Countering Misinformation via Emotional Response Generation
Authors: Daniel Russo
Shane Peter Kaszefski-Yaschuk
Jacopo Staiano
Marco Guerini
Venue: EMNLP 2023 Main (2023)
Keywords: Misinformation Countering
Automated Fact-Checking
Knowledge-Driven NLG
Automatic Text Summarization
Human-Machine Collaboration
Data Collection

Abstract: The proliferation of misinformation on social media platforms (SMPs) poses a significant danger to public health, social cohesion and ultimately democracy. Previous research has shown how social correction can be an effective way to curb misinformation, by engaging directly in a constructive dialogue with users who spread – often in good faith – misleading messages. Although professional fact-checkers are crucial to debunking viral claims, they usually do not engage in conversations on social media. Thereby, significant effort has been made to automate the use of fact-checker material in social correction; however, no previous work has tried to integrate it with the style and pragmatics that are commonly employed in social media communication. To fill this gap, we present VerMouth, the first large-scale dataset comprising roughly 12 thousand claim-response pairs (linked to debunking articles), accounting for both SMP-style and basic emotions, two factors which have a significant role in misinformation credibility and spreading. To collect this dataset we used a technique based on an author-reviewer pipeline, which efficiently combines LLMs and human annotators to obtain high-quality data. We also provide comprehensive experiments showing how models trained on our proposed dataset have significant improvements in terms of output quality and generalization capabilities.

Countering Misinformation via Emotional Response Generation
Daniel Russo 12 Shane Peter Kaszefski- Yaschuk 1.2 Jacopo Staiano", Marco Guerini
Fonddziooe Bruno Kessler, Via Sommarive 18, Povo, Trento, Italy
University of Trento, Italy {drusso, skaszefskiyaschuk, guerini eu, jacopp staiano@uni
Abstract
FullFact Dataset
The proliferation of misinformation social media platforms (SMPs) poses significant danger to public health, social cohesion and ultimately democracy. Previous research has shown how social correction can an effective way curb misinformation, by engaging di- rectly in a constructive dialogue with users who spread often good faith misleading mes- sages, Although professional fact-checkers crucial to debunking viral claims, they usually not engage conversations on social media. Thereby, significant effort has been made to automate the use of fact-checker material in so- cial correction; however, no previous work has tried to integrate it with the style and pragmat- ics that are commonly employed in social me- dia communication. To fill this gap, present VerMouth, the first large-scale dataset compris- ing roughly 12 thousand clain-response pairs (linked to debunking articles), accounting for both SMP-style and basic emotions, two factors which have significant role in misinforma- tion credibility and spreading. To collect this dataset we used a technique based on an author- reviewer pipeline, which efficiently combines LLMs and human annotators to obtain high- quality data. We also provide comprehensive experiments showing how models trained our proposed dataset have significant improve- ments terms of output quality and general- ization capabilities.
ARTICLE
CLAIM
VERDICT
Data Collection
PROMPT]
PRONPT
LLM
LLM
Claim HUMAN
Verdict Guldelines
HUMAN
Guidelinns
VerMouth Dataset
ARTICLE
CLAIM
VERDICT
Our dataset creation pipeline. Starting from <article, carticle,claim, verdict> clain,,erdicr>> triplets, we use an author- reviewer architecture (LLM human annotator) rich the dataset with style variations of claim and verdict while keeping the article constant.
Figure 1:
(Basol et al., 2020; Martel et al., 2020). Among the different countermeasures adopted, one of the most employed is fact-checking, í.e. the task assessing a claim's 's veracity. Although the work of professional fact-checkers is crucial for coun- tering misinformation (Wintersieck, 2017), it has been shown that most debunking SMP is ried out by ordinary users through direct replies misleading messages (Micallef et al., 2020). In the literature, this phenomenon called social correc- tion (Ma et al., 2023).
1 Introduction
Social media platforms (SMP) represent one of the most effective mediums for spreading misleading content (Lazer et al., 2018). Social media users interact with potentially false claims daily ba- sis and contribute (whether intentionally not) to their spreading. Several techniques commonly employed to construct false but convincing con- tent: mimicking reliable media posts, in the case so-called "fake news"; impersonating trustwor- thy public figures; leveraging emotional language
In order to keep up with the massive amount of fake constantly being produced, Natural Language Processing techniques have been pro- posed as viable solution for the automation of fact-checking pipelines (Vlachos and Riedel, 2014). Researchers have focused on both the automatic

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prediction of the truthfulness of statement (a clas- sification task, often called veracity prediction) and the generation a written rationale (a generation task called verdict production; Guo et al., 2022).
these limitations, our results show that verdicts written in a social and emotional style hold greater sway and effectiveness when dealing with claims presented in an SMP-style.
While generating rationale is more challenging than stating claim veracity, previous research has proven that it is more persuasive (Lewandowsky et al., 2012). Thus, automating the verdict genera- tion process has been deemed crucial (Wang et al., 2018) as an aid for both fact-checkers and for social media users (He et al., 2023).
Related Work
The fact-checking process is comprised of two main tasks: first, given news story, the truthful- ness/veracity of a statement has to be determined; then, explanation (verdict) has to be produced.
In the literature, the problem of determining a claim's veracity, has been framed as a binary (Nakashole and Mitchell, 2014; Potthast et al., 2018; Popat ct al., 2018) or multi-label (Wang, 2017; Thorne et al., 2018) classification task, and occasionally addressed under multi-task learn- ing paradigm (Augenstein al., 2019). Given the supervised nature of these methodologies, signifi- cant efforts have been directed towards the develop- ment of datasets for evidence-based veracity predic- tion, such as FEVER (Thorne et al., 2018), SciFact (Wadden et al., 2020), COVID-fact (Saakyan et al.. 2021), ), and PolitiHop (Ostrowski et al., 2021). For the more challenging task of Verdict Produc- tion, several methodologies have been explored, ranging from logic-based approaches (Gad-Elrab et al., 2019; Ahmadi et al., 2019) to deep learn- ing techniques (Popat et al., 2018; Yang et al., 2019; Shu et al., 2019; Lu and Li, 2020). More recently, He et al. (2023) introduced reinforce- ment learning- based framework which generates counter-misinformation responses, rewarding the generator to enhance its politeness, credibility, and refutation attitude while maintaining text fluency and relevancy. Previous works have shown how casting this problem as a summarization task start- ing from a claim and corresponding fact-checking article appears to be the most promising approach (Kotonya and Toni, 2020a). Under such framing, the explanations either extracted from the rel- evant portions of manually written fact-checking articles (Atanasova et al., 2020) or generated ex- novo (Kotonya and Toni, 2020b); these two ap- proaches correspond, respectively, to extractive and abstractive summarization. Finally, Russo et al. (2023) proposed a hybrid approach for the genera- tion of explanation, by employing both extractive and abstractive approaches combined into unique pipeline.
An effective explanation (verdict) is charac- terised as being accessible (i.e. adopting language directly and easily comprchensible by the reader)
and by containing a limited number of arguments to avoid the so-called overkill backfire effect (Lom- brozo, 2007; Sanna and Schwarz, 2006).
In this paper, contribute automated fact- checking by introducing VerMouth, the first large-scale and general-domain SMP-style dataset grounded in trustworthy fact-checking articles, comprising -2 thousand examples for the gen- eration of personalised explanations. VerMouth was collected via an efficient and effective data augmentation pipeline which combines instruction- based Large Language Models (LLMs) and human post-editing.
Starting from harvested journalistic-style claim- verdict pairs, we ran two data collection sessions: first, we focused on claims by rewriting them in a general SMP-style and then adding emo- tional/personalisationn aspects to better mimic con- tent which can be found online. Then, in the second session, the verdicts were rewritten according to pre-defined criteria (e.g. displaying empathy) to match the new claims obtained the first session. This process summarised in Figure
Finally, we tested the capabilities and robustness of generative models fine-tuned over VerMouth: automatic and human evaluation, as well as qual- itative analysis of the generated verdicts, suggest that for social media claims, verdicts generated through models trained on VerMouth are widely preferred and that those models are more robust to the changing of claim style.
Our analyses show that generated verdicts are deemed less effective if they are (i) either too long and filled with a high number of arguments or (ii) they are excessively empathotic. Generally, despite
Extractive and abstractive approaches suffer from known limitations: on the one hand, extrac-
'The resource is publicly available on https://githul http:/arcogurrinn//eeroooo

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tive summarization cannot provide sufficiently con- textualised explanations; on the other, abstractive alternatives can be prone to hallucinations under- mining the justification's 's faithfulness. Nonetheless, while the abstractive approach remains the most promising - also in light of the current advances in LLMs development- the problem of collecting an adequate amount of training examples persists: the few datasets available for explanation production are limited size, domain coverage or quality.
Each entry in our dataset includes triplet com- prising: a claim (i.e. the factual statement under analysis), a fact-checking article (i.e. a document containing all the evidence needed to fact-check a claim), and a verdicr (i.e. a short textual response to the claim which explains why it might be true or false). Both the claims and the verdicts were rewrit- ten according to the desired style using the author- reviewer pipeline. Still, given the different nature and purpose of claims and verdicts, we instructed the LLMs with different specific requirements dur- ing two different sessions of data collection. For the first session, we further considered two phases. The goal of the first phase was to obtain claims with generic "SMP-sttyle, i.e. something that resembles post which can be found online, rather than the more journalistic and neutral style. In the second phase, we add an emotional component the LLM's s instruction.
The most commonly used datasets are either machine-gennratten e.g. e-FEVER by Stamm- bach and Ash (2020), or silver data as for LIAR- PLUS by Alhindi et al. (2018). To the best of our knowledge, only three datasets include gold explanations, i.e. PUBHEALTH by Kotonya and Toni (2020b), the MisinfoCorrect's crowdsourced dataset by He et al. (2023), and FUlLFACT by Russo et al. (2023). However, PUBHEALTH and MisinfoCorrect datasets are domain -ppecic (re- spectively, health and COVID-19), and only the lat- ter comprises textual data written in an SMPs style (informal, personal, and empathetic if required), even if limited in size (591 entries). This style is very different from journalistic style, more direct and concise, meant for the general pub- lic. Other datasets, based on community-orrented fact-checking derived from Birdwatch? (Pröllochs, 2022; Allen et al., 2022), do not fit well our sce- nario, as users' corrections were proven to be often driven by political partisanship (Allen et al., 2022).
We considered Paul Ekman's 's six basic emotions: anger, disgust, fear, happiness, sadness, and sur- prise (Ekman, 1992). Verdicts were generated in a second session as responses to each newly gener- ated claim, using the same author-reviewer pipeline but different instructions for the LLM and different guidelines for the reviewer. This was done to ac- count for the characteristics a verdict should have, e.g. politeness, attacking the arguments and not the person, and empathy (Malhotra et al., 2022; Thor- son et al., 2010). In Table 1 we give an example of the obtained outputs using our methodology.
3 Dataset
3.1 Source Data
We leveraged FullFacct data (FF henceforth; Russo et al., 2023) as a human-curated data source for the derivation of our datuset. The FF data acquired from the FULLFACT website. 3 FF comprises all the data published on the website from 2010 and 2021, accounting for a total of 1838 entries. FF triplets were labelled with one or more topic labels: includ- ing crime (10.50%), economy (27.80%), education (11.15%), Europe (20.46%) health (32.37%), and law (8.05%). FF data are written in a journalistic style, dry and formal, very different from the style employed on SMPs.
In this work, we introduce VerMouth, new large- scale dataset for the generation of explanations for misinformation countering that are anchored to fact-checking articles. To build this dataset we adapted the author-reviewer pipeline presented by Tekiroglu et al. (2020), wherein a large language model (the author component) produces novel data while humans (the reviewer) filter and eventually post-edit them (Figure 1). Differently from their approach, based on GPT-2, we used an instruction- based LLM that does not require fine-tuning and applied it to the source data taken from popular fact-checking website. We leveraged the author- reviewer pipeline for a style transfer task, so to generate new data in SMP-style rather than in journalistic one.
3.2 Author: LLM Instructions
To provide more natural and realistic claims and more personalised verdicts resembling the SMP- style, we performed data augmentation on the origi-
'Twitter's crowdsourcing platform for fact-checking, re- named as Community notes at the end of 2022
hhtps://fullfact.org

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Original
Stye
Emotional style
The vaccine manufactur- ers do not have liability.
BREAKING: According to re- cent court ruling, vaccine mak- ers can't be held accountable for any issues that may arise from their products. #NoLiabil- ity Vaccines
As someone who has lost loved one due vaccine com- plications, it makes my blood hoil to think that the vaccine manufacturers have zero liability. How this fair? They should be held accountable for any harm caused by their prod- ucts, #vaccinesafety #justiceforvictmss
Covid-19 19 vaccine manu- facturers are immune to some, but not all, civil liability.
Actually, while it's true that vac- cine manufacturers are protected from some liability, they are still subject to civil liability for cer- tain issues, It's very important to be aware of this fact.
can't 't even begin to imagine the pain your loss has caused you, It's important to note that Covid- 19 vaccine manufacturers do have some immunity from civil liability. but this is not absolute. Also keep in mind that the government has set up compensation program for those who have experienced serious adverse reactions. hope this information helps, and hope you do better soon. #vaccinesafety #compassionforall
Table 1: An example of claim (first row) and verdict (second row): original versions from FullFact, then SMP-sttle and emotional versions, obtained via our author-reviewer approach. The original claim and verdict have dry and neutral style, while the variations we obtain resemble the content found on SMPs with hashtags, sensationalist expressions and informal style (in yellow). The emotional claim clearly contains emotional expressions, as well as, sometimes, personal stories grounding the emotional component (red) while the verdict also contains the qualities required by social response such as politeness and empathy (green).
nal FF dataset through an author- approach. This approach has the advantage of avoiding pri- vacy concerns (since no real SMP data is collected) and prevents dataset ephemerality (Klubicka and Fernández, 2018). As an author module, we tested instruction-based LLMs such as GPT3 (Brown et al., 2020) and ChatGPT.4
3.3 Reviewers: Post-Editing Guidelines
Two annotators were involved the post-editing process: one last-year master's student (native En- glish speaker) and Ph.D. student (fluent in En- glish). Adapting the methodology proposed by Fanton et al. (2021), both the annotators were exten- sively trained on the data and the topic of misinfor- mation and automated fact-checking. well the pro-social aims of the task. In addition, weekly meetings were organised throughout the whole an- notation campaign to discuss problems and doubts about post-editing that might have arisen.
To set the proper prompt/instruction, we run pre- liminary experiments by testing several textual vari- ants, providing the annotators with sample of the data generated for quality evaluation. We evaluated the prompts according to the following factors: gen- cralisability, variability, originality, coherence, and post-editing effort. Details on configurations and methodology of the quality evaluation are given in Appendix 1. The final instructions for claim and verdict generation are reported in Table 2.
The goal of the post-editing process was to min- imise the annotators' effort while preserving the quality of the output. For this reason, the guidelines focused not only on post-editing with consistency but also on minimising the amount of time needed to post-edit the data. Claims and verdicts are dis- tinct elements with different characteristics (c.g. claims can contain offensive or false content while verdicts can not), and they play different roles in dialogue. Thereby, the post-editing guidelines while preserving some overall commonalities be- tween these two components have to account for the specific roles each of them plays, as well as any claim or verdict-specific phenomena which arisc from the generation step. Examples of claim and verdict-specific phenomena, as well as effective post-editing actions, are discussed hereafter."
PROMPT(A) Write as if an ordinary person tweet- ing that [claim]. Use paraphrasing.
(BOMPT(B) Write a tweet from person who feels (emotion] about the idea that {claim} Use paraphrasing. Make it personal.
PROMPT(C) Rephrase this verdict [verdict) a po- lite reply the tweet (claim). Be empa- thetie and apolitical.
Table Instructions for claim generation: PROMPT(A) for StP-style; PROMPT(B) for the emotional style. Instructions for verdict generation: PROMPT(C).
"htpss//oppnai.com/bbggggggg
'See Appendix B for the full guidelines.

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3.4 Session 1: Claim Augmentation
verdicts, as they must follow stricter standards of quality: they have be always true, address the arguments made by the claim, avoid political polar- isation, and they must be empathetic and polite. It is important to highlight that LLM was re- quired to rewrite gold verdict and not to write a debunking from scratch, as can be seen in Table 2. For this reason, the main task of the annotators to check whether there were discrepancies between the gold and the generated verdicts, and, in case, to correct them. We took for granted that the gold verdicts are trustworthy (as they were manually written by professional fact-checkers), thus we are sure that a new verdict that differs only in style but not in content is trustworthy too.
Through our LLM-based pipeline and the avail- able FF claims, two sets of claims were generated: the "SMP-styyle claims, and the "emotional style" claims. What makes claim "good" can often be counter-intuitive since they do not need to be truthful. The generated claims exhibited specific characteristics which were accounted while creat- ing the post-editing guidelines. Some of the most relevant phenomena and the resulting post-editing actions follow:
1.
The generated texts occasionally copy the en- tire original claim verbatim, despite the model was prompted not In these instances, man- ual paraphrasing is necessary.
Some of the characteristics of the generated ver- dicts well actions which must be taken post-edit them effectively are listed below.
2. Sometimes, the generated claim debunks the original claim. For example, if the original claim says that "vaccines do not work", but the generated claim says the opposite, then it needs be changed to match the intent of the original claim.
Recurrent patterns, e.g. "thank you for.", "I understand your concern about...", "Ii's im- portant to...", were reworded or removed en- tircly.
3. Hallucinated information is usually undesired. However, since the claims might be mislcad- ing completely inaccurate, hallucinations can actually be useful for our task, making the claim seem more authoritative or convinc- ing by adding new false facts and arguments. For example, the model rewrote "Almost 300 people under 18 were flagged up ..." in "291 young people identified ..." making the poten- tial author of the post appear knowledgeable due to the precision in the stated number.
The generated verdicts often include "calls to action", i.e. exhortative sentences which call upon the reader to take some form of ac- tion (e.g., "it's important to continue advo- cating for fair treatment and stability in em- ployment."). To avoid potentially polarising verdicts as the main objective of verdict is simply provide factual arguments in favour or against a given claim was also neces- sary to neutralise or avoid overtly political or polarising calls to action.
4. For emotional claims specifically, we need to ensure that the emotion matches the claim and is reasonable. For example, being happy that people dying from vaccines is not something reasonable. A plausible correction can be that a person "happy as people are finally seeing the truth about the fact that the vaccine is causing deaths". If the correction is not possible, then the claim can be discarded.
Consistency regarding who exactly is 're- sponding' to claim was necessary. Some- times the first-person plural was used ("we understand that you' ree.."), and in other cases, the first-person singular was used ("l agree that...).. We decided that the verdicts should appear have been written by single per- son, rather than group, the case of social correction by single users. In some instances, the first-person plural can be used, but only when referring to a group that includes both the writer and the reader ("as society, we should...").
considering
3.5
Session 2: Verdict Augmentation
The verdict augmentation process was conducted similarly Session 1. However, in this case, the prompt included both the original FF verdict and the post-edited claim, since the generated verdicts are intended to be a specific response to it. A dif- ferent approach was required when post-editing
4. Sometimes the generated verdicts lack infor- mation or statisties contained in the original verdiet. whatever is missing is crucial to the argument being made, then including it is

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FullFact
SMP-style
Emotional style
claim
verdict
claim
verdict
claim
verdict
Tokens
18.0
35.5
34.1
52.3
52.8
61.3
Words
16.5
33.7
29.1
51.0
47.5
57.6
Sentences
1.0
1.9
2.6
2.5
3.4
3.0
Table 3: Average length of articles, claims, and verdicts in our dataset.
mandatory. If its exclusion does not detract from the strength of the argument, then it's not necessary to include it. fact, including extra information may actually be detrimental to the overall readability of the verdict (Lombrozo, 2007; Sanna and Schwarz, 2006).
RR measures the repetitiveness of text, by com- puting the geometric mean of the rate of n-grams occurring more than once in it. fixed-size sliding window while processing the text ensures that the differences in documents' size do not impact the overall scores. For our analysis, we computed the rate of word n-grams (with ranging from 1 to 4) with sliding window of 1000 words. Following previous works (Bertoldi al., 2013; Tekiroğlu et al., 2020), the RR values reported this paper range between and 100.
5. Conversely, claims arguments not con- tained in the original verdict could be gener- ated. If these claims support the argument being presented and are either factual or subjective opinion, then they were kept. Oth- erwise, they were removed or rewritten.
As can be seen in Table 4, the HTER values com- puted on the claims are very low, always less than 0.1, suggesting good quality machine-generated texts. In particular, the data generated accord- ing to general SMP-style were less post-edited, Machine-generated claims, which comprise also an emotional component, required more post-editing than SMP--tyle claims, shown by the higher HTER values.
3.6 Dataset Analysis
After the data augmentation process, we obtained -12 thousand examples (11990 claim-verdict pairs, 1 1838 written in general SMP-style and 10152 also comprising emotional component). Post- editing details can be found in Appendix A.2. In Table we report the average number of words, sen- tences, and BPE tokens for the articles, the claims and the verdicts of each stylistic version of our dataset. Then, to quantitatively assess the quality of the post-edited data we employed two measures: the Human-tane targeted Translation Edit Rate (HTER; Snover et al., 2006) and the Repetition Rate (RR; Bertoldi et al., 2013).
Moreover, HTER values for the post-edited ver- dicts are higher than those for claims. This can be explained by the need to ensure verdicts' truth- fulness, by adjusting or removing calls to action, possible model hallucinations or repeated patterns. However, even though HTER values vary across the single emotions, on average they lower than the 0.4 threshold.
This corroborated by the RR of the verdicts: substantial decrease in repetitiveness was obtained after post-editing at the expense of more editing op- erations. The average RR for the data comprising an emotional component comparable to the one obtained on the corresponding claims. However, this does not apply to the SMP-style data: in this case, the RR for the claims is more than 2 points lower than that the verdicts. This be plained by the tendency of the LLMs employed produce more recurrent patterns when the instruc- tions are enriched with specific details, such as the emotional state.
HTER measures the minimum edit distance, i.e. the smallest amount of edit operations required, be- tween
text and its post-edited version. HTER values greater than 0.4 account for low-quality generations; in this case, writing a text anew or post-editing would require similar effort (Turchi et al., 2013). In Table we report the HTER of the post-edited claims and verdicts
"We employed Spacy (https: /ttpa//sspcc///c/ep io) extracting words and sentences, and sentence-piece tokenizer used Pegasus (Zhang et al., 2020) for the BPE tokens.
'HTER values were averaged over the entire samples under analysis (including the non-post-edited data)
In summary, our pipeline facilitated the acqui-

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FF
SMP-sttyle
happiness
anger
fear
disgust
sadness
surprise
all emotions
# samples
1838
1838
1527
1590
1805
1675
1758
1797
10152
claims
HTER
0.028
0.066
0.055
0.058
0.060
0.047
0.073
0.059
generated post-edited
1.578
4.795
4.194
5.784
5.066
6,068
5.739
3,903
RR
1,501
4.803
4,206
5.800
5.089
6.149
5.692
3.945
verdicts
HTER
0.275
0.335
0.339
0.338
0.317
0.319
0.262
0.318
generated
6,359
6.742
7.155
7.266
7.355
6,938
6.761
RR
post-edited
3.872
4.292
4.128
4.250
4.149
4.476
4.168
4.200
Table For each dataset (column-wise):: number of samples, HTER and Repetition Rate (RR) values for both the post-edited claims and verdicts.
sition of a substantial volume of data while simul- taneously minimizing the annotators' workload." 8 Additionally, the intervention of the annotator sub- stantially increases the quality of the data as re- ported in the lowered values of RR.
cosine distance (SBERT-K henceforth, with de- noting the number of sentences). Under our exper- imental design, the top-k sentences with latent representation closer to that of the claim would be selected to construct the output verdict.
We used semantic retrieval approach rather than other common unsupervised methods for ex- tractive summarization, such as LexRank (Erkan and Radev, 2004), since the latter has no visibility into the claim itself. Nonetheless, we tested those approaches in preliminary analyses (reported in Ap- pendix C.1) and verified that the performance was significantly lower than that obtained with SBERT. We will consider SBERT-k baseline for the following experiments.
4 Experimental Design
Inspired by the summarization approaches pro- posed by Atanasova al. (2020); Kotonya and Toni (2020b), for the automatic generation of per- sonalised verdicts we leveraged an LM pretrained with summarization objective. To overcome the limitation of the model's fixed input size, we re- duced the length of the input articles, by adding extractive summarization step beforehand (fol- lowing the best configuration presented in Russo et al., 2023). This extractive-abssraattvve pipeline was tested on different configurations, both in in- domain and cross-domain settings." 9 The quality of the generated verdicts was assessed with both an automatic and a human evaluation. We present and discuss the results in Section 5.
4.2 Abstractive Models
We employed PEGASUS (Zhang et al., 2020), a language model pretrained with a summarization objective. In all the experiments, the length of the articles was reduced through extractive summariza- tion with SBERT (Reimers and Gurevych, 2019) in order to fit the maximum input length of the model (i.e. 1024). We opted for SBERT in light of its higher performances with respect other ex- tractive methods (see Appendix C.I). We explored four different configurations and tested them on all the versions of our dataset, i.e. FullFact, SMP, and emotional version (see Appendix C.2 and C.3 for fine-tuning and decoding details):
4.1 Extractive Approaches
Under an extractive summarization framing, we defined the task of verdict generation as that of extracting 2-sentence long verdicts from FullFFact articles, and 3-sentences long for the SMP and emotional data. Such lengths were decided accord- ing the averaged length of the verdicts, reported in Table 3. We employed SBERT (Reimers and Gurevych, 2019), a BERT-based siamese network used to encode the sentences within an article as well as the claim and to score their similarity via
PEG Zero-shot experiments with PEGA- SUS fine-tuned on CNN/Daily Mail, with the goal of summarizing the dehunking article.
PEGFF: Fine-tuning of PEG on FF data. claim and corresponding debunking arti- cle were concatenated and used as input, with the verdict as target.
"This corroborated not only by HTER values con- stantly lower than presented Appendix A.3, which revealed that creating con- tent from scratch takes roughly three times more than post-edit machine-geneee generated data.
also by supplementary experiment
"In-domain refers to train and test data having the same style, while cross-domain involves data with different styles.
"httpss:/huggiingface co/ wow.coolle/pegaaus-cco dailymail

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Model
VerMouth-test
R1
R2
RL
METEOR
BARTScore
BERTSccoe
SBERT-2
FullFact
.245
.092
.180
.328
-2,898
.874
SBERT-3
SMP-style
.230
.054
.149
.268
-2.981
.863
SBERT-3
emotional style
.228
.051
.144
.251
-3.091
.888
PEG
FullFact
.223
.073
.159
.291
-3.124
.856
PEGbase
SMP-style emotional style
.217
.045
.139
.213
-3.181
.852
PEG
.217
.044
141
.202
-3.253
.849
PEG
FullFact
.282
.104
.213
.345
-2.824
.886
PEG
SMP-style emotional style
.244
.058
.162
.227
-3,079
.873
PEGEE
.233
.052
.155
.203
-3.173
.867
PEG
FullFact
.660
.084
.184
.297
-3.038
.883
PEGamp
SMP-style emotional style
.337
.127
.240
.320
-2.864
.896
PEGsmp
.323
.121
.229
.301
-2.918
.890
PEG
FullFact
.266
.078
.175
.286
-3.084
.877
PEGemo
SMP-style
.326
.124
.233
.321
-2,858
.892
PEGemo
emotional style
.337
.131
.234
.331
-2.810
.893
Table 5: Results for each configuration, for both the in-domain and cross-domain experiments
Fine-tuning of PEGbase the SMP-style data. Training imput data were pro- cessed in the PEGFF configuration.
PEG
BERTScore (Zhang al., 2019) computes token-level semantic similarity between two texts using BERT (Devlin al., 2019).
PEGemo Fine-tuning of PEGbase the emo- tional data."' Training input data were in the PEGFF configuration.
BARTScore (Yuan et al., 2021), built upon the BART model (Lewis et al., 2020), frames the evaluation as text generation task by com- puting the weighted probability of the genera- tion of target sequence given source text.
5 Results
We assessed the potential of our proposed dataset in terms of generation capabilities via both automatic and human evaluation.
Table reports the results of all the experiments we carried out. For all metrics, the higher scores were obtained after fine-tuning the model in both in-domain and cross cross-domain experimental scenar- ios. Indeed, zero-shot experiments with PEGbus resulted in scores even lower than the SBERT base- line. This suggests that summarising the article is not enough by itself to obtain quality verdicts. Interestingly, the PEGASUS models fine-tuned on the SMP-style and emotional style samples ap- pear to generalise better. In fact, when tested against the other test subsets, they have simi- lar overall performance and smaller decrease in cross-domain settings compared PEGrr.
5.1 Automatic Evaluation
We adopted the following automatic measures:
(Recall-Oriented Understudy for Gisting Evaluation; Lin, 2004) measures the overlap between two distinct texts by examin- ing their shared units. We include ROUGE-N (RN, N=1,2) and ROUGE-L (RL), modified version that considers the longest common substring (L.CS) shared by the two texts.
ROUGE
METEOR (Banerjee and Lavie, 2005) deter- mines the alignment between two texts, by mapping the unigrams in the generated ver- dict with those in the reference gold verdict, accounting for factors such as stemming, syn- onyms, and paraphrastic matches.
5.2 Human Evaluation
We adapted the methodology proposed by He et al. (2023) to scenario: three participants were asked to analyse 180 randomly sampled items; each item comprises the claim and three verdicts pro- duced by PEGFr, PEG and PEG over that claim, compounding to 60 claims for each stylistic configuration present in VerMouth.
" order to fairly compare the models' performance, we carried out stratified subsampling of the emotional data, so that the size of the train and evaluation sets was equal across all the different configurations tested.

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We asked evaluate the model-generated ver- dicts by answering the following question:
and engaging manner is a very demanding task. On social media platforms, this usually done by or- dinary users, rather than professional fact-checkers. In this context, automated fact-checking can be very beneficial. Still, to fine-tune and/or evaluate NLG models, high-quality datasets are needed. To address the lack of large-scale and general-domain SMP-style resources (grounded in trustworthy fact- checking articles) we created VerMouth, novel dataset for the automatic generation of personalised explanations. The provided resource built upon debunking articles from a popular fact-checking website, whose style has been altered via a col- laborative maahnne strategy to fit realistic scenarios such as social-media interactions and to account for emotional factors.
Consider a social media post, which re- sponse is better when countering the pos- sible misinformation within the post (the claim)? Rank the following responses from the most effective (1) to the least effective(3). Ties are allowed.
After collecting the responses, we run brief in- terview to understand the main elements that drove the annotators' decisions. These interviews high- lighted some crucial aspects: (i) verdicts compris- ing too much data and information induced a nega- tive perception their effectiveness (overkill back- fire effect); (ii) verbose explanations are generally not appreciated: (iii) there was a positive apprecia- tion for the empathetic component in the response, however (iv) "over-empathisingg was negatively perceived.
Limitations
Table shows how PEGFF highly preferred for in-domain cases, possibly because avoids (i) excessively long verdicts and (ii) the stylis- tic/empathetic discrepancy between a journalistic claim from FF and other systems' output with a more SMP-like style. Still, PEGFF performs the worst in cross-domain settings. Conversely, PEG and PEG are somewhat more stable (consistently with the automatic evaluation). In general, style and emotions in the verdict have greater impact if the starting claim has style and emotions. Users reported that empathy mitigates the length effect. From a manual analysis, PEGsmp shows the ability to provide slightly empathetic re- sponses, so it sometimes ended up being preferred for its empathetic (but not overly so) responses.
There are some known limitations of the work pre- sented in this paper. First, the resource is limited to only English language only; nonetheless, the author-reviewer approach we adopted for data col- lection is language-agnostic and can be transferred as-is to other languages, assuming the availability of (i) a seed set of karticle,claim, verdict> triples for (or translated in) the desired target language, and (ii) an instruction based LLM for the desired language. Furthermore, this dataset limited in the sense that it only covers a particular style of language most commonly seen on specific Social Media Platforms short and informal posts (such as those typically found on Twitter and Facebook), rather than longer or more formal posts (which may be more typical sites such as Reddit on internet forums). We leave efforts tackle such limitations to future iterations of this work.
To sum up: for social claims, which resemble those found online, social verdicts are widely pre- ferred to FullFact journalistic claims.
Ethics Statement
FF
SMP
EM
PEGFF
1.55
2.08
2.00
The debate on the promise and perils of Artificial Intelligence, in light of the advancements enabled by LLMdbased technologies, is ongoing and ex- tremely polarising. A common concern across the community is the potential undermining of democratic processes when such technologics are coupled with social media and used with mali- cious/destabilising intent. With this work, we pro- vide a resource aiming at countering such nefari- ous dynamics while integrating the capabilities of LLMs for social good.
PEG smp
1.93
1.97
1.75
PEGemo emo
1.90
1.92
1.80
Table 6: Average rankings obtained via human evalua- tion. The ranks range from (most effective) to (least effective). The best results are highlighted in blue.
6 Conclusion
Producing a verdict, i.c. a factual explanation for a claim's veracity and doing so in a constructive

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Acknowledgements
Tom Brown, Benjamin Mann, Nick Ryder, Mclanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Ma- teusz Litwin, Scott Gray. Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei, 2020, Language models are few-shot learners. In Ad- vances in Neural Information Processing Systems, volume 33, pages 1877-1901. Curran Associates, Inc.
This work was partly supported by the AI4TRUST project- -1-based-tecchologii for trustworthy so- lutions against disinformation (ID: 101070190).
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A Data Augmentation Details
3. Originality: Do the generated claims and ver- diets resemble too much the original? Do they contain the original claim or verdict verbatim?
A.1 Prompt and Model Selection
The first step when comes to effectively lever- aging LLMs for one's 's specific use case is prompt engineering. In our case, carefully crafted prompt allows LLMs to produce quality claims and ver- dicts systematically, minimising the amount of post- editing required. Since ChatGPT's API was not yet available the public when we began our exper- iments, the initial prompt testing was performed using GPT3.
4. Coherence: Do the generated claims and ver- dicts make sense? Are they coherent? Are they saying what the original claims and ver- dicts are, or do they instead say something unrelated?
5. Amount of Post-Editing! On average, how much post-editing is required for each of the generated claims and verdicts? What propor- tion of these claims and verdicts requires any post-editing at all?
Initial tests focused on finding the optimal prompt and parameters for our specific use case. The parameters tested were the temperature T and the cumulative probability p for nucleus pling (Top-P).
Eventually, we opted for the following parame- ters: a temperature of and a Top-P of 0.9. These parameters were used with OpenAl's Davinci model during initial tests. No changes were made when we switched to ChatGPT after their public API was released.
The T byperparameter ranges between and and controls the amount of randomness used when sampling: value of O corresponds to a de- terministic output (i.e. picking exclusively the top- probability token from the vocabulary); conversely, value of provides maximum output diversity. Finding balance between not being overly de- terministic while remaining coherent was the goal when testing different temperature values 0.7 and were used when performing these initial tests.
A.2 Post-Editing
The post-editing of the data and the prompt eval- uation were carried out by two annotators either native English speakers or fluent in English. The time needed for post-editing dent the type of data (claim versus verdict) and on the configuration (SMP-style versus emotional style). On average, the annotators were able to process 250 SMP-style claims and 200 emotional claims per hour 12 For the emotional data, the time required to post-edit varied greatly depending on the emotion. Since verdicts are usually longer than claims and much more constrained, the post- editing process was much longer: average, 150 SMP-style verdicts and 70 emotional verdicts were able to be post-edited per hour.
heavily depen-
The Top-P parameter also ranges between 0 and and determines how much of the probability distri- bution of words is considered during generation. It necessary find value which was overly deterministic and that avoid using very rare words that may reduce coherence. During these initial tests, we tried using the default value of 0.5 as well as Top-P value of 1, which includes all words in the probability distribution. We also tested using a Top-P of 0.9.
Determining the "optimal" prompt and param- eters can be challenging because what makes a "good" personalised claim or verdict is subjective. Several factors were taken into account when se- lecting our prompts and parameters:
Not every claim and verdict required post- editing. Only -19% of the generated SMP-style claims and -68% of the emotional style claims were post-edited. Keep in mind that some of the generated emotional claims were discarded if spe- cific emotion and the content of the claim were mis- matched, as this resulted in forced and unnatural combinations, Figure 2 displays the distribution of post-edited, pon-podt-ddited, and discarded claims. Fear and surprise were the emotions with the least
1. Generalisability:: Do they perform well on a variety of claims, or do they only work well on specific ones (ie: it produces quality output for Covid-related claims, but struggles with claims about Brexit)?
2. Variability: Do the generated claims and ver- dicts vary between one another, or do they all follow similar patterns?
12The annotators were timed on several occasions during post-editing (ulways sessions of 20 minutes to make fair com- parisons); the results reported in the paper are the average of the claim/hour amount measured in each session.

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amount of discarded data, but they also required the most post-editing.
more effective in terms of time than writing new data from scratch. To this end, we provided one of the annotators with 60 claims and asked to write from scratch new tweets, 30 in SMP-style and 30 emotional tweets. In both cases, took the annota- tor (expert in the field) around 23 minutes to create 30 new tweets (thus, roughly 80 claims per hour as compared to the 250 SMP-style and 200 emo- tional tweets obtained with our pipeline). If in the creation of claims the time differences are consid- erable, we assume that this also applies to verdicts, which a task that requires more constraints.
As mentioned before, post-editing verdicts were a much longer process. Since verdicts are subject to stricter standards of quality (because they must be truthful and polite, for example) and are much longer average, many more of them required post-editing. Figure 3 shows this disparity: for some emotions such disgust and fear, there were fewer than 10 verdicts which did not require at least minimal post-editing. SMP-style verdicts also required post-editing at a much higher rate than StP-style claims, although less than emotional style verdicts. In total, there were 1838 SMP-style verdicts and 2609 emotional style verdicts, result- ing in post-editing rates of 91.4% and 96.3% re- spectively.
B Detailed Guidelines
B.1 Claim Guidelines
1. Generated claims copying original claims ver- batim:
Sometimes the wording of the original claim is copied verbatim in the generated claim. These should be rewritten to avoid resembling the original dataset. Determining whether gen- erated claim resembles the original claim "too much" can be subjective, so discretion must be used.
1998
1154
945
1067
1014
582
021
2. Reoccurring Patterns: Since the SMP-style claims have a lot more freedom to decide what sort of tone to adopt, they are much more di- verse. With emotional style claims, the emo- tional component an extra constraint which is applied during generation. This means that there are often reoccurring patterns which ap- pear in the resulting generated claims: "I'm livid!", "'So sad to hear that X". "Disgusting!", etc. If pattern can be removed while preserv- ing the overall emotional intent, then it is better to remove it entirely. Conversely, if removing pattern also removes any "emotion" " from the generated claim, then rewriting is preferred. In rare cases, the pattern can be kept, keeping in mind that too many occurrences may result in degraded performance during training.
239
182
33
8710-1hy
Figure 2: Graph representing the number of CharGPT generated claims that were deleted, post-edited, or not post-edited
soot-stited
non sott
2003
1680
1532
1032
158
3. Hashtags: Due the prompt used, hashtags often appear in the generated claims. We noted two different phenomena which may occur and which require post-editing:
21
8
511--00 5bi0
happinoss
anger
foar
siogust
Sod1e55
surrise
Figure 3: Graph representing the number of ChatGPT generated verdicts that were post-edited versus those which were not post-edited
(a) Debunking hashtags: There some currences where hashtag debunks a claim works against the claim's intent. If a claim is about how vaccines are not effec- tive, having the hashtag "#VaccinesSave- Lives" is not appropriate. These hashtags
A.3 Timing benefits of post-editing
We carried out an extra experiment to assess whether post-editing machine-gemerated data is

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can either be removed or edited to match the original intent.
stand that you feel X", "Let's continue to follow the recommended guidelines", etc.
(b) Unnecessary hashtags: There are some hashtags which are so vague that they di- minish the overall quality of the claim
2. Calls to action: As stated before, "call to ac- tion" is phrase or sentence which, as the name implies, calls upon the reader of the verdict take action in some way. For example:
(such "#miracle", "#goodjob", etc.). In emotional claims specifically, the emotion given in the prompt is turned into a hash- tag (such as "#happy" or "#sad"). Any hashtags which fit these criteria are to be removed.
"I understand your frustrationn and while the pro- portion of BME students at Oxbridge has ac- tually increased, agree that more needs be done to address the lack of diversity from disad- vantaged areas. It's important that we continue examining the root causes of this inequality and work towards equal opportunities for all. #diversitymatter #educationforall"
4. Generated claims debunking original claims: There are some instances where the generated claim actually debunks the original claim. These must be changed to reflect the intent behind the original claim. For example: if the original claim says that "wearing masks causes demen- tia and hypoxia" and the generated claim says " False info alert: wearing mask doesn't cause dementia and hypoxia", it should be rewritten match the original claim.
To avoid overtly political or polarising verdicts, many considerations need be kept in mind when call action in a generated verdict is encountered.
(a) Is the call to action well-integrated into the verdict? If call to action does not make meaningful contribution to the over- all quality of the verdict, then it will be re- moved. An example of poorly-integrated call to action is "let's 's focus on promoting peaceful and respectful discourse." This call to action is broad and vague and should be removed.
5. Dates and places:
The original claims con- tained many references to dates and places. These could be vague references ("last year", "in our nation". etc.) or specific references ("21 July 2021", "in England", etc.). Any dates and places in the generated claims should match the original claim's 's level of specificity.
Is the call action political or polaris- ing? One must determine whether or not the call to action is actually political or po- larising. With certain topics, is simply impossible to avoid having a call to action which contains political elements (such claim about politician, decided upon two possible approaches one can take when post-editing political calls to action.
6. Hallucinations: Since claims do necessarily need be true, hallucinations can often be ben- eficial. Consider an example where the original claim says "almost 300 people have died from the vaccine", but the generated claim contains a hallucination which states that "291 people have died from the vaccine" this number is specific, and this may give off the impres- sion that the person knows what they talking about.
law). We
The common sense approach (or the "rea- sonable person" approach) is employed for call to action expressing a political opinion which the most agree (such demandinn transparency and accountabil- ity from our government"). This call to action can be kept.
7. General formatting issues: While rare, there are cases in which grammatical errors, typos, malformed hashtags (such as "#endrape cul- ture") or other formatting issues occur in gener- ated claims. These should simply be corrected.
B.2 Verdict Guidelines
The empathetic approach is employed when dealing with opinions thoughts that simply have no "correct" answers, but rely on one's own beliefs. was decided that the call to action should be changed to empathise with the claim writer's beliefs
1. Reoccurring Patterns: As with generated claims, there are often patterns which occur of- ten generated verdicts. These should be re- moved or rewritten. Some examples of common patterns include "thank you for X", "I under-

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without agreeing or disagreeing with them. For example, if a claim expresses pro-life opinions, and a call to action such as "it's important to acknowledge the magnitude of lives affected by this issue" exists, chang- ing it to "it's important to acknowledge the magnitude of lives affected by this issue no matter what you believe" " is empathetic, but also explicitly avoids picking one side or the other in a polarising situation like this.
sistencies regarding "who" is writing the ver- dict. The assumption one should take when post-editing is that each verdict written by a single person. Therefore, if first-person plu- ral pronouns are encountered, they should be changed to first-person singular pronouns.
One exception exists: when the reader and writer of the verdict are grouped together, then first prsst-person plural pronouns can be kept: "surely we can all agree that this is a serious issue",
4. Confirmations: Sometimes a claim is fully or partially correct, and the original FullFact ver- dict notes this with a simple "correct", or "this is right, but..", ", but the generated verdict does not.
(c) How strong is a call to action, and who is the focus? Different actions require different amounts of effort. If a call ac- tion asks for too much from someone, then it should be changed. Asking someone, for example, to "advocate for stricter test- ing protocols" may be asking too much of them, but asking them to "hope that stricter testing protocols are implemented" is not. If a call to action is too strong, then it can either be weakened or neutralised. Weak- ening a call to action involves changing what is expected of the reader of the ver- dict: rather than "we must personally take action to end child poverty immediately", one can post-edit the call to action to say "let's try and do our part together to hope- fully end child poverty one day".
In this case, adding quick confirmation such as "yes, you're re right, but.." " or "absolutely, it's a serious issue" " can be done as long as it does not reduce the overall readability of the verdict. 5. Missing information: Sometimes the gener- ated verdicts do not include information from the original verdicts. This can make the gen- erated verdict easier to read without reducing its persuasiveness. If missing information nega- tively impacts how effective verdict is, then it should be added.
6. New claims:
Conversely, there are cases in which the generated verdicts actually include information which is not contained in the origi- nal verdict, but which is either objectively truc or is a subjective statement. If the claims made in the generated verdict are provably false, then removing them necessary. If they are prov- ably true, or if they are a subjective statement or opinion which can not be concretely proven true or false, they can be kept or removed at the post-editors discretion.
Neutralising a call to action takes the focus off the reader entirely. This involves either putting the onus on someone else who may be more capable of solving the issue or not demanding action from anyone all. Thus, "we must continue to keep an eye out for potential side effects of the vaccines" can be rewritten to "the experts must continue to keep an eye out for potential side effects of the vaccines".
An example of not demanding action from anyone at all is: "we must continue to ad- vocate for those struggling to make ends meet". It can be post-edited as "compas- sion and understanding for those strug- gling to make ends meet is crucial".
7. General formatting issues: As with generated claims, general formatting issucs such as gram- matical errors, typos, malformed hashtags, etc. should be corrected.
Experimental Details
C.1 Extractive Summarization Methods
3. Pronouns and Grammatical Personhood: As the original verdicts sometimes contain first- person plural pronouns ("we have contacted them for more clarification"". and at other times contained first-person singular pronouns ("I un- derstand your frustration "), there are incon-
Besides SBERT, we also considered other extrac- tive summarization methodologies, i.e. Lead-k which extract the first k sentences from the article, and LexRank (Erkan and Radev, 2004), a graph- based unsupervised methodology which ranks the

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R1
RL
METEOR
BARTScore
BERTScore
Lead-2
0.21
0.06
0.15
0.26
-3.23
0.86
Lexrank
0.18
0.06
0.12
0.31
-2.89
0.86
SBERT
0.25
0.09
0.18
0.33
-2.90
0.87
Table 7:
Results for the extractive summarization methodologies on FullFact dataset.
Gold Verdict
The network has been attacked, but the idea that it was Islamic State appears to be based on speculation from single, unnamed student.
claim
Islamic State has hacked major computer network used universities.
FullFact
model
This is not the case. The network provider, Jise, has told us that "the identity of the perpetrators remains subject to an ongoing criminal investigation"
claim
Just Islamic State breached large computer network utilized by universities! Super seary & unnerving hopefully authorities can help resolve the issue ASAP. #ishacked
SMP-style
model
It's definitely alarming to hear about the breach of university' 's computer network. How- ever, it's important to note that there is currently evidence linking Islamic State to the attack. Hopefully authorities resolve the issue quickly.
claim
As a student. am infuriated by the news that ISIS has hacked into major university computer network. This is a violation of our education and threut to safety. #stopISIS
Emotional
model
It's important to note that the news about ISIS hacking into major university computer network has not been confirmed by any official source. Nonetheless, it's understandable be concerned about the potential impact this may have on our education and safety, #stopISIS
Table 8: Examples of generated verdicts for each fine-tuning configuration tested in-domain.
sentences of document based on their importance by means of eigenvector centrality. We tested these approaches by extracting two sentence-long sum- maries from Fullfact articles. Subsequently, these summaries were evaluated against the gold verdicts. The results of this evaluation shown in Table 7. SBERT outperforms both Lead-2 and LexRank for all the metrics employed.
C.3 Decoding Configuration
At inference time, we employed nucleus sampling decoding strategy, setting the probability at 0.9, and repetition penalty, set at 2.0, for the verdict generation.
D Examples of Generated Verdiets
In Table 8 we report examples of verdicts gener- ated with PEGASUS model (Zhang et al., 2020) fine-tuned on the three different stylistic versions of our dataset, i.e. FullFact, SMP-style and emo- tional style. In particular, we report the generations obtained in the in-domain experiments.
C.2 Fine-Tuning Configuration
When fine-tuning, PEGbasc was trained for 5 epochs with batch size of and random seed set 2022. To this end, we employed the Hugging- face Trainer 13 using the default hyperparameter settings, with the exception of the Learning Rate values and the optimisation method. Instead, we used the Adafactor stochastic optimisation method (Shazeer and Stern, 2018) and a Learning Rate value of 3c-05. The training was performed on single Tesla V100 GPU, while the testing was performed single Quadro RTX A5000 GPU. The checkpoint with minimum evaluation loss was employed for testing.


Reviews:
[{"review_id": "7ZVlbg4wQE", "reviewer": "Reviewer_Emnp", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 3, "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": "Dear Reviewer,\n\nWe deeply thank you for the feedback you provided. \n\nWe understand that the information regarding the annotators could have been enriched and we apologise for the lack of details. Specifically, the data generated with the LLMs were post-edited by two annotators: one last-year master's student and a Ph.D. student. Both of them are included as authors of the present work (in addition, the master's student got their university credit and the PhD has a salary). Both annotators were extensively trained on the data and the topic of misinformation and automated fact-checking, as well as they had good experience with the communication style employed on the main social media platforms. In particular, before starting to work on the data they were properly trained for the task, by (i) providing them with relevant literature, (ii) walking them through the complete guidelines, (iii) providing and discussing with them several claims and verdicts examples, and (iv) answering possible doubts. Moreover, weekly meetings were organized throughout the whole annotation campaign to discuss problems and doubts about post-editing that might have arisen. We will include these details in the additional page provided for the final version of the paper upon acceptance. \n\nRegarding the human evaluators, we believe that a study representative of how larger social groups would perceive the responses to misinformation is a relevant topic but out of the scope of the present work. In our evaluation we wanted to check how the models' generations were perceived at first glance, trying to assess the quality of the generations both in in-domain and cross-domain settings. Further ad hoc studies employing a larger number of evaluators are indeed needed.\n\nFinally, as already mentioned in the paper, upon acceptance we will make publicly available the resource as well as the accompanying code for reproducibility purposes.", "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": "-\tExplanations for each step of dataset generation provided including assumptions, reasons, examples and references.\n-\tClear definitions for terms\n-\tWell-formatted paper\n-\tInteresting novel approach to counter misinformation\n", "reasons_to_reject": "-\tAnnotator selection criteria and salary not mentioned.\n-\tThough 3 human panel for evaluations is common in the computer science field, this use case doesn’t seem to be representative of how larger social groups would perceive the responses to misinformation. \n", "questions_to_authors": "", "paper_topic_and_main_contributions": "The paper proposes a new dataset consisting of claims-responses pairs to counter misinformation. The authors have leveraged the author-reviewer pipeline to create the dataset and performed both automatic and human evaluations.", "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": {}, "extra_text": {}}, {"review_id": "Z5CanTLhs2", "reviewer": "Reviewer_QBDx", "summary": "", "questions": "* Q1. Why emotional styles yielded consitently longer claims and verdicts compared to SMP style?\n* Q2. HTER is a measure for an individual pair. So in Table 4, is it averaged across the samples? According to the appendix, only ~19% samples required post-editing for SMP style, so is the average taken over the samples that required post-editing or over all the samples? \n* Q3. Why is the RR > 1? Acc. to the Bertoldi 2013 and Cettolo 2014 papers, RR is normalized to be between 0 and 1.\n* Q4. How many humans were involved in the reviewer stage? Were the same humans involved in session 1 and 2? I don't think there was much, if any discussion, about the human annotators in the paper about who the humans were, how they were chosen, what was their background, how they were trained, etc. Such questions are important to be addressed because past research (e.g., Sap et. al. 2019) have found that labels can be affected by annotator bias.", "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": "Dear reviewer,\n\nWe thank you for your valuable feedback. We will make sure to integrate the comments and suggestions you provided into the additional page provided for the final version of the paper upon acceptance. Hereafter, we will try to answer the questions and doubts you raised in your review.\n\n>*Q1. Why emotional styles yielded consistently longer claims and verdicts compared to SMP style?*\n\nProvided that all ChatGPT parameters were kept constant across generations and claim configurations, one reasonable explanation, derived from checking the outputs, is that the prompt used for emotional data was much more detailed - and contained more requirements - than the one used for SMP-style data (see Table 2). Thus, we can hypothesize that, for our task, ChatGPT tries to satisfy the multiple requirements (“SMP+emotional+personal style”) contained in the emotional prompt instruction by producing a longer output.\n\n>*Q2. HTER is a measure for an individual pair. So in Table 4, is it averaged across the samples? According to the appendix, only ~19% samples required post-editing for SMP style, so is the average taken over the samples that required post-editing or over all the samples?*\n \nHTER values were averaged over the entire samples under analysis (including the unmodified ones). We will specify this in the final version of the paper in case of acceptance and provide numbers also for modified ones alone. \n\n>*Q3. Why is the RR > 1? Acc. to the Bertoldi 2013 and Cettolo 2014 papers, RR is normalized to be between 0 and 1.*\n\nThe reviewer is right but, in order to be in line with previous works, e.g.  Tekiroglu et al. 2020 (Table 1)  and Cettolo 2014 (Table 3), our RR values range between 0 and 100. We will make this clear in the final version of the paper.\n\n>*Q4. How many humans were involved in the reviewer stage? Were the same humans involved in sessions 1 and 2? I don't think there was much, if any discussion, about the human annotators in the paper about who the humans were, how they were chosen, what was their background, how they were trained, etc. Such questions are important to address because past research (e.g., Sap et al. 2019) have found that labels can be affected by annotator bias.*\n\nWe apologise for the lack of details: annotators for sessions 1 and 2 were the same while the evaluation was carried out by different people to avoid any possible bias. \n\nWe understand that the information regarding the annotators could have been enriched and we apologise for the lack of details. Specifically, the data generated with the LLMs were post-edited by two annotators: one last-year master's student and a Ph.D. student. Both of them are included as authors of the present work (in addition, the master's student got their university credit and the Ph.D. has a salary). Both annotators were extensively trained on the data and the topic of misinformation and automated fact-checking, as well as they had good experience with the communication style employed on the main social media platforms. In particular, before starting to work on the data they were properly trained for the task, by (i) providing relevant literature, (ii) walking them through the complete guidelines, (iii) providing and discussing several claim and verdict examples, and (iv) answering possible doubts. Moreover, weekly meetings were organized throughout the whole annotation campaign to discuss possible problems and doubts about specific post-editing instances. We will include these details in the additional page provided for the final version of the paper upon acceptance.", "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 resource will be released publicly.\n* The resource could be valuable for researchers in the community to build models that differentiate between claims and counterclaims.\n* The generation of counterclaims with different styles and emotional registers can be used to study causal effects of presenting a counterclaim in a certain way.", "reasons_to_reject": "* Details about the human annotators background is largely missing (see Q4)\n* There are some inconsistencies in the calculation of certain metrics that should be clarified (see Q2 and Q3) ", "questions_to_authors": "* Q1. Why emotional styles yielded consitently longer claims and verdicts compared to SMP style?\n* Q2. HTER is a measure for an individual pair. So in Table 4, is it averaged across the samples? According to the appendix, only ~19% samples required post-editing for SMP style, so is the average taken over the samples that required post-editing or over all the samples? \n* Q3. Why is the RR > 1? Acc. to the Bertoldi 2013 and Cettolo 2014 papers, RR is normalized to be between 0 and 1.\n* Q4. How many humans were involved in the reviewer stage? Were the same humans involved in session 1 and 2? I don't think there was much, if any discussion, about the human annotators in the paper about who the humans were, how they were chosen, what was their background, how they were trained, etc. Such questions are important to be addressed because past research (e.g., Sap et. al. 2019) have found that labels can be affected by annotator bias.", "paper_topic_and_main_contributions": "The paper develops a dataset of 12K factually incorrect claims. Each claim is also accompanied with a counter response that is designed to be polite and empathetic. The novelty of this dataset is that the claim-response pairs are generated for the informal style of writing on social media platforms and express a specific emotional register. These pairs are generated by an LLM but are modified by humans in a post-generation step. The paper also presents experiments that show better performance in some tasks for models that are trained on this new dataset.", "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": {}, "extra_text": {}}, {"review_id": "ZYVygJBjRr", "reviewer": "Reviewer_Ysmd", "summary": "", "questions": "Are those 180 samples the same for all 3 human annotators? Is there a plan to release those samples when you will be releasing the dataset?", "limitations": "", "rating": 3, "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": "Dear reviewer,\n\nWe thank you for your insightful feedback. We will make sure to integrate your comments and suggestions into the additional page provided for the final version of the paper upon acceptance. Moreover, as already mentioned in the paper, upon acceptance we will make publicly available the resource as well as the accompanying code for reproducibility purposes. Hereafter, we will try to answer the questions and doubts you raised in your review.\n\nThe decision to move from GPT3 to ChatGPT was taken after some exploratory experiments meant to assess the quality of the generated outputs given the same prompt. To this end, starting from a subset of 50 FullFact claims, SMP-style and emotional claims were generated with both GPT3 and ChatGPT. One of the annotators was required to evaluate (on a scale from 0 to 3) the generated data according to two criteria: post-editing effort and quality of the generation. We opted for the model that showed less post-editing effort and higher quality, namely ChatGPT. In terms of quality, we noticed that ChatGPT: \n- **expands the claims in a more interesting way** (example below)\n\n    **Original claim [emotion: anger]**: \"On duty police officers participated in the London Pride parade.\"\n    \n    **GPT3**: \"So excited to see police officers participating in #LondonPride parade! This brings me hope that we can all learn to live together peacefully, despite our differences. #PoliceUnity #PrideAndUnity\"\n    \n    **ChatGPT**: \"I can't believe it! Just found out some on-duty cops took part in #LondonPride parade! As a member of the LGBTQ+ community, I feel so betrayed and disrespected. It's not just about them wearing rainbows, it's about the constant abuse and discrimination we face from the police force. #Unacceptable #HomophobiaInUniform\"\n    \n    *The original claim does not mention the police and homophobia, but the ChatGPT claim expands on the original by including this information.*\n\n- **Uses the emotions from the prompt in more interesting ways** (example below)\n    \n    **Original claim [emotion: sadness]**: \"A study on vitamin D and Covid-19 shows an 80% reduction in need for ICU and a 60% reduction in deaths. The study demonstrates that the clear relationship between vitamin D and Covid mortality is causal.\"\n\n    **GPT3**: \"Feeling so down about Covid-19. New study found an 80% reduction in the need for ICU & 60% reduction in deaths when Vitamin D is supplemented. Evidently, the link between Vitamin D & Covid-19 mortality is real. Time to up my Vitamin D intake! #StayHealthy #VitaminD #CovidMortality\"\n\n    **ChatGPT**: \"Just read this study on Vitamin D and Covid-19 and it's making me so sad! The study shows that if people have enough Vitamin D, their chances of dying from Covid goes down by 60%. And if they do end up in the ICU, they have an 80% chance of getting better. This means that so many lives could have been saved if we all just took our vitamins! It's heartbreaking to think about all the people who could have been saved if we just knew this sooner. #vitamind #covid19 #heartbroken\"\n\n    *The GPT3 claim just says that they feel sad about Covid-19, but then the emotion is dropped entirely. The ChatGPT claim expands upon the emotion throughout the generated claim – ‘if we knew this sooner, more people could have been saved!’*\n\n- **Has more interesting phrasing and personal connections in general** (example below)\n\n    **Original claim [emotion: fear]**: \"The mRNA Covid-19 vaccines are experimental.\"\n\n    **GPT3**: \"I'm really scared by the #mRNA #Covid19 #vaccines bc they're still experimental. I don't know if it's safe for me to take one & the uncertainty is really overwhelming. #experimentalvaccine\"\n\n    **ChatGPT**: \"Just heard about the mRNA #Covid19 vaccines being experimental and I'm freaking out. I don't want to be a guinea pig for some new technology! This is my health we're talking about. How am I supposed to trust it? #scared #vaccinehesitancy\"\n\n    *GPT3’s generated claim is very generic compared to ChatGPT – ‘I don’t know if it’s safe’ versus ‘I don’t want to be a guinea pig!’*\n\nOn top of these results, the prices of ChatGPT’s API are much lower than those of GPT3.\n\nAs regards the random samples of generated data, the evaluators were provided with different data. In particular, we created subsamples of data from the verdicts generated by our models (in the various configurations) while inferencing from the test set. The test set comprises data from FullFact (journalistic style) and from VerMouth (SMP-style and emotional-style data).", "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 paper has solid technical detail as well on the various aspects of augmentation. For example, the authors have pointed out various cases in sections 3.4 and 3.5. I liked the details presented in the paper.\n\nThe paper also proves that the dataset is not just another dataset but an important dataset by showing results with the dataset with various algorithms. \n\nThe paper builds up gradually on technical details which makes it easy to understand and technical details aren't missed much. \n\nOn top of the results with various algorithms, the authors have presented human evaluation as well.", "reasons_to_reject": "The paper has some details that should be in the main body instead of the appendix. \n\nFor example, the authors have mentioned that \"Initial tests focused on finding the optimal prompt and parameters for our specific use case\". This was done with GPT3 and wasn't changed while using ChatGPT. Any interesting findings that you have documented? \n\nThe random 180 samples were chosen for human evaluation using 3 evaluators. Are those 180 samples the same for all 3 annotators? Is there a plan to release those samples when you will be releasing the dataset?", "questions_to_authors": "Are those 180 samples the same for all 3 human annotators? Is there a plan to release those samples when you will be releasing the dataset?", "paper_topic_and_main_contributions": "The paper's main contribution is the claim verification dataset generated using data from the Full-Fact website (FF website). The authors have extensively used LLMs for the purpose of the generation of synthetic samples which are moreover emotion guided. The paper has done analysis with different algorithms to check if the emotion-guided dataset helped in getting clearcut summaries. The results were evaluated with various algorithms with various metrics used in the related works.", "excitement": 3, "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": {}, "extra_text": {}}, {"review_id": "AUWv7usWeN", "reviewer": "Reviewer_LBT1", "summary": "", "questions": "Question A: How exactly was the data “obtained from the FULLFACT-Website”? Particularly, from the description in the paper, there is no way of discerning whether the collected data represents all the fact-checks available from the source at the time of data collection or whether some selection criteria were explicitly or implicitly used. Connected, an overview of the number of collected instances per topic would be interesting, especially given that different topics are already mentioned in the paper. This could be an important piece of information for potential re-users of the dataset.\n\nQuestion B: Who were the annotators that post-edited the claims and verdicts, i.e., how were they recruited and selected? How were they remunerated? Were the same annotators responsible for the quality evaluation of different LLM prompt setups, for the post-editing of claims and verdicts, and the human evaluation study, or were these different sets of annotators? \n\nQuestionC: How much did ChatGPT hallucinate for the claims and verdicts? As the paper states, hallucinations are not very problematic for claims (unless it completely changes the topic), but it is for verdicts. How prevalent were these? And how can we conclude that this pipeline reduces the annotation burden instead of increasing it without a comparative experiment?\n", "limitations": "", "rating": 3, "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": "Dear reviewer,\n\nWe really appreciate your detailed review of our paper. While preparing the final version of this paper, if it is accepted, we will improve it by integrating your suggestions and fixing the highlighted typos. Moreover, as already mentioned in the paper, we will make publicly available the resource as well as the accompanying code for reproducibility purposes. Hereafter, we answer the questions raised in your review hoping to clarify your doubts and concerns as comprehensively as possible.\n\nWe agree that hallucinations in the verdict are a key issue, for this reason, manual post-editing is of paramount importance. Moreover, it is important to highlight that we prompt the LLM to rewrite a gold verdict and not to write a debunking from scratch. For this reason, the main task of the annotators was to check whether there were discrepancies between the gold and the generated verdicts, and, in case, to correct them. We took for granted that the gold verdicts are trustworthy (as they were manually written by professional fact-checkers), thus we are sure that a new verdict that differs only in style but not in content is trustworthy too. This also explains why the annotators managed to process a high number of data per hour (they did not need to read the whole debunking article to check if assertions in the generated verdict were supported, but only to read the -much shorter- gold verdict).\n \nWe did not keep track of the number of hallucinations in the data. Hallucinations were present in claims, but it was not a very common occurrence. As for the verdicts, there were even rarer occasions where it would come up with its own justifications for a specific fact, but it was mostly grounded in the original verdict  (in fact for verdicts we asked to “rephrase” rather than “write”, see Table 2). \n\nThere are two main reasons to state that our data augmentation pipeline reduces the annotator's effort: \n\n\n- The HTER values reported in the paper are always much lower than 0.4, meaning that it is easier to post-edit rather than write a tweet from scratch (Turchi et al., 2013). \n\n- We carried out an extra experiment (not reported in the paper, but it will be included in the final version upon acceptance) which proved that human effort is substantially reduced also in terms of time. We provided one of the annotators with 60 claims and asked them to write from scratch new tweets, 30 in SMP-style and 30 emotional tweets. In both cases, it took the annotator (expert in the field) around 23 minutes to create 30 new tweets (thus, roughly 80 claims per hour as compared to the 250 SMP-style and 200 emotional tweets obtained with our pipeline). If in the creation of claims the time differences are considerable, we assume that this also applies to verdicts, a task that requires more constraints.\n\nWe believe that this technique could be applicable also to other social media communication styles (such as Facebook and Reddit) after a proper study of the most effective prompt for the scenario under analysis.\n\nAs regards the FullFact data, we scraped all the fact-checking instances present on the website within the date range indicated in the paper. Further details about the dataset (including the topic distribution) will be provided in the final version of the paper, and we thank the reviewer for pointing this out.\n\nWe understand that the information regarding the annotators could have been enriched and we apologise for the lack of details. Specifically, the data generated with the LLMs were post-edited by two annotators: one last-year master's student and a Ph.D. student. Both of them are included as authors of the present work (in addition, the master's student got their university credit and the PhD has a salary). Both annotators were extensively trained on the data and the topic of misinformation and automated fact-checking, as well as they had good experience with the communication style employed on the main social media platforms. In particular, before starting to work on the data they were properly trained for the task, by (i) providing relevant literature, (ii) walking them through the complete guidelines, (iii) providing and discussing several claim and verdict examples, and (iv) answering possible doubts. Moreover, weekly meetings were organized throughout the whole annotation campaign to discuss possible problems and doubts about specific post-editing instances. We will include these details in the additional page provided for the final version of the paper upon acceptance.\t\n\nFinally, we really appreciated the suggestion of adding a reference to papers related to community-oriented fact-checking with Birdwatch. We will make sure to properly include them in the final version of our paper.", "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 authors make a very convincing claim that their proposed style transfer from journalistic fact-checks to social media-like claims and verdicts helps develop and evaluate automated methods to support social correction efforts. Their own Human Evaluation study shows that automated methods fine-tuned on their contributed dataset generate verdicts that are preferred over verdicts generated from models fine-tuned on journalistic claims and verdicts, with additional interviews with their study’s participants surfacing that the empathetic component in their model’s generation was positively appreciated. The strong points of the paper are:\n- The authors create a human-AI collaborative pipeline for data augmentation that, in principle, reduces manual annotation effort\n- The authors also conduct convincing quantitative analyses of the generated datasets.\n", "reasons_to_reject": "My main concern with the paper and the contributed dataset is only superficially addressed by the authors - the issue of LLM hallucinations. \n\nWhile the authors convincingly reason that hallucinations in the generated claims are not problematic and in some cases even improving the claim’s quality, this does not hold for the verdicts. While the authors also concede this - albeit only in the discussion of generated verdicts and the Verdict Guidelines included in the appendix - it is unclear how exactly they addressed this issue to ensure that no hallucinated false claims ended up in the verdicts of their datasets. Given the potentially devastating effects of sharing a dataset of claim-verdict pairs as a resource to develop and evaluate methods for the automated generation of factually correct verdicts to counter misinformation through social correction, I would expect the authors to provide more info on the prevalence of the problem (how many generated verdicts contained claims that were provably false and had thus to be removed, as laid out to the annotators in the Verdict Guidelines) and on the measures they took to ensure that their contributed dataset is free of hallucinated false claims, as the task of detecting potentially false claims, fact-checking them, and deleting them if false, seems to be rather difficult for single human annotators that post-edited at a speed of 150 (for SMP; 70 for emotional) verdicts per hour.\n\nNext, and related to the first point, is the hybrid data augmentation strategy, The paper claims that using ChatGPT to reword the claims and verdicts minimizes the annotators’ effort and then provides detailed guidelines that the annotators can check for post-editing in Appendix B. Tha main question is, does this really reduce annotators’ efforts? Generally, section 3.2 is missing details of the procedure, whether it saved the annotators time as well as who the annotators were, etc\n\nFinally, and to some extent, a minor point, but social media style is quite broad. The prompts and the resultant style indicate that the paper wants to investigate Twitter-style claims and verdicts. I would question to what extent this technique is applicable to other social media platforms like Facebook or Reddit. \n", "questions_to_authors": "Question A: How exactly was the data “obtained from the FULLFACT-Website”? Particularly, from the description in the paper, there is no way of discerning whether the collected data represents all the fact-checks available from the source at the time of data collection or whether some selection criteria were explicitly or implicitly used. Connected, an overview of the number of collected instances per topic would be interesting, especially given that different topics are already mentioned in the paper. This could be an important piece of information for potential re-users of the dataset.\n\nQuestion B: Who were the annotators that post-edited the claims and verdicts, i.e., how were they recruited and selected? How were they remunerated? Were the same annotators responsible for the quality evaluation of different LLM prompt setups, for the post-editing of claims and verdicts, and the human evaluation study, or were these different sets of annotators? \n\nQuestionC: How much did ChatGPT hallucinate for the claims and verdicts? As the paper states, hallucinations are not very problematic for claims (unless it completely changes the topic), but it is for verdicts. How prevalent were these? And how can we conclude that this pipeline reduces the annotation burden instead of increasing it without a comparative experiment?\n", "paper_topic_and_main_contributions": "The paper contributes a dataset of claim-response pairs to be used in developing and evaluating automated methods for countering misinformation through social correction. The presented dataset builds on a collection of fact-checked claims and professional fact-checkers’ verdicts and is extended through the transfer of the verdicts from the journalistic style of professionals to the style typically encountered on social media platforms, among other aspects richer in basic emotional content. The authors present this style transfer as their main contribution, as they claim that claim-verdict pairs in a typical social media style is crucial for developing automated methods to effectively support social correction efforts, ultimately helping to counter misinformation on social media platforms.", "excitement": 3, "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": {}, "extra_text": {"Missing_References": "Connected to studies on misinformation and fact-checking on Twitter, some papers look at community-oriented fact-checking with Birdwatch (now called ‘community notes’). While these approaches do not use automated fact-checking, these datasets model more naturalistic fact-checking behavior on social media sites and can be informative about the ideal characteristics of a good verdict. Some papers in this vein are:\n\nPröllochs, Nicolas. \"Community-based fact-checking on Twitter’s Birdwatch platform.\" Proceedings of the International AAAI Conference on Web and Social Media. Vol. 16. 2022.\n\nAllen, Jennifer, Cameron Martel, and David G. Rand. \"Birds of a feather don’t fact-check each other: Partisanship and the evaluation of news in Twitter’s Birdwatch crowdsourced fact-checking program.\" Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. 2022.\n", "Typos_Grammar_Style_and_Presentation_Improvements": "I found the paper mostly well-written and easy to follow, except for the organization of the experiments. It would be good to make clear that Section 4 deals with training and testing models for the verdicts alone. Experiments can also include human evaluations and post-editing, so I found it a bit abrupt that Section 4 only talked about automated experiments. Other than that (L stands for lines in the paper):\n\nL505 introduces ROUGE-L as R-L when abbreviated. Table 5 then uses RL as the abbreviation.\n\nThe caption of Figure 1 introduces the <Article, Claim, Verdict> triplets, which are then formatted (and spelled) as <article,claim,verdict> triples in L607.\n\nL948 introduces p as the hyperparameter representing the cumulative probability for nucleus sampling, which then seems to be picked up again only as the Top-P parameter in L960, L967, L969, and L997. \n\nL1024 refers to Figure 2 as Image 2, and L1034 to Figure 3 as Image 3. \n"}}, {"review_id": "jZUI5C76nK", "reviewer": "Reviewer_NTyP", "summary": "", "questions": "A. How were the annotators recruited and how many of them were there? I'm interested in those who did post-editing and also those doing evaluation. Please provide a line number if I simply missed this information.\n\nB. Were the articles used for evaluation also included in the SMP-style and emotional datasets? Or are these unseen articles?\n\nC. Why was the \"Make it personal\" sentence included in prompts for the emotional style in claim generation?", "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": "Dear reviewer,\n\nWe thank you for your feedback. We will make sure to integrate the comments and suggestions you provided into the final version of our paper if accepted. Hereafter, we will try to answer the questions and doubts you raised in your review.\n\n> *A. How were the annotators recruited and how many of them were there? I'm interested in those who did post-editing and also those* doing evaluation. Please provide a line number if I simply missed this information.\n\nWe understand that the information regarding the annotators could have been enriched and we apologise for the lack of details. Specifically, the data generated with the LLMs were post-edited by two annotators: one last-year master's student and a Ph.D. student. Both of them are included as authors of the present work (in addition, the master's student got their university credit and the PhD has a salary). Both annotators were extensively trained on the data and the topic of misinformation and automated fact-checking, as well as they had good experience with the communication style employed on the main social media platforms. In particular, before starting to work on the data they were properly trained for the task, by (i) providing relevant literature, (ii) walking them through the complete guidelines, (iii) providing and discussing several claim and verdict examples, and (iv) answering possible doubts. Moreover, weekly meetings were organized throughout the whole annotation campaign to discuss possible problems and doubts about specific post-editing instances. We will include these details in the additional page provided for the final version of the paper upon acceptance.\n\n> *B. Were the articles used for evaluation also included in the SMP-style and emotional datasets? Or are these unseen articles?*\n\nFor the human evaluation, we created subsamples of data from the verdicts generated by our models (in the various configurations) while inferencing from the test set. The test set comprises data from FullFact (*journalistic style*) and from VerMouth (*SMP-style* and *emotional-style* data).\n\n>*C. Why was the \"Make it personal\" sentence included in prompts for the emotional style in claim generation?*\n\nWe used the \"Make it personal\" sentence in order to obtain more realistic claims, but not all generated claims reported a personal story and as the reviewer correctly pointed out \"it is just a matter of that example claim\". We will clarify this aspect in the final version of the paper if it is accepted.", "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 task is well-motivated in that effective misinformation debunking must address the norms of social media. The dataset seems useful for future work, and the evaluation (particularly the human evaluations) show the benefit of the dataset toward this task.\n", "reasons_to_reject": "* There is a lack of sufficient detail on recruitment of the annotators. (See question).\n* I understand hallucinations in claim generation are expected, but the \"as someone who has lost a loved one\" sentence in the example claim makes me wonder about the impact of the \"make it personal\" prompt to author LLM for the emotional claims. Not everything on social media is personal even if emotional (people often just give impassioned statements of opinion) and could potentially lead to the model over-generating personal stories in claims. Or perhaps this is just a matter of that example claim. (See question.)", "questions_to_authors": "A. How were the annotators recruited and how many of them were there? I'm interested in those who did post-editing and also those doing evaluation. Please provide a line number if I simply missed this information.\n\nB. Were the articles used for evaluation also included in the SMP-style and emotional datasets? Or are these unseen articles?\n\nC. Why was the \"Make it personal\" sentence included in prompts for the emotional style in claim generation?", "paper_topic_and_main_contributions": "This paper focuses on debunking misinformation with the discourse style and proper emotional content of social media. They contribute a dataset of 12k claim-response pairs generated via an LLM with human post-editing. They show improvements over baselines that are not in the style of social media with automatic summarization-based evaluations and human evaluations.", "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": {}, "extra_text": {"Typos_Grammar_Style_and_Presentation_Improvements": "* Giving a ballpark range of the \"limited size\" of other datasets (last para of section 2) would be helpful\n* Line 41: \"fake news\"?\n* I found the RR explanation confusing. What is \"n-grams' rate\" exactly? Is it the rate of repeated n-grams?\n* Line 472: don't capitalize \"The\"\n* Table 6 took me a while to understand. It should be clearer (in the caption) that columns are different datasets (starting claims) and rows are models. I also would have expected the blue highlights for best results to be best per dataset instead of best per model, since that would give more expected information when comparing models for performance across datasets (which I think is the purpose) vs comparing datasets to see the areas in which models perform the best."}}]

Author rebuttal:

Meta-review: This study proposes a dataset consisting of approximately 12K claim-response pairs, which are linked to debunked articles. Each claim in the dataset is accompanied by an emotional response, aimed at countering misinformation.

All reviewers appreciated the contribution of the dataset. However, there are some concerns. Reviewers Emnp, QBDx, and NTyP mentioned the lack of details regarding the annotators, which the authors addressed in the rebuttal. Please include this information in the paper while making revisions. Additionally, an important point raised by Reviewer LBT1 regarding LLM hallucinations should be addressed in the paper.
Decision: Accept-Main