#  Identity Signals in Emoji do not Influence Perception of Factual Truth on
Twitter

Alexander Robertson, Walid Magdy, Sharon Goldwater

###### Abstract

Prior work has shown that Twitter users use skin-toned emoji as an act of
self-representation to express their racial/ethnic identity. We test whether
this signal of identity can influence readers’ perceptions about the content
of a post containing that signal. In a large scale (n=944) pre-registered
controlled experiment, we manipulate the presence of skin-toned emoji and
profile photos in a task where readers rate obscure trivia facts (presented as
tweets) as true or false. Using a Bayesian statistical analysis, we find that
neither emoji nor profile photo has an effect on how readers rate these facts.
This result will be of some comfort to anyone concerned about the manipulation
of online users through the crafting of fake profiles.

## Introduction

Hyp. | Condition A | Condition B | Description | BF (Black) | BF (White)
---|---|---|---|---|---
H1a | No photo, no emoji | No photo, Black-aligned emoji | Sensitive to presence of emoji | 0.109 | 0.443
H1b | No photo, no emoji | Black photo, no emoji | Sensitive to presence of photos | 0.099 | 0.087
H1c | No photo, no emoji | No photo, White-aligned emoji | Sensitive to presence of emoji | 0.186 | 0.073
H1d | No photo, no emoji | White photo, no emoji | Sensitive to presence of photos | 0.834 | 0.142
H2a | No photo, matching emoji | No photo, non-matching emoji | Preference for identity-matching emoji | 0.098 | 0.434
H2b | Matching photo, no emoji | Non-matching photo, no emoji | Preference for identity-matching photos | 0.284 | 0.086
H3a | Matching photo, matching emoji | No photo, matching emoji | Emoji encode distinct identity signal | 0.085 | 0.108
H3b | Matching photo, matching emoji | Matching photo, no emoji | Photos encode distinct identity signal | 0.084 | 0.075

Table 1: Overview of hypotheses tested, in terms of photo/emoji conditions.
These were tested separately for Black and White readers: matching/non-
matching means that the skin tone of the emoji was aligned with the self-
reported ethnicity of the reader. Final columns show Bayes Factors for t-tests
and are discussed in the analysis/discussion section.

As the influence of social media grows, it becomes ever more important to
understand factors contributing to the spread of misinformation and “fake
news”, as well as how people’s beliefs may be swayed online. In this paper, we
investigate the role of author identity, as signalled through emoji. Can these
identity signals influence the extent to which a reader accepts information as
being true? The answer has important consequences not only for whether and how
authors choose to signal their own identity, but also for studying the
deliberate manipulation of identity signals in online content.

In this paper, we focus on the connection between identity signals and beliefs
(rather than the behaviour that might result, such as retweeting). As the main
identity signal of interest, we use skin-toned emoji, building on previous
work showing that these are used as a form of self-representation (i.e.,
identity signalling) (Robertson, Magdy, and Goldwater 2018, 2020), and that
readers make use of these signals to infer aspects of the author’s identity
(Robertson, Magdy, and Goldwater 2021). We hypothesise that identity signals
in a tweet’s emoji will affect the extent to which a reader believes the
content of that tweet.

In a large scale (n=944) pre-registered controlled experiment, we manipulate
identity signals in tweets containing trivia facts and measure the extent to
which readers perceive those facts to be true or false. We find evidence
against any effect on perceptions for either emoji or profile photo. Under a
Bayesian statistical analysis, our experimental data supports the null
hypothesis over the alternative in all of our analyses. This result is
consistent across all experimental conditions, for two groups of participants
based on self-reported ethnicity, and persists following adjustments to the
experiment to address possible confounding factors in its design. Although
these findings did not support our hypothesis, we argue that skin-toned emoji
not affecting online behaviour in the way we expected is actually a positive
result in the context of online manipulation, misinformation and fake news.

## Background

Decades of sociology and linguistic research show that aspects of identity
(i.e. socially meaningful categories such as gender, class, or ethnicity) can
be signalled in many ways (Goffman 1959) and that these signals can influence
our attitudes, perceptions, and behaviour towards a person (Campbell-Kibler
2010; Labov 1972). Language is an important signal of identity and can affect
one person’s beliefs about another, such as whether they are trustworthy or
intelligent (Lambert et al. 1960). Identity signals can also affect behaviour:
for example, using African American-sounding names on Uber resulted in
increased wait times and more cancellations by drivers (Ge et al. 2016).

Emoji can also signal aspects of identity. For example, gender can be
signalled consciously through choosing to use a male or female emoji (or
neither) (Barbieri and Camacho-Collados 2018) or perhaps less consciously via
the frequency of use of particular emoji such as  or  (Chen et al. 2018).
Here, we focus on ethnic identity, as signalled by skin-tone modifiers.
Twitter users overwhelmingly use these to express their own identity
(Robertson, Magdy, and Goldwater 2018), though referring to _other_ people in
specific situations has been observed (Robertson, Magdy, and Goldwater 2020).
Expression of identity by authors is only one side of the story — readers of
tweets containing light or dark toned emoji also associate authors with a
congruent Black or White identity (Robertson, Magdy, and Goldwater 2021), and
this association is separate from linguistic signals such as use of particular
topics, phrases or syntax associated with Black or White speakers.

The connection between identity signals and behaviour has been studied in
experimental economics, using the Trust Game. Pairs of anonymous players are
designated sender and responder. The sender has a fixed amount of money and
can keep the whole sum or send a portion to the responder. Any money sent this
way will be tripled. The responder may keep everything they are sent, or
return a portion back to the sender. Pairs play one round consisting of the
sender making their decision, then the responder making their response.
Buchan, Croson, and Solnick (2008) found an effect of gender on trust (as
measured by the amounts sent/received), with women senders less likely overall
to send money, but to send more to male responders than to female. Stanley et
al. (2011) found a relation between the extent of a person’s racial bias (as
measured through the Implicit Attitudes Test (IAT) (Greenwald et al. 2002))
and the extent to which they trust players of a different racial background.

More recently, Babin (2020) examined the effect of emoji when trust game
players are allowed to communicate. Using emoji increased trust and
reciprocation between players, but of particular relevance to our work here is
the finding that use of skin-toned emoji may result in less trust when those
skin tones are darker. Babin did not set out to evaluate the impact of skin
tone and so this result must be regarded as preliminary. However, it motivates
our work here as it is suggestive of skin-toned emoji being able to induce
bias to the extent that behavioural outcomes are affected.

Our experimental setup is based on Lev-Ari and Keysar (2010), who looked at
the effect of a speaker’s accent on how we react to what they say.
Participants listened to speakers with native, mild foreign or strong foreign
accents reading trivia facts such as “Ants never sleep”. On a scale from 0 to
100, participants rated statements from definitely false to definitely true.
Accented statements were rated as less true even though participants were
informed that the speakers were simply reciting statements written by the
native-speaking experimenters. Lev-Ari and Keysar focused on processing
difficulty as the explanatory factor for this outcome, rather than out-group
or ethnic bias, but we would argue that the foreign accents used in the
experiments are likely a highly salient indicator of race/ethnicity. To
emulate the ‘reciting’ aspect, we can use retweets.

## Hypotheses

Before describing our experimental setup in detail, we will state the
hypotheses it is intended to test and the rationale for these hypotheses. The
experimental conditions arising from these hypotheses are shown in Table 1.

First, we predict that the identity signal encoded in emoji (Robertson, Magdy,
and Goldwater 2021) will have an effect on how readers evaluate trivia
statements in terms of being true or false, similar to the findings of Lev-Ari
and Keysar (2010). We also assume that a profile photo also encodes an
identity signal.

Second, we predict that readers will be sensitive to the kind of identity
encoded in emoji and photos. Readers may prefer their own identity (similar to
in-group bias in favour of own gender (Rudman and Goodwin 2004)) or they may
prefer another identity, for example if all users show a similar bias against
a particular group (Valla et al. 2018).

Third, we predict the presence of both a photo and an emoji signal will have a
larger effect than either one alone, as per Robertson, Magdy, and Goldwater
(2021) who found a “boosting effect” of an emoji and a language signal of
identity when users were asked to guess the identity of the author.

We test our hypotheses separately with Black and White participants (self-
identified; see Participants section). This is primarily to ensure we do not
mistakenly extend any effects found in one group to all groups, as prior work
has shown differences in how Black and White social media users use and
perceive skin-toned emoji in relation to expressions of identity (Robertson,
Magdy, and Goldwater 2020, 2021).

## Experimental setup

Before proceeding, we acknowledge that some terms do not have universally
agreed-upon definitions. Within the framework of Critical Race Theory
(Ogbonnaya-Ogburu et al. 2020), race and ethnicity are socially constructed.
As such, the meaning of these and related terms depends on context, audience
and the user. We use Black and White in our work to denote the self-identified
ethnic identity of the participants. For emoji, we use Black-aligned and
White-aligned to denote emoji which prior research (Robertson, Magdy, and
Goldwater 2018) shows to be preferred for self-representational usage by
people of Black or White identities. Furthermore, we acknowledge that Black
and White are not the only ethnicities which can be represented using emoji
skin tones. However, we restrict our experimental setup to consider only these
two options and make no claims as to how any findings may apply to other
identities.

The experiment follows the setup of Lev-Ari and Keysar (2010), with the main
difference that it was conducted online. Participants were shown 31 tweets
containing trivia and informed that the trivia could be true or false. Their
task was to mark on a slider from 0 to 100 whether they thought the trivia was
_definitely false_ or _definitely true_. Participants were explicitly asked
not to look up any trivia and informed that the correct answers would be shown
upon completion of the study.

### Stimuli

Figure 1: Example of a critical trial from one block, showing manipulation of
the identity encoded in profile photos and emoji.

Critical trials manipulated the identity signals present in emoji and photos,
as shown in Figure 1. Emoji were either dark-toned (aligned with Black
identities), light-toned (aligned with White identities) or absent. Only the
downwards pointing finger  was used. Photos signalled a probable Black
identity, White identity or no obvious identity (e.g. a nature photo). There
were 7 critical combinations of emoji/photo. Photos were female faces
generated by StyleGAN111https://github.com/NVlabs/stylegan, to avoid using
photos of real people, and blurred to obscure all detail except skin tone. In
a pre-norming study, 25 Black and 25 White participants guessed the ethnicity
of the blurred faces. We retained only those which the majority ($\geq$95%) of
participants considered to show a Black or a White person.

All trivia in the critical trials were true, but were likely to be neither
well-known to be true nor generally thought (mistakenly) to be false. This was
to prevent any ceiling effect in the ratings of the stimuli and was determined
through a pre-norming study of trivia facts taken from Snapple soft drink
lids.222See https://www.snapple.com/real-facts for a full list 20 Black and 20
White participants rated 100 random facts as either true or false. We randomly
sampled 7 from those facts where participants were at chance in deciding
between true and false. Mean ratings for these facts were 47.5 (stdev 0.0932)
for Black participants, 44.6 (stdev 0.0935) for White participants.

To control for any possible impact of individual facts on participant
judgements, each of these facts appeared in each critical combination of
emoji/photo. This was done in blocks, such that participants only saw each
fact once in a single emoji/photo combination, but across the entire
experiment all critical facts were judged in all combinations.

Filler trials (to obscure the intent of the experiment) were balanced across
two types of fact. Twelve were facts that pre-norming participants generally
considered to be true/false (6 of each), and twelve were facts we created
which were very obviously true or very obviously false (again, 6 of each). In
addition, profile photos could be colourful and non-human (e.g. scenery) and
blurred verified account ticks could be present in user names. Also, a wider
range of emoji, including skin-toned ones, could appear in the main
retweeter’s text.

All stimuli were presented as a main user retweeting another account, with
some comment expressing surprise or incredulity. Authentic-looking tweets were
automatically generated from a copy of the Twitter HTML/CSS and saved as PNG
files, to discourage participants looking for answers online by
copying/pasting the text. The surprise/incredulity comments were selected
based on a pre-norming study. 50 Black and 50 White participants provided
three examples of how they would comment if sharing such a tweet. The most
common responses shared between Black and White participants were chosen and
sampled from at random.

Display names, user names, dates, number of likes/retweets/comments were all
randomised before being blurred. In critical trials, the account being
retweeted was always unverified and had a default grey display photo. In
filler trials, these properties were randomly assigned. Finally, trials were
presented in randomised order per participant. All stimuli can be found in our
Open Science Framework pre-registration.333See https://osf.io/a8r6q/

### Participants

Participants were recruited through Prolific.co on the basis that they are
native speakers of English, US citizens resident in the US and self-identified
their ethnicity as either Black or White. We did not request or record any
non-pertinent personal information (e.g. age, gender), in accordance with the
University’s data handling protocols. In all pre-norming and experimental
tasks, participants were paid at a rate equivalent to the National Living Wage
of £9.50 per hour. Approval for the study was obtained in advance from the
University’s ethics board. In total, 944 participants (472 Black, 472 White)
were recruited for the main study, to ensure sufficient power to determine
with a good degree of confidence whether our null or alternative hypothesis is
true (using a Bayesian statistical analysis, described below). Power was
determined using the SSDbain package in R (Fu, Hoijtink, and Moerbeek 2020).

## Analysis plan

The experiment generates a sequence of true/false ratings (from 0 to 100) per
critical combination of emoji/photo. We analyse the means of these ratings for
differences using a Bayesian two-tailed independent samples t-test (van Doorn
et al. 2020), as implemented by the JASP444https://jasp-stats.org/ software
package. The Bayesian t-test is parameterised by a prior distribution over our
assumptions of the effect size. We follow standard practice of using a Cauchy
prior with mean 0 and scale 0.707 (Quintana and Williams 2018).

We chose a Bayesian version of the t-test for comparing sample means as it
allows us to report Bayes Factors rather than p-values, which means we can
quantify the amount of support for or against our hypotheses, rather than
simply testing whether the null hypothesis can be rejected (Rouder et al.
2009). The Bayes Factor (BF) is the ratio of the marginal likelihoods of two
competing hypotheses (H0 and H1), given some observed data. Multiplying the
prior odds of H0 and H1 (i.e. the ratio of P(H0) to P(H1)) by the BF gives the
posterior odds: the ratio of P(H0$|$data) to P(H1$|$data). The BF quantifies
the extent to which the observed data supports the hypotheses under
consideration. For our purposes, the two competing hypotheses are no
difference in means (H0) and a significant difference in means (H1). A BF
value of 1 means the observed data supports each hypothesis equally well (i.e.
it is uninformative), and normally indicates additional data is needed or
hypotheses need to be reformulated. A BF value greater than 1 means there is
more support for H1, while a value less than 1 means more support for H0.

## Results

Figure 2: Mean true/false ratings (with 95% confidence intervals) per
participant group. Each bar represents an experimental condition, with labels
XY representing Emoji status (Black, White, No emoji) and Photo status (Black,
White, No photo). Bars are coloured based on their prevalent identity signal
(grey = no signal, brown = Black signal, beige = White signal)

Mean true/false ratings per experimental condition for each participant group
are shown in Figure 2. The results of all analyses for both participant groups
were in favour of H0 — there was no effect on the mean true/false ratings in
any subsets of ratings selected for comparison. Table 1 reports the results of
all t-tests in terms of Bayes Factor, from which it can be seen that
experimental evidence offers between 1.2 and 13.4 times more support (i.e.
1/BF) for H0 in all cases than for the alternative hypothesis of a difference
in mean true/false ratings. From this, we conclude that neither emoji nor
profile photo, either alone or together, influence a reader’s behaviour in the
true/false trivia task.

## Further Analysis and Discussion

Although our experiments were motivated by a range of prior work, used
carefully designed and normed stimuli, and involved almost 1000 participants,
the evidence we found against any effect of identity signals prompted us to
reflect on the experimental design and explore possible issues.

Figure 3: Example of a critical trial from one block, with the user directly
tweeting the trivia, rather than retweeting another account.

Did the presentation of the fact as a retweet of an account with no identity
signals cause readers to focus only on the retweeted account? We ran a small
scale version (n=80, White participants only) of the experiment without the
retweet (Figure 3) and again obtained statistical support only for the null
hypothesis (BF range of 0.181-0.783, 1.2 to 5.5 times more support for H0).

Was participants’ familiarity with Twitter a factor? The Prolific.co platform
provided data on participants’ self-reported Twitter use and we separated
groups into Twitter users and non-users. Again, we obtained qualitatively the
same results, regardless of whether stimuli were tweets or retweets (BF range
of 0.144-0.385, 2.6 to 6.9 times more support for H0).

It may be that profile photos had no effect on behaviour because they were
blurred. We had normed these blurred photos to ensure the skin tone was
determinable, but this alone may not be sufficient to cause any bias or
preferential behaviour in this task. As emoji were not blurred but also had no
effect, we suspect it is unlikely that unblurring profile photos would have
any effect.

Perhaps we should have included a measure of bias by administering an IAT, as
per Stanley et al. (2011), and balanced participant numbers not only in terms
of self-reported racial identity but also in terms of pre-existing bias. This
we leave for future work and might give an interesting perspective on whether
emoji can trigger biases or preferences which are already known to exist, but
see prior work on issues with failure to IAT results and poor correlation
between IAT scores and behavioural outcomes (Blanton et al. 2009).

Finally, the true/false trivia task may itself not be best suited to detecting
the effect we set out to find. These facts are by definition trivial, and
whether they are true or false is likely unimportant to the reader. By
contrast, facts with real-world consequences or emotional valence might be
more subject to unconscious biases surrounding the identity of the source. We
did not explore that type of fact here, since the experiment would be far more
challenging to design, raising both ethical questions (we want to avoid
accidentally spreading misinformation) and design issues (it is difficult to
come up with plausible stimuli). However, such an experiment could provide
important further evidence regarding situations in which identity does or does
not affect perceptions of truth.

How any findings from such work would translate to the case of Twitter is not
clear. On the positive side, we found strong evidence that the participants in
our study were not swayed by identity signals to rate trivia as more or less
true/false. This has consequences for work in disinformation. In an analysis
of tweets from a Russian government-funded troll farm known as the Internet
Research Agency, Freelon et al. (2020) found that fake accounts included
aspects of racial presentation, using Black profile photos and names
associated with Black Americans. The practice of Black impersonation in this
case appeared to result in increased engagement with the content from real
Twitter users, in terms of likes and retweets. However, as Freelon et al.
note, there is no data on the identities of the users engaging with those
tweets. One way of reconciling the findings of Freelon et al. and ours is to
suppose that the increased engagement came only from additional fake bot
accounts (though not necessarily from the same troll farm, as the authors
found little evidence of self-amplification) rather than real users. The real
users in our study seem indifferent to such identity signals in the context of
behavioural influence. To better understand the interaction of users,
identity, behaviour and disinformation, future work in this area should
combine the experimental approach of our study and the content perspective of
Freelon et al. (2020).

## Conclusion

In a controlled experiment, we found that identity signals in emoji or photos
do not influence the extent to which readers perceive information as true or
false. This is potentially good news in light of attempts to manipulate social
media users, but future work should examine whether this result obtains for
different, more realistic, types of information. Our methodology is well-
documented due to the pre-registration process, making such work easier for
interested researchers.

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