Approaches to Identify Vulnerabilities to Misinformation A Research Agenda

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Approaches to Identify Vulnerabilities to Misinformation: A Research

Agenda

NATTAPAT BOONPRAKONG, University of Melbourne, Australia

BENJAMIN TAG, University of Melbourne, Australia

TILMAN DINGLER, University of Melbourne, Australia

Given the prevalence of online misinformation and our scarce cognitive capacity, Internet users have been shown to frequently

fall victim to such information. As some studies have investigated psychological factors that make people susceptible to

believe or share misinformation, some ongoing research further put these ndings into practice by objectively identifying

when and which users are vulnerable to misinformation. In this position paper, we highlight two ongoing avenues of research

to identify vulnerable users: detecting cognitive biases and exploring misinformation spreaders. We also discuss the potential

implications of these objective approaches: discovering more cohorts of vulnerable users and prompting interventions to

more eectively address the right group of users. Lastly, we point out two of the understudied contexts for misinformation

vulnerability research as opportunities for future research.

CCS Concepts: •Human-centered computing →Human computer interaction (HCI).

Additional Key Words and Phrases: misinformation; vulnerable users; user proling; cognitive bias

ACM Reference Format:

Nattapat Boonprakong, Benjamin Tag, and Tilman Dingler. 2022. Approaches to Identify Vulnerabilities to Misinformation:

A Research Agenda. In CHI ’22: ACM CHI Conference on Human Factors in Computing Systems, April 30–May 6, 2022, New

Orleans, LA. ACM, New York, NY, USA, 7pages. https://doi.org/10.1145/1122445.1122456

1 INTRODUCTION

There is a sheer amount of misinformation circulating on the Internet nowadays. Dened as false or misleading

information, misinformation misinforms people and bears many of the harmful eects to individuals and

society [

]. More recently, misinformation has undermined public health guidance about COVID-19 [

Through Internet users who consume online media, misinformation has been shown to spread faster than

factually accurate information [

]. This phenomenon, where users believe and share misinformation without

taking deliberate considerations, can be attributed to not only the presence of misinformation but also the

psychological vulnerabilities of information consumers [

]. Humans are not always rational [

]; therefore, they

are prone to make erroneous decisions, like misinterpreting news or sharing unveried information.

There are two main actions regarding misinformation: believing and sharing. In the literature, research has

explored several user-side factors that govern the susceptibility of people to believe online misinformation.

Cognitive biases–mental shortcuts we use to achieve faster but less deliberate decisions– have been marked

as factors that make people be gullible to false or unveried information [

]. Martel et al

. [21]

have shown

that reliance on emotion indicates higher belief in false information. Some research have shown that political

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permissions@acm.org.

CHI ’22, April 30–May 6, 2022, New Orleans, LA

ACM ISBN 978-1-4503-XXXX-X/18/06. . . $15.00

https://doi.org/10.1145/1122445.1122456

arXiv:2210.11647v1 [cs.HC] 21 Oct 2022

CHI ’22, April 30–May 6, 2022, New Orleans, LA N. Boonprakong, B. Tag, T. Dingler

partisans [

] or those who are older than 65 years [

] are prone to believing fake news. Roozenbeek et al

[31]

found that higher numeracy skills and better trust in science are indicators of lower susceptibility to

misinformation.

On the other hand, some research has investigated why people share unveried information online. Motivated

by [

], Karami et al

. [17]

listed ve motivational factors in spreading fake news: uncertainty, anxiety, lack

of control, relationship enhancement, and social rank. Laato et al

. [18]

found that trust in online information

and information overload are strong indicators of sharing unveried information. Chen et al

. [5]

suggested that

people share misinformation because of social factors, i.e., self-expression and socialization. In addition, Avram

et al

. [1]

found that social engagement metrics (i.e., the number of likes or shares) increased the tendency to

share misinformative contents.

Based on the above-mentioned psychological ndings, some research have put these ndings into practice by

building computing systems that objectively identify potential misinformation spreaders based on their online

and oine behaviours [

]. Unlike previous approaches that rely on self-report measures, these approaches

quantify how people behave with information and produce insights about their specic behaviours regarding

to misinformation. Through behavioural and psycho-physiological measures, these approaches can explicitly

identify when and which users are prone to believe or spread misinformation.

Dierent individuals have dierent backgrounds and dierent levels of cognitive capacity. Therefore, some

information consumers may be more prone than others to misinformation. Echoing the ndings in Geeng et al

[12]

that dierent design interventions may be eective for dierent users, the objective approaches to identify

vulnerable users would also provide a a guide on which misinformation intervention should be deployed to which

group of users. Ultimately, interventions would be made more eective as they address the right group of users.

In this position paper, we discuss empirical approaches to identifying people who may be susceptible to

misinformation: detecting cognitive biases and proling vulnerable users. We also highlight their implications of

identifying more cohorts of vulnerable users and prompting interventions to address the right group of users.

Lastly, we elaborate on challenges and future avenues to investigate vulnerabilities to misinformation. We intend

to generate a fruitful discussion in this workshop about the need to explore potential victims of misinformation,

identify their vulnerabilities, and employ the right interventions to address the right user cohorts.

2 APPROACHES TO IDENTIFYING VULNERABILITIES TO MISINFORMATION

Various studies have investigated why people believe and share misinformation on the Internet. However, most

of the studies addressed such questions based on self-report measures, e.g., questionnaires, which are prone to

many limitations such as subjectiveness. On the other hand, some research has proposed objective approaches

to identifying users who may be susceptible to misinformation. Combining prior psychological ndings and

behavioural measures, these research eorts explicitly identify which and when users tend to believe and share

misinformation. In this paper, we discuss two promising approaches: detecting cognitive bias detection and

proling social media users.

2.1 Cognitive Bias Detection

Humans possess limited cognitive capacity and employ cognitive biases as mental shortcuts, which can lead

to irrational judgements. One prominent example is “conrmation bias” (also known as selective exposure) as

a tendency to seek solely information to supports one’s perspectives or expectations, while ignoring other

dissenting information [

]. Taking the advantage of social media platforms that inform users with contents

that reinforce their viewpoints, conrmation bias is easily seen on social media platforms as many users tend to

consume and spread content items that match their belief without checking its veracity [39].

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摘要：

ApproachestoIdentifyVulnerabilitiestoMisinformation:AResearchAgendaNATTAPATBOONPRAKONG,UniversityofMelbourne,AustraliaBENJAMINTAG,UniversityofMelbourne,AustraliaTILMANDINGLER,UniversityofMelbourne,AustraliaGiventheprevalenceofonlinemisinformationandourscarcecognitivecapacity,Internetusershavebeensho...

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Approaches to Identify Vulnerabilities to Misinformation A Research Agenda

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