

###  5.1 Annotation task

We conduct an experiment on MTurk to evaluate whether the generated reframes
are perceived to be more receptive than the original replies. Section 4.3
described earlier how we filter the MTurk workforce to ensure quality of
collected data.

As discussed in Section 3.2, our data consists of comment-reply pairs, along
with generated reframes for two baselines and our receptiveness framework. We
use 9 versions of each of the 75 original replies: the original, the two
baselines, and the six strategy reframes. Annotators are asked to compare the
relative receptiveness between two alternative replies to a comment. One reply
is the original and the other is either one of our reframes or a baseline.
Annotation interfaces are shown in Figures 5, 6, and 7 in Appendix F.
Annotation is performed in multiple batches with 3-6 workers per example (4.4
on average). To evaluate the reframes, we develop a receptiveness index
translated from social science literature, as described below.

###  5.2 Dependent variable: Receptiveness index

We translate Minson et al. (2020)’s receptiveness index for dispositional
receptiveness to conversational receptiveness. Their index was determined via
an 18 dispositional statement scale. Based on participant responses, they
surfaced a set of four factors that underlie these 18 statements:

  1. 1.

F1 Emotion [reverse-coded]: negative emotional reactions towards opposing
(attitude-incongruent) views

  2. 2.

F2 Curiosity: intellectual curiosity one might have towards opposing views
(desire for greater insight/information about the beliefs of others)

  3. 3.

F3 Bias [reverse-coded]: derogatory attitude towards people who hold opposing
views

  4. 4.

F4 Openness [reverse-coded]: beliefs that some topics are not up for
discussion

To translate the scale to the fast-paced MTurk platform and to our
conversational receptiveness task, we reduce the quantity and transform two
statements into two questions per factor (Table 9 in Appendix D). The
questions compare the receptiveness level of the reframed or baseline reply
vs. the original reply, though annotators are not told which is which. We use
a 7-point Likert scale for each question: -3 indicates the original reply is
much more receptive, +3 indicates the opposite, and 0 indicates that original
and reframed reply are equally receptive. Answers are averaged over the eight
questions (and over all annotations) to determine our receptiveness index for
each example.

###  5.3 Results

To compare mean receptiveness scores between our reframed replies vs. baseline
replies, we use a mixed effects logistic regression model via the lme4 package
Bates et al. (2015). We model the receptiveness score with a random effect for
the comment ID and fixed effect for the strategy used.

We show the regression co-efficients in Appendix Table 11. The estimated
marginal mean and the 95% CI receptiveness scores of the strategies and the
baseline are shown in Figure 2. A marginal mean of zero implies the reframed
reply is no different from the original reply with respect to receptiveness.
From Figure 2, we find that neither the strategies nor baselines overlap with
0, indicating that they are more receptive than the original. The reframes
using our strategies have a statistically significant higher receptiveness
index than both baselines. This indicates that annotators consider generations
from our strategies more receptive than the paraphrases or baseline receptive
reframes. In other words, the replies generated using our social science-
informed in-context framework are more aligned with human notions of
receptiveness, compared to a generic prompting approach. The mean differences,
test statistics and p-values are in Table 12 in Appendix E.

Figure 2: Receptiveness scores for reframes compared to the original replies. The circle indicates the mean. Lines show the 95% CI. Positive values indicate that the reframed reply is more receptive than the original. “B” indicates a baseline. Strategy | Receptiveness Score
---|---
| F1 | F2 | F3 | F4 | Avg.
Hedging | 1.79 | 1.59 | 1.02 | 1.75 | 1.54
Acknowledge | 1.72 | 1.66 | 1.06 | 1.79 | 1.56
Elaboration | 1.85 | 1.71 | 1.09 | 1.78 | 1.61
Grounding | 1.86 | 1.72 | 1.11 | 1.90 | 1.65
Gratitude | 1.81 | 1.65 | 1.00 | 1.71 | 1.54
Agreement | 1.98 | 1.81 | 1.17 | 1.89 | 1.71
Avg. | 1.84 | 1.69 | 1.07 | 1.80 | 1.60
Table 4: Receptiveness over factors: Emotion (F1), Curiosity (F2), Bias (F3),
and Openness (F4). The highest value in each row is bolded, indicating the
factor that contributes most towards a particular strategy.

###  5.4 Factor breakdown of receptiveness

We have seen that the framework-generated reframes are more aligned with human
perceptions of receptiveness, but why is this the case? We investigate this by
examining the receptiveness scores split by the individual factors. To do so,
we averaged the values for the two questions in each factor. We can see from
Table 4 that while our reframes appear to decrease negative emotions, increase
openness, and increase curiosity towards opposing views, they interestingly do
not do as well in reducing bias towards the opposing party. We discuss this
further in Section 6. We note that such a factor analysis (drawing upon social
science theory) provides deeper insight into the reasoning for the
receptiveness scores than would be possible with the direct receptive
baseline.

###  5.5 Toxicity effects on perceived reception

In this section, we analyze whether reframing may improve receptiveness at
different rates based on the toxicity of the original reply. Toxicity scores
$\in[0,1]$ are predicted using the Perspective API, which defines toxicity as
“A rude, disrespectful, or unreasonable comment that is likely to make someone
leave a discussion.” Prior studies have validated use of the Perspective API
for toxicity classification on Reddit Rajadesingan et al. (2020). We use the
mixed effects regression model as before but also include an interaction term
for toxicity and strategy. As our previous regression did not reveal
differences in receptiveness between strategies, we only compare differences
between use of any strategy vs. baselines.

We show the results of the regression in Figure 3. The receptiveness scores
are split by toxicity for receptiveness reframes (all strategies together) and
each baseline. We operationalize low toxicity as [0, 0.5), medium as [0.5,
0.7), and high as [0.7, 0.9]. As noted in Section 3.2, we exclude examples
that have a toxicity score above 0.9.

We find that the difference in perceived receptiveness of our reframes based
on the toxicity level of the original reply is statistically significant for
some toxicity levels using the Ward test; the difference between low and high
toxicity and between low and medium is significant, but the difference between
medium and high toxicity is not. The differences between perceived
receptiveness for the two baselines are not significant between any of the
toxicity levels of the original replies. The mean differences between toxicity
levels, test statistics, and p-values for both receptiveness reframes and
baselines are shown in Table 13 in Appendix E.

These results suggest that the receptive reframes have higher impact on the
more toxic comments. Interestingly, neither paraphrase nor receptive baseline
generations were perceived to be any more receptive on toxic comments, further
drawing the distinction between our reframing strategies and the baselines.

Figure 3: Receptiveness at different toxicity levels (per the PerspectiveAPI):
low [0, 0.5), medium [0.5, 0.7), and high [0.7, 0.9]. Results are shown for
the two baselines and the average over receptive-reframed replies. “B”
indicates a baseline.

##  6 Discussion

#### Differences in receptiveness between factors

As discussed in Section 5, our reframes have a smaller effect on reducing bias
(F3) than on any of the other factors. This presents promising opportunities
for future work on receptiveness reframing aimed at generating reframes that
reduce outgroup bias. For example, this could include explicit instruction to
reduce bias in addition to our prompts, or attempting to signal impartiality
or humility AlSheddi (2020); Fisher (2000) as ways to implicitly reduce bias.
However, these methods might result in a tradeoff between reducing bias and
meaning preservation – discussed more in the limitations section.

#### Combining multiple strategies

In this work, we only generate reframes using one strategy at a time, but
future work could analyze combining multiple strategies. We do find that
certain receptiveness reframes contain strategies that were not explicitly
prompted for; this was especially prominent with hedges, which were commonly
added to generations using other strategies. Understanding how different
combinations of strategies might affect perceived receptiveness would allow us
to refine the current framework. Future work could also automatically optimize
for which strategies to use in different contexts.

#### Receptiveness for polarization

We have seen a vast increase in affective polarization, where increasingly,
opposing partisans dislike and distrust each other Iyengar et al. (2019). This
increase in affective polarization has contributed to partisan gridlock
Hetherington and Rudolph (2020), and more recently, limited compliance with
COVID-19 mitigation policies Druckman et al. (2021). A major intervention
known to reduce polarization is actually fairly straightforward: to engage in
conversations with the other side Levendusky and Stecula (2021). We believe
that our computational framework for reframing, if integrated into
conversation spaces, could improve the quality of conversations between
opposing partisans, potentially reducing polarization Argyle et al. (2023).

#### Receptiveness for content moderation at scale

The ability to automatically reframe content could strengthen moderation at
scale. Moderators could suggest receptiveness-signaling reframes to users as
rewrites for comments which might be removed otherwise, discouraging users
from commenting further (Myers West, 2018). Also, reframing one’s own comments
provides an opportunity to learn from one’s mistakes, which has been shown to
lead to better performance when learning a skill (Mason et al., 2016).

##  7 Conclusion

This work illustrates how transforming the social science construct of
receptiveness into a computational framework can make LM generations more
aligned with human perceptions. We found that our reframed replies are
perceived to be better aligned with human notions of receptiveness than a
reply generated with a generic receptiveness prompt. We also analyzed how our
reframes are perceived in terms of the different receptiveness factors, and
how some (emotion, curiosity, openness) are perceived to have improved more
than others (bias). Finally, we demonstrated that our reframes appear to be
more effective when the original replies are more toxic, and discuss
applications of receptiveness-signaling for content moderation and reducing
polarization.

## Limitations & Ethical Considerations

There are several limitations and ethical considerations with this work.

#### Overlap between strategies

We curate six linguistic strategies from communications literature that signal
receptiveness to another person. As we note previously, there is overlap
between some of these strategies, particularly between agreement and
grounding. However, given there are subtle differences in their implications,
we decide to differentiate them. In addition, we choose these six strategies
as a subset; there are potentially other strategies that we do not include
here.

#### Reframing and meaning preservation tradeoff

We acknowledge that there exists a tradeoff between receptive reframing and
meaning preservation; the most effective receptive reframe might not preserve
meaning to the same degree as a more subtle reframe, and conversely,
optimizing for meaning preservation alone would likely lead to reframes that
are not as receptive.

#### Confounds beyond linguistic strategies

The model-generated reframes make some changes beyond purely adding phrases
that signal a particular receptiveness strategy. Model generations tend to fix
grammatical and other syntax errors, make language more formal, and otherwise
slightly change the meaning of the original replies. We attempt to account for
changes in syntax and formality by comparing to the other model-generated
baselines, but we do not account for other potential changes in meaning,
besides what we measure (imperfectly) with automatic metrics in Section 4.2.

#### Receptiveness measure is US-centric

Our study is very US-centric; we use subreddits whose topics are mostly
centered in the US (r/democrats, r/Republican), and therefore only selected
annotators who live in the US. As a result, our measure of receptiveness and
our results are US-centric.

#### Limitations of the Perspective API

We rely on the toxicity classifier from the Perspective API for our toxicity
classifications, but this is based on only one notion of toxicity and may not
account for others. In addition, classifiers aren’t perfect and it is likely
there are some errors in the toxicity classifications.

#### Exposing annotators to toxic content

Our data contains some toxic content, which is shown to annotators during both
our reasonability evaluation task and receptiveness experiment. We note that
this exposure can be harmful to annotators Shmueli et al. (2021).

#### Reframes might still be offensive

We acknowledge that our generated reframes might still be offensive, as we
seek to preserve meaning with the original comments. We do not intend
potentially offensive reframes to be deemed non-toxic simply because they
incorporate one of our strategies.

#### Reframes might be biased

We note that model generations might be biased towards particular social
groups and/or political identities Santurkar et al. (2023), and this could
affect the content of our receptiveness reframes.

#### Intended use of data

Our reframes and annotations are released for research use. We emphasize that
(as discussed above) reframes might still be offensive or biased, and this
should be noted when this data is being used.

## Acknowledgements

We thank the online workers who provided annotations for this study. This
research was supported in part by the Knight Foundation and by Good
Systems555http://goodsystems.utexas.edu/, a UT Austin Grand Challenge to
develop responsible AI technologies. The statements made herein are solely the
opinions of the authors and do not reflect the views of the sponsoring
agencies.

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##  Appendix A Prompts and Examples for Model Reframes

###  A.1 Prompts

Pilot experiments led to the following prompt template across strategies:
“Rewrite the reply using <strategy>, following the examples. <Strategy> means
<strategy definition>. Use at most 30 words.” The final strategy definitions
used in the template were:

Hedging

“adding words or phrases that soften the force of a statement”

Acknowledgement

“conveying understanding and validating a person’s feelings”

Elaboration

“asking for further details, clarifying understanding, or repeating what was
heard”

Grounding

“establishing common ground, and explicitly mentioning mutual knowledge,
beliefs, or assumptions”

Gratitude

“showing appreciation for someone’s thoughts and opinions”

Agreement

“making explicit any agreement with the comment”

For the last strategy, we ultimately used “compromise and agreement” for
<strategy>, instead of merely “agreement”, since the latter tended to cause
the model to simply agree with the comment (even if this contradicted the
original reply); adding “compromise” helped to remedy this. Each prompt is
followed by the five in-context examples in A.2.

Our zero-shot paraphrase baseline was: “Paraphrase the following reply to the
comment. Use at most 30 words”. For the receptiveness baselines, we use the
prompt: “Rewrite the following reply to the comment to be more receptive. Use
at most 30 words.”

###  A.2 Examples

  1. 1.

Comment: Worth it to regain influence over the top executive who writes laws
that affect one’s life.
Reply: You think you have any influence over BoJo, Cummings, Rees-Mogg, Raab?
lol. They wouldn’t care if you died in a tower block fire.
Hedging: I’m not sure you would really have any influence over BoJo, Cummings,
Rees-Mogg, and Raab. They likely wouldn’t even care if you died in a tower
block fire.
Acknowledgement: I understand you think you have influence over BoJo,
Cummings, Rees-Mogg, Raab, but I don’t think they would care if you died in a
tower block fire.
Elaboration: From what I understand, you’re saying you would have influence
over BoJo, Cummings, Rees-Mogg, Raab. However, they likely wouldn’t care if
you died in a tower block fire.
Grounding: I agree it would be nice to have influence overBoJo, Cummings,
Rees-Mogg, and Raab, but I don’t think it would actually happen. They wouldn’t
care if you died in a tower block fire.
Gratitude: I appreciate your perspective, but I don’t think you have any
influence over BoJo, Cummings, Rees-Mogg, and Raab. They probably wouldn’t
care if you died in a tower block fire.
Agreement: I agree it would be nice to regain influence over top executives
like BoJo, Cummings, Rees-Mogg, Raab. But I don’t think you actually would,
they probably wouldn’t care if you died in a tower block fire.

  2. 2.

Comment: I love all of it. Except the proposed gun legislation. That might
actually make me walk away from Biden. It’s too much and too impractical.
Which is a shame because I was very excited and ready to vote for him.
Reply: Don’t be a single issue voter, it’s an absolutely ridiculous position
to hold.
Hedging: Perhaps being a single issue voter is not the best position to hold.
Acknowledgement: I would feel the same way if gun legislation was an important
issue to me, but it’s important to not be a single issue voter.
Elaboration: I think you’re saying that the one issue of gun legislation is
turning you against Biden. I don’t think being a single issue voter is the
best position to hold.
Grounding: I also think certain issues are more important than others, but I
don’t think being a single issue voter is the best position to hold.
Gratitude: Thank you for sharing your view, but I don’t think being a single
issue voter is the best position to hold.
Agreement: I agree that gun legislation is an important issue, but I don’t
think being a single issue voter is the best position to hold.

  3. 3.

Comment: "Um akshywally, cops kill more whites tham blacks, so while they’re
still murderers and thugs, at least they’re not racist." OP is an idiot.
Reply: Cops aren’t murderers and thugs. Like 1% of them are bad. You need to
respect the people that risk their lives to protect innocent people. The ones
that don’t do that are the bad 1%.
Hedging: I don’t think all cops are murderers and thugs. Maybe around 1% of
them are bad. It might be good to respect the people that risk their lives to
protect innocent people. Admittedly, the cops that don’t protect people are
bad 1%.
Acknowledgement: I realize this is a complex situation, but cops aren’t all
murderers and thugs, maybe like 1% of them are bad. I see how they can all
seem bad, but it’s important to respect the people that risk their lives to
protect innocent people. The ones that don’t do that are the bad 1%.
Elaboration: Would you mind clarifying what you mean about cops being
murderers and thugs? I don’t think all cops are murderers and thugs. Like 1%
of them are bad. You should respect the people that risk their lives to
protect innocent people. The ones that don’t do that are the bad 1%.
Grounding: I also believe that cops that don’t risk their lives to protect
innocent people are bad, but I think that’s only like 1% of them. Cops aren’t
all murderers and thugs.
Gratitude: I appreciate your opinion, but I don’t think all cops are murderers
and thugs. Like 1% of them are bad. It’s important to respect the people that
risk their lives to protect innocent people. The ones that don’t do that are
the bad 1%.
Agreement: I think you’re right that some cops kill people, but they aren’t
all murderers and thugs. Like 1% of them are bad. It’s important to respect
the people that risk their lives to protect innocent people. The ones that
don’t help people are the bad 1%.

  4. 4.

Comment: Is this a homework assignment? Either way, easy enough to learn about
what white privilege is if you just do a little Google searching.
Reply: Nope I want people to tell me what privileges they have over other
races. Not some dumb bs like "the black male makes .8 cent of the white males
dollar"
Hedging: Actually, I think I want people to tell me what privileges white
people have over other races. Statistics like "the black male makes .8 cent of
the white males dollar" usually aren’t very meaningful to me.
Acknowledgement: I know you could easily learn about white privilege online,
but I want people to tell me what privileges they have over other races. While
I understand that statistics like "the black male makes .8 cent of the white
males dollar" are important, they aren’t as meaningful to me.
Elaboration: I think you’re saying that it’s easy to learn about white
privilege from searching Google, but I want people to tell me what privileges
they have over other races. Not just statistics like "the black male makes .8
cent of the white males dollar".
Grounding: I also think it’s good to learn about white privilege, but I want
people to tell me what privileges they have over other races, rather than
finding statistics like "the black male makes .8 cent of the white males
dollar" online.
Gratitude: I appreciate your comment that we can learn about white privilege
online, but I want people to tell me what privileges they have over other
races, not just statistics like "the black male makes .8 cent of the white
males dollar".
Agreement: It makes sense that you can learn about white privilege by just
Googling, but I want people to tell me what privileges they have over other
races, not just statistics like "the black male makes .8 cent of the white
males dollar".

  5. 5.

Comment: You dont scare people when the recovered total is almost as big as
the total number.
Reply: Dude we are having a 9/11 in deaths from corona everyday and it has
become the leading cause of death in the u.s.. that still sounds like a pretty
big problem
Hedging: It seems like we are actually having a 9/11 in deaths from corona
everyday and it has become the leading cause of death in the u.s.. That still
sounds like a relatively big problem.
Acknowledgement: I agree that for a large number of people the corona virus is
recoverable. However, we are having a 9/11 in deaths from corona everyday and
it has become the leading cause of death in the u.s., which still sounds like
a pretty big problem.
Elaboration: Are you saying that because so many people recover, it means the
corona virus isn’t a problem? We are actually having a 9/11 in deaths from
corona everyday and it has become the leading cause of death in the u.s.. that
still sounds like a pretty big problem.
Grounding: I also believe that it’s good that many people recover from the
coronavirus, but we are having a 9/11 in deaths from corona everyday and it
has become the leading cause of death in the u.s.. that still sounds like a
pretty big problem.
Gratitude: I appreciate you bringing up that many people do recover from
corona, but we are still having a 9/11 in deaths from the disease everyday and
it has become the leading cause of death in the u.s.. that still sounds like a
pretty big problem.
Agreement: I agree that a lot of people recover from covid, but we are having
a 9/11 in deaths from corona everyday and it has become the leading cause of
death in the u.s.. that still sounds like a pretty big problem.

##  Appendix B Additional Validation Results

We study differences in trigrams found in generated reframes compared to the
original replies. The overlap of these trigrams between the different
strategies is given in Table 5. Overlap is defined as whether a trigram in a
strategy’s generations also appears in at least one other strategy’s
generations.

Strategy | Overlap
---|---
Hedging | 0.318
Acknowledgement | 0.340
Elaboration | 0.283
Grounding | 0.350
Gratitude | 0.371
Agreement | 0.364
Table 5: Proportion overlap between strategies.

##  Appendix C Additional Qualitative Examples

Examples of receptiveness reframes for comment-reply pairs are listed in Table
6. Examples of predicted contradictions between original and reframed replies
are shown in Table 7. Examples of original replies that might be seen as less
reasonable than the model-generated reframed reply are shown in Table 8.

|  Example 1 |  Example 2 |  Example 3
---|---|---|---
Comment |  Am I the only person reasonable enough to believe that both Democrats and Republicans love this country and that they both disagree on the ways that make it lovely? |  I’m white and my family worked in the fields of Alabama. Lived in a tiny house with no running water until the 70’s. Wtf |  No. Stop bringing racism into a movement trying to get rid of it.
Reply |  Nice false choice fallacy. Democrats hate America. Democrats hate Trump, the people who voted for Trump and they want retribution. End of story lol |  You’re white. You and your entire family are automatically privileged. |  Oh i said black people good and whites bad am not racist am "protesting"
Hedge rewrite |  Some people tend to generalize and assume that Democrats hate America, but perhaps it is possible that both Democrats and Republicans disagree on how to make America great. |  While I understand that you and your family have faced significant challenges, it is still important to acknowledge the existence of white privilege in society. |  It might not be helpful to bring racism into a movement trying to get rid of it. Saying things like "black people good and whites bad" could be seen as divisive, even if it’s meant to be a form of protesting.
Ack rewrite |  I recognize that both Democrats and Republicans love this country, but it seems like the polarizing rhetoric from both sides can make it difficult to see that they ultimately want the same thing for America. |  I understand that assumptions about privilege can be hurtful and dismissive. It is important to recognize that privilege is not a one-size-fits-all experience and your family’s struggles are valid. |  I understand your frustration, but it’s important to recognize that the Black Lives Matter movement is about addressing systemic racism and promoting equality, not about demonizing any particular race.
Elab rewrite |  I’m hearing your opinion that Democrats and Republicans both love this country but disagree on how to make it better. However, I don’t think it’s fair to say that Democrats hate America and want retribution against Trump and his supporters. |  It sounds like you’re saying that just because you’re white, you’re automatically privileged. I don’t agree with that. Your family had a tough life living in a tiny house with no running water until the 70s in Alabama. |  I think you’re saying that you don’t want racism brought into a movement trying to get rid of it. I believe it’s important to focus on the issue of racism and not make generalisations, like "black people good and whites bad".
Ground rewrite |  I agree that it’s possible for both Democrats and Republicans to love this country and simply disagree on how to make it better. However, it’s unfair to make a false choice fallacy by suggesting that Democrats hate America and want retribution against those who voted for Trump. |  I understand that not all white people have had easy lives or privileges, but I think it’s important to acknowledge that there is still systemic racism that affects people of color. |  I agree that bringing racism into a movement trying to get rid of it is not helpful, but I don’t think it’s fair to assume that all protesters are saying "black people good and whites bad."
Grat rewrite |  Thank you for your comment, but I don’t believe it’s a fair assumption to say that Democrats hate America. Both parties love this country, they just have different ways of approaching and achieving their goals. |  Thank you for sharing your personal experience. While privilege certainly exists, it’s important to recognize that every individual’s situation is unique and not defined solely by their race. |  I hear your point about not wanting to bring racism into a movement trying to eliminate it, but making sweeping statements about any race being inherently good or bad can be divisive and counterproductive.
Agree rewrite |  I agree that both Democrats and Republicans love this country, but it’s unfair to say that Democrats hate America. They may just have different visions for what makes it lovely. |  I acknowledge that your family faced struggles and challenges. However, it’s important to recognize that white privilege exists in society, regardless of individual circumstances. |  I understand that it’s not helpful to bring racism into a movement trying to eliminate it. Let’s focus on constructive ways to address racism without demonizing any particular group.
Table 6: Additional qualitative examples of Reddit comments and replies, as well as the reframes generated for each strategy. Original Reply |  Reframed Reply
---|---
Nice false choice fallacy. Democrats hate America. Democrats hate Trump, the people who voted for Trump and they want retribution. End of story lol |  I agree that both Democrats and Republicans love this country, and it’s important to recognize that they have different perspectives on what policies will make it better.

Dude we hold everyone to higher standards then a kid. A kid curses and
everyone says it’s wrong. An adult curses and no one cares???
|  I understand your point, but we should hold everyone, including the
president, to a higher standard than kids. Cursing is wrong regardless of age.

Dude we hold everyone to higher standards then a kid. A kid curses and
everyone says it’s wrong. An adult curses and no one cares???
|  You’re right that holding kids to a higher standard than the president is
problematic. We should hold everyone, including the president, to a higher
standard.

So if they were black it would have been fine you racist and the child where not harmed |  I share your horror at the thought of children being handcuffed. Regardless of race, it’s unacceptable. I’m relieved to hear the child wasn’t harmed.

Just playing by the “ballot harvesting” rules that the Democrats put in place. |  While I understand the Republicans are using “ballot harvesting” rules, it’s concerning to hear about the use of “unofficial” ballot boxes and potential voter fraud.
Table 7: Examples of contradictions (by the out-of-the-box NLI model) between original replies and model-generated reframes. Comment |  Reply |  Reframe
---|---|---
Well creepy isnt just a nickname anymore. It is an earned title. And let the games begin. |  Like trump hasn’t done the same shit? Tunnel vision at work |  Are you saying that Trump hasn’t done things that could also be considered creepy? It sounds like you might not be considering everything.
whales are not fish any more than seals or otters are. is that what u meant? |  I am positive that is not what the artist meant. Saving anything doesn’t mean fuck everything else. |  I understand your point about whales not being fish. But I’m sure the artist intended to convey saving wildlife in general, not disregarding other species.
No, this is going to be the start of the end of the republican party. |  This idiot is a genius in campaigning. I am dooming pretty hard for 22 and 24. |  I also think this could be the start of the end for the Republican party, but I’m worried about ’22 and ’24 since this "idiot" is actually a genius in campaigning.
Table 8: Examples of human-written original replies that might not make direct
sense from the original comment, compared to generated reframes that take into
account context from the comment.

##  Appendix D Receptiveness Factors

Table 9 contains each of the four factors that make up the receptiveness
index, as well as the corresponding questions that were given to annotators to
determine factor scores.

Receptiveness Factor |  Questions
---|---
(1) Negative emotional reactions towards opposing views |  (a)  Which reply would be more likely to make you angry when you read it?
|  (b)  Which reply would be less likely to make you mad?
(2) Curiosity towards opposing views |  (a)  Which reply makes you feel less interested in finding out why the user has a different view than you?
|  (b)  Which reply makes you feel genuinely curious to find out more about
why they have a different opinion than you do?
(3) Bias towards people holding opposing views |  (a)  Which reply makes you feel like the user’s view is biased by what would be best for them and their group?
|  (b)  Which reply makes you feel like the user’s opinion is based on
preconceived notions of what would be best for them and their group?
(4) Beliefs that some topics are off limits |  (a)  Which reply makes you feel like the user is unwilling to further discuss the issue?
|  (b)  Which reply makes you feel like the issue is just not up for debate?
Table 9: Receptiveness factors and corresponding questions asked to users.
Orange indicates the questions are reverse coded.

##  Appendix E Statistics Tests

This section contains the details of the statistical tests described in the
paper. Table 10 contains the mean differences, test statistics, and p-values
for the t-test on results from the reasonability annotation task. Table 11
contains the full regression coefficients and test statistics from the
receptiveness annotation task. Table 12 contains the mean differences, test
statistics, and p-values for the Wald test on results from the receptiveness
annotation task. And Table 13 contains the mean differences, test statistics,
and p-values for the Wald test on results from the toxicity interaction
analysis.

Strategy | Mean diff | Test stat. | p-value
---|---|---|---
Hedging | 0.454 | 3.814 | < 0.0001
Acknowled. | 1.008 | 7.776 | < 0.0001
Elaboration | 0.724 | 5.676 | < 0.0001
Grounding | 0.917 | 7.360 | < 0.0001
Gratitude | 0.729 | 5.113 | < 0.0001
Agreement | 0.852 | 6.384 | < 0.0001
Table 10: Mean difference between each strategy and the original replies, paired t-test statistics and p-values for the reasonability annotation task. The paired t-test is conducted for each strategy compared to the original reply. Strategy | Coefficient | Test stat. | p-value
---|---|---|---
Paraphrase (B) | 0.565 | 7.132 | < 0.0001
Receptive (B) | 0.416 | 6.085 | < 0.0001
Hedging | 1.153 | 11.479 | < 0.0001
Acknowled. | 1.196 | 12.048 | < 0.0001
Elaboration | 1.209 | 12.138 | < 0.0001
Grounding | 1.316 | 13.345 | < 0.0001
Gratitude | 1.142 | 11.463 | < 0.0001
Agreement | 1.153 | 13.318 | < 0.0001
Table 11: Regression coefficients for the mixed effects logistic regression model.“(B)” indicates a baseline. Strategy | Mean diff | Test stat. | p-value
---|---|---|---
Hedging | 1.15 | 11.478 | < 0.0001
Acknowled. | 1.20 | 12.048 | < 0.0001
Elaboration | 1.21 | 12.137 | < 0.0001
Grounding | 1.32 | 13.344 | < 0.0001
Gratitude | 1.14 | 11.463 | < 0.0001
Agreement | 1.33 | 13.317 | < 0.0001
Table 12: Mean difference between each strategy and the paraphrase baseline, Wald test statistics and p-values for the receptiveness experiment contrasts. The test is conducted for each strategy compared to paraphrased reply. Strategy | Pair | Mean diff | Test stat. | p-value
---|---|---|---|---
Avg. Strategies | high - med | 0.146 | 1.567 | 0.266
| high - low | $\textbf{0.385}^{**}$ | 4.107 | 0.0003
| med - low | $\textbf{0.239}^{*}$ | 2.540 | 0.0342
Paraphrase (B) | high - med | 0.094 | 0.497 | 0.873
| high - low | 0.273 | 1.458 | 0.312
| med - low | 0.181 | 0.934 | 0.619
Receptive (B) | high - med | -0.0734 | -0.457 | 0.891
| high - low | 0.0530 | 0.331 | 0.941
| med - low | 0.126 | 0.788 | 0.711
Table 13: Mean difference between each strategy and the paraphrase baseline,
Wald test statistics and p-values for the toxicity-receptiveness contrasts.
The test is conducted for the receptiveness score at each toxicity level
compared to the others. “B” indicates a baseline. Bolded values are
significant. ∗ indicates significance at p < 0.05, ∗∗ indicates significance
at p < 0.001

##  Appendix F Annotation Interfaces

This section includes the annotation interfaces used for all the annotation
tasks in this paper.

Figure 4: Reasonability validation task interface Figure 5: Receptiveness task
interface (a) Figure 6: Receptiveness task interface (b) Figure 7:
Receptiveness task interface (c)