# A Survey on the Role of Crowds in Combating Online Misinformation:
Annotators, Evaluators, and Creators

Bing He  Georgia Institute of TechnologyUSA<EMAIL_ADDRESS>,  Yibo Hu
Georgia Institute of TechnologyUSA<EMAIL_ADDRESS>,  Yeon-Chang Lee
Georgia Institute of TechnologyUSA<EMAIL_ADDRESS>,  Soyoung Oh
Saarland UniversityGermany<EMAIL_ADDRESS>,  Gaurav Verma
Georgia Institute of TechnologyUSA<EMAIL_ADDRESS>and  Srijan Kumar
Georgia Institute of TechnologyUSA<EMAIL_ADDRESS>
(2018)

###### Abstract.

Online misinformation poses a global risk with significant real-world
consequences. To combat misinformation, current research relies on
professionals like journalists and fact-checkers for annotating and debunking
false information, while also developing automated machine learning methods
for detecting misinformation. Complementary to these approaches, recent
research has increasingly concentrated on utilizing the power of ordinary
social media users, a.k.a. “the crowd”, who act as eyes-on-the-ground
proactively questioning and countering misinformation. Notably, recent studies
show that 96% of counter-misinformation responses originate from them.
Acknowledging their prominent role, we present the first systematic and
comprehensive survey of research papers that actively leverage the crowds to
combat misinformation.

In this survey, we first identify 88 papers111https://github.com/claws-
lab/awesome-crowd-combat-misinformation related to crowd-based efforts,
following a meticulous annotation process adhering to the PRISMA framework
(preferred reporting items for systematic reviews and meta-analyses). We then
present key statistics related to misinformation, counter-misinformation, and
crowd input in different formats and topics. Upon holistic analysis of the
papers, we introduce a novel taxonomy of the roles played by the crowds in
combating misinformation: (i) crowds as annotators who actively identify
misinformation; (ii) crowds as evaluators who assess counter-misinformation
effectiveness; (iii) crowds as creators who create counter-misinformation.
This taxonomy explores the crowd’s capabilities in misinformation detection,
identifies the prerequisites for effective counter-misinformation, and
analyzes crowd-generated counter-misinformation. In each assigned role, we
conduct a detailed analysis to categorize the specific utilization of the
crowd. Particularly, we delve into (i) distinguishing individual,
collaborative, and machine-assisted labeling for annotators; (ii) analyzing
the effectiveness of counter-misinformation through surveys, interviews, and
in-lab experiments for evaluators; and (iii) characterizing creation patterns
and creator profiles for creators. Finally, we conclude this survey by
outlining potential avenues for future research in this field.

misinformation, combat misinformation, crowd, survey, counter-misinformation

††copyright: acmcopyright††journalyear: 2018††doi: XXXXXXX.XXXXXXX††price:
15.00††isbn: 978-1-4503-XXXX-X/18/06††ccs: Information systems Data
mining††ccs: Information systems Social networks

##  1\. Introduction

Most individuals today rely on social media platforms as their primary source
of news and information (Walker and Matsa, 2021). However, such platforms
contain a plethora of unreliable information, including misinformation, which
unfortunately spreads more rapidly and widely than truth (Vosoughi et al.,
2018). Online misinformation harms individuals and society at multiple levels.
At the micro-level, misinformation harms well-being (Verma et al., 2022),
increases polarization (Stewart et al., 2018), and leads to online harassment
and violent attacks on individuals and communities (Chua et al., 2017). At the
macro-level, misinformation questions democratic processes and elections
(Silverman, 2016), impacts science and global public health (Memon and Carley,
2020; Dai et al., 2022; Xue et al., 2022). For instance, misinformation about
the COVID-19 vaccine (e.g., the vaccine causes infertility) has reduced
vaccine uptake and prolonged the pandemic (Memon and Carley, 2020; Dai et al.,
2022; Xue et al., 2022); misinformation about elections can undermined trust
in democratic processes and institutions (Cohen et al., 2020). Therefore, it
is crucial to curb the spread of online misinformation and to counter
misinformation (Sun et al., 2020; Zhao et al., 2016; Vosoughi et al., 2018).

Motivated by this, research has focused on detecting misinformation by
utilizing different approaches, including automated machine learning (ML)
solutions (Sharma et al., 2019; Guo et al., 2019; Islam et al., 2020) and the
use of professional fact-checkers (Micallef et al., 2022; Porter and Wood,
2021; Markowitz et al., 2023). Notably, there has been a growing interest in
research on developing ML models (Sharma et al., 2019) based on post content,
poster attributes, social network, temporal aspects, and propagation features
(Shu et al., 2020). These models have been deployed across widely-used web and
social media platforms (e.g., Twitter, Facebook, and Youtube). In the
meantime, ML solutions rely on ground truth labels of misinformation for their
training and validation, whereas professional fact-checkers typically label
the misinformation with fact-check labels. These professionals also write
fact-checking articles to explain their reasoning for the label
determinations. For example, Snopes.com provides fact-check labels that range
from “true” and “mostly true” to “mostly false” and “false”, accompanied by
corresponding explanations.

Despite these solutions, the “infodemic”, or the epidemic of misinformation
(Borah et al., 2021; Chen and Fu, 2022; Xue et al., 2022), continues to grow
at an alarming rate. One contributing factor is that automated ML models
respond slowly to changes in the information ecosystem, rely on fact-check
labels provided by professional fact-checkers, and are vulnerable to
manipulation by adversaries (Allen et al., 2021; He et al., 2021a; Roitero et
al., 2021). On the other hand, professional fact-checkers face limitations in
terms of the limited number of fact-checkers and the significant time required
for label generation; moreover, their fact-checks tend to address only a small
number of viral claims (Allen et al., 2021; Pennycook and Rand, 2019; Micallef
et al., 2022). Importantly, both approaches only detect misinformation but do
not actively engage with misinformation spreaders. In this context, it is
worth noting that there has been a noticeable absence of discussion on
structured methods for countering misinformation once it is identified.

###  1.1. Motivation

To address the drawbacks of these two approaches, leveraging crowds offers a
promising solution for combating misinformation in a scalable and proactive
manner (Allen et al., 2021; Ma et al., 2023). In this study, we focus on the
“crowd” — defined as ordinary users of social media platforms (i.e., not fact-
checkers, journalists, or organizations). They serve as eyes on the ground who
proactively question and counter misinformation, including emerging
misinformation (Bode and Vraga, 2018; He et al., 2023). Literature has shown
that crowd-based “social correction” is effective and works well across topics
(Bode and Vraga, 2018; Ma et al., 2023). Crowds engage in combating
misinformation by contributing significant benefits in diverse tasks,
including identifying misinformation, assessing counter-misinformation
effectiveness, and creating counter-misinformation. In addition, crowds also
have the key benefit of being “cost-effective,” compared to the expensive and
time-consuming recruitment of professional fact-checkers. Crowds on social
media platforms often counter online misinformation voluntarily (Mujumdar and
Kumar, 2021; Allen et al., 2022), making it cost-effective to leverage their
collective efforts. Therefore, there has been a surge of research efforts to
develop crowd-based methodologies to annotate misinformation, evaluate
counter-misinformation effectiveness, and characterize counter-misinformation
creation in recent years (Bhuiyan et al., 2020; Chen et al., 2021; Zhang et
al., 2022; Wang et al., 2022b, 2021; Allen et al., 2021; Mujumdar and Kumar,
2021; Shabani and Sokhn, 2018). Given the growing and significant interest in
this area, we investigate the crowd-based research efforts in combating online
misinformation.

for tree= grow’=0, draw, align=c, rounded corners, parent anchor=east, child
anchor=west, l sep=30, text centered, text width=90, edge path=[edge]
(!u.parent anchor) – ++(20pt,0) —- (.child anchor)edge label; , [Combating
Misinformation,text width=60 [By Automated
ML Classifiers, l=5, text width=60] [By Professionals, l=5, text width=60] [By
Crowds
(Our survey), l=5, text width=60 [Annotators for
Identifying Misinformation
(Section 4), [Individual Labeling
(Section 4.1)] [Collaborative Labeling
(Section 4.2)] [Machine-assisted Labeling
(Section 4.3)] ] [Evaluators for Assessing
Counter-Misinformation
Effectiveness (Section 5), [Evaluation Methods
(Section 5.1)] [Findings from Effective
Counter-Misinformation
(Section 5.2)] ] [Creators of Counter-
Misinformation
(Section 6), [Characterization Methods
(Section 6.1)] [Creation Patterns
(Section 6.2)] [Creator Profiles
(Section 6.3)] ] ] ]

Figure 1. Two existing approaches to combat misinformation and our proposed
taxonomy hierarchy related to crowd-based efforts.

###  1.2. Our Work

In this survey, we aim to provide a comprehensive overview of the
collaborative efforts made by crowds in combating misinformation. To clarify
our scope, we focus on online misinformation222The term “misinformation” in
this survey is commonly represented as the terms “fake news”, “rumor”, “false
information”, “false news”, and “conspiracy theory.” , rather than all
information or offline information. More specifically, our investigation
centers on recent and actively pursued initiatives in combat misinformation
within this field.

Figure 2. Illustration of the roles of crowds in combating misinformation.

We first identify 88 relevant papers by following the guidelines of the
preferred reporting items for systematic reviews and meta-analyses (PRISMA
framework) (Moher et al., 2009) (Section 2). We then present a detailed
overview of misinformation formats, topics, social media platforms, and crowd
inputs (Section 3). Upon holistic analysis of the papers, we propose a novel
taxonomy of the crowd-based efforts that aim to combat misinformation, as
depicted in Figure 1. To this end, we categorize crowd users based on their
roles, as shown in Figure 2:

  * •

Crowds as Annotators: Crowds help identify and label online misinformation
accurately at scale (Bhuiyan et al., 2020), leveraging their extensive numbers
compared to the limited professional fact-checker pool, and their widespread
presence across social media platforms. Additionally, crowds can amplify the
efforts of fact-checkers by sharing fact-checking articles to combat
misinformation out of a sense of social responsibility (Pal et al., 2019),
driven by emotions such as anger or concern provoked by fact-checks (Sun et
al., 2021), and a desire to warn others against misinformation (Veeriah,
2021).

  * •

Crowds as Evaluators: Crowds help evaluate the effectiveness and limitations
of different counter-misinformation messages across various aspects. They
contribute by providing their first-hand experiences and tangible responses to
misinformation and counter-misinformation. This valuable input facilitates the
design of effective strategies to counter misinformation and offers a unique
perspective not always accessible to professional fact-checkers (Borah et al.,
2021).

  * •

Crowds as Creators: Crowds create their own content on social media platforms
to combat misinformation through these crowd-generated posts (Nadamoto et al.,
2013; Chua et al., 2017). They also respond to and comment on misinformation
with accurate information (Guo et al., 2019). Analyzing such activities allows
us to characterize the creators and patterns of their counter-misinformation
messages.

We frame our examination of this topic around three research questions (RQs)
that naturally align with the aforementioned roles of the crowds:

  * •

(RQ1) Capabilities of crowds in identifying misinformation: How do crowds
contribute to identifying or detecting misinformation, and how effective are
they in this task?

  * •

(RQ2) Evaluation of counter-misinformation effectiveness by crowds: How can
crowds be leveraged to evaluate counter-misinformation efficacy, and how
effective are different types of counter-misinformation?

  * •

(RQ3) Characterization of counter-misinformation messages by crowds: What are
the characteristics of crowd-generated counter-misinformation messages and
crowds who counter misinformation?

However, addressing the above RQs systematically poses several challenges.
First, online misinformation spans a wide range of topics, including politics
and natural disasters, and is disseminated across various platforms like
Twitter and YouTube. Meanwhile, crowds engage differently based on their
roles, which include identifying misinformation, evaluating counter-
misinformation effectiveness, creating counter-misinformation content, and
responding to misinformation messages. Additionally, they counter
misinformation across a range of content formats, including text and images.
Lastly, researchers employ diverse approaches, such as in-lab experiments,
interviews, and surveys, when analyzing crowd-based efforts in countering
misinformation.

To navigate these challenges, our work to address each RQ can be summarized as
follows:

  * •

Crowds as annotators for identifying misinformation (for RQ1; Section 4): We
investigate the capability of crowds to identify misinformation and compare it
with that of professional fact-checkers. We analyze both individual and
collective labeling scenarios, as well as the machine-assisted setting where
humans and machines collaborate to enhance the annotation performance.

  * •

Crowds as evaluators for assessing the effectiveness of counter-misinformation
(for RQ2; Section 5): We examine direct and indirect evaluation methods,
including expressed sentiment and stance, in-lab experiments, interviews, and
surveys, to quantitatively evaluate the efficacy of a given counter-
misinformation message. Then, we investigate the distinct advantages offered
by each counter-misinformation approach.

  * •

Crowds as counter-misinformation creators (for RQ3; Section 6): We examine two
aspects of counter-misinformation characteristics: i) the characteristics of
counter-misinformation messages generated by the crowds on social media
platforms; ii) typical attributes of crowds who counter misinformation.

Through these investigations, we provide valuable insights into the
effectiveness and limitations of existing crowd-based efforts in combating
misinformation.

Table 1. Comparison with existing surveys. In this table, ‘✓’ and ’ TextRenderingMode=Stroke, LineWidth=1.0pt, LineDashPattern=[1 2]0, ✓ ’ denote that the relevant survey fully and partially covered the corresponding topic, respectively. | Crowds’ Roles
---|---
|  |  Annotators for identi-
---
fying misinformation
|  Evaluators for assessing
---
counter-misinformation
|  Creators of
---
counter-misinformation
(Wang et al., 2019; Suarez-Lledo and Alvarez-Galvez, 2021) |  |  |
(Sharma et al., 2019; Guo et al., 2019; Shuhud et al., 2017) |  TextRenderingMode=Stroke, LineWidth=1.0pt, LineDashPattern=[1 2]0, ✓ |  |
(Chan et al., 2017; Hartwig et al., 2023) |  |  (Chan et al., 2017): ✓, (Hartwig et al., 2023):  TextRenderingMode=Stroke, LineWidth=1.0pt, LineDashPattern=[1 2]0, ✓ |
Our work | ✓ | ✓ | ✓

###  1.3. Related Survey

While most surveys on misinformation primarily focus on automated machine
learning solutions (Islam et al., 2020), it’s worth noting that a few surveys
(Sharma et al., 2019; Shuhud et al., 2017; Guo et al., 2019; Chan et al.,
2017; Hartwig et al., 2023) explore the literature that covers crowd-based
efforts. However, these previous surveys have certain limitations, and we aim
to provide a more comprehensive perspective. Here’s a comparison between our
survey and previous ones, as shown in Table 1.

First, some surveys (Sharma et al., 2019; Shuhud et al., 2017; Guo et al.,
2019) have examined crowds’ ability to identify misinformation (i.e.,
annotators). They only deal with inferring misinformation-related signals from
indirect crowd behaviors, such as replies and comments, and incorporating
those signals into ML solutions as labels. However, as mentioned in Section
1.1, our survey recognizes and investigates the potential for crowds to serve
as a direct and potent means for accurately identifying and labeling
misinformation at a large scale. This distinction sets our survey
significantly apart from the aforementioned prior research. Second, Chan et
al. (2017) summarize the effectiveness of various types of counter-
misinformation evaluated by crowds (i.e., evaluators). However, this survey
has limitations as it only covers 8 papers, is restricted to literature
published before 2018, and does not provide a comprehensive summary of the
evaluation methods employed by crowds. Hartwig et al. (2023) analyze the user-
centered misinformation interventions where crowds implicitly or explicitly
evaluate certain intervention techniques. Nevertheless, some evaluated
interventions are not related to counter-misinformation contents, e.g., the
removal of misinformation. In contrast, our survey addresses these limitations
through a rigorous paper search process for recent papers and summarizes the
assessment metrics and methods used to evaluate the efficacy of counter-
misinformation. Third, none of the existing surveys cover the characterization
of counter-misinformation from the perspective of the crowds (i.e., creators).
This is a notable gap as understanding the characteristics of crowd-generated
counter-misinformation can offer valuable insights for devising effective
strategies to combat misinformation. Our survey fills this gap by providing a
comprehensive analysis of the patterns of existing counter-misinformation
messages generated by crowds, as well as identifying and profiling typical
attributes of these crowd creators.

###  1.4. Contributions

In sum, the main contributions of this survey are:

  * •

Comprehensive Survey: We systematically identify the relevant papers on the
crowd-based efforts in combating misinformation and then review them in terms
of detection of misinformation, evaluation of counter-misinformation
effectiveness, and characterization of counter-misinformation creation. To the
best of our knowledge, this is the first review of the literature that
encompasses crowds’ contributions to these three crucial aspects.

  * •

Key Statistics: We summarize important data statistics regarding
misinformation and crowds found in the literature (88
papers333https://github.com/claws-lab/awesome-crowd-combat-misinformation).
This encompasses the common formats and topics of misinformation, the social
media platforms where crowds engage, and the inputs made by the crowds.

  * •

Novel Taxonomy: We provide a novel taxonomy of approaches that comprehensively
covers the diverse functions of crowds. This is designed to help researchers
understand the current research trends in this area.

  * •

Thorough Analysis: We conduct a comprehensive analysis of each crowd role,
including individual, collaborative, and machine-assisted labeling for
annotators, as well as survey, interview, and in-lab experiment-based counter-
misinformation effectiveness assessment for evaluators, and the
characterization of creation patterns and creator profiles for creators.

  * •

Future Directions: We discuss the limitations of existing crowd-based
approaches to combat misinformation and suggest several promising research
directions for the future.

##  2\. Method of Identifying Relevant Papers

Following PRISMA guidelines (Moher et al., 2009), we conducted a comprehensive
search for relevant papers on www.scopus.com, a reputable scientific database.
The following query was executed on September 22, 2022, and yielded 3,956
papers.

TITLE-ABS-KEY ( (Category #1) AND ( Category #2 ) AND ( Category #3 ) AND (
Category #4 ) ) AND PUBYEAR > 1999 AND ( LIMIT-TO ( LANGUAGE, "English" ) )

We specifically targeted research articles in English published after 1999.
The search utilized information from titles, abstracts, and keywords of these
articles, referred to as “TITLE-ABS-KEY”. The search combined four categories
of information using logical “AND”. These categories and their definitions are
provided in Table 2. Keywords within each category were combined using the
“OR” operator to ensure that all related concepts were included, and the
wildcard “*” was used to account for multiple spelling variations.
Specifically, Category 1 deals with terms related to crowds, particularly non-
experts, engaged in countering misinformation. Category 2 is related to online
platforms where crowd reactions to misinformation are observed. Category 3
covers misinformation-related terms and synonyms. Lastly, Category 4 indicates
actions that hinder the spread of misinformation.

Table 2. The collection of keywords used to search relevant papers. Categories |  Keywords
---|---
Category 1 |  crowd OR citizen OR community OR crowdsourcing OR group OR user OR people OR society OR human OR individual
Category 2 |  web OR "social media" OR "social network" OR internet OR online OR twitter OR facebook OR instagram OR whatsapp OR weibo OR wechat OR reddit OR tumblr
Category 3 |  misinformation OR fake OR misleading OR disinformation OR conspiracy OR rumors OR "false information" OR hoax OR *infodemic
Category 4 |  counter* OR fight* OR respond* OR argu* OR negate* OR "fact check*" OR reply* OR dispute* OR respond* OR refute* OR debunk* OR flag* OR judg* OR combat* OR censor* OR correct* OR block*

To ensure the inclusion of only relevant papers in our survey, we rigorously
followed PRISMA guidelines and established the following inclusion criteria:
First, the paper must explicitly mention crowds’ active community engagement.
This involvement typically encompasses crowd inputs such as annotations,
replies to, or comments on online misinformation; or responses related to
counter-misinformation collected through surveys, in-lab experiments, and
interviews. Second, the purpose of the aforementioned efforts or the
associated research papers should be combating misinformation. This implies
that the paper should employ these efforts to mitigate the negative impacts of
misinformation and promote the positive effects of counter-misinformation, so
as to counter misinformation. These actions may include detecting
misinformation, evaluating counter-misinformation effectiveness, and
characterizing counter-misinformation.

These criteria were established through extensive discussions between two
authors to ensure the selection of papers within the intended topic. Each
author initially assessed a batch of 200 papers together. Papers were
categorized as “Yes” if they explicitly met both criteria, “No” if they didn’t
focus on the research topic or were proposal articles, and “Maybe” if there
was some degree of confidence in their relevance. Subsequently, an inter-rater
reliability analysis was conducted, yielding a Krippendorff’s alpha score
(Krippendorff, 2011) of 0.571, indicating a moderate level of agreement
(McHugh, 2012). This seemingly low but acceptable value accounts for some
overlap between “Maybe” and “Yes” labels, which was resolved during the
collaborative review. Moreover, only papers covering the aforementioned
aspects related to the detection, evaluation, or characterization were
retained. Ultimately, this comprehensive process identified 88 papers for our
survey. Figure 3 displays the annual distribution of our selected papers
categorized by the role of the crowd.

Figure 3. Annual distribution of annotated relevant papers in our survey
categorized by the role of the crowd.

##  3\. Data Statistics of Papers

We provide a summary of the selected papers in the survey, examining relevant
statistics regarding the formats of misinformation, covered topics, utilized
social media platforms, and crowd inputs. This overview offers scholars a
preliminary understanding of the research field.

###  3.1. Misinformation

Table 3. Overview of misinformation statistics in surveyed papers. | Categories | References
---|---|---
(a) Topics | Politics | (Chua et al., 2017; Masullo and Kim, 2021; Cohen et al., 2020; Chua and Banerjee, 2017; Goh et al., 2017; Shabani and Sokhn, 2018; Soprano et al., 2021; Li et al., 2020; Dang et al., 2016; Martel et al., 2021)
Natural Disasters | (Nadamoto et al., 2013; Flores-Saviaga and Savage, 2021; Bhuiyan et al., 2020; Hunt et al., 2020; Wang and Zhuang, 2018; Weber et al., 2020; Li et al., 2020)
Health Issues | (Gunaratne et al., 2019; Stojanov, 2015; Featherstone and Zhang, 2020; Kim et al., 2021; Vraga et al., 2021; van der Meer and Jin, 2020; Masullo and Kim, 2021; Allen et al., 2021; Pal et al., 2019, 2018; Memon and Carley, 2020; Ahmed et al., 2020; Micallef et al., 2020; Igbinovia et al., 2021; Borah et al., 2021; Veeriah, 2021; Al-Motlaq, 2021; Sun et al., 2021; Bode and Vraga, 2018; Xue et al., 2022; Kim and Chen, 2022)
Crisis Events | (Zeng et al., 2019; Wang and Zhuang, 2018; McCreadie et al., 2015; Jung et al., 2020; Lee et al., 2021; Babcock et al., 2019b; Arif et al., 2017; Zhao et al., 2016; Zubiaga et al., 2016)
Civic Topics | (Pal et al., 2018, 2017; Tanaka and Hirayama, 2019; Mancosu and Vegetti, 2021; Chua et al., 2017; Chua and Banerjee, 2017; Allen et al., 2021; Li et al., 2020; Pennycook and Rand, 2019; Babcock et al., 2019b, a; Vafeiadis et al., 2019; Tchakounté et al., 2020)
General Topics | (Vo and Lee, 2019; Kirchner and Reuter, 2020; Mitra and Gilbert, 2015; Ramachandran et al., 2020; Pal et al., 2019; Giachanou et al., 2022)
(b) Platforms | Social Media | Twitter | (Nadamoto et al., 2013; Lee et al., 2021; Chua and Banerjee, 2017; Gunaratne et al., 2019; Zubiaga et al., 2016; Micallef et al., 2020; Babcock et al., 2019b; Ahmed et al., 2020; Flores-Saviaga and Savage, 2021; Hunt et al., 2020; Jung et al., 2020; Goh et al., 2017; Babcock et al., 2019a; Arif et al., 2017; Memon and Carley, 2020; Wang and Zhuang, 2018; Weber et al., 2020; Pal et al., 2018, 2017; Charlesworth Z., 2021; Giachanou et al., 2022; Buchanan et al., 2022)
Facebook | (Al-Motlaq, 2021; Allen et al., 2021; Flores-Saviaga and Savage, 2021; Charlesworth Z., 2021; Xue et al., 2022)
YouTube | (Kim and Chen, 2022; Buchanan et al., 2022)
Reddit | (Dang et al., 2016; Achimescu and Chachev, 2020)
Sina Weibo | (Zeng et al., 2019; Li et al., 2020)
Zhihu | (Chen et al., 2022)
Whatsapp | (Kligler-Vilenchik, 2022)
Crowdsourcing | AMT | (Featherstone and Zhang, 2020; Pennycook and Rand, 2019; Soprano et al., 2021; Martel et al., 2021; Sun et al., 2021)
Others | (Orosz et al., 2016; Stojanov, 2015; Mancosu and Vegetti, 2021; van der Meer and Jin, 2020; Sun et al., 2020; Igbinovia et al., 2021; Bhuiyan et al., 2020; Borah et al., 2021; Veeriah, 2021; Pundir et al., 2021; Zhao et al., 2016; Masullo and Kim, 2021; Cohen et al., 2020; Vraga et al., 2021; Mitra and Gilbert, 2015; Tanaka and Hirayama, 2019; Kim et al., 2021; Bode and Vraga, 2018; McCreadie et al., 2015; Vafeiadis et al., 2019; Pal et al., 2019; Kirchner and Reuter, 2020; Shabani and Sokhn, 2018)
Other Platforms | (Ramachandran et al., 2020; Pal et al., 2019)

####  3.1.1. Formats and Topics

Countered misinformation mainly comprises textual content, encompassing
textual posts on social media platforms. Research has explored a diverse range
of misinformation topics, as summarized in Table 3-(a). These topics consist
of politics (e.g., elections (Cohen et al., 2020) and immigration (Masullo and
Kim, 2021)); natural disasters (e.g., earthquakes (Nadamoto et al., 2013) and
climate change (Bhuiyan et al., 2020)); health issues (e.g., COVID-19 pandemic
(Micallef et al., 2020; Xue et al., 2022; Kim and Chen, 2022; Buchanan et al.,
2022), vaccines (Stojanov, 2015; Gunaratne et al., 2019; Xue et al., 2022),
and genetically modified organisms (Chen et al., 2022)); crisis events (e.g.,
mass shooting (Lee et al., 2021)); and other civic subjects, including rumors
about brands KFC (Pal et al., 2018, 2017), celebrities (Chua et al., 2017;
Chua and Banerjee, 2017), and movies (Babcock et al., 2019b, a). The research
also addresses generic misinformation topics obtained from online fact-
checking sources like Snopes.com and PolitiFact.com (Vo and Lee, 2019;
Kirchner and Reuter, 2020).

####  3.1.2. Platforms

The crowds actively combat misinformation across various online platforms
where they primarily work as counter-misinformation creators, including social
media platforms like Twitter and Facebook. Additionally, crowd-sourcing
platforms like Amazon Mechanical Turk (AMT) are leveraged to collect
annotation of misinformation and evaluation of counter-misinformation from
crowds. A summary of these platforms can be found in Table 3-(b).

Table 4. Overview of crowd inputs statistics in surveyed papers. Categories | References
---|---
(a) Format |  | Text | (Zeng et al., 2019; Ahmed et al., 2020; Flores-Saviaga and Savage, 2021; Jung et al., 2020; Goh et al., 2017; Arif et al., 2017; Memon and Carley, 2020; Wang and Zhuang, 2018; Li et al., 2020; Pal et al., 2017; Xue et al., 2022; Giachanou et al., 2022; Micallef et al., 2020; Chen et al., 2022; Kligler-Vilenchik, 2022)
| Image | (Dang et al., 2016; Pal et al., 2017; Chua and Banerjee, 2017; Buchanan et al., 2022)
| Video | (Kim and Chen, 2022)
(b) Features | Explicit | Flagging Misinformation | (Mujumdar and Kumar, 2021; Allen et al., 2022)
Credibility of News | (Bhuiyan et al., 2020; Mitra and Gilbert, 2015; Allen et al., 2021; McCreadie et al., 2015; Vraga et al., 2021; Orosz et al., 2016; Mancosu and Vegetti, 2021; van der Meer and Jin, 2020; Kim et al., 2021; Kirchner and Reuter, 2020; Bode and Vraga, 2018; Martel et al., 2021; Vafeiadis et al., 2019)
Debunking Websites | (Micallef et al., 2020; Veeriah, 2021; Allen et al., 2021; Pennycook and Rand, 2019; McCreadie et al., 2015)
Countermeasures | (Ramachandran et al., 2020; Shabani and Sokhn, 2018; Kirchner and Reuter, 2020; Featherstone and Zhang, 2020; Masullo and Kim, 2021; Martel et al., 2021; Vafeiadis et al., 2019; Bode and Vraga, 2018; Kim et al., 2021; van der Meer and Jin, 2020; Cohen et al., 2020; Stojanov, 2015; Sun et al., 2021; Pal et al., 2018, 2019)
Demographics | (Pal et al., 2018, 2019; Igbinovia et al., 2021; Zhao et al., 2016)
Media Literacy | (Vraga et al., 2021; Veeriah, 2021; Pundir et al., 2021)
Emotion | (Sun et al., 2020; Pundir et al., 2021; van der Meer and Jin, 2020)
Conspiracy Mentality | (Mancosu and Vegetti, 2021; Orosz et al., 2016; Bode and Vraga, 2018)
Implicit | Textual Embedding | (Micallef et al., 2020)
Psycholinguistic Features | (Micallef et al., 2020; Shabani and Sokhn, 2018; Memon and Carley, 2020; Pal et al., 2018; Chua and Banerjee, 2017; Gunaratne et al., 2019; Xue et al., 2022; Giachanou et al., 2022)
Topic | (Shabani and Sokhn, 2018; Dang et al., 2016)
Sentiment | (Pal et al., 2018; Micallef et al., 2020; Shabani and Sokhn, 2018; Li et al., 2020; Dang et al., 2016)
Emotion | (Kim and Chen, 2022)
Hashtag | (Ahmed et al., 2020; Babcock et al., 2019a; Weber et al., 2020)
URL | (Chua and Banerjee, 2017; Ahmed et al., 2020; Hunt et al., 2020; Jung et al., 2020; Weber et al., 2020; Micallef et al., 2020)
Number of Likes/Shares | (Hunt et al., 2020; Pal et al., 2017)
Group Identity Language | (Chen et al., 2022)

###  3.2. Crowd Inputs

####  3.2.1. Content Formats

Crowds counter misinformation through diverse content formats, as outlined in
Table 4-(a). The primary involves utilizing various textual formats such as
posting counter-misinformation content, commenting on news articles, replying
to social media posts, and retweeting or sharing corrective information (Zeng
et al., 2019; Ahmed et al., 2020; Flores-Saviaga and Savage, 2021; Jung et
al., 2020; Goh et al., 2017; Arif et al., 2017; Memon and Carley, 2020; Wang
and Zhuang, 2018; Li et al., 2020; Pal et al., 2017). Additionally, images
often supplement textual content to enhance the effectiveness of countering
misinformation (Pal et al., 2017; Chua and Banerjee, 2017). Lastly, video
content serves as an effective tool to debunk misinformation on platforms like
YouTube and has demonstrated its potential to educate the general public (Kim
and Chen, 2022).

####  3.2.2. Features Extracted from Crowd Inputs

Crowds provide a diverse range of content in response to misinformation,
offering researchers valuable features that can be utilized for misinformation
detection and counter-misinformation characterization. In this survey, we
categorize them into explicit and implicit features, as shown in Table 4-(b).
Explicit features involve a direct examination of raw inputs. This includes
activities such as rating and flagging misinformation (Mujumdar and Kumar,
2021; Allen et al., 2022), assessing credibility scores of news articles
(Bhuiyan et al., 2020; Mitra and Gilbert, 2015; Allen et al., 2021; McCreadie
et al., 2015; Vraga et al., 2021; Orosz et al., 2016; Mancosu and Vegetti,
2021; van der Meer and Jin, 2020; Kim et al., 2021; Kirchner and Reuter, 2020;
Bode and Vraga, 2018; Martel et al., 2021; Vafeiadis et al., 2019),
identifying valuable debunking websites (Micallef et al., 2020; Veeriah, 2021;
Allen et al., 2021; Pennycook and Rand, 2019; McCreadie et al., 2015),
suggesting countermeasures (Ramachandran et al., 2020; Shabani and Sokhn,
2018; Kirchner and Reuter, 2020; Featherstone and Zhang, 2020; Masullo and
Kim, 2021; Martel et al., 2021; Vafeiadis et al., 2019; Bode and Vraga, 2018;
Kim et al., 2021; van der Meer and Jin, 2020; Cohen et al., 2020; Stojanov,
2015; Sun et al., 2021; Pal et al., 2018, 2019), and collecting demographic
attributes of users (Pal et al., 2018, 2019). Implicit features, on the other
hand, are derived through computational methods applied to raw inputs. These
methods generate new feature vectors, such as textual embeddings (Micallef et
al., 2020), psycholinguistic features (Micallef et al., 2020; Shabani and
Sokhn, 2018; Memon and Carley, 2020; Pal et al., 2018; Chua and Banerjee,
2017; Gunaratne et al., 2019; Xue et al., 2022; Giachanou et al., 2022), and
sentiment (Pal et al., 2018; Shabani and Sokhn, 2018; Li et al., 2020; Dang et
al., 2016) and other computational metrics (Li et al., 2020; Babcock et al.,
2019a; Chua and Banerjee, 2017; Ahmed et al., 2020; Hunt et al., 2020; Jung et
al., 2020; Weber et al., 2020; Micallef et al., 2020; Pal et al., 2017; Chen
et al., 2022) for (counter-)misinformation analysis.

##  4\. Crowds as Annotators for Identifying Misinformation

Following our meta-level summary of relevant papers, we now dive deeper into
the functional categorization of the roles of crowds. Our taxonomy,
represented in Figure 2, categorizes the contributions of crowds along three
axes: annotators, evaluators, and creators. In this section, we focus on the
“annotator” category, where crowds serve as annotators for identifying
misinformation.

Figure 4. Illustration of crowds’ role as annotators for identifying
misinformation.

To identify misinformation, many ML methods have emerged for automated
detection (Sharma et al., 2019). However, these methods heavily rely on the
expertise of fact-checkers to obtain ground-truth annotations (e.g., fact-
check labels), whose population and bandwidth are unfortunately limited. In
contrast, emerging approaches involve harnessing the fact-checking competence
of the crowd, especially laypeople, for efficient and effective misinformation
annotation (Ramachandran et al., 2020; Mitra and Gilbert, 2015). In this
context, crowds can directly label potential misinformation articles. Notably,
recent research suggests that crowd-based annotation performance rivals that
of professionals (Bhuiyan et al., 2020; Roitero et al., 2021), enabling the
use of these labels in downstream tasks such as misinformation classification
(Shabani and Sokhn, 2018) and (counter-)misinformation analysis (Goh et al.,
2017). Therefore, we review the relevant studies where crowds annotate
misinformation. These studies fall into three key categories: individual
labeling of misinformation, collaborative labeling by a group of annotators,
and machine-assisted labeling with crowd inputs. Figure 4 provides a visual
representation, and Table 5 offers a comprehensive list of reference papers.
We then provide detailed descriptions of each category in the subsequent
subsections.

Table 5. Taxonomy of crowd annotators for identifying misinformation. Categories | References
---|---
Individual Labeling | (Roitero et al., 2021; Ramachandran et al., 2020; Mitra and Gilbert, 2015; Xu et al., 2022; Bhuiyan et al., 2020)
Collaborative Labeling | (Mujumdar and Kumar, 2021; Allen et al., 2022)
Machine-assisted Labeling | (Shabani and Sokhn, 2018; Mitra and Gilbert, 2015; Ramachandran et al., 2020; Farooq et al., 2022)

###  4.1. Individual Labeling

Individual labeling of misinformation requires independent judgments about the
credibility of the given misinformation by each crowd member. To accomplish
this labeling, platforms such as Amazon Mechanical Turk (Roitero et al., 2021)
and Upwork (Bhuiyan et al., 2020) facilitate direct user labeling
(Ramachandran et al., 2020; Mitra and Gilbert, 2015). On these platforms,
labels obtained from crowds are typically verified through majority voting.
During this process, the credibility levels of the crowds increase as they
accurately identify misinformation.

Furthermore, several factors have been explored that influence the quality of
individual labeling (Xu et al., 2022; Bhuiyan et al., 2020; Roitero et al.,
2021). One significant factor is evidence from other peers (Xu et al., 2022),
which can either aid or mislead crowds in their judgments. Crowds who
effectively use provided evidence tend to make more accurate annotations.
Moreover, the demographic and political composition can also influence crowds’
credibility ratings and annotations. For example, Bhuiyan et al. (2020) found
that Democrats, males, those between the ages of 26 and 30, and those with
higher levels of education are more likely to agree with experts on climate
science.

Additionally, characteristics of the annotation task itself, such as the genre
of the article and partisanship of the publication, also have an impact.
Bhuiyan et al. (2020) found that crowds demonstrate a higher correlation with
experts in opinion articles and left-leaning publications. Lastly, the length
of the annotation period can also affect the quality of the judgments made by
crowds. Notably, Roitero et al. (2021) found that annotations collected at
different time spans for the same document may yield different results.
Annotations collected in close proximity to each other tend to produce similar
outcomes.

###  4.2. Collaborative Labeling

In addition to individual labeling, collaborative labeling indicates that
multiple individuals are contributing to annotations collaboratively, which
enhances quality through shared insights and community engagement (Mujumdar
and Kumar, 2021; Allen et al., 2022). Twitter’s Birdwatch/Community
Notes444https://help.twitter.com/en/using-twitter/community-notes, introduced
in 2021, is a prominent example. In this initiative, crowds review the
annotations of others and then label tweets that may contain misinformation by
providing supporting material and annotations of others. It is worth noting
that, unlike majority-based aggregation commonly used in individual labeling
scenarios, collaborative labeling involves group-level interactions that occur
before final label consensus. This aspect makes collaborative labeling a more
crowd-enabled process compared to individual labeling. Subsequently, research
efforts have emerged to investigate and address challenges within the
Birdwatch collaborative labeling ecosystem. For instance, crowds have
different levels of credibility in their annotations, which can result in
biased or unfair labeling. To tackle this issue, Mujumdar and Kumar (2021)
proposed HawkEye, a robust reputation system for fair user ranking and
misinformation labeling. Additionally, Allen et al. (2022) investigated the
impact of crowds’ partisanship within the Birdwatch annotation process. They
found that crowds tend to offer negative annotations for tweets from counter-
partisans and consider their annotation less helpful.

###  4.3. Machine-assisted Labeling

Machine-assisted labeling methods combine computational power and crowd inputs
to efficiently detect misinformation (Shabani and Sokhn, 2018; Mitra and
Gilbert, 2015). In the human-in-the-loop pipeline proposed by Shabani and
Sokhn (2018), crowds initially verify misinformation labels assigned by ML
classifiers, and this feedback is then used to refine the classifiers (Shabani
and Sokhn, 2018). Additionally, Farooq et al. (2022) proposed a blockchain-
based framework that leverages the inherent immutability and incentive
features of blockchains to record crowd annotations, ensuring accountability
while also rewarding accurate contributions and penalizing malicious
annotations. Similarly, Ramachandran et al. (2020) incorporated the blockchain
framework to collect the given genuineness score of news articles from crowds
to improve the downstream fake news detection algorithms. Lastly, topic
modeling techniques can be used to cluster tweets on similar topics, and
crowds can assess the credibility of these tweets to ensure a coherent and
consistent annotation process (Mitra and Gilbert, 2015).

##  5\. Crowds as Evaluators for Assessing Counter-Misinformation
Effectiveness

Figure 5. Illustration of crowds’ role as evaluators to assess counter-
misinformation effectiveness.

Once misinformation is identified by crowds, ML classifiers, or professionals,
different counter-misinformation messages are created on social media
platforms. In this case, crowds can help evaluate the effectiveness and
limitations of various counter-misinformation messages. To introduce
evaluation methods utilizing crowds as evaluators in the literature, we
categorize them into two categories, i.e., direct and indirect assessments by
crowds (see Figure 5). Each category consists of three sub-categories: direct
assessment–in-lab experiment, interview, and survey; indirect
assessment–sentiment, stance, and number of shares. Next, we describe the
details of each category and then discuss findings regarding effective
counter-misinformation across different factors, such as media formats,
countering order, word placement, content traits, communication styles, and
audience-related factors, in the subsequent subsections. The taxonomy of
evaluation methods and factors can be found in Table 6-(a) and -(b),
respectively.

###  5.1. Crowd-based Evaluation Methods

As mentioned above, two evaluation methods can be employed to evaluate the
efficacy of counter-misinformation using crowd input. The first method,
referred to as direct assessment, involves the use of direct quantitative
questions in which crowds rate the believability of misinformation after
encountering counter-misinformation messages. This approach directly assesses
the preferences and perceptions of the crowd, with lower believability
indicating more effective countermeasures. In contrast, the second method,
termed indirect assessment, involves the use of indirect indicators or proxy
metrics. To this end, we can analyze attitudes expressed in the comments and
fact-checking shares of the crowds to indirectly measure the effectiveness of
countering misinformation.

####  5.1.1. Direct Assessment

Direct assessment of counter-misinformation through quantitative questions
involves various research methodologies, including in-lab experiments,
interviews, and surveys.555Some reviewed papers use the term questionnaire. In
this work, we use “survey” to cover questionnaire- as well as survey-based
methodologies. In-lab experiments are a common approach in many studies
(Stojanov, 2015; Kim et al., 2021; Pasquetto et al., 2022; Kessler and
Bachmann, 2022; Tay et al., 2022; Dai et al., 2022; Pillai et al., 2022;
Masullo and Kim, 2021; Vraga et al., 2021). Crowds are presented with both
misinformation and various types of countering-misinformation articles. They
then answer questions to assess the plausibility and believability of
misinformation or counter-misinformation (Yu et al., 2022). As an advanced
design for this methodology, Orosz et al. (2016) ask crowds to first evaluate
their initial belief in the misinformation, view the counter-misinformation,
and then reevaluate their belief. The change in belief serves as a measure of
the effectiveness of counter-misinformation. In-lab experiments investigate
diverse countering strategies, including the impact of news sources, both
mainstream and private (Mancosu and Vegetti, 2021), and other factors (van der
Meer and Jin, 2020; Tanaka and Hirayama, 2019; Kim et al., 2021; Allen et al.,
2021; Featherstone and Zhang, 2020; Pennycook and Rand, 2019; Soprano et al.,
2021; Kirchner and Reuter, 2020; Bode and Vraga, 2018; McCreadie et al., 2015;
Martel et al., 2021; Sun et al., 2021).

Table 6. Taxonomy of crowd evaluators for counter-misinformation effectiveness. | Categories | References
---|---|---
(a) Methods | Direct | In-lab Experiment | (Stojanov, 2015; Kim et al., 2021; Pasquetto et al., 2022; Kessler and Bachmann, 2022; Tay et al., 2022; Dai et al., 2022; Pillai et al., 2022; Masullo and Kim, 2021; Vraga et al., 2021; Yu et al., 2022; van der Meer and Jin, 2020; Tanaka and Hirayama, 2019; Kim et al., 2021; Allen et al., 2021; Featherstone and Zhang, 2020; Pennycook and Rand, 2019; Soprano et al., 2021; Kirchner and Reuter, 2020; Bode and Vraga, 2018; McCreadie et al., 2015; Martel et al., 2021; Sun et al., 2021)
Interview | (Borah et al., 2021)
Survey | (Kessler and Bachmann, 2022; Kirchner and Reuter, 2020)
Indirect | Sentiment | (Zhang et al., 2022)
Stance | (Wang et al., 2022b, 2021)
Number of Shares | (Chen et al., 2021)
(b) Findings | Media Formats | (Kirchner and Reuter, 2020; Veeriah, 2021; Yu et al., 2022; Yang and Overton, 2022; Vraga et al., 2021; Kessler and Bachmann, 2022; Masullo and Kim, 2021; Pasquetto et al., 2022)
Content Traits | (Wang et al., 2021; Kessler and Bachmann, 2022; Orosz et al., 2016; Stojanov, 2015; Kirchner and Reuter, 2020; Yu et al., 2022)
Communication Styles | (Kim et al., 2021; Masullo and Kim, 2021; Orosz et al., 2016; Wang et al., 2022b; Yu et al., 2022)
Audience Factors | (Tanihara et al., 2022; Wang et al., 2022b; Pasquetto et al., 2022; Yang and Overton, 2022; Primig, 2022)
Countering Order | (Dai et al., 2022; Tay et al., 2022)
Word Placement | (Pillai et al., 2022)

Interviews offer personal interactions for crowds to engage in conversations
and ask open-ended questions about effective strategies for countering
misinformation. During the COVID-19 pandemic in 2020, Borah et al. (2021)
interviewed young adults and active social media users regarding their
COVID-19 perceptions, coping strategies, and recommended countermeasures.
Findings advocate calling out people sharing misinformation and bringing up
media literacy programs to combat misinformation.

Finally, online surveys efficiently expand sample sizes and enhance
inclusivity and accessibility for geographically dispersed or busy
participants, thereby complementing in-lab experiments and interviews. These
surveys collect demographic information and gather crowd opinions on
misinformation and counter-misinformation, including how individuals handle
potential false posts (Kirchner and Reuter, 2020) and their evaluations of
countering efforts (Kessler and Bachmann, 2022).

####  5.1.2. Indirect Assessment

In contrast to direct assessment, indirect assessment utilizes proxy metrics
to measure the effectiveness of counter-misinformation messages in a data-
driven manner. For instance, Zhang et al. (2022) analyze the sentiment in the
comments and replies of the crowds to counter-misinformation messages on Sina
Weibo, and use it as an indicator of the efficacy of counter-misinformation
messages. Similarly, other works assess the stance expressed in comments on
fake news rebuttals as a proxy for countering acceptance (Wang et al., 2022b,
2021). They categorize stances as supporting, denying, questioning, and
commenting, and define the normalized combined distribution of denying and
supporting stances among all comments as the debunking effectiveness index to
indicate countering effectiveness (Wang et al., 2021). Additionally, Chen et
al. (2021) use the number of shares of fact-checks as a proxy for the efficacy
of certain fact-checks. They explore the influence of peripheral and central
cues on fact-check shares, observing that stronger effects lead to higher
shares.

###  5.2. Findings from Effective Counter-Misinformation

After establishing the appropriate crowd-based evaluation methods, we utilize
crowd evaluators to gain comprehensive insights into the effectiveness of
counter-misinformation efforts. Specifically, we aim to uncover the underlying
factors contributing to these endeavors, considering dimensions such as media
formats, content traits, communication styles, audience-related factors,
countering order, and word placement.

####  5.2.1. Media Formats

We examine the impacts of different formats employed in counter-misinformation
efforts. The predominant format is to respond to textual misinformation with
text-based countermeasures (Kirchner and Reuter, 2020; Veeriah, 2021; Yu et
al., 2022; Yang and Overton, 2022). Furthermore, a study by Vraga et al.
(2021) explored text-based responses to video misinformation on health topics.
Crowd evaluators viewed misinformation videos and accompanied debunking
comments. The result showed that real-time crowd debunking in text partially
reduced belief in misinformation ($p<0.01$). Additionally, Kessler and
Bachmann (2022) investigated text and images, finding credible text-based
debunking supported by evidence effectively countered misinformation, while
images alone had limited impact. Moreover, Masullo and Kim (2021) studied
uncivil countering comments with emojis, discovering that while the uncivil
countering text has effects on the perception of the credibility of the news
stories, reactions like “angry” can mitigate incivility and reduce dislike of
news or comments, especially across different political leanings ($p<0.01$).
This finding potentially indicates the efficacy of emojis when countering
misinformation. Meanwhile, Pasquetto et al. (2022) found that audio-based
corrections on WhatsApp platforms generate more interest and are more
effective than text- or image-based messages in countering misinformation
($p=0.01$).

####  5.2.2. Content Traits

We further delve into the key content characteristics that are crucial to
effectively countering misinformation. These include the ability to uncover
the motivations behind misinformation (Stojanov, 2015), the use of evidence-
based and logical counter-arguments (Wang et al., 2021; Orosz et al., 2016),
the inclusion of warnings to alert readers to potential misinformation
(Kirchner and Reuter, 2020), and the careful selection of credible sources for
correction (Yu et al., 2022), which are elaborated on below.

Regarding revealing misinformation motives, Stojanov (2015) examined the
impact of debunking messages that reveal the motives behind conspiracy
theories, particularly in the context of countering vaccine-related medical
conspiracy theories. Their findings suggest that revealing the motives of
conspiracy theorists can effectively reduce belief in medical conspiracy
theories ($p<0.05$), although the effect on belief in general conspiracy
theories is less clear.

Regarding supporting claims with evidence, Wang et al. (2021) highlighted the
importance of citing evidence to enhance the debunking effect, which is better
than debunking methods with uncited evidence ($p<0.001$). Subsequently,
Kessler and Bachmann (2022) extended these findings by confirming that
credible debunking text (rather than images) with evidence is effective in
countering misinformation ($p<0.001$). Another related aspect involves logical
counter-arguments. Orosz et al. (2016) presented subjects with conspiracy
theory statements alongside countering posts containing rational counter-
arguments. They found that this approach was effective in reducing belief in
conspiracy theories ($p\leq 0.01$). Additionally, pointing out the fallacies
in the reasoning behind the original misinformation post, as demonstrated by
Stojanov (2015), was also effective in reducing belief in medical conspiracy
theories.

Regarding displaying warning messages, Kirchner and Reuter (2020) revealed
that German adults have a strong preference for displaying warnings on
inaccurate posts as a strategy to combat fake news (65% agreement compared to
the baseline of 51-57%). Crowd evaluators also stressed the importance of
social media platforms providing explanations for flagged misinformation (71%
agreement compared to the baseline of 51-57%). In summary, warning-based
approaches have demonstrated significant effectiveness in reducing the
believability of fake news, particularly when accompanied by explanatory text.

Lastly, when selecting correction sources, researchers have identified
variations in the persuasiveness of different sources, particularly
considering that the impact of corrections can vary across different
societies. For example, Yu et al. (2022) highlighted that in authoritative
societies, corrections from government sources tend to have greater
credibility than those from professionals or laypeople ($p\leq 0.05$). This
underscores the need to tailor correction strategies and source selection to
specific societal contexts.

####  5.2.3. Communication Styles

Analyzing different communication styles of counter-misinformation, such as
humorous and uncivil responses, provides an additional dimension to evaluating
its effectiveness. Regarding humor, researchers have explored the potential of
humor in correcting misinformation. For example, Kim et al. (2021)
investigated the impact of humorous versus non-humorous corrective messages in
countering misinformation about the HPV vaccine. The study found that when
correcting misinformation with humor, crowds’ visual attention increased
towards the image portion of the counter-misinformation contents, indirectly
reducing HPV misperceptions when measured by the reduced credibility of the
misinformation. Regarding uncivil responses, Masullo and Kim (2021)
investigated the effect of uncivil counter-misinformation comments on readers’
attitudes and perceptions. In an online survey, crowd evaluators were exposed
to misinformation articles, uncivil comments, and corresponding social
reactions to the comments, followed by questions about their feelings. The
findings revealed that while uncivil counter-comments influenced how readers
viewed the comments and the commentators themselves, they did not affect the
credibility of the underlying articles. This suggests that uncivil responses
may not contribute significantly to countering misinformation.

In addition, the literature has explored other factors such as ridiculing vs.
empathetic, readability, and tone of correction. According to Orosz et al.
(2016), ridiculing arguments effectively reduce belief in conspiracy theories,
while empathetic counterarguments have no significant impact on belief in
misinformation. Wang et al. (2022b) investigated the impact of the readability
of the rebuttal text on crowds’ acceptance of the rebuttal. The study found
that higher readability positively influenced users’ acceptance of the
rebuttal. Improved readability indicates that users can easily understand and
absorb the rebuttal, thereby enhancing its effectiveness. Lastly, Yu et al.
(2022) examined the impact of the tone of corrective messages on the
believability of the correction. The research indicated that a formal tone was
more believable than a less formal and conversational tone.

####  5.2.4. Audience Factors

In addition to the inherent characteristics of counter-misinformation, a
number of external factors related to the audience, including media literacy,
cognitive capability, political stance, trust in media, and concern about
misinformation, also have an impact on its effectiveness.

Research has extensively explored the impact of media literacy on the
perception of misinformation. For instance, Tanihara et al. (2022) found that
crowd evaluators with lower levels of media literacy are more likely to change
their perceptions of misinformation when exposed to corrective messages. In a
study conducted by Wang et al. (2022b), cognitive capability was assessed
based on an individual’s language-related cues, including vocabulary size. The
findings revealed that crowd evaluators with lower cognitive capabilities tend
to rely on the credibility of the source and the quality of the argument in
the rebuttal text to accept it. On the other hand, readers with high cognitive
capability have a greater demand for greater readability, even when the
sources and arguments are solid.

Regarding political stance, Pasquetto et al. (2022) found that the correction
from people with strong ties (e.g., the same political stance) can lead crowds
to more actively re-share these debunks and counter misinformation. This
tendency was corroborated by Yang and Overton (2022), who observed that crowd
evaluators are more likely to accept corrections from sources that align with
their existing attitudes. Regarding trust in media, Primig (2022) discovered
that greater trust in media sources corresponds to higher believability in
fact-checking sources and messages, especially when combined with trust in
politics. Conversely, for social corrections on social media platforms, Yang
and Overton (2022) noted their effectiveness is more pronounced among users
who experience higher levels of uncertainty on these platforms. Regarding
concern about misinformation, Yang and Overton (2022) found that social
corrections work better among users who are more concerned or worried about
the potential harm caused by misinformation on social media platforms.

####  5.2.5. Countering Order and Word Placement

Researchers have also examined other less prominent factors such as the order
of counter-misinformation and the placement of negation. Regarding countering
order, two contrasting approaches emerge: debunking starts by presenting
misinformation and then offers a counterresponse, while prebunking reverses
this order by introducing counter-misinformation as a preemptive warning or
reminder. Dai et al. (2022) confirmed that prebunking messages, especially
when combined with fact-checks or inoculation messages, effectively made crowd
evaluators more skeptical of misinformation. Additionally, Tay et al. (2022)
conducted a more comprehensive evaluation that extended beyond traditional
questionnaires to assess user behaviors, such as information seeking and the
promotion of online misinformation. The results highlighted the effectiveness
of both prebunking and debunking in reducing belief in misinformation.

Another nuanced aspect studied is how the placement of negation during
debunking impacts crowd memory and, consequently, the effectiveness of
debunking (Pillai et al., 2022). In social media, brief affirmations or
negations are often used to clarify claims. In this context, Pillai et al.
(2022) found that when crowd evaluators encounter negated messages (e.g.,
“This is wrong.”), they often remember the main claim but forget the negative
part. They tested placing the negation before or after the entire claim and
found that both methods were equally memorable. This result highlights the
possibility of diverse approaches to conveying affirmations and negations.

##  6\. Crowds as Creators of Counter-Misinformation

Figure 6. Illustration of crowds’ role as creators of counter-misinformation.

In addition to serving as annotators and evaluators, crowds have the ability
to create their own counter-misinformation messages on social media platforms.
In this section, we aim to examine their role as content creators (see Figure
6) to investigate their unique perspective and proactive involvement in
combating misinformation while characterizing their content. To achieve this
goal, we first present three characterization methods that measure how crowds
actively combat misinformation as creators. These methods are: data-driven
analysis, in-lab experiment, and survey (see Table 7-(a)). Based on these
methods, we discuss findings on analyzing counter-misinformation generated by
crowds, with a specific focus on creation patterns and creator profiles (see
Tables 7-(b) and (c)). Specifically, we conduct a comprehensive examination of
counter-misinformation generated by crowds, revealing the creation patterns of
counter-misinformation. Finally, we explore the intricate dynamics of the
human elements involved, focusing on the profiles of crowd creators.

Table 7. Taxonomy of crowd creators for analyzing crowd-generated counter-misinformation.  |  | Categories | References
---|---|---|---
(a) Methods |  Creation Patterns |  Data-driven Analysis | (Al-Motlaq, 2021; Micallef et al., 2020; Xue et al., 2022; Kim and Chen, 2022; Hunt et al., 2020; Pal et al., 2017; Giachanou et al., 2022)
Creator Profiles | In-lab Experiment | (Pal et al., 2018, 2019; Cohen et al., 2020; Igbinovia et al., 2021; Sun et al., 2020; Huber et al., 2022; Veeriah, 2021)
| Survey | (Chen and Fu, 2022)
(b) Creation Patterns | Language | Sentiment | (Al-Motlaq, 2021; Micallef et al., 2020)
Emotion | (Xue et al., 2022; Micallef et al., 2020; Kim and Chen, 2022)
Psycholinguistic Features | (Micallef et al., 2020; Xue et al., 2022; Giachanou et al., 2022)
URL | (Micallef et al., 2020; Pal et al., 2017; Hunt et al., 2020)
Hashtag | (Gunaratne et al., 2019; Babcock et al., 2019a)
Others | (Zubiaga et al., 2016; Nadamoto et al., 2013; Chen et al., 2022)
Network | Network Co-existence Pattern | (Gunaratne et al., 2019)
Information Transmission | (Zubiaga et al., 2016)
User Connectivity Pattern | (Ahmed et al., 2020; Chiu et al., 2022)
User Engagement | Post Volume | (Wang et al., 2022a; Micallef et al., 2020)
Post Sharing | (Micallef et al., 2020; Chua et al., 2017)
Temporal | Temporal Trend | (Gunaratne et al., 2019)
Life Cycle | (Zubiaga et al., 2016)
(c) Creator Profiles | Demographics | Age and Education | (Vijaykumar et al., 2022)
Political Ideology | (Cohen et al., 2020; Steinfeld, 2022)
Media Literacy | (Igbinovia et al., 2021; Vijaykumar et al., 2022; Veeriah, 2021; Huber et al., 2022)
Mindsets | Sharing Denials | (Pal et al., 2018, 2019)
Others’ Susceptibility | (Sun et al., 2020)
Third-person Effect | (Chen and Fu, 2022)
Harm Awareness | (Zhao et al., 2016; Vijaykumar et al., 2022)

###  6.1. Characterization Methods

To establish a comprehensive framework for analyzing the active counter-
misinformation creation process involving human elements, we present three
characterization methods in Table 7-(a). We note that different methods have
been utilized depending on what we aim to characterize. First, researchers
mainly rely on data-driven analysis to understand the creation patterns of
effective crowd-generated counter-misinformation. In a nutshell, they begin by
crawling large-scale crowd-generated data, including posts and social network
relationships. This data is then examined from different perspectives, such as
language patterns, networks, and temporal trends (Al-Motlaq, 2021; Micallef et
al., 2020; Xue et al., 2022; Kim and Chen, 2022; Hunt et al., 2020; Pal et
al., 2017; Giachanou et al., 2022), which will be elaborated in Section 6.2.

On the other hand, researchers employ a combination of in-lab experiments and
surveys to study creator profiles. For in-lab experiments (Pal et al., 2018,
2019; Cohen et al., 2020; Igbinovia et al., 2021; Sun et al., 2020; Huber et
al., 2022; Veeriah, 2021), crowd creators are exposed to misinformation text
and then asked to respond by either countering or endorsing it. In the
meantime, researchers collect information on creators, including their
political leanings and beliefs related to misinformation, in order to analyze
their attributes. Additionally, researchers conduct online surveys to assess
the creators’ levels of belief in misinformation and collect demographic
information, including their media competency (Chen and Fu, 2022). The results
are discussed in Section 6.3.

The goal of these methods is to characterize the creators themselves and
uncover the patterns and attributes associated with crowd-generated counter-
misinformation. In this context, it is important to note that this objective
contrasts with the goal of the crowd-based evaluation methods, which assess
misinformation countermeasure effectiveness.

###  6.2. Creation Patterns

In the battle against misinformation, crowds actively contribute by generating
a multitude of content that includes debunking posts and responses. A
significant amount of research has directed its attention towards
comprehending how such counter-misinformation messages are crafted and
pinpointing the patterns they contain (Micallef et al., 2020; Chua et al.,
2017; Nadamoto et al., 2013; Kim and Chen, 2022). Researchers typically first
manually label sampled text and visuals into categories such as misinformation
and counter-misinformation, and then train classifiers for extensive
categorization on unlabeled data points due to the high cost of manual
labeling. This comprehensive categorization serves as the basis for analyzing
counter-misinformation, including content characteristics, network dynamics,
temporal aspects, and social media engagement. For a detailed breakdown of
these dimensions, refer to Table 7-(b).

####  6.2.1. Language Analysis

Crowd-generated counter-misinformation messages exhibit distinct language
patterns when compared to other online content (Al-Motlaq, 2021; Xue et al.,
2022; Kim and Chen, 2022). We summarize these patterns through various
aspects, including sentiments, emotions, psycholinguistic features, and
textual components like URLs and hashtags.

First, sentiment analysis revealed differences among various topics. For
instance, during the COVID-19 pandemic, positive attitudes were expressed in
comments countering COVID-19 conspiracy theories on Facebook (Al-Motlaq,
2021), while counter-misinformation tweets regarding fake COVID-19 cures were
more neutral (Micallef et al., 2020). In addition, emotions expressed in
counter-misinformation have been explored in various contexts, demonstrating
that contexts affect different emotional responses. For instance, Xue et al.
(2022) used IBM Watson Tone Analyzer to extract emotions, revealing that
COVID-19 vaccine fact-checking posts on Facebook tended to maintain a neutral
tone, while public comments often displayed more emotionally charged
responses. Additionally, Kim and Chen (2022) analyzed emotions in videos
related to COVID-19 on YouTube. They utilized the NRC Emotion
Lexicon666http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm, which
contains a list of English words and their associations with emotions and
sentiments. The study identified two emotional dimensions, trust and fear, and
observed differential utilization of these emotions in debunking videos.

Furthermore, researchers have delved into the psycholinguistic features of
counter-misinformation using tools such as Linguistic Inquiry and Word Count
(LIWC) or IBM Watson Tone Analyzer (Micallef et al., 2020; Xue et al., 2022;
Giachanou et al., 2022). For instance, on Facebook, posts fact-checking
COVID-19 vaccine information exhibited higher levels of confidence compared to
general public posts (Xue et al., 2022). Additionally, Giachanou et al. (2022)
discovered that crowds tend to employ more positive language and causal
language, whereas misinformation spreaders often use more informal language.

Additionally, two noteworthy elements frequently integrated into counter-
misinformation messages are URLs and hashtags. Uniform Resource Locators
(URLs) serve as sources of evidence-based resources (Micallef et al., 2020),
often cited in tweets that counter rumors (Pal et al., 2017). Moreover, URLs
have proven effective in disseminating information for debunking during
disaster events, often citing news agencies as their primary information
sources (Hunt et al., 2020). Hashtags, on the other hand, are employed to
enhance the visibility and categorization of content. For example, Gunaratne
et al. (2019) analyzed vaccine-related tweets and found that 86% of users
exclusively used pro-vaccine hashtags, while 12% opted for anti-vaccine
hashtags. Additionally, Babcock et al. (2019a) found that the hashtag
“#fakenews” was widely used to attack fake news.

Besides these specific content attributes, other dimensions like stance
(Zubiaga et al., 2016), user impression (Nadamoto et al., 2013), politeness
(Micallef et al., 2020), and the group identity language (Chen et al., 2022)
have also been examined in counter-misinformation.

####  6.2.2. Network Analysis

Researchers employ various network analysis approaches and tools (e.g., NodeXL
and Gephi (Flores-Saviaga and Savage, 2021; Jung et al., 2020; Babcock et al.,
2019b; Memon and Carley, 2020; Dang et al., 2016; Weber et al., 2020)) to
uncover the intricate interplay between misinformation/counter-misinformation
information flow and user connections on social media platforms. The
representative approaches encompass patterns of co-existence in networks, post
sharing/(re)tweeting, and connectivity of users.

Regarding network co-existence patterns, Gunaratne et al. (2019) examined the
co-occurrence of anti-vaccine and pro-vaccine hashtags on Twitter. Their
analysis involved constructing a network with hashtags as nodes and co-
occurring hashtags as edges, revealing distinct community structures within
both pro-vaccine and anti-vaccine hashtags. The results highlighted that pro-
vaccine hashtags formed a dominant community along with a few closely
connected sub-communities. Conversely, anti-vaccine hashtags largely converged
into one community with a smaller, remote sub-community that focuses on
specific vaccines. This analysis enhanced our understanding of the discourse
and inter-relationship between misinformation and counter-misinformation.

To investigate information transmission within a social network, Zubiaga et
al. (2016) investigated the rumor (re)tweeting networks which included three
types of retweets: (1) unverified source tweets, (2) accurate tweets
supporting true information or denying false rumors (i.e., counter-
misinformation), and (3) inaccurate tweets denying true information or
supporting false rumors (i.e., misinformation). By examining retweeting
relationships between (re)tweet nodes, they discovered that retweets of
accurate tweets were primarily observed in certain misinformation topics, such
as the Ottawa shooting and the Sydney siege. In contrast, tweets sharing
unverified rumors had a broader spread across different topics.

User connectivity patterns on social network platforms such as Twitter have
also been studied in the literature (Ahmed et al., 2020; Chiu et al., 2022).
For instance, during the COVID-19 pandemic on Twitter, Ahmed et al. (2020)
regarded crowds as nodes and the “reply-to” or “mention” relationships in
tweets as edges in a graph. A “self-loop” relationship is added in the graph
if there is no “reply-to” or “mention” in the tweets. By analyzing such a
graph, researchers found that $32.2\%$ of the crowds denounced the COVID-19 5G
conspiracy theory. They also identified the two largest network structures,
consisting of an isolated group and a broadcast group. The results revealed
that there was a lack of an active authority figure to counter the spread of
misinformation. Instead, the crowds countered the conspiracy theory with
widespread denouncement. Similarly, Chiu et al. (2022) considered the
“mention,” “retweet,” and “self-loop” relationships between users to create
the graph and conduct the cluster analysis, while focusing on the diffusion
scope, speed, and shape for true and fake news across the users. They also
tested whether the attributes of the true or fake news spreaders would affect
the aforementioned three metrics. The results showed that true news from the
crowd tended to spread later and with less broadcast influence compared to
fake news. In addition, Wang et al. (2022a) investigated the follower-followee
relationships of refuters and non-refuters. They developed a deep-learning-
based text classifier to identify debunked and non-debunked posts on Sina
Weibo. They then identified associated refuters and non-refuters whose
follower-followee networks are crawled as well. Their analysis unveiled that
nodes with greater centrality had more follower-followee edges, and weakly
connected components could easily disseminate both debunked and non-debunked
posts. This suggests that misinformation and counter-misinformation have
similar propagation patterns, considering that both can spread from weekly-
connected nodes.

####  6.2.3. User Engagement Analysis

Social media engagement analysis explores how crowds interact with online
posts containing both misinformation and counter-misinformation. These
interactions can be measured through metrics like post volume growth, and
sharing behaviors can differ depending on specific scenarios.

For example, Wang et al. (2022a) observed that the post volumes of counter-
misinformation exhibited growth rates similar to those containing
misinformation. Nonetheless, Micallef et al. (2020) uncovered disparities in
their absolute quantities and growth trends during the COVID-19 pandemic.
Specifically, the number of counter-misinformation posts was significantly
lower than the number of misinformation posts. Apart from post volumes, they
also analyzed the imbalanced sharing behavior associated with these posts, and
found that the majority of posts, regardless of their classification as
misinformation or counter-misinformation, received minimal to no shares.
However, specific situations could lead to variations. For example, when
examining the rumored death of Singapore’s President Lee Kuan Yew, Chua et al.
(2017) found that tweets aiming to correct the rumor garnered more frequent
retweets than the initial rumor tweets. This suggests that, in certain
contexts, counter-misinformation may be more prone to be shared than
misinformation.

####  6.2.4. Temporal Analysis

Temporal analysis aims to uncover evolving misinformation and counter-
misinformation traits by studying temporal trends and life cycle patterns,
offering valuable insights into their dynamics.

For instance, Gunaratne et al. (2019) examined the temporal trends of disease
cases, pro-vaccine of diseases, and anti-vaccine of diseases tweets, as well
as the crowds who tweeted between 2010 and 2019. The results revealed that
pro-vaccine tweets consistently outpaced anti-vaccine tweets in volume, and
this trend continued to increase over time. Similarly, Zubiaga et al. (2016)
conducted a study on the life cycle of rumors and their countering. They aimed
to understand how users engage with rumors, in terms of both their spread and
debunking, before and after the veracity of the rumor is confirmed. Through
manual identification and annotation of rumor threads, interesting
observations were made. Notably, rumors that were eventually proved to be
true, were debunked more quickly than false rumors. Interestingly, when a
rumor tweet was countered by either the crowd or an organization, retweets
occurred more evenly over time, indicating sustained retweeting activities.
These findings shed light on the dynamics of rumor propagation and debunking
activities over time.

###  6.3. Creator Profiles

Beyond characterizing the creation patterns of crowd-generated counter-
misinformation, we also analyze the profiles of these crowds as counter-
misinformation creators. Specifically, we examine their demographic factors
and mindsets, as outlined in Table 7-(c).

####  6.3.1. Demographics

Literature highlights the significant influence of certain demographic factors
on crowds’ willingness and involvement in countering misinformation. One
noteworthy aspect is political ideology, which influences one’s approach to
governance and public policy. According to Cohen et al. (2020), the presence
of a social identity threat in fake news content related to one’s political
ingroup indirectly affects crowds’ readiness to publicly denounce fake news
articles. This threat attacks or undermines their group’s social standing and
political ideology. Similarly, Steinfeld (2022) found that individuals who
engage in violent or illegal political protests tend to actively combat
disinformation, even though they may occasionally share disinformation
themselves. These findings underscore the intricate interplay between
political ideology and countering misinformation.

Media literacy, also known as “Information Literacy Competence” and “News
Media Literacy,” is another widely investigated factor. It involves the
critical analysis of media content, particularly news, to discern its
credibility, bias, and intention. Several studies have highlighted its absence
among the general audience, as well as the necessity for improved media
literacy education. For instance, Igbinovia et al. (2021) emphasized the
importance of media literacy in limiting the spread of fake news during the
COVID-19 pandemic. They revealed that greater media literacy can aid in
identifying fake news, which in turn may facilitate social correction
(Vijaykumar et al., 2022).

Meanwhile, a concerning lack of media literacy has been observed. Veeriah
(2021) affirmed the crucial role of media literacy in motivating corrective
responses to misinformation. However, they found that even young crowds who
felt sure about identifying fake news still demonstrated only moderate levels
of media literacy. This observation implies the necessity of initiating media
literacy education amongst young crowds to bolster the fight against fake
news. Similarly, Huber et al. (2022) echoed this finding, indicating that
relying solely on general media literacy is insufficient in effectively
countering certain types of fake news. These collective findings highlight the
significance of high-level or even specialized media literacy for crowds
engaged in countering misinformation.

In addition to political ideology and media literacy, the above studies also
examined additional factors such as age and education. For instance,
Vijaykumar et al. (2022) found that crowds who are younger or less educated
tend to be less involved in countering misinformation.

####  6.3.2. Mindsets

The motivations of crowds to counter misinformation are influenced by their
beliefs and mindsets. For instance, the act of sharing denials has been
identified as a crucial strategy for countering misinformation (Pal et al.,
2018, 2019). Pal et al. (2018, 2019) delved into this area and identified
three key beliefs that encourage crowds to share messages that deny rumors:
(1) the belief that sharing denials helps spread the truth, (2) the belief
that friends and the online community would favor the behavior of sharing
rumor denials, and (3) the belief that the credibility of the source of rumor
denials encourages sharing, thus influencing crowds’ willingness to share for
countering.

Moreover, we note the presence of the phenomenon known as the third-person
effect, denoting the belief among individuals that media messages have a
greater influence on others than on themselves. For instance, Chen and Fu
(2022) found that this effect positively encourages crowds to debunk online
misinformation. Another related belief, known as the “others’ susceptibility”,
refers to the perception that “others will be affected by or susceptible to
misinformation”. Sun et al. (2020) discovered that such belief in others’
susceptibility can induce negative emotions such as guilt and anger, thus
motivating vaccine supporters to correct anti-vaccination misinformation
actively.

Lastly, harm awareness, or being aware of the negative consequences, can also
motivate efforts to counter misinformation. For example, Zhao et al. (2016)
found that perceiving the harm caused by rumors predicts a significant
increase in engagement to counter misinformation. Similarly, Vijaykumar et al.
(2022) discovered that when crowds perceive that the severity of COVID-19 is
not being properly addressed, they are motivated to take action against
related misinformation.

##  7\. Future Directions

In this section, we outline potential avenues for future research in this
field:

  * •

Improving Crowd Annotations: While collaborative labeling aims to overcome the
limitations in individual labeling (e.g., individual annotation biases (Mitra
and Gilbert, 2015)), it’s important to note that individuals often share
similar perspectives (homophily). Therefore, it’s unclear whether
collaborative labeling effectively captures diverse viewpoints. Future
research should explore this aspect and develop strategies to encourage
diversity within groups countering misinformation. Similarly, crowd-in-the-
loop identification of misinformation can benefit from agile classifiers that
do not require extensive labeled examples initially and can quickly adapt to
emerging misinformation (Mozes et al., 2023). One approach to achieve this is
by utilizing few-shot learning techniques (Mozes et al., 2023).

  * •

Multi-platform and Multimodal Countering: Current social media-related work
predominantly focuses on one specific platform like Twitter (He et al., 2021b;
Chua et al., 2017; Chua and Banerjee, 2017), Facebook (Xue et al., 2022), and
Sina Weibo (Wang et al., 2022b). However, crowds countering misinformation may
behave differently across various platforms due to variations in user
demographics and engagement dynamics. Exploring how crowds counter on multiple
platforms and whether countering on one platform influences others is
essential for a comprehensive understanding of crowd-driven misinformation
mitigation. Additionally, the crowd-generated counter-misinformation isn’t
limited to text alone; it can also involve the use of images or videos to
enhance the persuasiveness of their debunking efforts. Investigating these
multimodal aspects benefits the design of effective countering contents.

  * •

Multilingual and Topic-specific Countering: Most research works concentrate on
either a single language (e.g., English (Chua and Banerjee, 2017) or Chinese
(Wang et al., 2022b)) or a specific misinformation topic (e.g., COVID-19
(Veeriah, 2021)). But, misinformation spans across languages and topics,
leading to diverse countering actions. Analyzing how crowds in under-
representative languages combat various topics reveals variations in
countering strategies across languages and topics, thus contributing to a
comprehensive understanding of misinformation countering. On the other hand,
existing research on the profiles of crowds focuses on demographic factors
such as education, political leanings, and media literacy. However, it
overlooks misinformation topic-specific factors. For instance, an individual
having a background in health education may be effective at countering health
misinformation but susceptible to believing in climate misinformation.
Therefore, exploring these topic-specific factors can enhance our
understanding of human factors involved in countering misinformation.

  * •

In-thread Countering: While current research primarily examines the impact of
standalone or accompanied counter-misinformation messages on social media
platforms (Chua et al., 2017; Xue et al., 2022), there is limited exploration
into the dynamics of counter-misinformation within conversations or threads.
For example, a crucial question arises: “When others in a thread engage in
countering, does it lead to correcting the misinformation or, conversely,
amplifying it?” This form of in-thread countering (e.g., counter-
misinformation responses to original misinformation posts) is frequently
observed on social media platforms like Twitter and Reddit (Achimescu and
Chachev, 2020). Its impact is amplified due to the active engagement of
multiple users in the thread. Investigating these in-thread behaviors provides
valuable insights into the dynamics of countering and offers guidance for the
design of effective counter-misinformation strategies.

##  8\. Conclusions

While crowd-based efforts to combat misinformation have increasingly attracted
attention, there has yet to be a comprehensive survey paper that examines the
multifaceted roles of crowds. Our study fills this gap by systematically
categorizing the three primary roles of crowds: annotators, evaluators, and
creators. Toward this end, we present the inaugural systematic survey of 88
papers investigating crowd-based efforts, which were collected by following
PRISMA guidelines. Specifically, we presented key data statistics on
misinformation, counter-misinformation, and crowd inputs found in the
literature. Additionally, we proposed a novel taxonomy that covers the diverse
roles of crowds in a comprehensive way. Moreover, we provide detailed insights
and findings extracted from the surveyed papers, offering valuable resources
for effective counter-misinformation. By doing so, this survey helps readers
grasp the latest research developments in this field and establishes the
foundation for encouraging advanced crowd-assisted methodologies to combat
misinformation.

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