# NudgeCred: Supporting News Credibility Assessment on Social Media Through
Nudges

Md Momen Bhuiyan  Virginia TechUSA<EMAIL_ADDRESS>,  Michael Horning  Virginia
TechUSA<EMAIL_ADDRESS>,  Sang Won Lee  Virginia TechUSA<EMAIL_ADDRESS>and  Tanushree Mitra  University of WashingtonUSA<EMAIL_ADDRESS>
(January 2021; April 2021; July 2021)

###### Abstract.

Struggling to curb misinformation, social media platforms are experimenting
with design interventions to enhance consumption of credible news on their
platforms. Some of these interventions, such as the use of warning messages,
are examples of _nudges_ —a choice-preserving technique to steer behavior.
Despite their application, we do not know whether nudges could steer people
into making conscious news credibility judgments online and if they do, under
what constraints. To answer, we combine nudge techniques with heuristic based
information processing to design NudgeCred–a browser extension for Twitter.
NudgeCred directs users’ attention to two design cues: authority of a source
and other users’ collective opinion on a report by activating three design
nudges— _Reliable, Questionable,_ and _Unreliable_ , each denoting particular
levels of credibility for news tweets. In a controlled experiment, we found
that NudgeCred significantly helped users (n=430) distinguish news tweets’
credibility, unrestricted by three behavioral confounds—political ideology,
political cynicism, and media skepticism. A five-day field deployment with
twelve participants revealed that NudgeCred improved their recognition of news
items and attention towards all of our nudges, particularly towards
_Questionable_. Among other considerations, participants proposed that
designers should incorporate heuristics that users’ would trust. Our work
informs nudge-based system design approaches for online media.

Social Media; Twitter; Nudge; News; Credibility; Misinformation; Fake News;
Intervention; Heuristic; Bandwagon

††copyright: acmlicensed††journal: PACMHCI††journalyear: 2021††journalvolume:
5††journalnumber: CSCW2††article: 427††publicationmonth: 10††price:
15.00††doi: 10.1145/3479571††ccs: Human-centered computing††ccs: Human-
centered computing Human computer interaction (HCI)††ccs: Human-centered
computing Empirical studies in HCI††ccs: Human-centered computing User studies

##  1\. Introduction

Figure 1. Three types of interventions (marked by blue arrows) currently
employed by Twitter to tackle misinformation. Tweet (a) with a link to proper
authority regarding COVID-19, (b) with a warning, and (c) removed. Here, both
(a) and (b) are examples of nudges. Around the beginning of our work (July
2018), only (c) was operational. Twitter added others later.

Social media platforms have witnessed an unprecedented rise in misinformation
around public issues (e.g., COVID-19 and the 2016 US Presidential Election
(Worrall, 2020; Allcott and Gentzkow, 2017)). To tackle, they have responded
by experimenting with various design interventions (Twitter, 2019; Roth and
Pickles, 2020). These include attaching warning labels and links to show
additional context from trusted sources (see figure 1 for an example). With an
intent to raise awareness and lead users to investigate the veracity of a news
item (Safety, 2020), such design interventions can act as nudges—a choice-
preserving technique to steer behavior (Sunstein, 2015). Nudges differ from
methods such as real-time corrections that often act as mandates and can
backfire (Garrett and Weeks, 2013). Furthermore, nudges can overcome the scale
issue that many real-time systems face who rely on limited number of expert
fact-checkers and quality crowd workers (Babakar, 2018). Despite the benefits,
there is little empirical evidence whether nudge-based socio-technical
interventions affect users’ perception of credibility of online information.
Furthermore, what complicates such investigation is that people with strong
ideological leaning may resist nudges (Tannenbaum et al., 2017). Existing
works, again, lack empirical evidence of the effects of ideological leaning on
nudges regarding perception of credibility. Therefore, considering the
constraints under which these nudges may or may not work is crucial. This
paper does just that.

For the purpose of our investigation, we design nudges with heuristic cues,
i.e., mental shortcuts that people often use to judge credibility (Metzger et
al., 2010). The choice of heuristic cues over reflective ones reduces
cognitive burden on users, given the immense amount of content users see
online (Evans, 2003). Incorporating design guides from nudge and heuristics
literature (Sundar, 2008; Todd and Gigerenzer, 2000), we built NudgeCred which
operationalizes three design nudges— _Reliable_ , _Questionable_ and
_Unreliable_. Devised with two heuristic cues—the authority of the source and
other users’ opinions—each of our nudges designates a particular level of
credibility of news content on social media. These two heuristics comprise
both external authoritative sources of information and social interactions of
the crowd. Among the three nudges, both _Reliable_ and _Questionable_ are
applied to information originating from mainstream sources on Twitter, while
_Unreliable_ makes posts from non-mainstream sources less visible (see Figure
2). Here, _Questionable_ and _Reliable_ differentiate between mainstream news
items that raised questions in their Twitter replies compared to those that
did not. Questioned items are highlighted in yellow to warn of the potential
controversy in evolving news stories from mainstream media, while those
lacking questions are highlighted in green, signifying their reliability. By
directing users’ attention to the two heuristics, NudgeCred assists users in
making meaningful news credibility evaluations.

Figure 2. Our nudge design: [Top] A decision tree shows the intervention logic
and [Bottom] three nudge designs. (a). The _Reliable_ nudge on a tweet from
CNN Breaking News without questions in its comment thread. (b). The
_Questionable_ nudge is applied to a tweet with questions from Fox News, a
mainstream media outlet. (c). The _Unreliable_ nudge is activated on a tweet
from 100PercentFedUP.com, an extremely biased, non-mainstream website. The
numbers indicate: (1) a change in background, (2) a tooltip message shown when
hovered over, (3) a button to open a survey questionnaire for users to rate
the credibility of the news tweet, and (4) a button to show more questions in
the comments.

To demonstrate our nudge-based approach, we built NudgeCred as a Chrome
extension 111How NudgeCred works:
https://www.dropbox.com/s/2mt4tpdxebccokt/nudgecred_cropped.mp4. We followed
an iterative design process. We first tested our initial design of NudgeCred
by conducting a formative study with 16 university students and 36 Amazon
Mechanical Turk workers. Results from the formative study helped us refine our
design and suggested three confounds—political ideology, political cynicism,
and media skepticism—that may restrict impacts on users’ credibility
perceptions. We then conducted two sets of experiments using our final design:
Study 1, a controlled experiment to examine the impact of the nudges with a
representative US population ($n=430$); and Study 2, a qualitative field
deployment with Twitter users ($n=12$) to gain insight into how we can improve
NudgeCred’s design. Analyzing users’ credibility responses from Study 1
revealed that the _Unreliable_ nudge significantly reduced users’ perceptions
of credibility for non-mainstream news sources. For _Questionable_ , users in
the treatment group rated news tweets with questions as less credible than the
users in the control group, and those without questions as more credible. We
did not find any effect of users’ political ideology, media skepticism, or
political cynicism on the effects of nudges. These outcomes suggest that
NudgeCred worked irrespective of these confounds. Results from our field
deployment (Study 2) show that NudgeCred improved recognition of news content
and elicited attention towards all three nudges, particularly _Questionable_.
Participants also suggested additional design considerations, such as
incorporating heuristics that users would trust, applying nudges to share
buttons, and using nudges to distinguish news genres and biases. To conclude,
we offer design directions for news credibility nudging by exploring
transparency-mode of thinking nudge categories, other heuristics and nudging
methods from prior literature. Overall, our contributions include:

  * •

A novel approach using heuristic cues to nudge users towards meaningful
credibility assessment of news items in social media.

  * •

A quantitative evaluation of this approach by examining users’ credibility
perception while considering three confounds—political ideology, political
cynicism, and media skepticism.

  * •

A qualitative understanding of the opportunities and challenges of this
approach in designing credibility nudges.

##  2\. Related Works

###  2.1. Designing for Information Credibility

At present, social media platforms are taking three approaches to combat
misinformation—removing misinformation, reducing their reach, and raising
awareness (Rosen and Lyons, 2019; Roth and Achuthan, 2020). The first line of
action falls under the practices of crowdsourced (in-house and community-
driven) and technology-assisted moderation (Hegeman, 2018; Silverman, 2019;
Harvey and Gasca, 2018) by enforcing established community guidelines (Brown,
2019; Roth and Achuthan, 2020). The second approach involves reviews from
fact-checking services followed by downranking (Lyons, 2018; Buelow, 2019) and
the application of warning/correction labels (Mosseri, 2016; Roth and Pickles,
2020; Twitter, 2019). The third approach largely focuses on contextualizing
misleading content through design interventions, such as providing source
transparency (Facebook, 2019; Thulasi, 2019; Support, 2020; Smith et al.,
2018), prioritizing content from trusted authorities (Harvey, 2019; Rosen,
2020), and showing related news stories from various sources (Lyons, 2017).
Some of these interventions also target particular issues (e.g., voting
(Safety, 2019)) or particular interactions (e.g., message forwarding
(Sullivan, 2020)). While contextualizing with additional information has its
benefits, producing unsorted information only adds to the confusion (O’neill,
2002). In this regard, we simplify design cues to aid users in distinguishing
news coming from mainstream and non-mainstream news sources.

Aside from these platform-led efforts, researchers have also taken up the
challenge of designing tools to aid in assessing information credibility.
These works span several approaches, including fact-checking systems,
interventions, media literacy programs and games (Diakopoulos et al., 2012;
Caulfield, 2017; Roozenbeek and van der Linden, 2019; Lapowski, 2018; Maertens
et al., 2020). There are multiple scholarly efforts for computationally
assessing content credibility (Castillo et al., 2011; Gupta et al., 2014;
Mitra et al., 2017; Qazvinian et al., 2011; Marinova et al., 2020). There are
some scholarly works on establishing appropriate credibility signals for
online content, as well as on designing guides for labeling manipulated media
(Zhang et al., 2018; Im et al., 2020; Saltz et al., 2020). Some works examine
particular crowd-led credibility labeling, including ratings by partisan
crowds and the relationship between ratings from crowds and experts (Pennycook
and Rand, 2019; Becker et al., 2019; Bhuiyan et al., 2020; Mitra et al.,
2015). Compared to actively seeking labels from the crowd, our work uses both
authoritative sources of information as well as passive crowd interaction with
content for labelling content credibility in NudgeCred. This combination helps
us overcome the scale issue regarding recruiting quality crowd workers for
active labelling.

Scholars have employed multiple types of messages as interventions against
misinformation, including theory-centered messages (Chen et al., 2015),
warning messages (Pennycook et al., 2019; Bush et al., 1994), corrective
messages (Pluviano et al., 2017; Garrett and Weeks, 2013; Johnson and Seifert,
1994), and opposing argument messages (Cook et al., 2017). Studies examined
the efficacy of interventions in various genres of news, including public
health (Pluviano et al., 2017; Pennycook et al., 2020b) and politics
(Pennycook et al., 2019). Some research examined the effectiveness of
interventions across countries (Guess et al., 2020). Others examined effects
for interventions across time by offering real-time correction versus delayed
retraction (Garrett and Weeks, 2013; Johnson and Seifert, 1994). Real-time
correction tools utilize various strategies, including mining databases of
well-known fact-checking websites (such as, Snopes and PolitiFact) or
crowdsourcing fact-checking. Paynter and colleagues looked into how to
strategize corrections by combining several existing techniques (e.g.,
salience of a graphical element in a warning); they call this approach
“optimized debunking” (Paynter et al., 2019). Some suggest that while
corrections can be effective, they can also backfire by inadvertently
provoking users into attitude-consistent misperceptions (Pennycook and Rand,
2017). However, others were unable to replicate such backfiring effects (Wood
and Porter, 2019). Warnings about information credibility have been more
successful than corrections and are not prone to the same backfiring effect
(Bolsen and Druckman, 2015). While there are various methods (e.g., warnings,
reminders, and default options (Sunstein, 2015)) to operationalize nudges, we
utilize warnings in NudgeCred.

###  2.2. Nudges to Steer Human Behavior

The concept of nudges has been frequently used to steer civic behavior for
achieving important societal goals (Halpern, 2016; Sunstein, 2014, 2016). The
idea stems from behavioral economics and psychology, which define it as a
“choice architecture” that encourages citizens to act in a certain way while
allowing them to act in other ways, thereby being a favorable alternative to
imposing mandates (John et al., 2009). Such approaches have been highly
effective in areas such as environmental protection, financial regulation, and
anti-obesity policy (Halpern, 2016; Sunstein, 2014, 2016). In online settings,
technology-mediated nudges have been applied for such purposes as encouraging
better password management, improving mobile privacy, and encouraging frugal
shopping (Bachour et al., 2012; Balebako et al., 2011; Kankane et al., 2018).
Comparatively, nudges regarding online news is getting traction recently in
works such as Pennycook and colleagues’ “accuracy nudge” (an accuracy
reminder) and Nekmat’s “fact-check alert nudge” (Pennycook et al., 2020b;
Nekmat, 2020), who investigated impact of nudging on misinformation sharing
intention. This work not only extends existing line of research by employing
heuristics to assist credibility judgment, but also shows a method of devising
such heuristic cue design.

###  2.3. Heuristic Cues for Credibility

Cognitive psychologists have long argued that when information overload
occurs—as it typically does in online social media environments—humans turn to
the cognitively effortless route of peripheral processing (Chaiken, 1987;
Petty and Cacioppo, 1986). While existing HCI works focus extensively on
reflective processing, we use automatic or peripheral processing in this study
(Adams et al., 2015). Peripheral processing means that they depend on
heuristic cues, such as the attractiveness of the source or the font color, to
evaluate message content (Petty and Cacioppo, 1986). Communication
researchers, in response, have offered a well-established list of technology-
mediated heuristic cues, highlighted in the MAIN model, which attempts to
explain how certain cues influence users’ perception of credibility in online
contexts (Sundar, 2008). The MAIN model suggests that four technological
affordances influence perceptions of credibility: Modality, Agency,
Interactivity, and Navigability. These technological affordances trigger
various peripheral cues by which users then judge the credibility of online
content. For example, the agency affordance focuses on how users perceive
source information in computer-mediated contexts. Often, the perceived agent
or source of authority can be the machine, the user themselves, or the
perceived authors of information on a particular website. For online news,
agency is often attributed to the message’s source, and these sources can
trigger the _authority_ heuristic—the perception that the source is an expert
on the subject matter (Sundar et al., 2015). Similarly, information
surrounding a message, such as ratings and recommendations, may also provide
contextual information. For example, when a group of users likes or shares a
news article on social media, the action signals that the group deems the
information trustworthy. This signal, in turn, can influence users’ perception
of the information’s credibility while serving as a bandwagon heuristic
(Sundar et al., 2008; Sundar, 2008). In summary, the space of all possible
heuristics under the four affordances is vast, offering us numerous
possibilities for designing credibility nudges. Among these heuristics, we
utilize _authority_ and bandwagon heuristics in our nudge design. We discuss
the remaining design possibilities later (see section 8).

###  2.4. Factors Affecting Credibility Perception: Partisanship, Attitude
towards Politics, and Media

Scholars have found numerous factors that may influence information
credibility. Based on findings from our formative study (discussed in section
3), we contextualize our research on three behavioral confounds—partisanship,
attitude towards politics, and attitude towards media. Historically, scholars
have failed to reach a consensus on the role of partisan bias in credibility
perception. Earlier work suggested that users perceive unbiased information as
more credible compared to one-sided information (Allen, 1991; Pechmann, 1992).
Compared to this result, other research found that users would perceive news
conforming to their own attitudes as more credible than unbiased news (Clark
III and Maass, 1988; Mackie and Queller, 2000; Metzger et al., 2015). For this
reason, users with strong partisan biases might even resist nudges on
attitude-challenging news items, rather than be influenced. Sunstein
hypothesized that a considerably large number of people evaluate nudges based
on whether they approve of the underlying political objective, naming this
“partisan nudge bias” (Sunstein, 2016; Tannenbaum et al., 2017). Hence, we
made sure to test our nudge design across a population with balanced partisan
affiliations, allowing us to measure the effects of partisanship.

Similar to users’ partisan attitude, users’ media skepticism can influence
their perceptions of the credibility of mainstream and non-mainstream content.
Media skepticism is “the feeling that the mainstream media are neither
credible nor reliable, that journalists do not live by their professional
standards, and that the news media get in the way of society rather than help
society” (Tsfati and Cappella, 2003). Scholars have found that media
skepticism is negatively associated with exposure to mainstream media and
positively associated with non-mainstream media (Tsfati, 2010). Media is also
generally blamed for its role in enhancing institutional distrust by depicting
most governmental policies negatively and causing cynicism towards politics
(Cappella and Jamieson, 1997). Studies have also demonstrated that users with
high media skepticism and political cynicism rated citizen journalists as more
credible than mainstream ones (Carr et al., 2014). Drawing from these works,
we examine and provide the first empirical evidence of the effects of
political ideology, media skepticism, and political cynicism on credibility
nudge.

##  3\. Formative Study

We designed NudgeCred in an iterative fashion. Initially, we built a prototype
and conducted a pilot study to evaluate it 222All of our studies have been
approved by our Institutional Review Board..

##### Method

We built our prototype as a Chrome extension with two types of nudges
(described in section 4.2); namely, _Questionable_ (tweets highlighted in
yellow, which indicate caution) and _Unreliable_ (tweets that are less
visible). This extension would alter the Twitter homescreen in real-time when
users visit them. As mentioned in Figure 2, users could click on a survey
button added by the extension. Clicking the survey questionnaire button would
open a pop-up overlay comprising our study measurements for credibility. We
discus them further in section 5.1.2 (refer Figure 3 to see how it looked).
With this setup, we conducted a study with 52 participants from Amazon
Mechanical Turk (n=36) and the university (n=36) (Bhuiyan et al., 2018). For
recruitment from the university, we used a university portal available for
participant recruitment. For the MTurk users, we used MTurk portal with some
filtering conditions, such high rate of work acceptance (¿95%), over 18 years
of, US resident and familiarity with Twitter. In a pre-study survey, we
collected users’ demographic details, such as gender and political leaning.
Participants were divided into two groups of treatment (seeing tweets with
nudges) and control (not seeing any nudge). In a 2-week study period, we asked
our participants to rate the credibility of three to five tweets from their
Twitter feeds every day. We did so by reminding them everyday to spend around
20 minutes on Twitter by completing an MTurk HIT. Afterwards, we reached out
to 16 users—8 control and 8 treatment users—to get feedback on our design
where 8 of them finally agreed. In all studies, we compensated our
participants adhering to Federal minimum wage requirements ($7.25).

##### Result

In our study, we hypothesized that users in the treatment group would rate
tweets with both _Questionable_ and _Unreliable_ nudges as less credible
compared to users in the control group. A Mann-Whitney U test on the
credibility ratings showed that our hypothesis was true for _Unreliable_ nudge
(avg. cred. (Control) = $0.51$, avg. cred. (Treatment) = $0.43$ and
$Z=210236,p<0.001$, Cohen’s $d=1.291$). However, we found the opposite for
_Questionable_ nudge, i.e., the treatment group rated those tweets as more
credible than the control group (avg. cred. (Control) = $0.67$, avg. cred.
(Treatment) = $0.71$ and $Z=502140,p<0.001$, Cohen’s $d=0.188$). Furthermore,
in our post-hoc analyses, we found that for Republican users the effects of
nudges were not significant.

To make sense of the discrepancies in our quantitative result, we conducted
interviews followed by a thematic analysis. We identified three themes in the
interviews. First, when asked which news organization users follow,
participants showed a trend of interest in ideologically aligned news sources.
While a majority of Democrats mentioned mainstream sources (e.g., CNN, NBC,
the New York Times, and the Washington Post), most Republicans named a mixture
of mainstream and non-mainstream sources (e.g., the Wall Street Journal, Fox
News, Joe Rogan, and Candace Owens). This trend led us to assume that our
intervention may be less effective if it contradicts users’ political stances.
Second, we found several hints that _cynicism towards politics_ and _media
skepticism_ can influence the impact of nudges. For example, one participant
suggested that he prefers news without biases which mainstream media does not
do anymore. Another (Republican) participant expressed frustration that she
had to stay away from discussing politics on social media, as she often ran
into arguments with others. If Republicans are indeed more skeptical of
mainstream media on the whole, and also equally mistrusting of social media
platforms, then our intervention could be perceived as yet another attempt by
social media to integrate ideologically motivated interventions into their
news feeds. Therefore, we decided to examine whether these sentiments of media
skepticism and political cynicism adversely affect the interventions. Third,
consistent with our quantitative result, we found the opposite of the expected
reaction to the _Questionable_ intervention. For example, a participant
responded: “I found that these tweets [with Questionable intervention] seem …
more accurate than things that I normally read”. This conflicting reaction may
have stemmed from the lack of a clear hierarchy, i.e., the absence of nudges
on more credible news tweets. Subsequently, we revised our design with a third
nudge called _Reliable_ (tweets highlighted in green to indicate reliability).
These findings suggest that our initial prototype did not adequately support
better news credibility judgments by users, and informed us to consider three
confounds (users’ political ideologies and attitude towards politics and
media) in evaluating our system.

##  4\. Designing NudgeCred

###  4.1. Design Guides

To design nudges with heuristic cues, we employ design guides from two strands
of literature: the nudge perspective and the heuristic perspective.

####  4.1.1. Nudge Perspective

To design effective nudges, the literature suggests two primary aspects to
consider: the mode of thinking involved (automatic vs. reflective) and the
degree of transparency (transparent vs. non-transparent) (Hansen and
Jespersen, 2013).

Mode of Thinking: Cognitive psychologists developed dual process theories, a
set of psychological theories for understanding human decision-making. These
theories describe two main modes of cognition: _automatic_ and _reflective_
(Evans, 2003). The automatic mode is fast and instinctive. It uses prior
knowledge or past repeated behavior and minimal cognitive capacity to decide
on actions. Reflective thinking, on the other hand, is slow and effortful. It
uses greater cognitive capacity to make a goal-oriented choice by critically
examining the effects of choices before selection.

Transparency: Scholars introduced epistemic transparency (i.e., whether users
would understand the purpose of a nudge) to divide existing nudge designs into
two categories: transparent and non-transparent (Hansen and Jespersen, 2013).
Thaler and Sunstein adopted transparency as a guiding principle for nudges
(Thaler, 2008). This is because of the concern that a designer may manipulate
people into their own preferred direction using systems for behavioral
changes.

Using the combination of these two dimensions, Hansen and Jespersen grouped
existing nudges into four categories: reflective transparent, automatic
transparent, reflective non-transparent, and automatic non-transparent (Hansen
and Jespersen, 2013). In designing technology-mediated nudges for credibility,
we pick one quadrant from these categories: _transparent_ nudges with the
_automatic_ mode of thinking. We chose the automatic mode as it requires less
cognitive effort to process information, especially given the information
overload in social media and the instant nature of media consumption. Scholars
in the past argued that use of automatic mode over reflective mode for design
could address two potential problems–lack of motivation and lack of
ability–that typically restrain users from performing tasks such as critically
evaluating credibility (Adams et al., 2015). Furthermore, our design does not
prevent users from critically reflecting on the news content. We chose the
_transparent_ design to explicitly reveal the motives behind it. We later
discuss the potential for nudge designs in the remaining three quadrants (see
section 8).

####  4.1.2. Heuristic Perspective

This work applies heuristics to design nudges for social media in order to
enhance users’ perceptions of the credibility of news. Cognitive psychologists
have proposed models of how effective heuristics work (Koehler and Harvey,
2004). One of the models, called Fast and Frugal Heuristics suggests that
users should be able to make inferences using “fast, frugal, and accurate”
heuristics when faced with environmental challenges (e.g., information
overload) (Todd and Gigerenzer, 2000). According to Todd et. al., simple
heuristics work when they follow two principles: they exploit the structure of
the environment and are robust. In social media, structures include sources of
news items, popularity (indicated by the number of shares or replies), and the
way that information is organized by time and personal interactions. They
argued that heuristics that exploit existing structured information can be
“accurate without being complex” (Todd and Gigerenzer, 2000). Another success
criteria for heuristic design is the robustness of the decision model. A
computational strategy utilizing a limited set of information can yield more
robustness (Todd and Gigerenzer, 2000). Employing these principles, our design
includes only two heuristics, outlined below. These heuristics seem useful to
users to investigate misinformation (Geeng et al., 2020).

  * •

Authority Heuristic: We limit the source of news to a handful of known
organizations followed by a binary classification of the organizations.

  * •

Bandwagon Heuristic: We utilize the conversational structure (or replies) of
the environment as an indicator of credibility assuming a skew in the reply
distribution.

###  4.2. Outlining the Design

Our design of NudgeCred is built on the idea of applying subtle heuristic cues
in certain contexts in social media. It is powered by three types of socio-
technical interventions— _Unreliable_ , _Questionable_ , and _Reliable_. Using
the principles of fast and frugal heuristic design, our design uses a two-
level decision tree with two heuristics (see figure 2). They are triggered
based on whether a news tweet originates from an official authority. Thus, the
first step of our tool design relies on the _authority heuristic_.
Communication scholars have long argued that revealing the official authority
of content results in applying the authority heuristic in credibility
judgments (Sundar, 2008). We apply the authority heuristic by differentiating
between mainstream and non-mainstream news tweets. We do not apply nudges to
tweets that do not come from mainstream and non-mainstream sources. We opt to
use source-based credibility annotation due to the challenging nature of
annotating article-level credibility. While we may flag some accurate articles
from non-mainstream media in this method, other work demonstrated that this
number could be few (14%) compared to the accuracy (82%) (Shao et al., 2018).
For such false-positives, users still have the opportunity to fact-check them.

To flag inaccurate content from mainstream media, we apply another criteria of
whether someone replied to a mainstream tweet with a question. Literature
suggests that such questions, depending on users’ prior knowledge of the
subject matter, can instigate curiosity and motivate them to investigate to a
varying degree (Litman et al., 2005). We employ this property by showing the
number of questions as a _bandwagon heuristic_ (“if others question this
story, then I should doubt it, too”) on mainstream news tweets. Thus, our
study had three nudges: mainstream news tweets that did not have questions
raised about their content (_Reliable_), mainstream news tweets that had
questions raised about their content (_Questionable_), and non-mainstream news
tweets (_Unreliable_). To employ epistemic transparency in the design, our
design includes a tooltip with the reasoning behind each nudge. Figure 2 shows
the overall design. Before delving into each intervention, we propose a
classification of news sources that enables our application of the _authority
heuristic_.

####  4.2.1. Classifying Authority of News

Our nudge-based interventions work on two types of news sources: mainstream
and non-mainstream. In journalism and communication literature, the term
“mainstream media” lacks an official definition. For the purposes of our
study, mainstream news sources are sources recognized as more reliable in
prior scholarly work. Opting for a heuristic approach, we use such existing
literature to create a reliability measure which may later be replaced; this
is not our primary contribution. In this approach, the first two authors
iteratively collected a list of mainstream news websites by referring to two
prior works, including Pew Survey and NPR (Mitchell et al., 2014; Selyukh et
al., 2016). Next, we refined our list with the help of our in-house journalism
and communication media expert by referring to the most circulated and the
most trusted news sources (Cision, 2017; Mitchell et al., 2014), subsequently
removing a news aggregator (Google News) and a local source (AMNewYork). Table
2 shows a sample. Every source on our final list of 25 news sources follows
standard journalistic practices in news reporting (AP, 2018).

Mainstream Source
---
The Economist | CNN | The Blaze
New York Times | NPR | BBC
Washington Post | MSNBC | Fox News
Chicago Tribune | WSJ | Politico
New York Post | Newsday | NY Daily
Table 1. Example sources in our _mainstream news_ category.

Website | Category | Inaccuracy Type Message
---|---|---
abcnews.com.co | Fake news | misinformation
breitbart.com | Extreme Bias | partisan stories
americantoday.news | Rumor Mills | rumor
infowars.com | Conspiracy Theory | conspiracy
rt.com | State News | state propaganda
Table 2. Example _non-mainstream news sources_ and their categories of
reporting inaccuracy. The tooltip messages read: “This source is considered
unreliable because it promotes $<$InaccuracyType$>$”.

For non-mainstream news sources, we refer to Opensources.co, a professionally
curated list of websites known to spread questionable content (OpenSources,
2018). Each source in this list is categorized based on its level of
information reliability (e.g., ‘extreme bias,’ ‘rumor,’ ‘clickbait’). The
curators manually analyzed each source’s domain-level characteristics,
reporting and writing styles before assigning a particular category. From this
list, we remove the ‘politics’ and ‘reliable’ categories to retain sources
which were explicitly labeled as promoting unreliable news, a total of 397
sources spanning 10 categories. Table 2 shows a sample from this list. We do
not intervene in the rest of the sources that do not fall into these two
categories. Using this notion of mainstream and non-mainstream news sources,
we apply three nudges.

####  4.2.2. Three Nudges

The _Unreliable_ nudge detects whether a tweet comes from an unreliable
authority. Our design applies this nudge by examining whether a tweet from a
user’s feed originates from a non-mainstream news site and subsequently
reduces the item’s opacity, rendering it harder to read. We call these tweets
unreliable non-mainstream tweets ($T_{U}$) [See (c) in figure 2]. To instigate
epistemic transparency, _Unreliable_ provides an explanation of its action
through a tooltip message: “This source is considered unreliable because it
promotes $<$InaccuracyType$>$.” Table 2 shows the list of $<$InaccuracyType$>$
messages based on the source and its category in opensources.co.

The _Questionable_ nudge is applied to mainstream news tweets ($T_{Q}$) when
at least one question is raised about the information in the corresponding
Twitter reply thread333Though Twitter has recently rolled out a threaded reply
structure, at the time of the study, it did not exist. Thus, we only took
direct replies into account.. Prior studies suggest that less credible reports
on an event are marked by questions and inquiries (Zhao et al., 2015; Mitra et
al., 2017). To detect questions, our algorithm is kept intentionally simple.
Our algorithm looks for “?” mark to identify questions—a simple but
transparent method that is understandable by users. It is worth noting that
our focus is not to develop the most sophisticated algorithm to detect
questionable news, rather testing the effectiveness of nudge in credibility
assessment. In that regard, using the simple heuristic serves its role and
have benefits in simplicity and transparency for users to understand. While
investigating advanced natural language parsing methods to identify relevance
of the questions to the news article or more advanced machine learning
techniques to detect questions is worth looking into (Lee et al., 2017; Xiong
et al., 2016), such investigations would require significant work, perhaps
amounting to a separate full contribution. Hence we leave those as future
paths to pursue. Instead our approach works as a minimum baseline to identify
questions.  To make users aware of these questioned mainstream tweets, the
_Questionable_ nudge is applied by changing the background color of the tweet
to yellow while showing the number of questions (see (b) in figure 2). By
showing this number, we promote a collective endorsement that multiple users
have doubts about this news (Sundar et al., 2008). Additionally, a tooltip
message offers transparency by explaining the reason behind the nudge
activation. For $T_{Q}$, the tooltip message follows the format: “Several
users have questioned this item. Questions include: $<$first reply tweet with
a question$>$” (e.g., for a tweet containing a news report with missing
details, such as time of an event, a reader may have replied to ask: “When did
this happen?”), thus directing further attention to other users’ comments in
an effort to stimulate the bandwagon heuristic. The bandwagon effect has been
demonstrated to be powerful in influencing credibility judgments (Knobloch-
Westerwick et al., 2005).

The _Reliable_ nudge is triggered when the source of the news tweet is an
official, mainstream source and was not questioned in the replies;
specifically, reliable mainstream tweets ($T_{R}$) were emphasized with a
green background highlight (see figure 2 (a)). A tooltip message is formatted
for $T_{R}$ as follows: “This tweet seems more reliable. Nobody has questioned
this item yet.” The colored highlights and the corresponding tooltip messages
create contrast within the mainstream news tweets, helping users navigate them
better. Note that we included this nudge in response to the findings from the
formative study.

##  5\. Study 1: Evaluating Impact on Perceptions of Credibility in a
Controlled Setting

To evaluate our design, we conducted two studies. Study 1 evaluates impact on
credibility perception in a controlled setting while Study 2 is a field
deployment. In Study 1, we examine three research questions on the effect of
nudges on users’ credibility perceptions in a controlled setting simulating a
Twitter feed with multiple tweets for each nudge.

RQ1. Can heuristic-based design nudges on an online social news feed help
users distinguish between reliable, questionable, and unreliable information?

RQ2. Do users’ partisan attitudes affect their responses to credibility
nudges?

RQ3. Do users’ political cynicism and media skepticism affect their responses
to credibility nudges?

###  5.1. Method (Study 1)

Source Type | Twitter Account | Political Bias |  | Source Type | Twitter Account | Political Bias
---|---|---|---|---|---|---
Mainstream | CNN Breaking News | Left |  | Non-mainstream | Daily Kos | Left
Mainstream | NY Post | Right |  | Non-mainstream | Breitbart | Right
Mainstream | Politico | Center |  | Non-mainstream | Zero Hedge | Conspiracy
Table 3. Example news sources and their political biases.

####  5.1.1. Selecting news tweets for a controlled environment

For this study, we simulated a Twitter feed with a fixed set of tweets which
would be shown to every user. To simulate a realistic Twitter feed, we
selected news sources from our previously compiled list of mainstream and non-
mainstream sources, and then selected several tweets from each source (see
Table 2 and 2). We used a balanced approach to select sources with left-wing,
centrist, and right-wing biases. For bias categorization, we used
mediabiasfactcheck.com, a source used in other scholarly works
(Mediabiasfactcheck, 2019; Kiesel et al., 2019). For each news source under
each bias category, we first found the source’s Twitter handle. We retained
the Twitter accounts that had the greatest numbers of followers444We excluded
Fox News’ Twitter handle due to its inactivity for several months until the
time of the study. (a mark of popularity of the news source on Twitter). Table
3 shows sample Twitter accounts with their perceived political biases. For
each source, we selected the tweet within the last 48 hours that had the
highest number of shares. With three nudges working across three political
leaning categories, our feed comprised 9 tweets (3 political leanings $\times$
3 nudges). Appendix A shows several tweets from this list. To add variation,
we created another set of 9 tweets using the second most followed Twitter
accounts from our list of news sources, resulting in a second feed for our
controlled environment. Users were randomly shown one of the two feeds,
totaling 9 tweets in each case. To evaluate our RQs, we needed to measure
users’ credibility perception, political ideology, political cynicism, and
media skepticism.

Figure 3. Screenshot of how clicking on the survey button would pop open the
five-item credibility questionnaire.

Item |  IRR
---|---
Is/not biased |  0.83
Is/not fair |  0.79
Does/not biased tell the whole story |  0.80
Is/not accurate |  0.79
Can/not trusted |  0.79

Table 4. IRR of the five-item questionnaire on credibility in the formative
study.

Pol. |  1\. Elected officials put their own interests ahead of public’s interest
---|---
Cyn. |  2\. It seems like politicians only care about special interests
|  1\. The media provide accurate information
Med. |  2\. The media provide trustworthy information
Skep. |  3\. The media deal fairly with all sides
|  4\. The information provided by the media needs to be confirmed

Table 5. Items used in measuring political cynicism and media skepticism. We
used a five-point Likert scale (Strongly Agree – Strongly Disagree) with a
“Don’t know” option.

####  5.1.2. Measuring News Credibility, Political Ideology, Political
Cynicism & Media Skepticism

We used a five-item questionnaire by Meyer et. al. (Meyer, 1988) to measure
users’ perceptions of credibility for every news tweet (see Figure 3). In our
formative study, we found this measure had a high Cronbach $\alpha$
($\alpha=0.95$) and individual inter-item correlations (see Table 4), showing
a high level of internal consistency. To capture partisan attitudes, we survey
participants for their political ideology on a seven-point Likert scale
ranging from “strong Republican” to “strong Democrat”. We survey participants
on media skepticism and political cynicism using a validated questionnaire
from journalism scholarship (see Table 5) (Carr et al., 2014). For both
variables, we average the responses across questions and use the median to
create a binary response variable with values “low” and “high.” Note that we
had to revert the first three media skepticism questions before averaging.

Figure 4. Distribution of demographics, political ideology, political
cynicism, and media skepticism in our participants in Study 1.

####  5.1.3. Recruitment

Our study participants were recruited starting the third week of July 2019 and
spanning a period of three weeks. We required three qualifications for user
participation: (1) age of 18 or older, (2) US resident, and (3) familiarity
with Twitter. This choice of US population was purposeful due to the
difficulty in measuring our confounds across global population. Users’
political leaning has different meanings in different countries (e.g.,
political left-right are different in the US and Europe). Similarly, levels of
skepticism/cynicism might vary by country. We focused on US-population due to
the availability of well-established measurements for our confounds from the
communication literature (Carr et al., 2014; Meyer, 1988). We recruited 430
users from Qualtrics, well-balanced by partisan affiliations. Figure 4 shows
their demographics. This sample is mostly balanced across gender, age, and
education, with a slight skew toward females.

####  5.1.4. Study Procedure

We presented our participants a set of tweets collected right before the start
of recruitment. We chose this approach because studies have shown that there
is a lag in terms of the amount of time media coverage takes to influence
public opinion, with some exceptions (e.g. mass shootings) (Roberts et al.,
2002). As a result, we anticipated that participants would be least likely to
be familiar with the most current tweets. Participants were randomly assigned
to either the treatment or the control group, with a quota check to ensure
balanced allocation across political ideology. To counter order effects, we
presented tweets in random order. We added attention checks–questions with
options reversed–right before the Twitter feed. Taking recommendations from
Qualtrics, we also discarded participants who spent less than six minute to
respond–the half of the median time spent by users in a soft-launch of 50
users. Participants saw each tweet and answered the questions for that item
before scrolling down to the next one. This approach reflects a natural
setting of modern social media sites, where users browse feeds in real-time,
click links in-situ, and the same post usually do not appear again at a later
time point. To reduce response bias, we framed the questions to ask for
credibility of the items (e.g., how do you feel about the post?) instead of
the effects of nudges (e.g., how does the nudge affect your perception?). The
unexpected effect on Questionable in our formative study suggests a lack of
response bias.

####  5.1.5. Method of Analysis

We initially perform mean comparison with Mann-Whitney U-test. However, note
that each user saw multiple tweets with each intervention. To model such
repeated measurements for the same intervention, we further use a mixed-
effects logistic regression.

(1) $\mathbf{y}=X\mathbf{\beta}+Z\mathbf{u}+\epsilon$

In Eq. 1, the response variable (credibility score) ($\mathbf{y}$) is the
dependent measure for our experiment. While fixed effects ($X$) are the
independent measure, random effects ($Z$) are the variables repeated in
multiple observations; that is, tweets. The residual ($\epsilon$) is the error
in fitting. Finally, $\beta$ and $\mathbf{u}$ are the coefficients of fixed
and random effects, respectively. We used an R implementation of a linear
mixed-effects regression model, lme4.lmer, on our dataset (Bolker, 2018).

Dependent Variable: Our dependent measure is the credibility score, a
continuous variable computed by averaging the five credibility question
responses (see Figure 4) followed by standardization. We perform robustness
checks by rerunning mixed-effects ordinal logistic regressions on each of the
five credibility questions. We find no significant differences in the
resulting model coefficients, suggesting sufficiency in modeling the
credibility score as continuous.

Independent Variables: The independent variables related to RQ1 include main
effects and interaction effects derived from the two experimental conditions:
users’ group (control or treatment) and intervention type ($T_{R}$, $T_{Q}$,
$T_{U}$). For RQ2 and RQ3, we examine three variables, including political
ideology, political cynicism, and media skepticism. For the sake of analysis,
we map political ideology, measured on a seven-point Likert scale, to three
groups consisting of Democrats, Independents, and Republicans. Following a
prior scholarly work (Carr et al., 2014), we used the median score across all
the questions in each variable (political cynicism and media skepticism) to
split the participants into two groups. In our representative US sample, the
median for media skepticism was $2.75$ ($\alpha=0.72$, $M=2.48$, $SD=0.98$)
and the median for political cynicism was $4.00$ ($r=0.53$, $M=4.09$,
$SD=0.81$). Similar to Carr et al., we considered values greater than the
median as high on that category and vice versa (Carr et al., 2014). In other
words, media skepticism of 3.00 would be labeled high media skepticism, while
2.75 would be labeled low media skepticism. We also include the political
leanings of the news sources used in our tweet selection procedure as an
additional independent variable.

Control Variables: Prior studies indicate that the level of interest in a news
story can influence users’ credibility assessment (Metzger, 2007). Therefore,
we include participants’ interest in a tweet as a control variable, measured
on a five-point Likert scale ranging from low to high interest. Other control
variables include users’ demographics, such as gender, age, education, and
Twitter usage frequency.

###  5.2. Results (Study 1)

| Control | Treatment | Bet. Subj. MWU-test
---|---|---|---
| Avg. Cred. | Avg. Cred. |  (Cohen’s $d$)
$T_{R}$ | 0.62 | 0.67 | 187488.0(0.162)***
$T_{Q}$ | 0.58 | 0.55 | 198180.5(0.072)*
$T_{U}$ | 0.46 | 0.37 | 171763.0(0.296)***
n | 693 | 597 |

Table 6. Mann-Whitney U test results for Study 1. Here, ‘n’ denotes the number
of tweets rated in each condition. Avg. Cred. is the mean of ‘n’ credibility
scores; * $p<0.05$, *** $p<0.001.$

Figure 5. Shows interaction effects between user groups and nudge types in
Study 1. The numbers inside the brackets are the effect sizes, Cohen’s $d$.

####  5.2.1. RQ1: Effect of the Nudges

For RQ1, initially we investigated our data using mean comparison. Table 6
shows the mean values and Mann-Whitney U test results of our experiment and
Figure 5 shows corresponding interaction plots. Users in the treatment group
rated the credibility of non-mainstream news tweets ($T_{U}$) significantly
lower than did users in the control group, suggesting the effectiveness of our
intervention ($Z{=}171763,p{<}0.001$, Cohen’s $d{=}0.296$ ). Additionally,
treatment users rated mainstream news tweets without questions ($T_{R}$) as
more credible than corresponding control users ($Z{=}187488,p{<}0.001$,
Cohen’s $d{=}0.162$ ). Our participants showed significant decrease in their
rating of mainstream news tweets with questions ($T_{Q}$)
($Z{=}198180,p{<}0.05$, Cohen’s $d{=}0.072$).

| Base Model |  | Politics & Media Model |  | 3-Way Interaction Model
---|---|---|---|---|---
|  $\beta$ |  SE |  |  $\beta$ |  SE |  |  $\beta$ |  SE
(Intercept) |  0.29*** |  |  |  0.27*** |  |  |  0.32*** |
Control Variables |  |  |  |  |  |  |  |
Gender (Male) |  0.03* |  0.05 |  |  0.03* |  0.05 |  |  0.04 |  0.07
Education |  -0.00 |  -0.01 |  |  -0.01 |  -0.03 |  |  -0.01 |  -0.04
Age |  0.00 |  0.01 |  |  0.01 |  0.03 |  |  0.01 |  0.04
Social Media Usage |  0.01 |  0.01 |  |  0.01 |  0.02 |  |  0.01 |  0.02
Interest in the Tweet |  0.09*** |  0.45 |  |  0.09*** |  0.45 |  |  0.08*** |  0.36
Experimental condition |  |  |  |  |  |  |  |
Type(Mainstream-Question) |  -0.03 |  -0.05 |  |  -0.03 |  -0.05 |  |  -0.07 |  -0.11
Type(Non-mainstream) |  -0.16** |  -0.26 |  |  -0.16** |  -0.26 |  |  -0.23** |  -0.38
Group(Treatment) |  0.04** |  0.07 |  |  0.04** |  0.07 |  |  0.08 |  0.13
Type(Mainstream-Question):Group(Treatment) |  -0.06*** |  -0.08 |  |  -0.06*** |  -0.08 |  |  -0.11* |  -0.14
Type(Non-mainstream):group(Treatment) |  -0.10*** |  -0.13 |  |  -0.10*** |  -0.13 |  |  -0.21*** |  -0.26
Politics and Media |  |  |  |  |  |  |  |
Ideology(Democrat) |  |  |  |  0.03* |  0.05 |  |  0.01 |  0.01
Ideology(Republican) |  |  |  |  -0.03* |  -0.04 |  |  -0.03 |  -0.05
Political Cynicism(Low) |  |  |  |  0.01 |  0.02 |  |  0.01 |  0.02
Media Skepticism(Low) |  |  |  |  0.05*** |  0.08 |  |  0.06** |  0.11
Account Leaning(Conspiracy) |  |  |  |  0.07 |  0.08 |  |  0.06 |  0.07
Account Leaning(Left) |  |  |  |  0.09 |  0.14 |  |  0.09 |  0.14
Account Leaning(Right) |  |  |  |  0.00 |  0.01 |  |  0.00 |  0.00
Experimental Condition x Other Variables |  |  |  |  |  |  |  |
Group(Treatment):Gender(Male) |  |  |  |  |  |  |  -0.08* |  -0.11
Type(Non-mainstream):Interest in the Tweet |  |  |  |  |  |  |  0.02* |  0.11
Type(Non-mainstream):Group(Treatment):Gender(Male) |  |  |  |  |  |  |  0.09* |  0.08
Type(Non-mainstream):Group(Treatment):Interest In a Tweet |  |  |  |  |  |  |  0.03* |  0.10
Adj. $R^{2}$ (Marg./Cond.) | .310/.483 |  | .347/.494 |  | .358/.508
N = 3870 | * p<.05, ** p<.01, *** p<.001

Table 7. Regression models on the credibility score. The base model contains
nudge type, user group, control variables and the interaction between user
group and nudge type. The politics and media model adds users’ political
ideology, media skepticism and political cynicism variables to the base model.
The 3-way interaction model further includes the interactions of nudge type,
user group and other variables with significant main effects in the politics
and media model (Gender, Interest in the Tweet, Ideology and Media
Skepticism).

Our experimental setting with each user rating multiple tweets prompted us to
further analyze the data using a series of mixed-effects regression models.
Table 7 shows this analysis. To determine the effects of the experimental
conditions, our base model includes news source type, group assignment, and
their corresponding interactions. We find that tweet type—mainstream or non-
mainstream—is strongly correlated with a tweet’s credibility score. Non-
mainstream tweets are generally rated less credible than mainstream news
sources with a small effect size ($\beta{=}-0.16$, $p{<}0.01$ and Cohen’s
$f{=}.07$). This result suggests that users can differentiate between
mainstream and non-mainstream tweets even without our nudges. However,
treatment users (those who received nudges) generally rated tweets as slightly
more credible than control users ($\beta{=}0.04$, $p{<}0.01$, Cohen’s
$f{=}0.04$). Nevertheless, there is an interaction effect between tweet type
and user group. Treatment users scored non-mainstream tweets lower than
control users with a medium effect size ($\beta{=}-0.10$, $p{<}0.001$ and
Cohen’s $f{=}0.16$). Treatment users also rated mainstream tweets with
questions as less credible than did control users ($\beta{=}-0.06$,
$p{<}0.001$, Cohen’s $f{=}0.16$). The decrease in the credibility perception
scores of both mainstream questioned tweets ($T_{Q}$) and non-mainstream ones
($T_{U}$) suggests that our nudges can help users consume these news items as
less credible, thereby answering RQ1.

####  5.2.2. RQ2: Effect of Political Ideology

To answer RQ2, we examine the political ideology variable in our politics and
media regression model. Politically Independent users serve as the point of
reference for this variable. We find that Democrats generally rated all tweets
slightly higher in credibility than did Independent users ($\beta{=}0.03$,
$p{<}0.05$, Cohen’s $f{=}0.06$), whereas Republicans rated them slightly lower
than Independents ($\beta{=}-0.03$, $p{<}0.05$, Cohen’s $f{=}0.06$). Due to
this main effect, in our 3-way interaction model, we further explore whether
political ideology had any interactions with the three nudges and the users’
group assignments. However, we find no significant interaction. Therefore,
nudges changed users’ credibility perceptions irrespective of their political
leanings. We discuss these findings later.

####  5.2.3. RQ3: Effect of Political Cynicism and Media Skepticism

To answer this RQ, we examine two variables (political cynicism, and media
skepticism) in our politics and media regression model. Between media
skepticism and political cynicism, only media skepticism had a significant
effect, where users with lower media skepticism rated tweets as more credible
($\beta{=}0.05$, $p{<}0.001$, Cohen’s $f{=}0.10$). In our 3-way interaction
model, we further explore whether media skepticism had any interactions with
our key variables of interest—treatment and control groups, and the three
nudges. We do not find any significant interaction effects. Therefore, nudges
changed users’ credibility perceptions irrespective of their attitudes towards
politics and media. We elaborate on these findings in the Discussion section.

####  5.2.4. Effect of Control Variables

We examine whether user demographics and users’ interest in a news story had
any effect on how they rated the credibility of the news tweet. Across all
three models, the effects exerted by our control variables are consistent.
Independent of whether a user was assigned to the control or treatment group
and independent of the type of news (whether mainstream or non-mainstream)
they saw, users provided higher credibility scores when they were interested
in a story, with a large effect (base model effects:$\beta{=}0.09,p{<}0.001$,
Cohen’s $f{=}0.5$). Among demographic variables, male users rated tweets as
more credible with a small effect size (base model effects:
$\beta{=}0.03,p{<}0.05$, Cohen’s $f{=}0.03$). The remaining demographic
variables did not show any significant effect 555Additionally, we examined
whether there was any learning effect compounded from seeing multiple nudges.
To do so, we added the order (from 1 to 9) of the tweets in which participants
evaluated their credibility and its interaction with nudge type and user group
in our regression models. We found no significant effect of the order..

##  6\. Study 2: Field Deployment

To gain insights into how we can understand and improve the current design
nudges for credibility, we conducted a qualitative study. For this purpose, we
recruited participants to use NudgeCred on Twitter for five days. This process
allowed us to evaluate it in a more ecologically valid environment than Study
1 (Brewer and Crano, 2000). Below, we describe the process.

###  6.1. Method (Study 2)

####  6.1.1. Recruitment

To recruit users for this study, we used Twitter advertising, following a
prior strategy (Im et al., 2020). We devised several targeting mechanisms to
promote the advertisement to our desired group, including age range (¿=18),
language (English), location (USA) and whether users followed top mainstream
and non-mainstream news accounts in our list. Initially, we were not
successful in getting responses from broader regions, so we iteratively
revised the location to target nearby states for effective recruitment.
Additionally, we promoted the advertisement within our academic network on
Twitter. From 50 interested participants, we recruited 12 participants for the
study by filtering our spams and users with less than 100 followers . Overall,
our participant group consisted of 5 females and 7 males with an average age
of 29.5 (std. dev. = 6.5) and a political tilt towards Democrats (Democrat =
6, Independent = 4, Republican = 2).

####  6.1.2. Procedure

Followed by an informed consent process, we instructed users to install the
NudgeCred browser extension. To promote a natural setting, we encouraged users
to use Twitter during the five-day study as they normally would. However, it
is possible that Twitter’s news feed algorithm may not have surfaced news
items on their feed each time. Hence, we also encouraged users to visit the
Twitter profiles of some news sources each day to ensure that users experience
how NudgeCred works. After five days, we asked them to fill out a post-study
survey on demographic details followed by an interview in a semi-structured
manner (see Appendix B for the interview questions). To facilitate their
responses, we asked users to walk us through their news feed during the
interview. Each participant received a $30 gift card for participating.

###  6.2. Results (Study 2)

We analyzed the interview data using a grounded theory approach (Strauss and
Corbin, 1994). The first author transcribed the audio from the interviews and
analyzed the data to come up with a set of themes. These themes were discussed
and refined with the other authors. Below, we present our final set of themes.

####  6.2.1. NudgeCred facilitates more conscious news consumption

Most of our participants (9/12) provided positive feedback on their overall
experience with NudgeCred, referring to the design and application. Some
participants (U1,U6,U9) particularly mentioned that they liked the bandwagon
heuristic with the questions in replies.

> “It [NudgeCred] quickly highlights. So you know what to look for. Especially
> when it’s a question mark, I do actually open up the comment section and
> read what the question is.” (U9)

Others liked it because it served as an educational tool to “train the user to
thoughtfully think about” news (U4) or because it did not add overhead to
their current Twitter experience (U2). Overall, users reported two phenomena.
We describe them below.

Improved Recognition of News Content and News Genres: One of the impacts that
participants (5/12) mentioned was the perceived difference in the amount of
news content in their feed compared to their prior experience. For example,
they perceived that there was more news content in their feed than before.
NudgeCred even helped some participants pay more attention to the types of
content that news sources produced.

> “It [NudgeCred] really just told me that NPR produces more articles that are
> opinionated [the participant was referring to an Op-Ed] on Twitter than I
> thought.” (U1)

The article labeled as Questionable made them realize that it was an op-ed
article, rather than news.

Attention towards Bandwagon Heuristic: Out of the three nudges, participants
(7/12) noticed the Questionable tweets highlighted in yellow the most, and the
number of questions below them.

> “I noticed the one [question icon] at the bottom more … most people who use
> Twitter a lot … 9 out of 10 are more likely in tune with the
> replies/retweets. Usually I use those as a sign of popularity.” (U1)

> “If I see it [a tweet] as yellow … I do get the information that this is
> either … a lot of people don’t like the news article or this article might
> have controversial or incorrect facts.” (U10)

While users, as U1 indicated, may see traditional retweet or reply numbers as
an indicator of popularity, one participant (U10) correctly pointed out the
bandwagon cue as an indicator of controversy. Thus, nudges imitating existing
designs on social media can be useful.

Overall, these phenomena support that our nudges can improve users’ news
perception in two ways: (i) with an overall impression of total news items on
users’ feeds broken down based on the reliability of sources, facilitating
better perception on its genres; and (ii) with individual attention towards
particular news items.

####  6.2.2. Concerns in Using Heuristics for Nudging News Credibility

Interviews also revealed two concerns regarding our nudge design. We discuss
these concerns below.

Trust Issues with Heuristics: A majority of our participants (7/12) questioned
the use of bandwagon heuristic to differentiate _Reliable_ and _Questionable_
news items. Because audience composition can vary by the source and the topic
of a news item, and influence bandwagon heuristic, they were concerned about
its disparate impact. One participant pointed out that followers of the New
York Times (NYT) are comparatively more diverse than followers of Fox News.
Consequently, audiences with an opposing stance on a news report from the NYT
may question it. In contrast, Fox News, having a homogeneous audience which
mostly supports its reporting, may hardly question their reports. Therefore,
our bandwagon heuristic would be skewed based on the audience of a report.

> “NYT [The New York times] and MSM [mainstream media] in general have a lot
> more reactions from skeptical readers given the current administration. And
> to some, the color-coding and the number of questions may indicate that the
> news is subjective or “fake” when you compare it with other outlets such as
> Fox News that have fewer reactions on Twitter and a more homogeneous
> audience.” (U7)

These responses suggest that even though a user may understand the bandwagon
heuristic, the heuristic itself may have some shortcomings, which makes it
challenging for the user to trust it as a metric for gauging credibility.

Adverse Effects of Nudges: Our participants (2/12) suggested two adverse
effects of the nudges. One participant (U11) proposed that users may use the
bandwagon heuristic based on like-minded questions as a justification for
their attitude-consistent belief in politically opposing news, thus promoting
a confirmation bias.

> “If I agree with you and you are questioning an article that I questioned as
> well … since you personally agree with me, it confirms my bias against that
> piece of information.” (U11)

A similar effect has been suggested by scholars in the past, who show that
rating mechanisms on online platforms (e.g., Facebook “likes”) may guide
users’ news selection process (Messing and Westwood, 2014). If such effects
exist, users may become more polarized. Compared to confirmation bias stemming
from the existence of a nudge, the absence of nudges, mentioned by U4, could
also have a harmful effect.

> “I think the ones that I subconsciously ignore are the ones that have no
> color at all. If there aren’t any flags … no color blocks, I am more
> inclined to assume that the content is valid.” (U4)

This participant suggested that the absence of nudges creates an illusion of
validity of content without nudges. Indeed, recent research points out the
same phenomenon when false reports are not tagged, resulting in a false sense
of being validated (Pennycook et al., 2020a). One way to address this concern
is, again, to be transparent about not being nudged with an additional tool
tip message for the news items that are not nudged.

Overall, our participants’ concerns suggest that designers need to evaluate
two aspects of nudges: (i) How trustworthy the design components of the nudges
are (ii) Whether the presence and absence of nudges adversely affect users.

####  6.2.3. Opportunities for Credibility Nudge Design

In addition to differentiating news credibility, we asked participants what
other functions they would like in NudgeCred. Participants suggested
improvements in three directions.

Extending the News Source List: Our participants were concerned on the limited
set of news sources we considered. They (5/12) suggested that they often see
misinformation from non-news entities, including their acquaintances. To allow
more news identification, some (2/12) asked us to include local news sources.
With our participants following diverse sources for information, our limited
news source list was naturally inadequate for them.

Indicating News Genres and Reporting Biases: Suggestions from the participants
included distinguishing opinion items from news (3/12) and indicating bias in
a report as a nudge (2/12).

> “Give a notification that say what I am seeing is an op-ed rather than
> straight facts.” (U4)

> “Is it possible to state that this news article is biased towards this
> particular claim?” (U10)

A recent survey shows that about 50% US adults “are not sure what an op-ed is”
and that about 42% of respondents perceive that opinion and commentary are
often posed as news in most news articles (PROJECT, 2018). Therefore, a
significant share of the population may appreciate having nudges that
differentiate op-eds from news as well as other indicators of bias stems.
Incorporating such attributes in nudging might help users better determine the
credibility of news content.

Curbing Misinformation Sharing: To prevent the sharing of misinformation, some
participants (2/12) proposed implementing nudges on share (or retweet)
buttons.

> “[when someone clicks the share button] If there is a notification that says
> this source is not credible then people would be less likely to share it.”
> (U2)

Research indicates that about 59% of links shared on Twitter have never been
clicked (Gabielkov et al., 2016), i.e., users often share news items without
reading them. If nudges can help user determine the unreliability of news from
misinformative sources, they might be less likely to share them.

In summary, our participants proposed improvements to our nudge design in
three key areas: (i) improving existing classifications by extending the
source list, (ii) expanding news classifications of nudges in alternate areas,
and (iii) targeting users’ interactions with news on social media.

##  7\. Discussion

Below, we elaborate on our results, starting with each research question from
Study 1, followed by opportunities and challenges suggested by the
participants in Study 2.

###  7.1. RQ1: Effect of Nudges on Credibility

Our regression analyses in Study 1 revealed that users’ credibility ratings
were considerably different between the treatment (nudged) group and the
control group. While other nudge designs have proven effective in reducing
sharing intentions of misinformative content, their effectiveness was shown on
a particular news genre, such as COVID-19 and HIV (Pennycook et al., 2020b;
Nekmat, 2020). In contrast, we examined the effects of our nudges on a wide
variety of popular news items surfacing over multiple days, thus offering a
more generalized result. Our intervention provides a less authoritative
approach that gives users simple but transparent information for them to make
their own judgments. News feeds, as they typically present limited information
on social media, have few features to distinguish content quality. Tweets from
bots, friends, mainstream news, and non-mainstream sources are all given equal
weight in terms of visual representation in any feed, making it difficult for
users to sift through them. Though people are capable of identifying
misinformation, social media makes it challenging to make informed analytical
judgments (Pennycook et al., 2019). Our results suggest that users might
appreciate it if social media sites provide tangible signals to work through
this clutter, which is further exemplified by participants’ suggestion to
differentiate news and op-eds in Study 2.

Apart from facilitating better perceptions of the credibility of news,
NudgeCred may also act as a “translucent system” (Erickson and Kellogg, 2000).
The theory of social translucence posits that we should aim for systems that
make online social behavior _visible_ to facilitate _awareness_ and
_accountability_. Note that our participants in Study 2 suggested improved
recognition of particular types of news content on their feed and were more
aware of what they were seeing on their feeds. Our nudges on news content that
are liked or shared by users’ followers or friends could also have similar
impacts, wherein users become more _aware_ of their peers’ news consumption
behaviors. When their peers like/share misleading news, one may hold the peers
_accountable_ by pointing out the misleading content. Besides, after seeing
the nudges on unreliable content, users may restrain themselves from sharing
such content and reflect on their sharing habits.

###  7.2. RQ2: Influence of Political Partisanship on Nudge Effects

Our regression results suggest that NudgeCred changed users’ perception of
credibility irrespective of their political views; that is, there were no
interaction effects between political characteristics and the effects of
interventions. This result is consistent with recent studies showing the
success of interventions in limiting sharing intentions of misinformation,
irrespective of users’ political affiliation (Wood and Porter, 2019;
Roozenbeek and van der Linden, 2019). Although some prior literature argue
that citizens may view nudges as partisan policies and may not support nudges
when they conflict with users’ partisan preference (Sunstein, 2016), other
scholars suggest that this behavior can be countered by reducing partisan cues
in nudges (Tannenbaum et al., 2017). We incorporated this suggestion in our
design by showing nudges on news content from all political leanings and
nudging news content in both directions (Reliable, Questionable, and
Unreliable). However, in practice, users tend to typically follow news based
on their partisan preferences (Kohut et al., 2012). In such a setting, users
who follow only alternative fringe sources may see mostly _Unreliable_ nudges
triggered on their reports and perceive NudgeCred as partisan. One potential
design solution is to show the similar news item from reliable sources, with
the same partisan view, to help them understand the alternatives.

###  7.3. RQ3: Influence of Political Cynicism and Media Skepticism on Nudge
Effects

In our study, we did not find any impact of political cynicism or media
skepticism on nudge effects. This convincing nature of our nudges—that nudges
worked irrespective of users’ prior media skepticism and political cynicism—is
promising. Our result for media skepticism is aligned with a recent work where
media skepticism did not affect nudge effects (Nekmat, 2020). Research
suggests that media skeptics, despite significant exposure to alternate
sources, still seem to have moderate to high consumption of mainstream news
(Tsfati and Cappella, 2005). Therefore, our nudges could improve news
credibility assessment of both mainstream and non-mainstream sources by
skeptics in the wild. Scholars suggest that exposure to fake news mediated by
belief in its realism increases political cynicism (Balmas, 2014). Thus, if
nudges can reduce belief in fake news, it could help mitigate increasing
cynicism towards politics. Furthermore, our nudges can be utilized as an
alternative to censorship by social media, thus helping mitigate the concern
that social media apply censorship in a disparate manner across different
political affiliation, as raised by participants in our formative study.

###  7.4. Opportunities in Designing News Credibility Nudges

Our field deployment of NudgeCred showed several opportunities in designing
credibility nudge in the future. First, participants’ attention to the
bandwagon heuristic reveals how designers can utilize existing Twitter
infrastructure in their design. Though the impact of the bandwagon effect in
collaborative filtering has been discussed in the literature (Sundar et al.,
2008), it has been underutilized in a news credibility context. Our study
suggests that applications similar to ours can act as valuable markers of
information credibility. Second, participants seeking nudges on a wider set of
sources (e.g., news and non-news sources), and alternate types of taxonomies
(e.g., news and op-eds) suggests their need for tools to differentiate
information reliability. Comparatively, nudges on tweet actions (e.g., retweet
and share) may play a stronger role in curbing the spread of misinformation,
as research indicates that sharing without thinking is often weaponized for
this purpose (Wardle, 2019). For example, when users click on the share
button, activating a “timer nudge”—a visual countdown to complete the
action—could make users rethink before they share (Wang et al., 2013).

###  7.5. Challenges in Designing Nudges with Heuristics

Our final evaluation presented several challenges in designing credibility
nudges. First, participants showed their skepticism towards the selection of
heuristics (e.g., bandwagon heuristic) in design. Though the bandwagon
heuristic can be robust and hard to falsify, in an open forum such as Twitter,
it is open for manipulation. Perhaps, as one of the participants suggested,
feedback on the validity of the question may be helpful. Still, problems may
also exist with feedback. For one, due to partisan attitude, feedback wars,
similar to edit wars in Wikipedia, might result (Sumi et al., 2011).
Additionally, due to frequent updates and the scale of social media content,
feedback from volunteers might be scarce on most items. Perhaps a system
designer can show the distribution of feedback by users’ leanings to reduce
the effects of feedback wars and solicit user contributions by promoting items
with scarce feedback. Second, participants’ concerns with the bandwagon
heuristic promoting confirmation bias might be an extension of prior findings
that users tend to prefer selecting information (including political
information) consistent with their preexisting beliefs (Knobloch-Westerwick
and Meng, 2009). However, scholars have shown that the extent of confirmation
bias in information selection, particularly in news selection, is small (Hart
et al., 2009). In the event of confirmation bias, we can computationally de-
bias the heuristic cue, as in prior works (Adomavicius et al., 2014). Lastly,
audience misperceptions of non-nudged content being credible indicate an
additional challenge in design. This effect has also been demonstrated in a
recent work (Pennycook et al., 2020a). One way to solve this problem would be
to add nudges to all content. Aside from these challenges, one participant
(U7) pointed out that “News might change and the user will not see the update
when more legitimate questions are added to the replies”. As user interactions
accumulate over time, the number of questions in replies could change, wherein
the same tweet would be categorized as _Reliable_ at first and _Questionable_
at a later time. This change in nudge category stemming from our choice of the
bandwagon heuristic could imply an inconsistency in nudge design. System
designers can incorporate delayed intervention mechanisms and inform users of
this cold-start issue. Overall, these challenges inform designers about
considerations for designing nudges with heuristics.

##  8\. Implications And Opportunities for Designing Credibility Nudges

Figure 6. Types of nudges based on transparency and mode of thinking. This
figure emulates Figure 1 by Caraban et al. (Caraban et al., 2019). This work
lies in the bottom-right quadrant.

We built NudgeCred by fusing the theoretical background on nudges with
theories underpinning credibility evaluation. Researchers have the opportunity
to explore the nudge categories we built our design around, experiment
effectiveness of other heuristics and utilize alternate nudging method. Below,
we elaborate a few possibilities around these areas while discussing potential
considerations.

##### Exploring Additional Nudge Categories

Referring to the four categories of nudges, divided along the two dimensions
of _transparency_ and _mode of thinking_ (automatic or reflective), we have
illustrated how NudgeCred resides on one of the four quadrants— _transparent
automatic_ (see Figure 6). Nudge theorists describe transparent nudges with
reflective and automatic mode as, respectively, reflective decision making and
instinctive decision making (Hansen and Jespersen, 2013). Technology-mediated
nudging research focus more on transparent reflective quadrant (Caraban et
al., 2019). For example, Facebook’s “Related Articles” feature to tackle
misinformation exemplifies such a nudge design (Smith, 2019). Twitter’s blue
check “verified” marker on profiles is another example of a transparent
automatic credibility nudge. Between reflective and automatic mode of
thinking, each has their own benefit. For example, while nudges with automatic
mode of thinking can be diminished over time, reflective mode of thinking can
educate users and have a lasting impact. On the other hand, design considering
reflective mode of thinking requires additional considerations such as
motivating the users and assisting in reflection (Adams et al., 2015). For
example, to motivate users we can show statistics of how often users misread a
news tweet as a nudge and to assist them in reflection this nudge can include
the list of the most common mistakes. For non-transparent quadrants, scholars
propose reflective ones as a manipulation of behavior and automatic ones as a
manipulation of choice, in both cases without users’ awareness. An example of
non-transparent automatic nudging could be showing the average reading time of
an article which could prompt users to think shorter articles as less detailed
and less credible. Comparatively, research show fewer work in non-transparent
reflective quadrant (Caraban et al., 2019). Due to their deceptive nature,
designers need to consider ethical consideration while experimenting on non-
transparent quadrants. Overall, this is the start of work in this space; much
research needs to be done in cataloging and evaluating news credibility nudges
along the other dimensions spanning the four quadrants.

##### Exploring Alternate Heuristics for Nudging Credibility

Our nudge design is based on two heuristics under the Agency affordance, drawn
from the MAIN model’s list of technology-mediated affordances affecting
credibility judgment. Designers have the opportunity to explore the other
heuristics in their design. For example, designers could offer design cues to
distinguish news items with/without video footage and prompt a realism
heuristic from Modality affordance. Or, design cues distinguishing news tweets
with interactive content (e.g., interactive 3D footage or charts) could prompt
interaction heuristic from Interactivity affordance. For Navigability
affordance, designers can prompt browsing heuristic by providing additional
hyperlinks to journalists’ past activities (e.g., MuckRack profile (Rack,
2019)) besides news items.

##### Examining Alternate Nudging Method

While the original proposers of the concept did not lay out a fixed method for
creating successful nudges (Selinger and Whyte, 2011), Caraban et. al.
recently devised six broad categories for 23 nudging methods used in prior HCI
works, namely, Facilitate (e.g., default choice and hiding), Confront (e.g.,
remind consequence and provide multiple viewpoint), Deceive (e.g., add
inferior alternative and deceptive visualization), Social Influence (e.g.,
enable comparison and public commitment), Fear (e.g., reduce distance and
scarcity) and Reinforce (e.g., ambient feedback and subliminal priming)
(Caraban et al., 2019). Under these categories, NudgeCred utilizes two methods
from two categories. First, it works as a Facilitate category by facilitating
decision making through a combination of color-coding and translucent hiding
method. Second, it operates as a social influence nudge category with enabling
social comparison method through the use of number of questions asked by other
responders. Technology-mediated credibility nudges can utilize other methods
from this classification. For example, similar to NewsCube (Park et al.,
2009), designers can use confront category by offering multiple viewpoint
method on news items. Or, flashing keywords around news items (e.g.,
“reliable” or “questionable”) utilizing subliminal priming method under
Reinforce category (Pinder et al., 2015), could affect users’ credibility
perception of those items.

##### Designing Against Adversarial Agents.

Our approach to be transparent suggests that adversarial entities, upon
knowing algorithmic detail, can manipulate the system. For example, by
commenting on a factual mainstream news with a question, they can create a
misperception of it being questionable. This is a problem that most online
platforms struggle with–balancing between being transparent about their
algorithm while safeguarding against adversaries. Indeed, platforms like
Reddit while publishing their ranking algorithms include “some fuzzing” to
avoid manipulation of their voting system (Coldewey, 2016). Hence, some
opaqueness in the algorithm might be desirable. At the same time, platform
developers could also misuse this fuzziness in the algorithm for their own
benefit, such as to drive engagement (Hao, 2021). Indeed, Twitter’s content
moderation practice has been controversial in the past, in some cases
resulting in reversal of moderation decisions (Lopatto, 2020). Similar
controversy could arise regarding nudging policy. Therefore, designing nudges
requires consideration for the multiple stakeholders of a social
platform—platform developer, consumers and news producers. Designers would
need to consider the degree to which a nudge would be resistant to adversarial
attacks by each stakeholder. For example, crowds’ question based bandwagon
heuristic has a high-level of susceptibility of manipulation by the consumers
compared to the platform developers and news producers. On the other hand, our
authority heuristic is more susceptible to manipulation by the platform
designers compared to the news producers and consumers. Overall, a potential
solution to this problem would be creating a collaborative standard authorized
by all stakeholders. For example, Facebook has already created a third-party
oversight board for content moderation (Board, 2020). A similar strategy can
be applied to determine nudging criteria.

##### Considering Shortcomings of Nudging as a Design Approach

Despite its success, nudging has its own shortcomings. Prior literature
proposes that nudges may have diminished effects in the long term for two
reasons: (i) nudges relying on automatic cognition may fail to educate the
user, and (ii) prolonged exposure may have various effects such as transform
nudge effects into background noise (Lee et al., 2011; Sunstein, 2017), invoke
a feeling of intrusiveness (Wang et al., 2014), and reduce users’ perception
of autonomy (Lehmann et al., 2016). Such effects may lead to unforeseen
consequences. For example, a default choice nudge promoting vaccination that
reduced users’ perception of autonomy resulted in users unsubscribing from the
program (Lehmann et al., 2016). In our case, if users repeatedly encounter
their favored political news source labeled as _Questionable_ or _Unreliable_
, they could become averse to the design. However, designers can apply several
strategies to counter this problem. On one end, they can alter the design over
time or prompt users to change the intervention settings over time (Kovacs et
al., 2021). As an alternate, they can also choose to deploy reflective nudges
which are less susceptible to the diminishing effect. A potential problem with
the altering design is that news consumers may need to re-learn how nudges
operate. Regardless, designers would first have to understand the rate at
which nudge effects diminishes, a direction for future research.

##  9\. Limitations and Future Work

Our work is not without limitations. Our Study 1 was conducted in a controlled
setting as opposed to in the wild. However, we see two reasons that suggest
that our results demonstrating the utility of nudges in credibility assessment
could extend to naturalistic Twitter setup as well. First, research shows that
self-reported measures pertaining to social media usage correlate with
observed behavior in the wild (Guess et al., 2019; Pennycook et al., 2020a).
Second, large-scale survey-based research on nudges pertaining to news on
social media, show that nudges affect related attitude, such as sharing
intention of misinformation (Nekmat, 2020; Pennycook et al., 2020b). Our
second limitation relates to choice of population. Because we tested variables
(e.g., political ideology and media skepticism) that has different meaning
across countries, we had to limit our experimental population to the US. To
generalize our findings to the global population, future research could
replicate our study in the context of each country. Third, our recruitment had
limitations that are characteristic of any online service-based recruitment.
Though we may not have obtained the true nationally representative sample in
the US, research suggests that Qualtrics provides reasonably representative US
sample (approximately 6% deviance from the US census) around demography such
as gender, age, race and political affiliation (Heen et al., 2014). Overall, a
large-scale Twitter deployment might reconcile these concerns in the future.
We initially attempted to do so by contacting a large number of Twitter users,
without much success due to a lack of platform-level cooperation (Our research
account was repeatedly blocked). While a 2015 study had successfully
piggybacked on Twitter’s infrastructure to run large-scale recruitment efforts
on the platform (Grevet and Gilbert, 2015), we were unable to do so, despite
following similar strategy. We anticipate that changes to Twitter’s policies
may have prevented us from running large-scale recruitment on the platform
(Inc., 2019).

##  10\. Conclusion

In this study, we provide evidence that a nudge-based design that directs
users’ attention to specific social cues on the news can affect their
credibility judgments. We used three nudges: _Reliable_ , applied to
mainstream news tweets without questions in replies; _Questionable_ , applied
to mainstream news tweets with questions in replies; and _Unreliable_ ,
applied to non-mainstream news tweets. Our experiment suggests that users who
saw tweets with _Reliable_ nudge as more credible, and tweets with
_Questionable_ and _Unreliable_ nudges as less credible compared to the
control users. Moreover, our nudges were not affected by users’ political
preferences, political cynicism, and media skepticism. Through interviews, we
found evidence of how nudges can impact users’ news consumption and how the
current design can be improved. This research proposes further exploration of
nudge-based system design approaches for online platforms.

##  11\. Acknowledgements

This paper would not be possible without the support from National Science
Foundation through grant #$2041068$. We also appreciate valuable feedback from
the members of the Social Computing Lab at Virginia Tech and University of
Washington.

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##  Appendix A Example Tweets Used in Study 1

Figure 7 shows several tweets used in Study 1. These tweets were selected by
finding the most popular tweets from the last 48 hours from various partisan
sources. Notice the partisan nature of the items including immigration, racism
and LGBTQ+ issues.

Figure 7. Sample tweets used in Study 1 without the interventions. The
examples include reliable, questionable and unreliable tweets from
left-/center-/right-leaning sources. Here, there is a mix of politically
contentious (e.g., immigration, racism and LGBTQ+) and not so contentious
issues (e.g., flood and national security).

##  Appendix B Study 2: Interview Questionnaire

  * •

Could you tell me about your news reading on twitter? How often do you read
news? and what type of news do you read?

  * •

How often do you come across misinformation?

  * •

Have you ever felt the need for any tools to improve news reading on twitter?

  * •

Have you ever used any tools to improve news reading on twitter? What tool?
How did it work?

  * •

(Asking them to share their screen for twitter feed) How would you compare
your Twitter use during the study to how you normally do?

  * •

Could you tell me about a time when you paid more attention to a news on
Twitter in the last 5 days? Why? What was it about?

  * •

How satisfied were you with what you saw on Twitter in the last 5 days?

  * •

What aspect of the intervention did you notice most? How did that impact you?

  * •

When using the extension, did you think of anything it was missing? What more
should it do?

  * •

Can you think of any other application of this extension that you would like?

  * •

During the study, did you have any issues with NudgeCred? Is there any aspect
of the usability (eg., design, speed, accuracy) you thought could be improved?

  * •

How did you feel about the plugin overall? What did you like about it? What
did you dislike about it?

  * •

Would you continue using it after this study?