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Do People Trust Their Eyes More Than Their Ears? Media Bias While Seeking Expert Advice

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Abstract

Enabling users to identify trustworthy actors is a key design concern in online systems and expertise is a core dimension of trustworthiness. In this paper, we investigate (1) users' ability to identify expertise in advice and (2) effects of media bias in different representations. In a laboratory study, we presented 160 participants with two advisors – one represented by text-only; the other represented by one of four alternate formats: video, audio, avatar, or photo+text. Unknown to the participants, one was an expert (i.e. trained) and the other was a non-expert (i.e. untrained). We observed participants' advice seeking behavior under financial risk as an indicator of their trust in the advisor. For all rich media representations, participants were able to identify the expert, but we also found a tendency for seeking video and audio advice, irrespective of expertise. Avatar advice, in contrast, was rarely sought, but – like the other rich media representations – was seen as more enjoyable and friendly than text-only advice. In a future step we plan to analyze our data for effects on advice uptake.
Do People Trust Their Eyes More Than Their Ears?
Media Bias While Seeking Expert Advice
Jens Riegelsberger, M. Angela Sasse, John D. McCarthy
Department of Computer Science
University College London
Gower Street, London WC1E 6BT, UK
{jriegels, a.sasse, j.mccarthy@cs.ucl.ac.uk}
ABSTRACT
Enabling users to identify trustworthy actors is a key design
concern in online systems and expertise is a core dimension
of trustworthiness. In this paper, we investigate (1) users’
ability to identify expertise in advice and (2) effects of
media bias in different representations. In a laboratory
study, we presented 160 participants with two advisors –
one represented by text-only; the other represented by one
of four alternate formats: video, audio, avatar, or
photo+text. Unknown to the participants, one was an expert
(i.e. trained) and the other was a non-expert (i.e. untrained).
We observed participants’ advice seeking behavior under
financial risk as an indicator of their trust in the advisor. For
all rich media representations, participants were able to
identify the expert, but we also found a tendency for
seeking video and audio advice, irrespective of expertise.
Avatar advice, in contrast, was rarely sought, but – like the
other rich media representations – was seen as more
enjoyable and friendly than text-only advice. In a future
step we plan to analyze our data for effects on advice
uptake.
Author Keywords
Trust, CMC, interpersonal cues, video, audio, avatar, photo
ACM Classification Keywords
H5.1 Multimedia Information Systems: Animation, Audio,
Evaluation, Video; H5.2 User Interfaces: Evaluation, H5.3:
Group and Organization Interfaces: CSCW, Evaluation.
INTRODUCTION
As technology-mediated interaction gradually replaces
face-to-face (f-t-f) interaction in many areas of life, trust
becomes a central concern for designers and researchers.
Systems should not be designed simply to increase user
trust, but to enable users to discriminate between
trustworthy and less trustworthy actors [3]. To date,
research investigating users’ ability to discriminate mainly
focused on deception (e.g. [4,6]). However, in many
everyday situations, questions of trust do not arise from the
risk of willful deception, but because one is uncertain about
the other’s expertise [1,2,3]: an individual might mean well,
but lack the expertise to be truly helpful. Investigating these
issues, we focus on the perception of interpersonal cues of
expertise in advice given in the rich media representations
video, avatar, audio, photo+text, and – for baseline
comparisons – text-only. These media are chosen for their
practical relevance: with more bandwidth available to users,
video and audio are becoming increasingly common.
Avatars and animated assistants are now marketed as cost
effective off-the-shelf solutions to enrich the user
experience. Photos are simple additions that have long been
used online with the aim to build trust.
For these media, we are investigating (1) whether they
introduce a media bias, and users’ ability to discriminate
between expert and non-expert advice (2). Bias occurs
when advice is preferred due to its media format,
irrespective of its expertise. We are also investigating the
influence of risk. After an overview on online trust
research, we introduce our predictions and experimental
approach. Then we present and discuss the results of the
study and we close with conclusions for researchers and
practitioners.
BACKGROUND AND RESEARCH QUESTIONS
Trust has been defined as a willingness to be vulnerable
based on positive expectations [1]. This implies that trust is
required in the presence of risk and uncertainty. Relying on
an online advisor can pose several risks, ranging from lower
than expected entertainment (e.g. in the case of a film
recommendation) to bodily harm (e.g. in the case of
medical advice). Uncertainty arises from the fact that the
trustor cannot directly observe the trustee’s ability (e.g.
expertise) and motivation (e.g. desire to deceive) [2], but
needs to infer these from the available information.
Interpersonal cues are an important type of signals for
trustworthiness in f-t-f situations [5]. They include visual
cues (e.g. appearance, facial expressions) and audio cues
(e.g. pitch, modulation) [5].
If interactions are mediated, some interpersonal cues are
lost. Text chat, for instance, removes all visual and audio
cues. In the discussion on online trust, it is often assumed
Copyright is held by the author/owner(s).
CHI 2005, April 2–7, 2005, Portland, Oregon, USA.
ACM 1-59593-002-7/05/0004.
MANUSCRIPT – Do Not Circulate
that this reduction in cues will result in lower trust.
However, there is also evidence that trust cannot be linked
unequivocally to such a one-dimensional understanding of
media richness. Firstly, in the presence of cues for lack of
expertise (e.g. nervousness), a rich channel is unlikely to
result in a high level of trust compared to one that
suppresses such cues. Secondly, Walther [10] found that
narrow-bandwidth channels can result in over-reliance on
the few cues available, which may lead to unwarranted high
levels of trust. Hence, richer representations may result in
(P1) positive media bias (i.e. more trust) or they may result
in (P2) better discrimination between expert and non-expert
advice as they convey more information.
Video and Audio: Swerts et al. [9] in a study on
interpersonal cues of uncertainty found that users’ ability to
discriminate was lowest for video-only, higher for audio-
only and highest for video+audio; thus supporting P2.
Investigating the detection of deception in video, Horn et al.
[6] found that slight visual spatial degradation reduced
participants’ ability to discriminate; giving further support
to P2. However, severe degradation of the visual channel
resulted in better discrimination. They hypothesized that
this effect may result from a reduced bias in the absence of
recognizable visual cues. Such an effect would provide
support for P1 and suggest that visual cues in particular
introduce a positive bias.
Avatar: Virtual humans (avatars and animated assistants)
are sometimes presented as simple means to enrich user
experience and build trust. However, they can prompt
mixed reactions from users [3]. In a study that varied agent
implementation and expertise (albeit not the interpersonal
cues given off) van Mulken et al. [7] found a strong effect
of expertise on perceived trustworthiness but only a
marginally positive effect for the embodied representation.
Rickenberg & Reeves [8] found a positive effect of a simple
animated agent on user trust (P1).
Photos: Photos do not give additional cues with individual
advice, but they are widely used with the aim to increase
social presence and trust. Previous studies found that they
can bias users’ trust in websites [3] (P1).
None of the studies above induced risk to measure trust and
none systematically investigated P1 and P2 across different
media representations. To specifically address our
predictions we contrasted expertise and media richness: we
gave each participant two advisors – one in a rich media
representation and the other text-only. For one group of
participants expert advice was given by the rich media
advisor; for the other group by the text-only advisor. On
each question participants could ask only one advisor.
Figure 1, illustrates P1 and P2 for this approach. In the
hypothetical case of total bias (P1), we would expect
participants to always seek rich media advice, irrespective
of expertise. In the case of perfect discrimination (P2),
participants would always prefer expert advice.
Perfect Bias (P1)
0
0.25
0.5
0.75
1
Advice Seeking Proportion
Ri ch Me dia i s Expe rt
Rich Medi a is No n-Exper t
Perfect Discrimination (P2)
0
0.25
0.5
0.75
1
Advice Seeking Proportion
Ric h Me dia i s Expe rt
Rich Me dia is Non-Expert
Figure 1. Illustration of predictions P1 and P2.
METHOD
Participants and Design
160 highly computer-literate participants (median age
23.75, 49% female) took part in the study, which was
framed as a quiz, similar to the well-known TV show
Millionaire. The questions used in the study had been tested
for their difficulty in a pre-study with 80 participants. After
two easy practice questions on which only correct advice
was given, participants went through 30 assessed questions.
Feedback on the correctness of participants’ answers was
only given at the end of the study.
The study had a 4 (type of rich media representation) x 2
(rich media advisor is expert vs. rich media advisor is non-
expert) between-subjects design. Each participant was
presented with a pair of advisors (Fig. 2). In all conditions
one advisor was represented as text-only and the other in
one of the four rich media representations (video, avatar,
audio, photo+text; Fig. 2, 3). Depending on the condition,
either the text-only or the rich media advisor gave expert
advice, while the other gave non-expert advice. The order
of the questions and answer options (A-D, Fig. 2) was
randomized; the position (left, right) and names (Katy,
Emma) of the advisors were counterbalanced.
Figure 2. Experimental system (video and text-only advisor).
Figure 3. Avatar, audio, and photo+text advisor.
MANUSCRIPT – Do Not Circulate
Independent Variables
Expertise: Non-Expert and expert advice was recorded
from the same individual before and after training,
respectively. Hence, the expert and non-expert advisors
only differed in the ratio of correct to incorrect advice and
in their cues to confidence about the answers. In the interest
of ecological validity, answer formats were not prescribed.
Based on experience with a pilot study, we added 6
incorrect (and less confident) answers from the untrained
recording to the expert so she did not seem artificially
perfect. The proportion of correct (and confident) advice
was .80 for the expert and .36 for the non-expert.
Media Representation: The media representations were
created from the same video clips ranging from 1 sec. to 8
secs. in length. The original clips were used for the video
representation. The avatar was created with a commercially
available animation tool (V1 by DA Group) directly from
the audio stream without any manual scripting of nonverbal
behavior. The tool synchronized lip movements and added
cues of liveliness (e.g. blinks). Video and avatar were
streamed with Windows Media Encoder (350 kbps,
320x240). Audio was encoded with 48 kHz, 16 bit, mono.
Photo+text included a facial photo of the advisor, otherwise
it was identical to the text-only representation; for both, the
text appeared dynamically with a delay of 107 ms per letter
to ensure that all representations had equal ‘playing time’.
Risk: Participants’ pay depended on the number of
correctly answered questions and thus on their ability to
identify the expert advisor, as the quiz questions were
extremely difficult. Pay ranged from the equivalent of $15
to $26. To investigate the effect of level of risk, we
included a high-stakes question worth an additional $5.50.
Dependent Variables
The measure advice seeking was defined as the proportion
of one advisor being asked out of the total number of times
advice was sought by a participant. As only one advisor
could be asked on each question, expert advice seeking = 1
non-expert advice seeking. A final questionnaire elicited
users’ subjective assessments of the two advisors.
RESULTS
On average, participants sought advice on 26 out of 30
questions. One participant did not seek advice at all. Figure
4 shows a main effect for expertise (P2) on participants’
advice seeking (F (1, 154) = 51.56, p < .001). There is also
some indication for an effect of the type of rich media
representation (F (3, 154) = 2.50, p = .062). To conduct
within-subject tests for bias (P1, Fig.1) and discrimination
(P2, Fig.1) in individual conditions, we investigated rich
media non-expert advice seeking (grey bars in Fig. 4). A
value < .5 would provide evidence for discrimination (see
Fig. 1), a value > .5 would be a sign of bias outweighing
discrimination. Figure 4 shows non-expert avatar and
photo+text advice seeking significantly below .5. No such
effect was present for video and audio, indicating that a
media bias interferes with users' ability to discriminate.
Rich Media Advice Seeking
0.3
0.4
0.5
0.6
0.7
0.8
Video Avatar Audio Photo+Text
Advice Seeking Proportio
n
Rich M e dia Expe rt Rich Media Non-Expert
Figure 4. Seeking advice from the rich media advisor.
Stars (*) indicate results for one-sided t-tests
(H: non-expert advice seeking < .5; p < .05).
Further evidence for a media bias in video and audio is
given by the finding that for these representations rich
media expert advice was chosen more often than text-only
expert advice (video: t(38) = 3.60, p < .001, audio: t(37) =
1.69, p < .05; both one-sided). This effect was not present
for the avatar and photo+text representations. Expert
avatar advice was less often sought than advice from the
other rich media experts combined (t (77) = 2.45, p < .05).
Participants increasingly sought advice from the expert as
they gained experience with the advisors (Fig. 5), but the
increase in financial risk for the final high-stakes question
resulted in an increase in seeking advice from the rich
media advisors (McNemar: χ2 (131) = 6.25, p=.012, Fig. 6).
Expert Advice Seeking over Time
0.3
0.4
0.5
0.6
0.7
0.8
1 2-8 9-15 16-22 23-29 High
Stake s (30)
Question
Advice Seeking Proportio
n
Video Avata
r
Audio Photo+Te x
t
Figure 5. Expert (rich + text-only) advice seeking over time.
Rich Media Advice Seeking over Time
0.3
0.4
0.5
0.6
0.7
0.8
1 2-8 9-15 16-22 23-29 High
Stak es (30)
Question
Advice Seeking Proportio
n
Vid e o Avatar Audio Photo+Text
Figure 6. Rich (expert + non-expert) advice seeking over time.
**
MANUSCRIPT – Do Not Circulate
Participants stated that they trusted the video advisor more
than the text-only advisor, irrespective of expertise (t (39) =
2.83, p < .01); a finding not replicated for the other media
representations. All rich media representations were rated
as more friendly (t (159) = 7.24, p < .001) and enjoyable (t
(159) = 6.71, p < .001) than text-only.
DISCUSSION
Overall, we found that participants mostly chose expert
advice in all media representations (P2). However, there
was also some indication that video and audio
representations can interfere with users’ ability to
discriminate effectively (P1). Increased risk led to an
increase in media interference.
Video: When the non-expert was represented in video,
preference for choosing video almost matched the
preference for choosing expert advice. Also, in the post-
experimental ratings, participants stated that they trusted the
video advisor more, irrespective of expertise. Hence, with a
view to well-placed trust, video can be seen as problematic:
users’ preference for receiving video advice led them to
disregard better text-only advice.
Audio: Similar to video, the preference for seeking non-
expert audio advice almost matched the preference for
expert advice. However, participants did not say they
trusted audio more than text-only irrespective of expertise.
This finding supports Horn et al. [6] in that visual
interpersonal cues in particular appear to induce a bias.
Avatar: The avatar did not result in a bias; rather it was
less preferred than other rich media experts. This finding
cannot necessarily be generalized to other avatars or
animated assistants, but it indicates that using an off-the-
shelf avatar to increase trust may not be advisable at this
stage. Finally, we did find that the avatar, like all other rich
media representations, was perceived as friendlier and more
enjoyable than the text-only advisor.
Photo+Text: Lexical cues alone, as given in the photo+text
representation, were sufficient for identifying the expert.
The photo was not found to bias advice seeking, but it did
result in higher ratings of friendliness and enjoyment
compared to text-only advice.
CONCLUSIONS
We observed participants’ advice seeking in a situation of
limited advice and under financial risk. In all media
representations, participants were able to identify expert
advice (P2), but the data suggest that video and audio
representations can interfere with users’ ability to
discriminate effectively (P1). One interpretation of this
finding is that users chose the rich media representations
because they considered them to give the best insight into
the trustworthiness of a piece of advice. An analysis of our
data for effects on advice uptake will clarify whether this is
really the case. The relatively good performance at
perceiving expertise in the photo+text representation
suggests that sufficient information about expertise was
contained in text alone.
For designers interested in high levels of trust (even at the
risk of inducing media bias), video is the best
representation, followed by audio. Finally, the avatar, and
even just a simple photo lead to higher ratings of
friendliness and enjoyment than text-only. So, if the design
goal is engagement rather than trust, our data suggests that
these representations can be effective.
With a view to methodology, our results provide further
support for measuring trust by observing decision-making
under risk, since we found that the level of financial risk
influenced participants’ behavior. Our next step will be to
analyze the data for effects on participants’ advice uptake.
ACKNOWLEDGEMENTS
We would like to thank Cyril Scott at DA Group
(www.dagroupplc.com) for the V1 animation tool, as well
as M. Garau, P. Bonhard, H. Knoche (UCL), S. Mahlke
(TU Berlin), and K. Chorianopoulos (IC London).
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