AI Optimism, Pessimism, or Indifference? Challenges of Combating AI-Made
Misinformation Under Mixed Perceptions of AI
Yuya Shibuya, 1* Tomoka Nakazato,1 Soichiro Takagi1
1*

The University of Tokyoyuya-shibuya@iii.u-tokyo.ac.jp

Abstract
There is growing concern about misinformation and propaganda being spread through AI-generated content that is often
indistinguishable from human-made content. As a response,
major platforms (e.g., Google, Meta, TikTok) have introduced
policies to warn users of AI-generated content. One potential challenge here is the divergent perceptions of AI in the
public may cause different reactions to such warnings. Whilesome see AI’s potential benefits for society, others are more
pessimistic about the potential risks. It is not yet clear how
these polarized attitudes affect efforts to combat misinformation. Our experimental study investigated how people’s attitudes toward AI influenced their perceptions of AI-generated
posts. In our experiment, participants were asked to reportwhat factors influenced their judgments about the accuracyof AI-generated video content in an open-ended response format. The study found that most participants relied on theirpre-existing knowledge and beliefs to evaluate AI-generated
posts, even when shown a warning message to AI-made contents. Interestingly, some participants mindlessly evaluatedthe accuracies of all of the videos positively or negatively
based on their positive or negative beliefs about AI. The finding suggests that incorporating a simple warning with GenAImade content may be insufficient and has varying effects on
users, ranging from underestimated to no reactions to overestimated reactions.

1

Introduction

The public’s perception of Artificial Intelligence (AI) is
divided (Schepman and Rodway 2020; Amoozadeh et al.
2024; Baek, Tate, and Uci 2023; Patrick Mikalef andPopovič 2022; Google 2024; Hyun Baek and Kim 2023;
Mou and Xu 2017; Fui-Hoon Nah et al. 2023). Some people are excited over its capabilities and potential, while others fear unanticipated consequences and the negative side of
AI. Research on human-AI interactions has researched how
AI-generated content can be perceived and accepted. For instance, trust in writers decreased when individuals learned
that AI was involved in email communication (Liu et al.
2022), and AI-generated online profiles were considered lesstrustworthy than human-generated profiles (Jakesch et al.
2019). Conversely, AI “smart reply” functions can enhance
Copyright © 2022, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.

trust between human communicators (Hohenstein and Jung
2020). Moreover, perceptions and acceptance of AI canbe influenced by environments (Zhang et al. 2021), contexts (Shin and Park 2019; Lee 2018; Ragot, Martin, andCojean 2020; Böhm et al. 2023; Castelo, Bos, and Lehmann2019; Wang 2023; Morewedge 2022), pre-existing attitudes
toward AI (Kocielnik, Amershi, and Bennett 2019), confidence in AI technology (Balakrishnan, Abed, and Jones
2022; Hong 2022; Hyesun Choung and Ross 2023), experiences (Jeung and Huang 2023), socioeconomic backgrounds (Zhang et al. 2021), and individual characteristics (Böhm et al. 2023; Castelo, Bos, and Lehmann 2019;
Wang 2023; Morewedge 2022).
Meanwhile, for individuals’ perceptions of misinformation, credibility and trust in information sources have beenrecognized as the essential factors (Van Der Meer and Jin
2020; Nakazato, Shibuya, and Takagi 2023; Rodrı́guezPérez and Canel 2023; Xiao, Borah, and Su 2021). For
example, trusted established entities, such as government
agencies, organizations, news media, and social peers, areeffective in correcting misinformation (Van Der Meer andJin 2020). Both unthinking trust and unthinking skepticism
of information sources could stimulate misinformation herding (Santoro and Sydnor 2024; Xiao, Borah, and Su 2021).
While much work investigates credibility and trust in information sources, we know little about how people see GenAI
information sources in the context of misinformation (Wittenberg et al. 2024). Do people believe content more or lessif they know a GenAI created a social media post? Somemay see GenAI as a trusted expert, while others believe
GenAI is a biased, untrustworthy source.
By conducting an online experiment, this study investigates how individuals evaluate the accuracy of AI-generated
video posts when they are informed that a GenAI created thevideos. This article contributes to the literature on HumanAI interactions in the context of combating misinformation
by:• The study conducted an experiment study to compare theimpact of informing users that GenAI created social media video posts on how they evaluate the accuracies ofthe videos.
• The results showed that there are several types of participants: for the majority of the participants, GenAI-created
content warning messages seemed not to have a strong

Table 1: Headlines used in the experiment

ID

AI
Video
creation
service

Post style

Topic

Label in Politifact

Date

Headline in Politifact

False 1

Lumen5

Facebook

HealthMostly false

May-20-2016

False 2Invideo AI

TikTokHealthMostly false

April-18-2022

False 3

Invideo AI

TikTokCovid

Mostly false

April-28-2023

False 4

Lumen5

Facebook

Covid

Mostly false

January-19-2022

False 5

Lumen5

TikTokClimateMostly false

October-9-2023

True 1

Invideo AI

Facebook

Covid

True

True 2Invideo AI

TikTokClimate

True

September-27-2019

True 3

Lumen5

Facebook

Climate

True

November-29-2018

True 4

Invideo AI

TikTokClimate

True

April-10-2020

True 5

Lumen5

Facebook

Climate

True

October-13-2020

Says a WHO proposal “portrays milk and dairy products as an obstacle
to a healthy start in life.”“It’s been proven that sugar triggers and causes cancer. ... So the bestthing to do to prevent cancer is to avoid sugar.”“Face masks may raise risk of stillbirths, testicular dysfunction andcognitive decline, study warns.”“Mask mandates on children lead to learning loss that harms earlychildhood development.”
1,609 scientists signed a declaration saying “There is no climate emergency.”
Amount of COVID Viral RNA Detected at Hospital Admission Predicts
How Patients Will Fare
Energy suppliers accused of ‘greenwashing’ tariffs to attract environmentally friendly customers
Heatwaves linked to climate change pose increasing global health risk
worldwide, study shows
Some experts believe climate change may increase the emergence of
new animal-to-human transmitted diseases like COVID-19
UN: Climate change means more weather disasters every year

influence on their reported process of accuracy evaluations of the videos.
• Also, there are participants who over-distrust or overtrust in AI. They seemed to unthinkingly judge video accuracies negatively or positively.
• We shed light on the importance of continuous investigation on how to address AI-generated misinformation
under divergent AI perceptions.
The rest of the paper is organized as follows. In the next
section, we will introduce our experiment design. After that,
we will present the results we obtained from the experimentand discuss them in detail. We will also provide some future
research and practical implications. Finally, we will conclude the article by giving some potential research questions
for future studies.

2
2.1

Research Design

Participants Recruitment

This study focuses on video content created by a GenAI andusers’ perceptions of its accuracy with an experiment fieldedin the United States (N = 1, 493). We recruited social media users (aged from 20 to 99 years old and living in the
United States) via PureSpectrum. We asked the participants
to assess the accuracy of video content created by a GenAI;
half of the participants were shown warning messages thatthe videos were created by a GenAI, and the other half werenot (see the following subsection). After their assessment,
the participants were asked to report detailed explanationsof factors that influenced their accuracy ratings in an openended format.
The current study’s survey protocols were reviewed andapproved by the human-subject research ethics board of thecorresponding author’s institution (approved on December
28, 2023). Informed consent was obtained from all experiment participants, and all procedures were performed incompliance with relevant laws and related guidelines. Outof the 1,571 participants who originally completed the experiment, 44 were excluded from the analysis because theyfailed to meet our exclusion criteria (those failed all of thetrivial attention checks, reported not having any social media
accounts, or reported that they are 20 years old or younger).
Therefore, the observations of 1,493 are analyzed in total.

2.2

Social media video content creation by
GenAIsIn this study, the participants were presented with ten AIgenerated videos, five of which were true and five including false information. Ten videos covering topics of health,
COVID-19, and climate change. To create ten videos, wefirst manually selected various headlines from Politifact(https://www.politifact.com/), covering a wider range of topics that are globally relevant and not outdated. This firststep gave us the 26 Polifact headlines. We then conducteda prestudy (N=285, number of observations = 2,164) using
these 26 headlines and chose the final headlines for the main
study with the following criteria. Regarding true headlines,
we chose five true headlines with the highest skewness of theaccuracy scores in the prestudy, indicating that more people
recognized them as correct. Regarding false headlines, wechose five false videos with the largest variances of the accuracy scores in the prestudy, representing the most differing
opinions among people when it came to judging their accuracy. The final ten headlines are listed in Table 1.

Table 2: Categories of accuracy evaluation reasoning among the participants

Category label

Explanation

Example responses by the participantsNPre-existing knowledge

Using pre-existing knowledge and information that a participant has
previously heard and seen from various media sources.

“I based it upon my own knowledge as I pay attention and read a lotof science based articles and check facts to back up news.”322

Own belief /views

Relying on a participant’s own beliefs and worldviews.

“Rated based on my personal beliefs and opinions”

107

AI

Relying on whether posts are created by GenAI.

“The knowledge that they were created by AI made me skeptical to
my beliefs that they could be true.”

49

Exaggeration/misleading

Relying on usages of exaggeration, misleading language, or failingto acknowledge ambiguity in unproven or controversial information.

“Most of the videos seemed to be overly assertive.”

49

Tactics

Relying on whether a post seems to use manipulative tactics, scaretactics, or propaganda to push a specific political stance or to instillfear in people.

“I think some were fear mongering, some tried to convey statements
as facts without any evidence or backing.”

44

Video quality

Relying on the quality of video posts.

“The information looked trustworthy because of how professional
the post looked.”

42

Information source

Relying on whether a post has credible or scientific sources orbackup information to support claims.

“Unless any of these videos can cite sources with verifiable scientific
data I wouldn’t trust any random video on the internet that anyone
could easily edit together themselves.”

41

Post’s sounds/wording

Relying on language usage and how it sounds like.

“The way they express and the words they use can help you identify
how accurate the posts are.”34

Common sense

Using common sense or common knowledge.

“Some were accurate while others were not. The factors that influenced me were just common sense.”33

Make sense

Relying on whether a post’s content makes sense to a participant.

“A lot of what I watched made sense and therefore impacted myratings”

33

Lack of information

Relying on whether a post provides sufficient information to support
its claims.

“Most of these short-form videos are also missing a lot of othercrucial information.”

27

Feeling

Relying on a participant’s feeling toward a post.

“Based on how I felt.”24

Own opinion

Relying on a participant’s own opinion toward a post.

“I made the decisions that I made reflected my own opinion.”

21

OpinionatedRelying on whether a post sounds opinionated instead of providing
the information based on a fact.

“Most are opinions over solid facts. Hard to prove or disprove”

20

OthersAll the answers that do not fit into the above categories. Responses
that did not explicitly provide reasoning were included here. Other
reasons include trust in the social media platforms.

“Because most of them were made on TikTok it’s hard to believe
them. There are many false facts on TikTok so anything I see on
TikTok I take with a grain of salt.”

638

Note1: Column ‘N’ shows the number of participants whose answers were categorized into a category. See Figure 1 for the differences between the treatment and control groups.
Note2: Most “Others” responses did not give reasons explicitly. No difference in size between the treatment and control groups.

2.3

Video creation

We first generate optimized text for social media posts with
Chat GPT 3.5 by prompting it to create a post for social networking sites to spread the word about each headline in 30-50 words. Next, we use two commercial text-tovideo GenAI services to automatically create videos fromthe above Chat GPT-created texts (Invideo AI and Lumen5)with two social media formats (Facebook and TikTok video
post style). We asked both applications to create videos
about 15 seconds long videos based on the above Chat GPTcreated texts. Four types of videos for each headline werecreated (Invideo AI - Facebook style, Invideo AI - TikTok,
Lumen5 - Facebook, Lumen5 - TikTok) and then randomly
selected one of four videos for each headline (Table 1). In
Table 1, labels of truth and falsehood are given based on
PolitiFact’s fact-checking website.

2.4

Experiment design

We ask participants to complete a 20-minute survey programmed in Qualtrics at their own pace without backtracking. Participants were randomly allocated to one of two conditions: half of them were shown warning messages thatvideo posts were created by a GenAI (treatment group)
while the rest were not shown the warnings (control group).
After answering several questions about social media use
and their perceptions of AI, eligible participants were asked
to watch each video post and evaluate its accuracy with a
6-point Likert scale. Before this task started, the participantsin the treatment group were shown the GenAI-create video
warning messages (‘The contents of the posts (video andtext) are created by a generative AI’). In addition, the warning message was attached just below each social media post;‘Caution: The text and video in the above post are created by

150

Pre-existing knowledge
39

Own belief/views

172

***

68

28
30

Exaggeration/misleading

48

AI 1
15

Tactics
Video quality

15
12

Information source

***
**

29
27

**
**

29

21

Post's sounds/wording

13
15
18
17
16
16
11
14
10

Make sense
Common sense
Lack of information
Feeling
Own opinion

5

Opinionated

5

0

16

**

15

**
25

50

75

Count

100

Treatment

Control

150

175

125

Figure 1: Most common reason categories among treatment and control group participants. For category names, see Table 2.
The category “Others” was excluded from the figure. The asterisk marks in the figure indicate if there is a statistical significancebetween ratio of responses between the treatment and control groups (two-sided). ∗∗∗ : p < 0.01, ∗∗ : p < 0.05
a generative AI’). Once the task was completed, participants
were asked to provide an open-ended response explaining
the reasoning behind their accuracy judgments. Finally, participants were debriefed by re-presenting the text contents
of social media posts with the fact check summary and linkto the fact check website. The control groups went throughthe exact same procedures without the waring messages, butthey were informed that a GenAI created the posts when they
were debriefed. Informed consent was obtained from all participants.

2.5

Data Analysis

We use mixed methods apporach to deepen our understanding of user behaviors and their contexts on social media (Shibuya, Hamm, and Cerratto Pargman 2022). To quantitatively understand the trends of participants’ reasoningof video accuracy evaluations, we use Natural Language
Processing techniques, counting word frequency and creating word co-occurrence networks. In doing so, we firstpreprocess the text datasets by removing stop words (seethe Appendix). Then, we construct a bigram word pair frequency matrix and draw networks on it. To gain a deeper
understanding of the participants’ reasoning and identify
consistencies among them, we conducted a qualitative content analysis of the open-ended question answers (Creswell

and Poth 2021). We manually analyzed all participants’ answers and developed categories inductively from their responses, using a systematic interpretative structuring of thecontent (Mayring 2014). Our focus was on categories that
helped explain the participants’ decision-making process regarding their accuracy assessment of social media posts. Our
goal was to gain a more profound knowledge of the participants by reading through their answers directly and better
understanding their perceptions toward AI as well as social
media posts. We closed off the qualitative content analysis
once we had developed a sufficient and thorough categorysystem from the material.

3

Results

In this study, we aim to comprehend the reasoning behind
the accuracy judgments of the participants by focusing on
how the experiment participants answered open-ended questions about their ways of accuracy assessment. Through thequalitative content analysis, we found several categories ofparticipants’ reasoning behind their accuracy assessment on
AI-created videos (Table 2). We compared the occurrence of
these reasoning categories between the treatment and control groups, and the results are shown in Figure 1. Additionally, we use NLP and network analysis methods to conduct
a quantitative analysis on the open-ended answers, and thea Treatment group
togetherput

nevernews hearprettymuch

b Control group

actual decision
factorinfluence

questionanswerfact
base
mediumgood topic social
subject
first
timecommonknowledgereadnews hear
never

sense
make
reallyknow

say video

everything
believeopinion give muchpersonal provide
factwatch saygooddecision
prior
issue
influencefactorsoundbaseinformation
video
knowledge
manythingalreadytry useenoughseem
reasonable

feel likereal

believable
sound
ai
look
generate

seemlike

topic
know

lot

informationsocialmediummake

lot feelsense
common claim
farfetchedpeoplescare
tacticFigure 2: Word co-occurrence networks of treatment group (a) and control group (b). These figures imply that the majority of theparticipants used their pre-existing knowledge and beliefs to judge the accuracy of videos. The words shown in the figure werenormalized forms of morphemes after tokenizing all open-question answers. The color groups present network communities
identified by the Louvain community detection method. The edge widths represent the co-occurrence frequencies and the node
sizes represent the word occurrence.
results are presented in Figure 2.
Below, we summarize our findings through interesting
both qualitative and quantitative analysis results.

3.1

Many Participants rely on pre-existingknowledge and feelings to judge accuracy

We found that both the control and treatment groups often
relied on their pre-existing knowledge and personal beliefs
to assess the accuracy of videos (Figure 1). This indicatesthat even when participants were shown a warning message about GenAI-made videos, most of them were not significantly influenced by the GenAI labels, at least at theirself-reported answer level. These tendencies are also observable in bigram-based word co-occurrence networks ofopen-question responses (Figure 2), where most participantsin both groups reported that they rated the video accuracy
based on their knowledge as the word “knowledge” is mostfrequently used with other various words (e.g., “based [on][my] knowledge” and “prior knowledge”).
However, we did observe slight differences in tendencies
between the treatment and control groups. As shown in Figure 1, fewer participants in the treatment group (who were
informed that the videos were made by GenAI) self-reported
relying on their personal beliefs and worldviews, comparedto the control group. Instead, more participants in the treatment group answered that they relied on video quality andhow the posts use language and expressions, compared to thecontrol group (Figure 1). We will discuss these differences
between the treatment and control groups in the following
subsections and in Section 4.

3.2

Pessimistic views of AI

In addition, we recognized not a small number of treatedparticipants reported that their accuracy judgments were influenced by the fact that GenAI created a post (Figures 12). Among the participants in the treatment group, 48 participants explicitly mentioned AI as an influenced factor in
the experiment responses (Figure 1). By manually examining their responses, we found that most of the participants
expressed skepticism or negativity toward AI-generated content. For example, one participant in the treatment group reported as following:“Some seem kind of believable but if this is AI I’m nota fan! It will have everyone in a panic. Life will be consumed with disputes and negativity! No thanks”
Another treated participant also expressed his/her belief in
AI as follows:“I am not a big believer in AI”
One participant answered that he/she rated all of the AImade posts as inaccurate:“I rate the Ai 0 the reason why is that I don’t believe AI
has humanities best interest at heart [...]”
Some others mentioned in more detail why they assess AImade content negatively. For instance, one treated participant pointed out how AI-made information was made:“The real problem is giving to much power to the socalled professionals AI does what it is told and learns
from the human nature it is dangerous in the long term.”

Similarly, another treated participant expressed skepticism
about the data source of AI-made content.
“All of these are speculation and are very inaccurate as
the AI are getting fed info that is speculative”
These responses indicate that there are not a small number ofindividuals with pessimistic AI views. Their responses in theexperiment suggested that most of the AI pessimists seemed
to perceive videos with AI-generated content warnings as
more negative without critically checking other components
of information.

3.3

Optimisitc views of AI

Compared to individuals who are pessimistic about AI,
we found fewer participants who explicitly reported that
their optimistic views of AI influenced their video accuracy
evaluations. Meanwhile, some participants expressed optimistic views of AI in their responses. Interestingly, there aresome participants who seem to unthinkingly believe the AIgenerated content.
“The general AI has done a great and fantastic job to thesociety of human beings”
Similarly, others expressed their astonishment at how wellAI-created content are:
“They were AI generated to make them look real.”
Some participants even answered that they believed the contents simply because it was created by AI:“I felt like these were pretty accurate because of the ai
generated content.”
According to our study, there are several participants who
accept AI-generated content without any scrutiny. Therefore, it is crucial to conduct further research to determine
the extent to which they utilize critical thinking skills whenevaluating AI-generated content. Additionally, we need toinvestigate how displaying warning labels may affect theirability to distinguish between authentic and false information. We will discuss the results further in the next section.

4

Discussion

By dissecting the open-ended question responses, we discuss in this section the potentials and challenges of the softintervention approach, which involves attaching labels to indicate that videos were created by AI. Our focus is on how
user perceptions and acceptance of AI-made videos are affected by the presence or absence of such labels differently.

4.1

Critical thinking skills for AI-made content

Critical thinking skills and media literacy are the key elements to combat misinformation (Machete and Turpin 2020;
Chen, Xiao, and Kumar 2023). Here, we are interested in
how the warning label about AI affects people’s thinking
process of video accuracy. Through qualitative content analysis, we observed that fewer participants in the treatmentgroup who received the warning message reported that they
checked the posts’ tactics and the information sources in thevideos. This is evident as the “Tactics” and “Information

Source” categories occurred significantly less frequently inthe treatment group, as shown in Figure 1.
Conversely, more participants in the treatment group
showed increased interest in checking video quality. Thisis reflected in the higher occurrence of the “Video Quality” category within the treatment group, as depicted in Figure 1. For instance, one participant in the treatment groupanswered as follows:“I used the voice and the music to determine the field of
the ads and overall determine the authenticity in the AI
presentation of the videos.”In this case, participants seemed to rely more on the authenticity of AI presentation rather than checking the contents
critically. Meanwhile, others seemed to also check the content itself by checking the videos.
“The videos and information seemed like it was true, butthere could have been a lot more facts about the stuff thatthey were talking about.”
These findings suggest that the warning message prompts
some individuals to assess how well videos present information. However, it may also reduce some users’ intentions to scrutinize the tactics and information sources behind the posts compared to those not exposed to the warning
message. Further investigation is needed to understand how
warning messages on social media platforms can enhance ordiminish users’ critical thinking skills.

4.2

Human factors in intervention design

Major social media platfomers (e.g., YouTube, Meta, TikTok) recently introduced new policies to label a warning
message to GenAI-made content. However, there is little direct evidence regarding the effectiveness of attaching suchwarning labels to AI-generated content (Wittenberg et al.
2024). Because our study underscores the diversified reactions to the warning labeling, it is critical to examine how
individuals react to interventions from multiple scopes. To
better design the interventions for combating misinformation, there is a need to continuously investigate the humanfactor and design perspective of misinformation-related interventions in AI context.
In addition, there is a need to develop tools and methodologies to benchmark and evaluate interventions (Boonprakong, Tag, and Dingler 2023). Despite various efforts indesigning digital interventions to combat misinformation in
the age of GenAI, there is a lack of direction and methodology to make sure that these solutions are effective. We, inparticular, need to measure effectiveness in diverse individual circumstances and attributes as well as context becauseof people’s diversified attitudes toward AI.

4.3

Multiple approaches are needed instead ofmere soft interventions

Through the investigation of how people react to AIgenerated content, it appears that simple warning messages
may be insufficient to invoke critical thinking skills amongusers. Therefore, relying solely on warning message approaches, which has become defact-stanrad among majorplatofmrs recently, may not be effective for combating AImade misinformation. To address this issue, it is important toaddress generative-AI-based misinformation through multiple approaches (Xu, Fan, and Kankanhalli 2023). We also
believe that a design that empowers users to develop theirown resilience and civic engagement is crucial. User-levelinterventions, such as improving user literacy in generative AI, and policy-level interventions should accompany theuser interface interventions (Wilner et al. 2023).

4.4

Limitations

This study holds several limitations. First, our field of experiment study was limited to participants from the UnitedStates. Considering the perceptions of AI differ dependingon geographical, political, and socioeconomic characteristics, research should be expanded to other regions in the
future. Secondly, this study relied on the participants’ selfreported responses to an open-ended question. Due to thenature of open-ended questions, the quality of the given answers varies among the participants. Moreover, most of theanswers were labeled as “Others” in the analysis because
they did not give explicit answers to the question. Therefore, further research is needed to explore the heterogeneous
impacts of AI-generated video warnings on individuals withmore robust quantitative identification strategies.

5

Conclusion

Given the recent trends to add labels to AI-made content byplatforms (e.g., TikTok, YouTube), it is crucial to understandhow such soft interventions impact user perceptions and acceptances of AI-made content. Based on scrutinizing the experiment participants’ answers to our open-ended question,
this study sheds light on the importance of understanding theimpacts of labeling AI-made content. While warning messages did not have a significant impact on most participants’evaluations of the content, the diverse responses of participants highlighted the need to consider individual perceptions and acceptances of AI. This study also emphasizes theimportance of designing effective human-AI interactions tocounter misinformation.
Further research on the design of human-AI interactions
is necessary to counter misinformation. In particular, ourstudy emphasizes the criticality of analyzing diversified individual acceptances and perceptions of AI. Additional research is needed to explore the heterogeneous impacts ofAI-generated video warnings on individuals with more robust quantitative identification strategies.

Acknowledgments
This work was supported by JSPS KAKENHI Grant Number JP21K13278.

References
Amoozadeh, M.; Daniels, D.; Nam, D.; Kumar, A.; Chen, S.;
Hilton, M.; Srinivasa Ragavan, S.; and Alipour, M. A. 2024.
Trust in Generative AI among Students: An Exploratory

Study. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1, 67–73. Portland
OR USA: ACM. ISBN 9798400704239.
Baek, C.; Tate, T. P.; and Uci, M. W. 2023. “ChatGPT Seems
Too Good to Be True”: College Students’ Use and Perceptions of Generative AI.
Balakrishnan, J.; Abed, S. S.; and Jones, P. 2022. The roleof meta-UTAUT factors, perceived anthropomorphism, perceived intelligence, and social self-efficacy in chatbot-based
services? Technological Forecasting and Social Change,
180: 121692.
Böhm, R.; Jörling, M.; Reiter, L.; and Fuchs, C. 2023. People Devalue Generative AI’s Competence but Not Its Advice
in Addressing Societal and Personal Challenges. Communications Psychology, 1(1): 32.
Boonprakong, N.; Tag, B.; and Dingler, T. 2023. Designing Technologies to Support Critical Thinking in an Age of
Misinformation. IEEE Pervasive Computing, 22(3): 8–17.
Castelo, N.; Bos, M. W.; and Lehmann, D. R. 2019. TaskDependent Algorithm Aversion. Journal of Marketing Research, 56(5): 809–825.
Chen, S.; Xiao, L.; and Kumar, A. 2023. Spread of misinformation on social media: What contributes to it and how
to combat it. Computers in Human Behavior, 141: 107643.
Creswell, J. W.; and Poth, C. N. 2021. Qualitative Inquiry
and Research Design.
Fui-Hoon Nah, F.; Zheng, R.; Cai, J.; Siau, K.; and Chen,
L. 2023. Generative AI and ChatGPT: Applications, Challenges, and AI-human Collaboration. Journal of Information
Technology Case and Application Research, 25(3): 277–
304.
Google. 2024. Global Study Shows Optimism About AI’s
Potential.
Hohenstein, J.; and Jung, M. 2020. AI as a moral crumple
zone: The effects of AI-mediated communication on attribution and trust. Computers in Human Behavior, 106: 106190.
Hong, J.-W. 2022. I was born to love AI: The influence ofsocial status on AI self-efficacy and intentions to use AI.
International Journal of Communication, 16: 20.
Hyesun Choung, P. D.; and Ross, A. 2023. Trust in AI
and Its Role in the Acceptance of AI Technologies. International Journal of Human–Computer Interaction, 39(9):1727–1739.
Hyun Baek, T.; and Kim, M. 2023. Is ChatGPT Scary Good?
How User Motivations Affect Creepiness and Trust in Generative Artificial Intelligence. Telematics and Informatics,
83: 102030.
Jakesch, M.; French, M.; Ma, X.; Hancock, J. T.; and Naaman, M. 2019. AI-Mediated Communication: How the Perception that Profile Text was Written by AI Affects Trustworthiness. In Proceedings of the 2019 CHI Conferenceon Human Factors in Computing Systems, CHI ’19, 1–13.
New York, NY, USA: Association for Computing Machinery. ISBN 9781450359702.
Jeung, J. L.; and Huang, J. Y.-C. 2023. Correct Me If I Am
Wrong: Exploring How AI Outputs Affect User Perception

and Trust. In Companion Publication of the 2023 Conference on Computer Supported Cooperative Work and Social
Computing, CSCW ’23 Companion, 323–327. New York,NY, USA: Association for Computing Machinery. ISBN
9798400701290.
Kocielnik, R.; Amershi, S.; and Bennett, P. N. 2019. Will
You Accept an Imperfect AI? Exploring Designs for Adjusting End-user Expectations of AI Systems. In Proceedings ofthe 2019 CHI Conference on Human Factors in Computing
Systems, CHI ’19, 1–14. New York, NY, USA: Association
for Computing Machinery. ISBN 9781450359702.
Lee, M. K. 2018. Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response
to algorithmic management. Big Data & Society, 5(1):2053951718756684.
Liu, Y.; Mittal, A.; Yang, D.; and Bruckman, A. 2022. WillAI Console Me when I Lose my Pet? Understanding Perceptions of AI-Mediated Email Writing. In Proceedings ofthe 2022 CHI Conference on Human Factors in Computing
Systems, CHI ’22, 13. New York, NY, USA: Association for
Computing Machinery. ISBN 9781450391573.
Machete, P.; and Turpin, M. 2020. The Use of CriticalThinking to Identify Fake News: A Systematic Literature
Review. In Hattingh, M.; Matthee, M.; Smuts, H.; Pappas,
I.; Dwivedi, Y. K.; and Mäntymäki, M., eds., Responsible
Design, Implementation and Use of Information and Communication Technology, 235–246. Cham: Springer International Publishing. ISBN 978-3-030-45002-1.
Mayring, P. 2014. Qualitative content analysis: theoreticalfoundation, basic procedures and software solution. Klagenfurt.
Morewedge, C. K. 2022. Preference for human, not algorithm aversion. Trends Cogn. Sci., 26(10): 824–826.
Mou, Y.; and Xu, K. 2017. The Media Inequality: Comparing the Initial Human-Human and Human-AI Social Interactions. Computers in Human Behavior, 72: 432–440.
Nakazato, T.; Shibuya, Y.; and Takagi, S. 2023. Characterizing the Behavior of Healthcare Experts Towards COVID-19
Vaccine on Twitter. In 2023 IEEE International Conferenceon Web Intelligence and Intelligent Agent Technology (WIIAT), 608–615. Venice, Italy: IEEE. ISBN 9798350309188.
Patrick Mikalef, J. E. L., Kieran Conboy; and Popovič, A.
2022. Thinking responsibly about responsible AI and ‘thedark side’ of AI. European Journal of Information Systems,
31(3): 257–268.
Ragot, M.; Martin, N.; and Cojean, S. 2020. AI-generated
vs. Human Artworks. A Perception Bias Towards Artificial
Intelligence? In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, CHI EA’20, 1–10. New York, NY, USA: Association for Computing
Machinery. ISBN 9781450368193.
Rodrı́guez-Pérez, C.; and Canel, M. J. 2023. Exploring European Citizens’ Resilience to Misinformation: Media Legitimacy and Media Trust as Predictive Variables. Mediaand Communication, 11(2): 30–41.

Santoro, L. R.; and Sydnor, E. 2024. Blind Trust, Blind
Skepticism: Liberals’ & Conservatives’ Response to Academic Research. American Politics Research, 52(1): 52–66.
Schepman, A.; and Rodway, P. 2020. Initial validation of thegeneral attitudes towards Artificial Intelligence Scale. Computers in Human Behavior Reports, 1: 100014.
Shibuya, Y.; Hamm, A.; and Cerratto Pargman, T. 2022.
Mapping HCI Research Methods for Studying Social Media Interaction: A Systematic Literature Review. Computersin Human Behavior, 129: 107131.
Shin, D.; and Park, Y. J. 2019. Role of fairness, accountability, and transparency in algorithmic affordance. Computersin Human Behavior, 98: 277–284.
Van Der Meer, T. G. L. A.; and Jin, Y. 2020. Seeking Formula for Misinformation Treatment in Public Health Crises:
The Effects of Corrective Information Type and Source.
Health Communication, 35(5): 560–575.
Wang, S. 2023. Factors Related to User Perceptions of Artificial Intelligence (AI)-Based Content Moderation on Social
Media. Computers in Human Behavior, 149: 107971.
Wilner, T.; Mimizuka, K.; Bhimdiwala, A.; Young, J. C.; andArif, A. 2023. It’s About Time: Attending to Temporality in
Misinformation Interventions. In Proceedings of the 2023
CHI Conference on Human Factors in Computing Systems,
CHI ’23, 19. New York, NY, USA: Association for Computing Machinery. ISBN 9781450394215.
Wittenberg, C.; Epstein, Z.; Berinsky, A. J.; and Rand, D. G.
2024. Labeling AI-Generated Content: Promises, Perils, andFuture Directions. An MIT Exploration of Generative AI.
Https://mit-genai.pubpub.org/pub/hu71se89.
Xiao, X.; Borah, P.; and Su, Y. 2021. The Dangers of Blind
Trust: Examining the Interplay among Social Media News
Use, Misinformation Identification, and News Trust on Conspiracy Beliefs. Public Understanding of Science, 30(8):977–992.
Xu, D.; Fan, S.; and Kankanhalli, M. 2023. Combating
Misinformation in the Era of Generative AI Models. In
Proceedings of the 31st ACM International Conference on
Multimedia, 9291–9298. Ottawa ON Canada: ACM. ISBN
9798400701085.
Zhang, R.; McNeese, N. J.; Freeman, G.; and Musick, G.
2021. ”An Ideal Human”: Expectations of AI Teammates in
Human-AI Teaming. Proceedings of the ACM on HumanComputer Interaction, 4(CSCW3).

Appendix
Stop words
The stop words used for this study are listed below:
having, hers, couldn’t, don’t, was, it, while, rate, some,with, very, he, would, on, at, been, she’s, didn, shouldn’t,
yourself, y, animal, m, s, inaccurate, o, for, because, your,mask, what, herself, from, as, that’ll, should’ve, didn’t,t, weren’t, below, ourselves, all, change, aren’t, ours,
themselves, 10, hadn’t, weren, do, other, this, did, then,which, here, why, wasn’t, warming, am, if, each, above,before, whom, is, him, you’re, until, that, how, ma, it’s,
only, they, such, nor, doesn’t, wouldn’t, mustn, sugar,
during, climate, its, mustn’t, no, have, covid, of, after,
by, wasn, most, not, where, we, shan, she, false, wouldn,
under, them, once, our, over, global, any, few, mightn,
an, be, hadn, further, my, more, aren, had, himself, a,own, hasn’t, 19, ain, me, but, or, yours, isn’t, his, to,the, there, who, myself, out, doing, about, re, off, couldn,won, won’t, her, isn, should, both, when, now, cancer,
being, in, needn, post, i, ve, up, d, itself, ll, those, same,than, can, yourselves, through, haven, accurate, into, don,just, their, true, haven’t, so, were, again, down, shan’t,has, and, you, hasn, mightn’t, between, accuracy, these,
doesn, needn’t, you’ll, too, will, against, shouldn, you’ve,
you’d, are, does, theirs
The stop words were comprised of Python natural language processing package nltk’s pre-defined stop word
list (nltk.corpus.stopwords) and this study’s specific words including the topics of videos (covid 19, mask,
cancer, sugar, climate change).