=Paper=
{{Paper
|id=Vol-3300/paper_6284
|storemode=property
|title=Predicting User Engagement in Video Advertisement: Insights from Pupillary Response and Heart Rate
|pdfUrl=https://ceur-ws.org/Vol-3300/paper_6284.pdf
|volume=Vol-3300
|authors=Gregor Strle,Andrej Košir,Evin Aslan Oğuz,Urban Burnik
|dblpUrl=https://dblp.org/rec/conf/hci-si/StrleKOB22
}}
==Predicting User Engagement in Video Advertisement: Insights from Pupillary Response and Heart Rate==
https://ceur-ws.org/Vol-3300/paper_6284.pdf
Predicting User Engagement in Video Advertisement:
Insights from Pupillary Response and Heart Rate
Gregor Strle1,2 , Andrej Košir1 , Evin Aslan Oğuz1,3 and Urban Burnik1
1
University of Ljubljana, Faculty of Electrical Engineering, Tržaška 25, 1000 Ljubljana, Slovenia
2
ZRC SAZU, Novi trg 2, 1000 Ljubljana, Slovenia
3
Nielsen Lab d.o.o., Obrtniška ulica 15, 6000 Koper, Slovenia
Abstract
The article presents the results of predicting user engagement with in-video ads using physiological
sensor signals. Specifically, we examine pupil response and heart rate as possible predictors of user
engagement. To this end, we conducted an experiment with 33 young participants (age M = 21.70, SD =
2.36; female = 68%) in which their psychometric (engagement score) and physiological responses (pupil
dilation and heart rate) to four in-video ads were recorded. The ground truth for the ad engagement was
collected using the User Engagement Scale Short Form (UES-SF), a standardized psychometric instrument
for measuring user engagement. The UES-SF dimensions Aesthetic Appeal (AE) and Perceived Usability
(PU) were used to calculate the combined User Engagement Score. Several machine learning classifiers
were evaluated that used heart rate and pupil response as predictors of engagement. The best overall
results were obtained by Random Forest Classifier (’weighted’ F1 score = .76, Precision=.84, Recall=.95),
Logistic Regression and Support Vector Classifier (the latter two with the same scores: ’weighted’ F1
score = .74, Precision=.82, Recall=1)
Keywords
user engagement, perceived usability, aesthetic appeal, advertisement exposure, pupil dilation, heart rate
1. Introduction
Advances in technology and digital media advertising have enabled new approaches to measur-
ing consumer engagement and exposure to online marketing [1]. These go beyond traditional
frequency measurements (reports by recall, number of views, likes, etc.) and now include
online consumer behavior, social media metrics and trends, and user engagement and attitude
ratings [2, 1]. One area of advertising that is growing particularly strongly is ad-supported
video streaming, which has overtaken video-on-demand streaming [3])
The goal of the research presented here is to assess heart rate and pupil dilation as predictors
of user engagement with in-video advertising used by online streaming services. The ground
truth for the ad engagement was collected using the User Engagement Scale-Short Form (UES-
SF) [4], an established psychometric instrument for measuring user engagement. The focus
group of the presented research was younger consumers who use streaming services extensively
and are accustomed to in-video advertising [5].
Human-Computer Interaction Slovenia 2022, November 29, 2022, Ljubljana, Slovenia
Envelope-Open gregor.strle@fe.uni-lj.si (G. Strle); andrej.Kosir@fe.uni-lj.si (A. Košir); evin.aslan-oguz@fe.uni-lj.si (E. A. Oğuz);
urban.burnik@fe.uni-lj.si (U. Burnik)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
The contribution of the presented study is to examine the potential of physiological cues as a
measure of advertising exposure. It is well known that advertising evokes emotional arousal and
triggers cognitive processes in consumers [6]. These, in turn, influence a person’s measurable
physiological responses and can give us new insights into consumer behavior. Due to the steady
development of wearable devices with physiological sensors (e.g., smartwatches), physiological
measurements may be used in the future as novel marketing strategies and technologies related
to the impact of advertising and consumer behavior [1].
In the following, we briefly present related work. Next, we present the experimental design
and procedure for collecting user responses and the selection of materials and tools. The
statistical analysis and the evaluation of machine learning models are presented in the Results
section. The article concludes with a summary of the main results and possible directions for
future work.
2. Related Work
Contemporary advertising strategies and services require rapid and accurate insight into con-
sumer engagement and exposure to media content. Fast and efficient measurement methods
with little interference with the observed subject are preferred. We hypothesize that measuring
physiological signals known to be associated with emotional arousal and cognitive processes
could provide a good basis for unobtrusive measures of engagement and exposure to media
advertising.
Richardson et al. examined engagement with video and audio narration using wrist sensors
that measure heart rate variability, electrodermal activity, and body temperature [7]. They
found a significant physiological response to all 3 observed measures. Ayres et al. provided a
comprehensive review of physiological measures of intrinsic cognitive load, including heart rate,
heart rate variability, respiratory measurements, pupil dilation, blink rate, fixation, electrodermal
measurements, functional near-infrared spectroscopy, electroencephalography, and functional
magnetic resonance imaging [8]. The most sensitive physiological measurements were blink
rate, heart rate, pupil dilation, and alpha waves.
Recent research has shown that pupil dilation can be used to assess cognitive processes in
participating subjects. P. van der Wel [9] reported that with respect to the cognitive control
domains of updating, switching, and inhibition, an increase in task demands leads to increased
pupil dilation. However, the study does not establish a clear model for the relationship between
pupil dilation and performance. The use of pupil dilation in studies of advertising effectiveness
has been known since the early 1970s Hensel1070. Using pupil dilation to measure emotional
arousal during video consumption is challenging because pupil dilation is sensitive to changes
in brightness. In [10], a linear model of the pupillary light reflex is proposed that predicts
a viewer’s pupil diameter based only on incident light intensity. The model can be used to
subtract the effects of brightness to determine subjects’ emotional arousal as a function of
the observed scene. Jerčić et al. [11] examined pupil dilation and heart rate as measures of
physiological arousal and identified them as possible indicators of cognitive ability in serious-
gaming participants. In [12], the application of personalized advertising systems based on the
measurement of heart rate variability on the go is proposed. Pham et al. [13] found that heart
rate variability (HRV) can be a stable and affordable source of information for neurophysiological
and psychophysiological studies, provided that appropriate acquisition procedures and well-
developed indices are available. Schaffer et al. [14] have addressed the complexity of heart
oscillations and reviewed existing methods for monitoring HRV in the time and frequency
domains using nonlinear metrics.
Much of the evidence cited above suggests that among physiological measures, pupil dilation
and heart rate are good choices for detecting fluctuations in emotional arousal, mental activity,
and cognitive processes. In the presented study, we focus on the putative effects of the two on
advertiser engagement and media use.
3. Materials and Methods
A pre-selection of 12 videos and 12 ads was taken from the online streaming service YouTube.
These materials were selected in collaboration with three marketing experts from The Nielsen
Company to address different levels of engagement. All materials were in English and aired in
the United States.
A crowdsourcing study with Clickworker1 was conducted to determine engagement with
YouTube’s 12 videos and ads. The ads were inserted into the videos at random positions, with
combinations of different engagement levels. The goal of these combinations was to simulate
the experience of ad-supported video streaming. Engagement was measured on a 5-point
scale (How engaging is the ad?: none-medium-strong-very strong). Engagement scores were
collected from study participants (N=360, age=18-24).
Based on the results of the crowdsourcing study, different video and in-video ad combinations
were created based on the engagement scores. The final selection for the experiment was made
in collaboration with Nielsen media experts. For the experiment, four combinations of ads and
videos were created based on the engagement level and brand awareness criteria for the ads
(2x2: known vs. unknown brand with higher and lower engagement scores). The number of
combinations was limited to make the experiment feasible (see 3.2). The following four ads
were used: Dior Joy Perfume2 , Coca Cola3 , Little Baby’s Ice Cream4 , Waring Ice Cream Maker5 .
3.1. Psychometric and Physiological Measures
Ground truth for user engagement was measured using the User Engagement Scale-Short Form
(UES-SF) [4]. The UES-SF is a 12-item questionnaire covering four dimensions of engagement:
Focused Attention (FA), Aesthetic Appeal (AE), Perceived Usability (PU), and Reward (RW).
The dimensions are rated on a 5-point scale and a total score can be calculated as an average
across the selected dimensions. For the purposes of this study, AE and PU were selected as the
most relevant aspects of ad engagement. This is in line with the guidelines of UES-SF, where a
subset of the dimensions from UES-SF (relevant to a particular case) can be used to calculate
1
Clickworkerhttps://www.clickworker.com
2
https://www.youtube.com/watch?v=vfOnEaaPaF4
3
https://www.youtube.com/watch?v=vUMQeNw2QDA
4
https://www.youtube.com/watch?v=erh2ngRZxs0
5
https://youtu.be/GJ4P6ko_aLU
user engagement [4]. According to [4], PU is defined as ”negative affect experienced as a result
of the interaction and the degree of control and effort expended”, while AE is defined as ”the
attractiveness and visual appeal of the interface” (or, in our case, the ad ). Example items for
both dimensions, tailored to our case: ”PU.1: I felt frustrated while watching this Ad.” and
”AE.1 This Ad was attractive.” [4]. The combined user engagement score was then calculated by
averaging the scores of both dimensions.
Several physiological sensor signals were collected from the participants: eye-tracking data
with the Tobii Pro Glasses 2 eyetracker (pupil dilation, saccades, fixations) and heart rate and
electrodermal activity (EDA) with Empatica 3. In this article, we report pupil dilation and heart
rate.
3.2. Experimental Procedure
An experimental design was used in which all participants watched all four ads within their
assigned shuffle set. This was done to control for potential carryover effects from the preceding
combination to the next, as the engagement response elicited by the preceding sequence could
influence the participant’s response in the next sequence. Four sets were created, yielding four
combinations (Set1: Ad1, Ad2, Ad3, Ad4; Set2: Ad1, Ad3, Ad2, Ad4; Set3: Ad4, Ad2, Ad3, Ad1;
Set4: Ad4, Ad3, Ad2, Ad1). Note that the number of combinations was limited to four sets to
keep the duration of the experiment per user still workable, but shuffle ads so that no ad has
the same preceding ad more than once.
Next, the four sets were randomly and evenly assigned (taking into account age and gender) to
participants (N=44, age=18-24), with each participant being assigned only one set. Within each
set, the four combinations of video ads were interspersed with a 2-minute break to further isolate
possible carryover effects of the preceding combination to the next and to give participants a
break.
The experiment was conducted in a controlled environment (the Lucami laboratory at the
Faculty of Electrical Engineering) to ensure a uniformly lit and quiet environment. First,
informed consent and demographic data were collected from each participant. The participants
were familiarized with the goal of the experiment (to collect physiological data and engagement
scores for the ads) and given time to familiarize themselves with the procedure. They then sat
down on a sofa and watched each video sequence on TV. After viewing each ad, they were asked
to rate their engagement with the ad (on a laptop) using UES-SF. Participants also recorded other
aspects related to advertising exposure, including affective state (valence and arousal), brand
familiarity and recall, and purchase intention. The average duration of the experiment was
45 minutes. The experiment was completed by 44 participants. Due to incomplete responses
and errors in sensor measurement, a final sample of 33 participants (age M=21.70, SD =2.36;
female=68%) was used for further analysis.
3.3. Data Preprocessing
Within-subject normalization of pupil response data was performed to account for individual
differences in pupil dilation between the participants. Pupil dilation and heart rate data were
analyzed directly; no additional features were extracted. For each individual participant, the
average pupil dilation across all four ads was calculated and then subtracted from the mean
values of pupil size for each ad. In this way, only the relative differences in pupil dilation per ad
were recorded. Heart rate data were averaged, and median heart rates per ad per participant
were calculated. Outliers (outside the threshold SD =2.5) for both pupil dilation and heart rate
were imputed with the medians per participant and per ad. Next, both heart rate and pupil
dilation were normalized using MinMaxScaler (sklearn), heart rate data to a range [0, 1] and
pupil dilation to a range [-1,1].
Statistical analyses (ANOVA and pairwise t-tests) and classification using machine learning
were performed in Python with the Scipy, Pinguoin, open-cv, and sklearn libraries. Visualization
of the data was done using the matplotlib and seaborn libraries. The boxplots in Figures 1,
2, and 3 represent the minimum, first quartile, median, third quartile, and maximum of the
visualized data.
4. Results
4.1. User Engagement
The dimensions Aesthetic Appeal (AE) and Perceived Usability (PU) were selected for modeling
user engagement. For each participant and for each dimension, the average scores were first
calculated from the average scores of the respective items of the dimensions. Figure 1 shows
the distribution of AE and PU across the ads. We can observe a strong negative correlation
between the two dimensions (r(117)=-.67, p <.001).
AdID = 1 AdID = 2 AdID = 3 AdID = 4
5
Engagement Score
4
3
2
1
AA PU AA PU AA PU AA PU
Dimension Dimension Dimension Dimension
Figure 1: The average scores of Aesthetic Appeal (AE) and Perceived Usability (PU) for each ad. We can
observe a strong negative correlation between AE and PU across all four ads.
Next, both dimensions were summed and averaged to produce a combined engagement
score, which was later used in machine learning. Statistical analysis examined differences in
participant engagement between the four ads. Summary statistics are provided in Table 1. The
Kruskal-Wallis test revealed no significant differences in engagement scores between the four
ads (H=6.83, p=.078).
4.2. Pupil Dilation and Heart Rate
In our earlier work [15] we had reported preliminary summary statistics for each physiological
dimension. The Shapiro-Wilk test showed that the data for both dimensions were normally
distributed. A Pearson correlation coefficient was calculated to assess the relationship between
N Mean SD SE 95% Conf. Interval
Ad1 29 2.61 0.49 0.09 [2.43 – 2.80]
Ad2 30 2.89 0.39 0.07 [2.75 – 3.04]
Ad3 28 2.66 0.41 0.08 [2.50 – 2.82]
Ad4 30 2.85 0.43 0.08 [2.69 – 3.00]
Table 1
Summary statistics for the combined User Engagement Score. No significant differences were found
between the ads.
1.0 1.00
Average Video Brightness ( )
0.75
0.8
0.50
0.25
Pupil Dilation
0.6
Heart Rate
0.00
0.4 0.25
0.50
0.2
0.75
0.0 1.00
1 2 3 4 1 2 3 4
Ad ID Ad ID
(a) Heart Rate (b) Pupil Dilation
Figure 2: (a) The differences in heart rate among the ads. (b) The differences in pupil dilation between
the ads. The red line represents the average video brightness of each video ad.
pupil dilation and heart rate, and a weak negative correlation was found between the two sensor
signals (r(117)=-.17, p=.08).
One-way ANOVAs were performed to determine whether the median of pupil dilation and
heart rate (Figure 2) was the same between the four ads (Ad1-Ad4). For the heart rate (F=2.80,
p=.043; M=.4429, SD =.1736, SE =.02, 95%CI=[.41, .47]), multiple comparison t-tests (Bonferroni-
corrected) revealed no significant difference between the ads. For pupil dilation (F=76.44, p
<.001; M=-.037, SD =.28, SE =.026, 95%CI=[-.089, .014]), several significant differences were
found, as also visible in Figure 2(b). Multiple pairwise t- tests (with Bonferroni correction)
showed significant differences in the increase in pupil dilation between the following ads: Ad1
and Ad2 (T=-7.74, p <.001), Ad1 and Ad3 (T=-9.69, p <.001), Ad1 and Ad4 (T=-13.70, p <.001),
Ad2 and Ad4 (T=-6.32, p <.001), and Ad3 and Ad4 (T=-6.88, p <.001). No significant difference in
pupil dilation was observed between Ad2 and Ad3.
A moderate negative correlation was found between the engagement dimensions AE and PU
and pupil dilation (AE and pupil dilation: r(117)=-.39, 95%CI=[.23, .53], p <.001, power=.99; PU
and pupil dilation: r=-.36, 95%CI=[-.51, -.19], p <.001, Hedges’ g=.98). No significant correlations
were found between heart rate and the dimensions AE and PU.
4.3. The Effect of Ad Brightness on Pupil Dilation
Some studies have reported that pupil dilation can be affected by both light and contrast [16].
Also in our case, the differences in pupil dilation between the ads could be due to the differences
in brightness and contrast between the ads and not to the content of the ads.
To this end, we analyzed the brightness and contrast characteristics of the ads. The overall
average brightness (median) and contrast (standard deviation) of an ad were calculated from
the averages of the 1-second frames (first converted to grayscale) for each in-video ad. A
strong positive correlation was found between brightness and pupil dilation, r(117)=-.67, p <.001.
Although the results show a significant difference in overall pupil dilation, the brightness of the
ad may not be the most important predictor of pupil size. As Figure 2(b) shows, the average
brightness is different for all but Ad1 and Ad2. The connected red line shows the average
brightness value for each ad, which is given here along with the standard deviation (average
contrast). For example, while there is a significant difference in pupil dilation between Ad1
and Ad2, there is no difference between the brightness levels of the two ads (Ad1: M=.26, SD
=.37; Ad2: M=.26, SD =.26). On the other hand, while there is not much difference in pupil
dilation between Ad2 and Ad3, there is a significant difference in brightness (Ad2: M=.26, SD
=.26; Ad3: M=.48, SD =1.8). While the difference in brightness between Ad4 (M=.6, SD =1.9) and
Ad3 (M=.48, SD =1.8) is smaller than between Ad2 and Ad3, the pupil dilation is significantly
larger for Ad4 compared to all the other ads. From these results, it appears that there is no
independent effect of brightness on pupil dilation.
4.4. Heart Rate and Pupil Dilation as Predictors of User Engagement
Several classifiers were used without optimizing the parameters of the models: Logistic Re-
gression, Support Vector Classifier, Decision Tree, Random Forest, Gaussian Naive Bayes, and
AdaBoost. Features ’Age’, ’Gender, ’Heart Rate, and ’Pupil Dilation’ were used as predictors,
while ’Engagement Score’ was used as a binary target. The decision to model engagement as a
binary score was due to the bimodal distribution of engagement scores. The midpoint of the
5-point scale was set as the threshold for the binary engagement classes: lower engagement
class < 2.5 vs. higher engagement class > =2.5. Because the target distribution has a relatively
large class imbalance (only 22% of the target represents the ’lower engagement’ class) and the
data set is small, repeated stratified cross-validation (folds=10, repeats=5, fixed number of seeds)
was used to preserve the distribution of samples for each target class. Consequently, model
performance was evaluated primarily using F1 scores and ”weighted” F1 to account for class
imbalance, as these provide more reliable evaluation metrics when data are imbalanced. Table 2
shows the evaluation metrics for each model. Logistic Regression, Support Vector Classifier,
and Random Forest Classifier achieved the highest overall scores.
For the feature evaluation, the permutation importance metric was used because of the
imbalaned class distribution of the target variable. Another advantage of the permutation-based
estimation is the ability to assess whether individual feature is useful as a predictor for the test
set. Permutation importance was calculated based on the Random Forest Classifier. Figure 3
shows the permutation importance of the predictors for the training set (a) and the test set (b).
We can see how the importance of each predictor changes.
Table 2
Classifier Performance Evaluation. Classifiers: LogReg: Logistic Regression, SVC: Support Vector
Classifier, DT: Decision Tree, RFC: Random Forest, GNB: Gaussian Naive Bayes,Ada: AdaBoost
LogReg SVC DT RFC GNB Ada Best Score
Accuracy 0.82 0.82 0.68 0.8 0.81 0.73 Logistic Regression
Precision 0.82 0.82 0.83 0.84 0.82 0.83 Random Forest
Recall 1 1 0.78 0.95 0.99 0.86 Logistic Regression
F1 Score 0.9 0.9 0.8 0.89 0.89 0.84 Logistic Regression
F1_weighted 0.74 0.74 0.69 0.76 0.73 0.71 Random Forest
Pupil Dilation Pupil Dilation
Heart Rate Heart Rate
Age Age
Gender Gender
0.000 0.025 0.050 0.075 0.100 0.125 0.150 0.175 0.02 0.01 0.00 0.01 0.02 0.03 0.04 0.05
Decrease in accuracy score Decrease in accuracy score
(a) Permutation importance: Train set (b) Permutation importance: Test set
Figure 3: Feature estimation with permutation importance by Random Forest Classifier. (a) The
permutation importance of features on the training set. (b) The permutation importance of features on
the test set.
5. Discussion and Conclusion
The evaluation metrics for several classifiers provide good results and show potential for
predicting ad engagement based on pupil dilation and heart rate. However, the presented
models still need to be evaluated on a larger number of ads to reliably assess their robustness
and predictive power. An interesting finding of the presented study is the strong negative
correlation between the dimensions AE and PU, which is consistent throughout all the ads.
These differences are partially lost in the combined engagement score. In our future work, we
will evaluate both dimensions separately.
The results on the effects of brightness on pupil dilation are inconclusive, as no independent
effect of brightness on pupil dilation was found across the ads. Further research is needed
to evaluate the effects of brightness, again with a larger sample of ads and specifically in the
context of the effects of ad exposure.
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