Finding Strategies Against Misinformation in Social Media: A
Qalitative Study
Jacqueline Urakami

Yeongdae Kim

urakami.j.aa@m.titech.ac.jp
Tokyo Institute of TechnologyJapankim.y.ah@m.titech.ac.jp
Tokyo Institute of Technology
Japan

Hiroki Oura

Katie Seaborn

houra@rs.tus.ac.jp
Tokyo University of ScienceJapanseaborn.k.aa@m.titech.ac.jp
Tokyo Institute of Technology
Japan

ABSTRACT1

Misinformation spread through social media has become a fundamental challenge in modern society. Recent studies have evaluatedvarious strategies for addressing this problem, such as by modifyingsocial media platforms or educating people about misinformation,
to varying degrees of success. Our goal is to develop a new strategy for countering misinformation: intelligent tools that encouragesocial media users to foster metacognitive skills "in the wild." Asa frst step, we conducted focus groups with social media users
to discover how they can be best supported in combating misinformation. Qualitative analyses of the discussions revealed thatpeople fnd it difcult to detect misinformation. Findings also indicated a need for but lack of resources to support cross-validation of
information. Moreover, misinformation had a nuanced emotional
impact on people. Suggestions for the design of intelligent tools that
support social media users in information selection, information
engagement, and emotional response management are presented.

Misinformation is regarded as a fundamental challenge in the contemporary communication landscape of social media. Even thoughmisinformation is not a new phenomenon, its increase in volume
and the large number of people exposed to it has caused widespread
alarm in recent years [14, 23]. It is therefore not surprising that how
to address the issue of misinformation has received a lot of attention, not only from researchers but also from social media platforms
themselves (e.g. "Birdwatch" by Twitter). Research has focused onunderstanding how misinformation spreads in social media (e.g.
[3, 38, 49], designing interventions for correcting misinformation(e.g. [7, 44, 46], exploring the (in)efectiveness of warnings (e.g.
[25, 33]), or studying users reactions to fake news posts [16]. Other
work has acknowledged the impact of emotion on technology use[43] and the mediating efect of emotions on people’s behavior insocial media [37]. As yet, there is still much that we do not understand well about the relationship between emotion and how people
handle misinformation on social media. Moreover, there is room
to design new user-centred strategies that harness and/or extend
what people already do to combat misinformation in social media.
To this end, our goal was to uncover the role of emotion in themetacognitive strategies people un/knowingly deploy when confronted with social media content. We asked: RQ1: What strategies dopeople use when confronted with misinformation? RQ2: What role does
emotion have in social media use? Our contributions are fourfold.
First, we ofer qualitative evidence that people struggle to detectand manage misinformation. Second, we provide evidence supporting the view that cognition and afective reactions are intimately
entwined. Third, we ofer a synthesis of the high-level metacognitive strategies we derived from participants’ storied experienceswith misinformation. We end with an initial set of guidelines fordeveloping an intelligent metacognitive tool for everyday use onsocial media platforms.

CCS CONCEPTS• General and reference → Empirical studies; • Networks →Social media networks; • Human-centered computing → Userstudies.

KEYWORDS
misinformation, social media, focus group, opinion discussion, emotional reactions
ACM Reference Format:
Jacqueline Urakami, Yeongdae Kim, Hiroki Oura, and Katie Seaborn. 2022.
Finding Strategies Against Misinformation in Social Media: A Qualitative
Study. In CHI Conference on Human Factors in Computing Systems Extended
Abstracts (CHI ’22 Extended Abstracts), April 29-May 5, 2022, New Orleans, LA,
USA. ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3491101.
3519661
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https://doi.org/10.1145/3491101.3519661

INTRODUCTION

2 THEORETICAL BACKGROUND
2.1 Role of Emotion in Social MediaSocial media difers from traditional information media in beinguser-based, interactive, relationship- and community-driven, andvaluing emotions over content [27]. Indeed, user emotion is a driving force on social media. So far, most of the literature has focused

CHI ’22 Extended Abstracts, April 29-May 5, 2022, New Orleans, LA, USA

on deriving emotional expressions from social media content, particularly through sentiment analysis and other forms of large-scale,algorithmic approaches to content analysis [1, 42, 50]. For example,
Alduaiji and Datta [2] conducted a sentiment analysis on threediferent Twitter data sets and found that positive tweets are shared
more often than negative tweets. Others have found that people’semotional reactions to a topic can change over time [28] and beinfuenced by the emotions of others [12, 20]. In short, social media
can convey and evoke emotion. However, less is known about how
emotional reactions to content, especially misinformation, relates
to other aspects of social media engagement. Valence–the afective
quality of emotion as pleasant or unpleasant–and arousal–the intensity of the emotion–seem to be related to how content is sharedon social media. Nelson-Field et.al. [29] analyzed user behavior insharing video ads on Facebook. They found that videos rated withpositive valence and high arousal were shared 30% more often thanvideos with negative valence and low arousal. Similarly, Yu [51]reported a positive relationship between brand posts with positive
valence and users’ sharing and liking of these posts. A small bodyof work (e.g., [47]) has started to explore the potential relationship between emotion and cognition as a mediator of interactions
with misinformation on social media, however studies have also
shown that emotion recognition is often perceived as being invasive and scarey [4]. Cognitive ability has long been identifed asa key variable in misinformation of all kinds, online and of [15].
In the past, the relationship between emotion and cognition was
widely discussed and often contested in the psychological literature [10, 22, 52]. However, recent fndings in neuroscience (e.g.,[30, 31]) support the view that emotion and cognition infuenceeach other and are closely related. More work is needed to understand whether and how this plays out across a variety of topics
subject to misinformation within social media.

2.2

Employing Metacognitive Strategies in
Social MediaOne cognitive strategy that could address susceptibility to and emotional reactions towards misinformation in social media is metacognition. Originally coined by John Flavell [13], metacognition isdefned as cognition about cognitive phenomena or more colloquially"thinking about thinking." It has been described as higher order
thinking that involves active control over one’s cognitive processes
while engaged in learning [39, 40] and executive control involving
self-monitoring and self-regulation [18, 26]. Lab and questionnaire
studies have shown that supporting and encouraging the use of
the metacognitive processes involved in processing information
can enable social media users to detect misinformation more easily
and to react appropriately to them [34]. In general, encouraging
people to engage in the evaluation of information is a successful
approach to reduce susceptibility to inaccuracies [5, 8]. However,people are often not motivated to engage in information evaluation,even when it seems obvious and reasonable [25, 33].
Social media users may be encouraged to employ metacognitive
strategies in social media contexts with the right design scafolds
and user experience (UX) design. However, with no known applied
work to date, it is not clear how best to approach this as a design or
technical problem. We start by exploring the role of emotion and

Urakami, et al.

cognition in people’s orientations and responses to social media
content, especially misinformation, as a way of deriving strategies
and potential design guidelines. As a frst step, we conducted afocus group study with social media users to better understandhow they grapple with social media content that is emotionallyevocative and potentially misinformed.

3

METHODS

We conducted an online qualitative study with social media users.
We qualitatively analyzed transcripts and quantitatively assessed
questionnaire responses. Our qualitative approach focused on describing the experiences of the participants in everyday language[21, 36]. We chose the divisive and emotionally-charged topic ofusing masks as a preventive measure for COVID-19, assuming thatparticipants would be aware of the ongoing discussions and may
have already formed their own opinions about it. Furthermore, numerous recent studies have highlighted the urgency of addressing
misinformation on COVID-19 in social networks (e.g. [9, 32, 35, 41]).

3.1

Participants

We recruited a diverse group of participants from diferent countries using Facebook, word-of-mouth advertising, and our personal
online networks. Fifteen participants (5 men, 10 women, aged 18-54)
representing six diferent ethnicities and being located in Japan,
the UK, and Canada took part in the study. Most were non-native
English speakers (n = 12) but all could use English at a conversational level or higher. The study was approved by the university’s
ethical board.

3.2

Questionnaires

A pre- and post-questionnaire was administered to participantsof the focus group using SurveyMonkey1 . Tweets and comments
underwent a rigorous rating process by three raters.
The questionnaires were identical except that demographic questions were not included in the post-questionnaire. The questionnaire started with an explanation of the purpose of the researchand requested participants’ consent. The frst section containedquestions about average social media platform usage on weekdays
and on the weekend, based on the questions used by Walsh etal. [45]. Typical social networking activities were captured using
the Social Networking Activity Intensity Scale (SNAIST) [24]. In
the second section, participants were asked to read four tweets
from Twitter and four comments from the New York Times about
diferent opinions on wearing masks as preventive measure for
COVID-19. These were presented in random order to each participant using SurveyMonkey’s randomization tool. After reading each
tweet/comment, participants were asked to rate their emotional
reaction according to valence and arousal (9-point Likert scales),as well as estimate their agreement and familiarity (5-point Likert scales) with the content. Emotional reactions were measured
with the Tactile Self-Assessment Manikin (T-SAM) [19]. The third
section of the questionnaire contained demographic questions andquestions about participants’ ideological leaning and degree ofagreement on whether or not masks protect against COVID-19.
The questionnaire took about 20 minutes.
1 Seehttps://www.surveymonkey.com

Finding Strategies Against Misinformation in Social Media: A Qalitative Study

3.3

Equipment

We used the Zoom video conferencing platform2 . Each Zoom meeting was recorded and stored for later analyses. Zoom recordings
were transcribed and double checked by a second experimenter.

3.4

Procedure

Participants were assigned to one of four online focus groups basedon their availability and time zone. We also aimed to maximize
a large diversity of group members in terms of gender, age, andethnicity. Participants had to complete the questionnaires by a day
before and after the focus group meeting. The same protocol wasused in each session. Each session was scheduled for 1 hour. Eachsession started with a 10 minutes opening (greeting participants,
introducing study goals, confrming basic rules for discussion), followed by a 40 minutes discussion section (Sharing own experienceswith social media and misinformation / Importance of social media
in everyday life / Experience with misinformation), followed by a
10 minutes Wrap up and Summary of the discussion. At the end of
the session participants were informed about the procedure for the
post-questionnaire.

3.5

Data Analysis

Descriptive statistics and correlations were generated for the quantitative data. A thematic analysis [17] of the transcripts was conducted by two raters who identifed three thematic blocks (importance of social media, experience with misinformation, important
discussion points). Responses were fltered according to the mainquestions for each block (see 3.4). Rater agreement of fltered responses was 85%. Discrepancies were resolved through discussion.

4 RESULTS
4.1 Questionnaire: General Usage of Social
Media
Questionnaire data revealed that participants were regularly using
a variety of diferent social media platforms, such as YouTube (n =15), Instagram (n = 11), Line (n = 11), Twitter (n = 8), WhatsApp(n = 7), Facebook (n = 6), and WeChat (n = 4). Only one participant
used TikTok. Estimated time spent on each platform difered greatly
between participants. Averaging usage time across participants
showed that most time was spent on YouTube on weekdays (Md =30min) and weekends (Md = 60min), with some participants (n =4) spending an average of 2-4 hours on weekdays and weekends on
the platform, probably because YouTube provides a large amountof content for entertainment. All participants used at least one
type of social messenger application, such as WeChat, Line, and/or
WhatsApp spending between 30-300 minutes on these apps on
average weekdays and weekends.

4.2

Questionnaire: Response to Tweets andComments

The main purpose of the questionnaire was to familiarize focus
group participants with conficting information on social media
and to use these experiences as a reference for discussion. Because
2 See https://zoom.us/

CHI ’22 Extended Abstracts, April 29-May 5, 2022, New Orleans, LA, USAof our small sample, the results of the questionnaire were not representative of the general population. Overall, participants were
supportive of the argument that mask-wearing protects against
COVID-19 (M = 3.73, SD = 1.16).
Furthermore, a signifcant negative correlation between participants’ attitudes towards wearing a mask as a COVID-19 prevention measure and rejecting tweets/comments was found in the pre(r = .825, p < .01) and post-test (r = .797, p < .01).

4.3

Focus Group Discussion

Findings from the focus group discussions are organized into fve
themes: 1) General social media use, 2) Responses to the content
of tweets/comments in the pre-questionnaire, 3) Experience withmisinformation, 4) Recognizing misinformation, and 5) Insightsfrom the discussion. Participants’ quotes are provided in “italics”.
4.3.1 General Social Media Use. Most participants agreed that social media is an important part of everyday life. Social media had
become especially important during the COVID-19 pandemic toovercome feelings of isolation and distance from family and friends(n = 2). Participants felt that it was “very inconvenient when notavailable”, and “can’t imagine to live without it”. Social media wasmainly used to stay in touch with family and/or friends (n = 14).
Three mentioned that they used it to connect with new people andtwo interacted with colleagues. Social media was also largely used
as a tool to collect diferent types of information (n = 6) such as“getting the latest news especially when something had just happened”,“looking up information what is going on in the world”, gathering political and world news or information about happenings and eventsin the local area, but also for “getting information about new people
before meeting them”. However, one participant acknowledged at
the same time that social media is the “least trustable information
source”.
4.3.2 Responses to the Content of Tweets/Comments in the PreQestionnaire. Participants felt that the content was representativeof what is on social media right now. They noticed diferences informality of language and emotional tone, with some “pretty emotional” and even “aggressive”. Furthermore, participants recognizedthat these tweets/comments mainly voiced personal opinions with
only some providing scientifc evidence. Participants’ initial reactions fell into two categories: either triggering an afective response
or a cognitive evaluation. Reported afective responses were being“shocked”, feeling “hurt”, “upset” and “surprised” about anti-mask
comments. Cognitive evaluations were made in regard to the originof the poster: “they try to convey to you an idea, an opinion, an
emotion and like you read them and you get an idea of the person[who wrote them]”. Some evaluated the value of the information(n = 6), as indicated by comments such as “I think about which
is most relevant to me” , “I chose which comment I want to hear
and which not”, and “It’s like a piece of news and before I made myjudgement, I would like to read the whole information”.
All noticed that conficting information was represented in thetweets/comments. Notably, we received mixed answers when we
asked about their impression of the balance in terms of supportingor rejecting mask-wearing. Two participants could not remember,two stated that their own viewpoint was mainly supported, four felt

CHI ’22 Extended Abstracts, April 29-May 5, 2022, New Orleans, LA, USA

that the content was balanced, and another four indicated that mostof the content rejected mask-wearing as a preventive measure. Participants then realized that their impressions of tweets/comments
difered. They elaborated that their impression could be biased because of their own position: ”because I think mask is important so I
just feel like I just want to avoid this comment”, “I think if it’s [thecomments] more negative because my perception is positive”. Reading the tweets/comments also impacted how participants thought
about the topic of mask-wearing. While just over half of participants stated that it did not change their way of thinking (n = 9),
fve participants said that it slightly changed or made them think
more about how they think about the topic: “it kind of swayed me abit”, “good to see the other side of the beliefs”, “concerns some things
which I never thought about before” or “they gave me fresh points ofview to some problems”.
4.3.3 Experience with Misinformation. There was a general agreement that misinformation is an increasing problem in social media.
Participants stated that there is “so much misinformation, it is getting harder to flter it out”, that they “don’t trust social media to get
important information”, and there is “much misinformation even on
credible sources”. When asked how they have acted on misinformation in their past, four diferent patterns emerged: 1) ignoring“I perceive this information as noise”, “there are a lot of false information, we are getting used to the false information”, to “brush itof”, 2) warning or educating friends and family about it “I told myfather don’t listen to him it’s all wrong”, “I re-posted to my friends totell this is not true”, 3) reporting it to the platform “I actually was
like reporting my uncle’s post because they were completely racist”,“it is important to report posts of fake news”, and 4) searching foradditional information sources "should follow up on it if it is a new
information”, “you have to double check where that is coming from”,“fnd a diferent source that’s talking about the same news”. Onedecisive factor in the type of action taken was whether the information was of personal concern or not. Participants stated “evaluate
how important it is, if not important than brush it of”, “if like thecontent is related too closely to my life then I try to check it out todiferent sources”, and “if it is not personal than ignore, need to choose
which info you care about”. Participants also expressed that thelarge amount of misinformation makes it harder to follow up on:“getting a lot of misinformation in social media but it is getting harder
to flter it out”, “checking info is extra work, everybody can just post
anything”.
Many participants (n = 8) also mentioned the negative emotional
impact that encountering misinformation can have. Participants
made comments such as “I am angry that others believed false information”, “so worried because I didn’t expect him to be somebody who
can fall to that kind”, “feel sad when people I respect post misinformation”, “sometimes it’s a bit shocking”, when “I have a bad day, makes
me feel upset but can’t really do anything”, “a lot of misinformation
makes me worry about my family if they are OK or not”, “feel angry if
it is bad info about other person”. Four participants emphasized thatmisinformation can be harmful and dangerous, creating division
among people, whereas one participant noted that “I think it’s really
important to actually still have people that post false information on
your Facebook because they’re trapped in like a social bubble so youkind of actually post something that might change their mind”.

Urakami, et al.

4.3.4 Recognizing Misinformation. All participants had frequently
encountered misinformation and agreed on what misinformationis. However, when refecting on their approach to identifying misinformation, participants found it difcult to give a straightforward
answer. Source credibility was mentioned as one way to make a
judgment call (n = 10), which also included checking the politicalbackground of the poster. Another was a lack of scientifc evidence(n = 2), or, alternatively, the presence of specifc keywords that areoften used in misinformation: “also there’s a typical way like they
use the term share it as much as you can, these statements probably
80 percent just fake news”. Participants also acknowledged that it isoften difcult to tell what is misinformation (n = 7). In such cases
they rely on their intuition “you just know”, “it does not seem to makesense”. Misinformation that mixes facts with false information was
regarded as especially difcult to identify (n = 2). Participants also
agreed that cross-referencing social media information with othersources, such as an ofcial agency, other news sites, and original scientifc articles, is a good way to verify information (n = 14). These
alternative information sources also included friends, colleagues,
and family members.
4.3.5 Participants’ Insights from the Discussion. Participants wereasked to refect on the discussion to stimulate metacognitive processes on their own perceptions of social media and misinformation.
Seven said that the discussion about how to identify misinformation
was especially important: “I guess it helped me to think about likehow I detect misinformation”, “the nuances between how to tell ifsomething is misinformation or fake news”, “the most important is
the ability to discriminate between false and fraud information”, “itis very important for us to be able to divide which is false information
and which was correct information and for me the most takeaway
information here is how can we understand from other people’s perspective about what such kind of information what to say”. Fiveparticipants pointed to the discussion about the importance andefects of social media in everyday life, realizing that “it is very
important to actually try and take time away from social media”
and the increasing infuence social media has “when I was growing
up there was no social media so it’s really interesting to see how it
became really like so important in our life”, “not to spend much timewith social media and look more for information from the books orinteract directly with other people”. Thinking about cognitive biases
and how to deal with views other than their own was mentionedby only two participants.

5

SUMMARY AND DISCUSSION

Our fndings clearly show that social media is an important part ofpeople’s everyday life. Moreover, focus group discussions indicatedthat social media is not simply about connecting with family andfriends; rather it has become a source of information, even thoughparticipants were conscious about the lack of reliability of thisinformation.
Misinformation was widely regarded as a common occurrence.
The discussion revealed three main challenges that participants
were struggling with: 1) the large amount of (mis)information, making it more difcult to flter any information; 2) problems withidentifying misinformation, especially when facts and false information are mixed; and 3) the mainly negative emotional impact

Finding Strategies Against Misinformation in Social Media: A Qalitative Studyof misinformation on their own feelings but also their feelings towards others. Four patterns related to acting on misinformation
also became apparent: 1) ignoring it, especially when deemed not
personally relevant; 2) warning family and friends; 3) reporting itto the platform; and 4) cross-validation using other information
sources.
Participants also experienced diferent levels of engagement
when processing information on social media, ranging from low(e.g., ignoring), to medium (e.g., evaluating personal relevance,
checking information about the author or information source, identifying keywords that could signal misinformation), to high (e.g.,looking up the original information source, discussing with friends,
family, or colleagues, searching for additional information about
the same topic in other information sources). However, none of theparticipants had a rigorous strategy for identifying misinformation.
Indeed, participants noted that the focus group discussion itself
allowed them to understand how misinformation can be detected.
A limitation of our study is the small sample (n = 16) whichmight not be representative of social media users in general. Furthermore, limiting the topic to mask usage might have infuenced
how participants responded in the discussions. Further studies ona variety of topics are necessary to validate our fndings.

6

IMPLICATIONS

Our fndings provide insights into how social media users confront
and react to information in general and misinformation specifcally.
Based on these, we have identifed three areas where social media
users may need support.

6.1

Information Selection

Social media users are confronted with a large volume of information coming from diferent sources and platforms. People struggle
to flter the information fow and identify the truthfulness of theinformation. Intelligent tools could support users in two diferent
ways. (1) Making misinformation less visible. Misinformation can becompletely removed (an approach already used by some providers),
or it can be camoufaged with opaque overlays or patterns thatmake it less visible (for images) or less readable (for text). Many
previous studies have looked at highlighting misinformation byadding warnings (e.g., [6, 25, 33]. However, as our fndings indicate,people are overloaded with information. Additionally, salient cues
like warnings might draw users’ attention to misinformation rather
than away from it. In general, warnings are designed to capture
attention and hold it long enough for the user to process the content (e.g. [48]). Camoufaging misinformation without removing itcompletely increases algorithmic transparency. Providing a way forthe user to control the overlay obscuring the information ensuresthat they maintain autonomy. Importantly, content obfuscation andcontrols over it must be considered in light of disability. (2) Highlighting key information. Highlighting information based on simple
strategies to determine its truthfulness might speed up recognition of the degree of truthfulness and ease anxieties about credible
sources being infltrated by misinformation. Such key information
can be common phrases often used in misinformation ("share thisquickly!!!"), or easily accessible information about the information

CHI ’22 Extended Abstracts, April 29-May 5, 2022, New Orleans, LA, USA

source (original source, author, evidence cited). Subtle visual emphasis and timed animations could draw attention to these elements,
especially if the user has been attending to the information forsome time.

6.2

Information Engagement

Given the large amount of information that social media users are
exposed to on a daily basis, it is understandable that many choose
to ignore those that they do not consider relevant or that they deem
misinformation. However, engaging with difering viewpoints canencourage a re-evaluation of one’s beliefs and motivate users tothink more deeply about an issue. Of course, this infuence could go
both ways: in opposition to misinformation or towards accepting
misinformation as a legitimate alternative view. Indeed, only a couple of participants identifed raising awareness of cognitive biases
and learning how to handle opposing views as important takeaways.
Encouraging users to engage more deeply with the information thatthey are exposed to could therefore trigger metacognitive processes
to reconsider one’s own position, reiterate pro and con arguments,
and raise awareness of one’s own thought processes. An intelligent
tool could do this by asking thought-provoking questions, such as"Do you think this information is true or false?" "Why do you thinkso?" "What are the arguments against this position?". In addition,
an intelligent tool could help users cross-reference information by
providing links to other sources that contain factual information
or references to discussion groups that address the issue, i.e., diversifcation of information, similar to diversifcation of authors
and communication styles [11]. The usefulness of the focus groupformat itself suggests that it might also be helpful to encourage
people to discuss and cross-reference content with friends, family,
or colleagues.

6.3

Emotion Management

Emotions play an important role in people’s judgments, information evaluation, and information selection, and thus also afect how
we react to certain stimuli [10]. Previous research [12, 20, 28] aswell as our study show that encountering misinformation or conficting viewpoints triggers a variety of emotional reactions, manywith a negative valence. For example, emotion recognition systems
could provide feedback to users about their emotional responses toinformation, helping them to better understand their own reactions
and the infuence of their emotions on thoughts and actions. They
could also highlight in advance the sentiments expressed in content
not yet read. Emotion monitoring systems could prompt users totake time out or focus more on self-care when extreme emotions aredetected (e.g., anger, intense resentment, shock) or when negative
emotions persist for an extended period of time.

7

CONCLUSION

Misinformation is difcult to detect and emotionally reactive. Current platforms and tools, and arguably education and social environments, are not sufcient to help people make sense of thecontent they are exposed to online, especially if it is divisive. Yet, as
our fndings show, people do exercise metacognitive skills without
training or prompting. Yet, these skills may not be sufcient to recognize and handle responses to all forms of negative social media

CHI ’22 Extended Abstracts, April 29-May 5, 2022, New Orleans, LA, USA

content, or indeed all forms of misinformation, at all times. As wehave suggested, these informal strategies may be translated into
design guidelines for intelligent tools that take on the burden of
detecting divisive content and assisting people during social media
use. Such tools may help everyday social media users to develop themetacognitive skills that they need to handle the infodemic while
retaining their autonomy and continuing to enjoy the brighter sideof social media.

ACKNOWLEDGMENTS
This research was supported by DLab Challenge : Laboratory for
Design of Social Innovation in Global Networks (DLab) Research
Grant.

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