Hater Is Not the Only Source of Toxic Online Communication, But Also Fan
Akira Matsui1 * , Taichi Murayama1 , Mitsuo Yoshida2
1

Yokohama National University, Japan
2
University of Tsukuba, Japan

Abstract
Online platforms often witness harmful communication.
While the previous research has mainly focused on the linguistic aspect of such communication, the role of the authors’ stance toward the discussed topic remains unclear. To
address this gap, we first construct a unique dataset from an
online platform containing self-disclosure annotations aboutauthors’ stances. Our analysis of the constructed dataset indicates that anyone can be a target of toxic communication,
whether a real person or a virtual entity. We, then, demonstrate that toxic communication is not limited to haters, butalso fans. Writing toxic comments does not necessarily indicate a negative attitude towards the subject or individual being
discussed.

1

Introduction

Online platforms have significantly transformed our daily
communication, but we often face abusive online communications. This prevalence of abusive behavior demands computational techniques to detect the patterns of such toxic online communication (Kowalski, Limber, and McCord 2019;
Giumetti and Kowalski 2022; Avalle et al. 2024).
However, only linguistic features do not fully capture suchcomplex form of communication because a highly toxic
comment about a particular individual does not always guarantee the author’s intention to target that individual; rather,it may be a defensive response (Edwards, Kontostathis, andFisher 2016). For instance we often find partisans using
highly offensive language to defend their preferred party’s
candidate in political discussion (Kalmoe 2014; Faizah,
Mustika, and Prasetyo 2019; Lyu 2023). Despite this, moststudies focus on the toxicity of comments within online discussions and pay little attention to the stances of authors toward topics.
Interactions among two groups with opposed views canincrease their positional distance in discussion. Particularly,
in political discussions, disagreements between opposing
groups can escalate the intensity of discussions. Bail et al.
(2018) show that exposure to opposing political views on
Twitter amplifies polarization, highlighting the challenge in* Corresponding author. E-mail adress: matsui-akirazr@ynu.ac.jp
Copyright © 2024, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.

mitigating online political polarization. This escalation ofpolitical stances can be facilitated by interactions between
the two with opposing views. However, it remains uncertain
whether this phenomenon extends to all discussions, particularly those involving differing preferences or conflicts overvarious issues. Moreover, as Avalle et al. (2024) point out,
such disagreements do not always result in escalating debates, and we little know what kind of groups or interactions
exacerbate the situation.
While the literature on toxic behavior in online platforms often focuses on specific characteristics of individuals (Chandrasekharan et al. 2017; Rieger et al. 2021), any
entity can become a subjects of highly toxic comments regardless of their traits. This suggests that personal distinctive traits alone do not fully elucidate why anyone can be a
victim of toxic communication. For instance, terms like “idiot” or other aggressive synonyms are prevalent in abusive
language, but such language can point to any individual orentity. Even fictitious entities, such as cartoon characters or
virtual streamers, can become targets of hurtful comments inthe online sphere (Boi 2015; Wikipedia 2024). An excessive
focus on specific instances can veil toxic communication on
online platforms.
To bridge the aforementioned research gaps, we conduct
an extensive analysis of toxic discourse on an online platform that mandates users to disclose their stance towards
the subject of discussion before commenting. These selfdisclosure annotations enable us to categorize comments aseither written by fans or haters of target individuals in forums. The website covers a wide range of genres, from famous actors to fictional characters, and user discussions often take the form of replies. To make the presented paper
easy to follow, we introduce several terms to facilitate discussion, which are summarized in Table 1.
Leveraging this unique dataset, we address the following
research questions:• RQ1: Do toxic comments share a common pattern regardless of the target, or do they vary based on the target’s characteristics?
• RQ2: Do haters predominantly drive toxic communication in online fandoms, or can fans be contributors as
well?
By answering these questions, we provide the follow-

Figure 1: Schematic of the dataset with a mock example. This schematic is created to avoid presenting harmful content in thepaper to be presented in public. In real scenarios, the forums on the platform are related to entities like celebrities, athletes,or virtual characters. The schematic serves as a conceptual illustration of user interactions and stance declarations within theplatform’s context.
Table 1: Glossary of Terms
Term
TargetDescription
The subject of cyberbullying, encompassing tangible individuals, virtual entities, or fictional characters.
Fan
An individual who reveals supports the target on theplatform.
Hater
An individual who reveals supports the target on theplatform.
On the platform, users are required to state their stance towards
the target. Through these self-declared annotations, we canclearly categorize users as either Fans or Haters.

ing insights into toxic communication in online platforms.
First, RQ1 reveals that although the toxicity of comments
varies across target categories, certain categories witness
toxic comments from fans, not just haters. Our subsequentcase study shows that virtual targets receive a comparable
level of toxic comments to non-virtual ones, albeit with adistinct composition. Additionally, in RQ2, we discerned thescenarios in which fans write toxic remarks, a situation influenced by the friction between fans and haters.

2

Related WorkIn this section, we review the related work of this study,
mainly focusing on toxic communication on online platforms. The advent of online platforms has transformed ourmeans of communication, but toxic communication has beena serious issue. To combat this dark side of the online
community, many researchers have investigated efforts todevelop robust methods to keep the community safe andpositive (Almerekhi, Jansen, and Kwak 2020). Such interventions can improve user experience on online platforms,
for example through safe ranking algorithms (Garcia-Pueyo
et al. 2023). However, in terms of communication, online
interactions take multifaceted forms, and there is a potential
disconnect between authors’ intent and readers’ interpretation (Chang, Cheng, and Danescu-Niculescu-Mizil 2020).

Among many forms of toxic communication on online
platforms, cyberbullying has been gathering a large amountof attention from not only researchers but also school officials, policymakers, and parents in general (UNICEF;
Gradinger, Strohmeier, and Spiel 2010). Cyberbullying
tends to take the form of a sequence of comments rather thana single message (Yao, Chelmis, and Zois 2019). On cyberbullying, various individuals can be victims of these malicious acts (Kowalski, Limber, and McCord 2019; Giumetti
and Kowalski 2022), while specific individual traits are also
being targeted (Smith et al. 2019; Li 2006). Therefore, it
is crucial to capture the sequence of toxic communication
about various topics to understand cyberbullying.
There has been research on hate speech on online platforms (Castaño-Pulgarı́n et al. 2021), and the literature
has developed computational models to detect and mitigate hate speech using machine learning models (Nobata
et al. 2016; MacAvaney et al. 2019; Bourgonje et al. 2018).
Not only hate speech detection, such computational method
is applicable to a wide range of offensive language communication (Pradhan et al. 2020; Bertaglia et al. 2021).
The detection and mitigation of abusive language on online platforms also benefit to online communities such as
Wikipedia (Cheriyan, Savarimuthu, and Cranefield 2021;
Smirnov, Oprea, and Strohmaier 2023) or Stack Overflow (Kumar et al. 2023), Reddit (Kumar et al. 2023; Hebert,
Golab, and Cohen 2022; Hiaeshutter-Rice and Hawkins2022). Regarding this line of the literature, it is worth noting that Ashraf, Zubiaga, and Gelbukh (2021) indicate that
understanding not only the textual but also the contextual
elements is important for comprehending hate speech.
Therefore, to understand the mechanism of the toxic phenomenon on online platforms, it is crucial to comprehend
the interactions among users because not only negative users
play a pivotal role in such form of communication (Kayes
et al. 2015; Avalle et al. 2024). Even if a very toxic communication occurs within a community or section of an online
platform, it does not necessarily imply we find toxic com-munication in other part on that platform (Caines et al. 2018;
Mohan et al. 2017). In addition, the user interactions especially conflict between users plays a pivotal role in online
communication (Levy et al.; Avalle et al. 2024).
In conclusion, the spread of toxic communication in online realms presents several challenges, particularly in understanding the role of users’ stances toward topics and thesequence of communication. Additionally, the role of tensions among users have not been fully explored. Moreover,while most studies focus on specific factors or personal traits
in understanding abusive language usage online, few investigate the banality nature of toxic communication and thereality that anyone can be a victim of such malicious acts.

3

Data

In this section, we explain the dataset employed in thepresented study using the terminologies described in Table 1. This study constructs a distinct dataset comprising online fandom communities where authors openly reveal theirstances toward the subjects of discussion.

3.1

Overview of the Platform

The dataset employed in this manuscript originates from an
online forum1 . The platform primarily centers on Japanesecelebrities, enabling visitors to gauge the popularity of thesecelebrities. Notably, the platform includes a wide range ofentities from different domains, such as actors, movie stars,
and politicians. This diverse set of entities ensures the representation of the dataset without bias towards any specificgenre. In this paper, we refer to the individuals receiving
comments in the described format as “target”.

3.2

Like or Dislike, Fan or Hater

The crucial functionality of the platform is that it requests
users to disclose their stance toward the target in a binary
form, indicating whether a given user is a fan or a hater of
the target. Users who have commented on the target are assigned labels that reflects their preference, either liking or
disliking the target. Consequently, all users are aware of theirown stance toward the target when writing posts, including
responses to others’ posts. We refer to the participant’s vote
“like” toward the target person are fan of them, and those
who cast “dislike” vote for the target person are hater ofthem. We retrieve this self-reveal stance of each user andcategorize whether each post is written by fan or hater.
The platform also has a function that displays the tension
between fans and haters in each forum of a target, showing
the number of fans and haters of that target. The ratio of fansto haters in a forum plays a pivotal role as it reflects the discourse’s intensity on the forum. When the majority of users
participating in a forum are fans, that forum is fan-driven.
When dominated by haters, that forum is hater-driven. In
cases where there is a well-balanced distribution of posts between these two groups, it signifies a forum where they arein competition. We will employ this information to explore
whether the tension between these two groups is associatedwith the prevalence of toxic behavior on a forum platform.
1

https://suki-kira.com

3.3

Tags and Categories of Targets

In addition to the comments and stances of users, the platform incorporates category tags associated with each target,
thereby facilitating comparisons across the targets’ spheresof influence. We present the top 30 frequent categories inTable 2. The table shows a wide range of categories of targets persons, and as discussed in the introduction we seesome categories that represent non-human targets such asCharacter or V-tuber. After collecting data, we consequently
have amassed approximately 83,004 comments pertaining to
4,700 users, strictly keeping users’ anonymity.

4

Methods

In this section, we will outline the methods employed to analyze toxic communication on the platform. We first discuss
the use of the Perspective API and provide a brief overview
of the regression model utilized in this study.
To classify the toxicity of individual comments, we leverage the Perspective API 2 . This API offers an array of prebuilt toxicity classifiers, and its reliability and validity havebeen established in numerous studies (Hua, Naaman, andRistenpart 2020; Kumar et al. 2023; Saveski, Roy, and Roy
2021; Xia et al. 2020; Rajadesingan, Resnick, and Budak2020) 3 . Given the API’s capability to process a broad rangeof languages, from Japanese to English, we believe our results possess a certain degree of generalizability.
The Perspective API analyze and score abusive language
in text through six distinct metrics: Toxicity, Severe Toxicity,
Identity Attack, Insult, Profanity, and Threat. This paper isinterested in toxic behavior, therefore we conduct the analysis with a primary emphasis on the Toxicity Score, following by related studies (Hua, Naaman, and Ristenpart 2020;
Saveski, Roy, and Roy 2021; Xia et al. 2020; Rajadesingan,
Resnick, and Budak 2020; Avalle et al. 2024). We incorporate the other metrics to elucidate the distinctions between
fans and haters. The comprehensive descriptions of these
metrics are available on the Perspective API’s documentation page4 .
We investigate the relationships between the scores provided by the Perspective API and attributes such as the categories of target persons. Also, we study whether each comment is written by a fan or a hater of the target. We employ a
linear regression model to estimate the relationship between
these attributes and the toxicity of the comments.

5

Results

This section discusses the results of the analysis to answer
the research questions we proposed in the introduction.
2
In the document page, the Perspective API defines Toxity
as “We define toxicity as a rude, disrespectful, or unreasonable
comment that is likely to make someone leave a discussion”(https://perspectiveapi.com). We follow this definition in this paper.
3
https://perspectiveapi.com
4
https://developers.perspectiveapi.com/s/about-the-apiattributes-and-languages?

Category Tag Name
Female Model
Stage Actor
Host/MCChild Actor
Virtual YouTuber (VTuber)
TV Station Announcer
Singer-Songwriter
Businessperson
SingerComedian
Politician
Infielder (baseball)Figure Skating
Lyricist
Female Voice Actor

Description
Female Model.
Actor in theatrical plays.
Introduces guests and manages events.
A child, especially in films and plays.
A YouTuber with a CG avatar.
An announcer for a TV station.
Musician who writes/performs songs.
Manages commercial activities.
Produces musical tones with voice.
Makes people laugh by jokes or actions.
In professional politics, elected office.
An inner field player in baseball.
Skating in prescribed patterns.
Writes the words to songs.
Female who provides voice-overs.

Category Tag Name
Male Idol
Idol
Member of Parliament
TalentMale Voice Actor
CharacterYouTuber
General/Comprehensive
Actor
Female Idol
Idol Groups†
A VTuber Group†
Actress
Game Commentator
BaseballDescription
Male performer in pop groups.
Performer in pop groups.
Representative in the parliament.
Celebrity in entertainment industry.
Male who provides voice-overs.
Person in a work of art.
Manages content on YouTube.
Relating to the whole.
Person who acts in films, stage shows.
Female performer in pop groups.
Idol groups under talent agency.
VTubers under Nijisanji company.
Female who acts in films, stage shows.
Plays games with commentary.
Sport played by two teams of nine.

Table 2: Description of Entity Category Tag. The table that delineates the entity category labels for the analysis depicted in
Figure 2. These entity category labels serve to categorize and identify entities within the dataset. In order to refrain from directly referencing specific individuals or persons, we have anonymized the names of such groups with the symbol “†”.

5.1

RQ1: Patterns in Toxic Comments Based onTarget Characteristics

We first answer the RQ1 “Do toxic comments exhibit common patterns regardless of the traits of targets, or do theyvary depending on targets’ attributes?” To explore this, we
calculate the toxicity of comments across various categories,
as outlined in Table 2. We then compare the differences intoxicity of comments among the categories. In this analysis,
we take into account the users’ stances toward the target,
distinguishing between fans and haters.
Categorical Differences in Toxicity We calculate the differences in toxicity across the categories and plot them in
Figure 2. The values presented in the figures represent deviations in toxicity from the average of the other categories.
Therefore, the estimated value indicates the deviation of toxicity from the average, and those values demonstrate what
categories receive more toxic comments by which type ofusers (i.e., fan or hater). A positive value implies that subjects within that category tend to attract more toxic posts
compared to others, and vice versa.
The figure also indicates some variations in its toxicity
levels with regard to the stance towards targets. As anticipated, haters tend to write more toxic comments comparedto fans. However, some categories receive higher toxic comments from fans, such as in the case of Idol Groups. Additionally, we observed that there are categories where bothfans and haters employ highly toxic language directed at thetarget, for example, the politician category. These observations imply that even fans can engage in toxic communication in specific contexts.

5.2

Case Study: Virtual vs. Non-Virtual Targets

We have discerned a link between target categories andthe usage of toxic language and, we find that virtual entities can also receive toxic communication. To comprehend
this interesting phenomenon, we conduct a deeper analysis

to unveil why even virtual entities can receive toxic comments. For this aim, we conduct a case study contrasting
extreme categories: virtual versus non-virtual targets. The
rationale behind this case study is twofold. First, the recent
work highlights the harassment in metaverses even if they
are in their virtual avatar personas (Wiederhold 2022; Ortiz
2022). Second, even if targets are non-human, prevailing offensive language in online form can distort the safety of theonline community. In addition, such heated phenomenon canbe recognized as a social problem (St. Michel 2015)
We examine the targets classified as either Charactersor VTuber categories. VTubers, or Virtual YouTubers, areonline personalities who employ digital avatars, frequently
powered by live motion-capture technology. They create
content and engage with their audience on YouTube, gaining significant popularity in recent year (Lu et al. 2021;
Bredikhina et al. 2020).
We begin this case study with comparing the distributionof toxic comments between these two categories and plot
the results in Figure 3 where we observe subtle differencesamong the two. Although we find lower overall toxicity levels when considering various facets in aggregate, we stillobserve that virtual targets receive toxic comments as real
persons do.
While both categories experience toxic comments, we investigate the contents of such comments and try to find thedifferences between them. To address this, we calculate thesimilarity among the toxic comments. We focus on the comments whose toxicity scores are in the top 75th percentile
and create a set comprising the top 1000 words from thesecomments. We then calculate the Jaccard similarity among
those vectors. Also, we construct vectors with normalized
word frequencies and calculate the cosine similarity among
them. We employ this analysis for both the Virtual and Nonvirtual categories, as well as for Fans and Haters for comparison purposes. We present the reults in Figure 4, and observethat toxic comments directed at virtual targets consist of distinct words compared to the other groups (Figure 4(a)). This

Figure 2: Toxicity Across Different Categories
Note: The estimated variations in toxicity across different categories for Hater in red color and “Fan” in blue color; • indicates pval< 0.01, and otherwiwse ×; Bars represent 95% CIs. The figure illustrates that in most categories, Haters tend to post more toxic
comments than Fans. However, there are certain categories in which Fans write comments with either higher or equivalent levels of
toxicity as compared to Haters.

pattern is consistent when examining cosine similarity (Figure 4(b)). However, the consistent findings in the analysis ofnormalized vectors (cosine similarity) imply that toxic comments directed at virtual targets manifest differently thanthose directed at other subjects.
While we anticipate common words related to toxic language across all groups, the findings above suggest that even
within toxic comments, there exists a degree of diversity intheir contents. On the observation of the fan, we find relatively lower values( Figure 4), which suggests that fans also
employ distinct words in their toxic comments. These disparities indicate the possibility of predicting attitudes toward
the target (Fan/Hater) based on textual analysis.

Figure 4: Comparative Analysis of Toxic Comment Content. The comparison involves comments authored by four
distinct groups: comments on Virtual entities and Nonvirtual entities, composed by Haters and Fans.
the role of user stance (fan or hater) towards targets and observed that fans can also write toxic comments. To convey a
better understanding of this phenomenon, we direct our focus toward the context in which they employ toxic language.

Figure 3: Toxicity Distribution: Virtual VS. Non-virtual

5.3

RQ2: Toxic Communication: Haters vs. Fansin Online Fandoms

Next, we aim to address the RQ2: “To what extent do individuals with negative attitudes (haters) dominate toxic communication within online fandoms, and can fans also contribute significantly?” Our investigations in RQ1 underscore

Distribution of Toxic: Fans VS Hater To gain a comprehensive understanding of the distinctions between fans andhaters, we have plotted the CCDF of toxicity scores in Figure 6. The figure illustrates that haters tend to compose more
highly toxic content than fans, which aligns with our expectations. All four measurements from the API yield similar
results.
While haters write more toxic, fans may also write at thesame level as haters in specific contexts. To study this point,
we focus on the distribution of scores more than 90 percentile of each score measurement, and we reveal different
patterns as depicted in Figure 8. While the distribution of the
toxicity scores still indicates that haters generate more toxic
comments than fans, we observe that both fans and haters
exhibit similar distributions in identity attacks and insults

Figure 5: Detailed Comparison of Toxicity Measurement:
Virtual VS. Non-virtual. The figure provides a detailed representation of toxicity measurements for each group (Virtual vs. Non-virtual) along with 95% confidence intervals(represented as bars).

scores (the bottom two categories in Figure 8). This finding
supports the notion discussed earlier that fans can also contribute toxic comments under specific circumstances.

Figure 7: Joint Distribution: Fan/Hater Ratio vs. Toxicity

the fans employ toxic comments. The figure illustrates boththe fans and haters as having a negative correlation between the dominance of fans and the toxicity of their comments. Conversely, in a fan-dominated forum, both users
write fewer toxic comments. We also note that the distribution for fans (orange) broadens at its lower end, indicating a context where fans become toxic. This implies thatfans can be inclined to employ toxic language when they
are in forums dominated by haters. One possible interpretation of this observation is that fans employ toxic language
as a means to defend their favored targets against haters
because fans typically hold a positive attitude towards thetarget by definition. Therefore, examining the interactions
between fans and haters within fandom communities holds
substantial value.

Figure 6: Overall CCDF of Perspective Scores: Fan VS
Hater. The plot represents the distribution across the entire
population
Toxic and Intensity of Fandom To examine the context
of the intensity of fandom, we study if the tension between
fans and haters can potentially affect the likelihood of generating toxic comments. As previously discussed in Section 3.1, each forum of target reveals the ratio of fans tohaters. In fact, we find an association between the ratio offans and toxicity score (Figure 9), and we depict the relationship between comment toxicity and the ratio of fans withineach category of the target in Figure 7.
The figure demonstrates that as the ratio of fans decreases,
which corresponds to an increase in the presence of haters,

Dependence on User Attitude: Fans vs. Haters We investigate whether fans and haters employ toxic language intheir responses to individuals with opposing attitudes (haters
and fans, respectively). Within the comment section of each
forum, users can reply to other users’ comments. To comprehend the contextual impact, we compute the differencesin toxicity score between replies and non-replies using a linear regression model. Our regression model assesses the difference of toxicity score from the baseline (Intercept) that is
the toxicity score of non-reply comments by haters. variations in toxicity compared to a baseline. In other words, we
calculate the effect of attributes on the toxicity score as thedifferences from the average scores of haters’ comments 5 .
The results presented in Table 3 support the hypothesis
5

In other wards, Intercept represents the average toxity scoresof the comments by haters that are not Reply, Virtual, and not
being replied.

Figure 9: Average Toxicity Score in Each Quantile. The
bars in the figure represent the mean values for each quantile group, along with 95% confidence intervals.

6

Figure 8: Top 10% CCDF of Perspective Scores: Fan VS
Hater. The plot focuses on the distribution among the top
10% most toxic comments in each measurement.
introduced in the previous section. We first find that the absolute toxicity of haters’ comments is much higher than fans.
The first two rows of the table reveal that fans employ more
toxic language in their replies. The estimations indicate thatin their replies to haters, fans write comments with a toxicity score 0.088 higher than normal6 . Interestingly, fans also
exhibit an inclination to use more toxic language when replying to fans. In contrast, haters tend to compose less toxic
comments in their replies. Hence, we observe a certain degree of homophily among haters. These effects surpass theimpact of post popularity, as measured by the number of
replies a post receives.
TOXICITY
Reply: Fan to Hater
Reply: Fan to Fan
Reply: Hater to Hater
Reply: Hater to Fan
Is Virtual#of Being Replied
Is FanIntercept (hater)
R-squared

0.088*** (0.004)
0.033*** (0.004)-0.036*** (0.003)-0.007* (0.004)-0.036*** (0.002)
0.003*** (0.002)-0.137*** (0.002)
0.249*** (0.001)
0.091

Discussion and Conclusions

We investigated the complexities of toxic discourse within
an online platform, focusing on users’ stances toward targets
and the interactions between fans and haters. Our analysis
first revealed that almost any entity can be a target of toxic
discourse, regardless of whether it is a virtual or real person. However, they received comments composed of different contents, even though these comments were toxic. In addition, when focusing on highly toxic comments, we foundthat haters and fans can write almost equally toxic comments. We also observed that fans tend to employ abusive
language when haters are predominant in a forum and when
they reply to other users. These insights help us better understand the onset of toxic online communication and underscore the importance of understanding the authors’ stance.
However, as with any study, ours too has its limitations.
First, we analyzed the platform notorious for toxic content.
While we ensured a robust dataset for our study, it might not
be entirely representative of online platform. Our conclusions, particularly those centered around fan-hater dynamics, are rooted in specific fandom contexts. Our binary categorization of users into fans and haters might obscure thesubtle shades of user attitudes because it is not always possible for us to decide for or against a topic. Therefore, ourbinary classification omit the discourse written by “neutral”
authors. We also focused on textual interactions, neglecting
other modes like emojis or images. We believe future research endeavors could expand our dataset and conduct acomprehensive analysis using full information of the forum
and understand the dynamics of the communications on theplatform.

Ethical Considerations
Table 3: The Toxicity and the Context of Reply. ∗ markindicates the statistical significance (***p-val< 0.01, **pval< 0.05, *p-val< 0.1).
6
Since the average score of non-reply comments by Fan is0.112, the toxicity of fans reply to hater increases by about 78%.

The data in this paper is derived from publicly-accessibleuser-generated content. We pay the utmost attention to theprivacy of individuals in this study. We did not discuss theresults regarding specific figures (described as “targets”
in this paper), organization to keep the privacy of theindividual studied in this paper.

Acknowledgements
This work was supported by JSPS KAKENHI Grant-in-Aid
for Scientific Research Number JP22K20159, JP24K16359,
JP23K16889, JP21H03557. Also, this work was supported
by Research Institute of Science and Technology for Society,Japan, Grant Number JPMJRS23L4.

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7

Paper checklist

1. For most authors...
(a) Would answering this research question advance science without violating social contracts, such as violating privacy norms, perpetuating unfair profiling, exacerbating the socio-economic divide, or implying disrespect to societies or cultures? Yes(b) Do your main claims in the abstract and introduction
accurately reflect the paper’s contributions and scope?
Yes(c) Do you clarify how the proposed methodological approach is appropriate for the claims made? Yes(d) Do you clarify what are possible artifacts in the data
used, given population-specific distributions? Yes(e) Did you describe the limitations of your work? Yes(f) Did you discuss any potential negative societal impacts of your work? Yes(g) Did you discuss any potential misuse of your work?
Yes(h) Did you describe steps taken to prevent or mitigate potential negative outcomes of the research, such as data
and model documentation, data anonymization, responsible release, access control, and the reproducibility of findings? Yes(i) Have you read the ethics review guidelines and ensured that your paper conforms to them? Yes
2. Additionally, if your study involves hypotheses testing...
(a) Did you clearly state the assumptions underlying all
theoretical results? Yes(b) Have you provided justifications for all theoretical results? Yes(c) Did you discuss competing hypotheses or theories thatmight challenge or complement your theoretical results? Yes(d) Have you considered alternative mechanisms or explanations that might account for the same outcomes observed in your study? Yes(e) Did you address potential biases or limitations in yourtheoretical framework? Yes(f) Have you related your theoretical results to the existing
literature in social science? Yes(g) Did you discuss the implications of your theoreticalresults for policy, practice, or further research in thesocial science domain? Yes
3. Additionally, if you are including theoretical proofs...
(a) Did you state the full set of assumptions of all theoretical results? NA(b) Did you include complete proofs of all theoretical results? NA
4. Additionally, if you ran machine learning experiments...
(a) Did you include the code, data, and instructions
needed to reproduce the main experimental results (either in the supplemental material or as a URL)? NA(b) Did you specify all the training details (e.g., data splits,
hyperparameters, how they were chosen)? NA(c) Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)?
NA(d) Did you include the total amount of compute and thetype of resources used (e.g., type of GPUs, internalcluster, or cloud provider)? NA(e) Do you justify how the proposed evaluation is sufficient and appropriate to the claims made? NA(f) Do you discuss what is “the cost“ of misclassificationand fault (in)tolerance? NA
5. Additionally, if you are using existing assets (e.g., code,
data, models) or curating/releasing new assets, withoutcompromising anonymity...
(a) If your work uses existing assets, did you cite the creators? NA(b) Did you mention the license of the assets? NA(c) Did you include any new assets in the supplemental
material or as a URL? NA(d) Did you discuss whether and how consent was obtained from people whose data you’re using/curating?
NA(e) Did you discuss whether the data you are using/curating contains personally identifiable information or
offensive content? NA(f) If you are curating or releasing new datasets, did you
discuss how you intend to make your datasets FAIR
(see ?)? NA(g) If you are curating or releasing new datasets, did you
create a Datasheet for the Dataset (see ?)? NA
6. Additionally, if you used crowdsourcing or conducted
research with human subjects, without compromisinganonymity...
(a) Did you include the full text of instructions given toparticipants and screenshots? NA(b) Did you describe any potential participant risks, withmentions of Institutional Review Board (IRB) approvals? NA(c) Did you include the estimated hourly wage paid toparticipants and the total amount spent on participantcompensation? NA(d) Did you discuss how data is stored, shared, and deidentified? NA