11institutetext: RWTH University, Aachen, Germany
11email: {ali.norouzifar, majid.rafiei<EMAIL_ADDRESS>22institutetext: UWV Employee Insurance Agency, Amsterdam, Netherlands
22email<EMAIL_ADDRESS>
# Process Variant Analysis Across Continuous Features: A Novel
Framework††thanks: This research was supported by the research training group
“Dataninja” (Trustworthy AI for Seamless Problem Solving: Next Generation
Intelligence Joins Robust Data Analysis) funded by the German federal state of
North Rhine-Westphalia.

Ali Norouzifar 11 0000-0002-1929-9992 Majid Rafiei 11 0000-0001-7161-6927
Marcus Dees 22 0000-0002-6555-320X Wil van der Aalst 11 0000-0002-0955-6940

###### Abstract

Extracted event data from information systems often contain a variety of
process executions making the data complex and difficult to comprehend. Unlike
current research which only identifies the variability over time, we focus on
other dimensions that may play a role in the performance of the process. This
research addresses the challenge of effectively segmenting cases within
operational processes based on continuous features, such as duration of cases,
and evaluated risk score of cases, which are often overlooked in traditional
process analysis. We present a novel approach employing a sliding window
technique combined with the earth mover’s distance to detect changes in
control flow behavior over continuous dimensions. This approach enables case
segmentation, hierarchical merging of similar segments, and pairwise
comparison of them, providing a comprehensive perspective on process behavior.
We validate our methodology through a real-life case study in collaboration
with UWV, the Dutch employee insurance agency, demonstrating its practical
applicability. This research contributes to the field by aiding organizations
in improving process efficiency, pinpointing abnormal behaviors, and providing
valuable inputs for process comparison, and outcome prediction.

###### Keywords:

process mining process comparison business process improvement.

##  1 Introduction

Process mining techniques are used to analyze event data generated by
different types of information systems. For instance, performance analysis
using process mining techniques has provided a range of new opportunities for
business owners to analyze and improve their processes. From the performance
point of view, we often observe that the execution policies may vary
significantly for different groups of process instances based on their
characteristics.

For example, in the claim handling process shown in Fig. 1(a), a risk score
assigned to the cases affects the handling procedure. On top, the business
process explaining the whole process is shown without giving any clue that the
process could be different for cases in different ranges of the risk score.
After creating an application, cases with a risk score higher than 10 are
canceled. The remaining cases go through the check documents step and require
an in-person interview, then there is an exclusive choice that decides whether
the cases are required to submit more documents and have another interview, or
they can skip these two steps if the risk score is lower than 3.

(a) The BPMN on top shows a claim handling process in which each case has a
risk score and may have a different handling procedure based on the risk
score.

(b) The proposed framework for process variant analysis based on the risk
score.

Figure 1: A claim handling process as a motivating example.

Different dimensions for each case in the event log can be extracted using the
available data in information systems and process analysis tools. The
introduced approach in Fig. 1(b) considers risk score as an example of a
continuous dimension and has two main parts. First, the control flow change
detection is designed by using a sliding window shifting over the dimension
range. Then, local peaks are used for segmentation and pairwise comparison.

In this paper, we have the following goals: 1) Motivating the problem of
finding process variants over the range of a continuous dimension, e.g., risk
score or case duration. 2) Introducing a sliding window approach using the
earth mover’s distance to find the changes in control flow. 3) Case
segmentation based on continuous dimensions and control flow, and pairwise
comparison of the segments. 4) Testing the usability of the framework with a
real-life case study.

The output of the framework is an automated segmentation of cases based on key
dimensions influencing the control-flow behavior which can be used as input
for other types of analysis, such as process comparison [13], outcome
prediction [14], or labeling the traces as desirable or undesirable in order
to use them in process discovery algorithms working with desirable and
undesirable traces [11, 5].

##  2 Related Work

Extracted event logs from information systems often consist of a variety of
process executions containing deviating behaviors, manual interventions,
infrequent patterns, process drift over time, and many other inconsistencies
resulting in high complexity [6]. While filtering out infrequent behavior can
mitigate data complexity [4], it is important to note that infrequent behavior
may sometimes include significant deviations that need further investigation.
In [8], the earth mover’s distance is used to identify distinct cohorts of
traces based on trace attributes, offering a framework that explores all trace
attributes to uncover combinations leading to the most diverse control-flow
behavior. Unlike this paper, we do not need the assumption that the dimensions
are discretized in advance.

Trace clustering can also be considered as related work to our research.
Different trace clustering approaches may use a control-flow perspective,
other available attributes, or a combination of both [7]. In [1], different
similarity measures to cluster the traces based on the control flow are
introduced and compared. In [17], an event log is divided into sub-event logs
considering an attribute value, then hierarchical clustering is used to merge
similar clusters. However, choosing a distinctive attribute is not
straightforward. In addition, when considering continuous attributes it is
even more challenging to set distinctive thresholds. In [15], the active trace
clustering framework is introduced which takes process models explaining the
clusters into account while doing trace clustering. The trace clustering
methods cannot directly provide solutions for our problem, i.e., considering
the performance perspective.

Considering time as an important dimension in event data analysis, the
identification of concept drift is an interesting research question [12].
These methods usually use a feature space to characterize the control flow and
use some techniques to pinpoint the changes. If a change is not observable
using the selected feature space, these algorithms fail to detect it. In [16],
a concept drift detection algorithm is introduced which leverages declarative
constraints to represent control-flow behavior. A multi-variate change point
detection algorithm is implemented to find the changes over time. In [10],
another method is proposed which is based on the statistical tests applied to
the distribution of partially ordered runs in two consecutive time windows. In
[2], the earth mover’s distance is used to find drifts in the control flow.
The mentioned approaches only focus on changes in control flow behavior over
time and not on different continuous dimensions. Similar to the framework
proposed in [2], we employ a window-based approach using the earth mover’s
distance function to identify the changes across continuous features. Our
framework works effectively without a large feature space, which might
struggle to capture all potential changes in control flow. Additionally, it
does not use statistical tests that often rely on some assumptions.

##  3 Preliminaries

$\mathcal{B}(A)$ is the set of all multisets over some set $A$. Considering
$B\in\mathcal{B}(A)$, $B(a)$ denotes the frequency of element $a\in A$. We
write $x\in B$ if $B(x)>0$. The event log is an important concept in our work,
therefore we formally introduce it.

######  Definition 1 (Event Log)

Let $\mathcal{C}$, $\mathcal{A}$, and $\mathcal{T}$ be the universe of case
identifiers, the universe of activities, and the universe of timestamps
respectively. $e{=}(c,a,t)$ is a tuple representing an event where
$\pi_{\mathcal{C}}(e){=}c\in\mathcal{C}$,
$\pi_{\mathcal{A}}(e){=}a\in\mathcal{A}$, and
$\pi_{\mathcal{T}}(e){=}t\in\mathcal{T}$.
$\mathcal{E}{=}\mathcal{C}\times\mathcal{A}\times\mathcal{T}$ is the universe
of events. A trace is a finite sequence of events $\sigma{=}\langle
e_{1},e_{2},...,e_{n}\rangle\in\mathcal{E}^{*}$ with size $n\in\mathbb{N}$
such that for each $1\leq i<n$,
$\pi_{\mathcal{C}}(e_{i}){=}\pi_{\mathcal{C}}(e_{i+1})\wedge\pi_{\mathcal{T}}(e_{i})\leq\pi_{\mathcal{T}}(e_{i+1})$.
An event log $L$ is a set of traces such that each trace belongs to a
different case. $\mathcal{L}$ is the universe of event logs. $|L|$ is the
number of traces in event log $L$. In addition, we define
$cf:\mathcal{L}\rightarrow\mathcal{B}(\mathcal{A}^{*})$ as a function that
extracts the control flow of $L$, i.e., the multiset of traces projected on
activities such that
$cf(L){=}[\langle\pi_{\mathcal{A}}(e_{1}),...,\pi_{\mathcal{A}}(e_{n})\rangle|\langle
e_{1},e_{2},...,e_{n}\rangle\in L]$.

In addition to the control flow and time dimension, other dimensions could be
assigned to cases from other sources of information. We use
$\kappa_{L}:L\rightarrow\mathbb{R}$ to show a case-level indicator. Case-level
indicators assign a numerical value $\kappa_{L}(\sigma)$ to each trace
$\sigma{\in}L{\in}\mathcal{L}$. If the context is clear, we drop $L$ from the
notation $\kappa_{L}(\sigma)$.

###### Definition 2

(Ordering function) Let $L\in\mathcal{L}$ be an event log and $\kappa$ be a
case-level indicator that assigns a value to each case in $L$. We define
$rank_{\kappa}(L)=\langle\sigma_{1},\sigma_{2},...,\sigma_{|L|}\rangle$ such
that $L{=}\\{\sigma_{1},\sigma_{2},...,\sigma_{|L|}\\}$ and for
$1{\leq}i{<}j{\leq}|L|:\kappa(\sigma_{i}){\leq}\kappa(\sigma_{j})$.

For example,
$L=\\{\langle(c_{1},a,13),(c_{1},b,23)\rangle,\langle(c_{2},a,14),(c_{2},b,16),(c_{2},c,20)\rangle,$
$\langle(c_{3},a,17),(c_{3},b,20),(c_{3},c,35)\rangle\\}$ is an event log with
$cf(L){=}[\langle a,b,c\rangle^{2},\langle a,b\rangle^{1}]$. Consider $\kappa$
as a function that calculates the duration of cases, therefore,
$\kappa=\\{(\sigma_{c_{1}},10),$ $(\sigma_{c_{2}},6),(\sigma_{c_{3}},18)\\}$
and
$rank_{\kappa}(L)=\langle\sigma_{c_{2}},\sigma_{c_{1}},\sigma_{c_{3}}\rangle$.

###### Definition 3

(Stochastic Language) Given the universe on activities $\mathcal{A}$,
$f:\mathcal{A}^{*}{\rightarrow}[0,1]$ is a stochastic language iff
$\sum_{s\in\mathcal{A}^{*}}f(s){=}1$. $\mathcal{F}$ is the universe of
stochastic languages.

###### Definition 4

(Stochastic Language of an Event Log) Let $L\in\mathcal{L}$ be an event log.
$stoch:\mathcal{L}\rightarrow\mathcal{F}$ is a function that extracts the
stochastic language of an event log such that $stoch(L)=\\{(s,p)|s\in
cf(L)\wedge p=\frac{cf(L)(s)}{|L|}\\}$.

The earth mover’s distance calculates the distance between two stochastic
languages. We use this distance to compare the control flow in two event logs.

###### Definition 5

(The earth mover’s distance) Let $L\in\mathcal{L}$, and
$L^{\prime}\in\mathcal{L}$ be two event logs,
$\delta:\mathcal{A}^{*}\times\mathcal{A}^{*}\rightarrow[0,1]$ be a trace
distance function, and $r:L\times L^{\prime}\rightarrow[0,1]$ be a function
that indicates the movement of frequency between two event logs. $\mathcal{R}$
is the universe of all reallocation functions. The earth mover’s distance
between $L$ and $L^{{}^{\prime}}$ is defined by
$EMD(L,L^{\prime})=\min\limits_{r\in\mathcal{R}}\sum_{s\in cf(L)}\sum_{t\in
cf(L^{\prime})}r(s,t).\delta(s,t)$ such that $\forall s\in cf(L):\sum_{t\in
cf(L^{\prime})}r(s,t)={stoch(L)}(s)$, $\forall t\in cf(L^{\prime}):\sum_{s\in
cf(L)}r(s,t)={stoch(L^{\prime})}(t)$, and $\forall s\in cf(L),\forall t\in
cf(L^{\prime}):r(s,t)\geq 0$.

We use the Levenshtein distance $\delta$ to calculate the distance between the
traces. The calculation of the efficient reallocation amounts, i.e., $r$ in
Def. 5 is solved as a linear programming problem. For more details, we refer
to [9].

##  4 Process Variant Identification Framework

As illustrated in Fig. 1(b), the framework proposed in this paper consists of
two main parts, a control flow change detection over the range of a case-level
indicator and a segmentation and comparison framework. Unlike concept drift
frameworks which focus on time dimension, we identify changes in control flow
across continuous dimensions. Our framework does not generate a large feature
set that might overlook certain types of changes. Instead, it leverages the
earth mover’s distance to effectively capture the variability in control flow.

###  4.1 Control Flow Change Detection

Our proposed method consists of a bucketing step and then moving a sliding
window. In Fig. 2(a), three different event logs, i.e., $L_{1}$, $L_{2}$, and
$L_{3}$ are illustrated. Each of the event logs has 150 cases and the cases
are ordered based on a case-level indicator. A bucketing strategy is used to
divide the event logs into 15 buckets each containing 10 traces. The buckets
are shown as patterned boxes such that different patterns show that the traces
are different. In this example, two different window sizes $w_{1}{=}1$ and
$w_{2}{=}3$ are used. Considering event log $L_{1}$ and window size $w_{1}$,
we start from $i=1$ and each time we move the central point one unit to the
right up to $i=14$ and compare $w_{1}$ bucket on the left-hand side of the
central point to $w_{1}$ bucket on the right-hand side of it using the earth
mover’s distance. Comparative results for some combinations of the event logs
and time windows are illustrated in Fig. 2(b) using a color range proportional
to the difference level. Next, we formally define buckets and sliding windows.

(a) Event logs $L_{1}$, $L_{2}$, and $L_{3}$ with different types of changes
in the control flow.

(b) Visualised $ldist$ value with moving the sliding window.

Figure 2: Some examples illustrating how the framework works considering three
different event logs, $b=15$, and two time windows $w_{1}=1$ and $w_{2}=3$.

###### Definition 6

(Buckets) Let $L{\in}\mathcal{L}$ be an event log, $\kappa$ be a case-level
indicator, $\sigma_{i}=rank_{\kappa}(L)(i)$, and $b\in\mathbb{N}^{[2,|L|]}$ be
the number of buckets given by the user. For the sake of simplicity, we assume
that $|L|$ is divisible by $b$. Then, $l{=}\frac{|L|}{b}$ is the number of
cases in each bucket such that
$B_{i}=\langle\sigma_{(i-1).l+1},...,\sigma_{i.l}\rangle$ for $1\leq i\leq b$.

###### Definition 7

(Left and Right Windows) Let $L{\in}\mathcal{L}$ be an event log, $\kappa$ be
a case-level indicator, $\sigma_{i}=rank_{\kappa}(L)(i)$,
$b\in\mathbb{N}^{[2,|L|]}$ be the number of buckets given by the user, and
$w\in\mathbb{N}^{[1,\frac{b}{2}]}$ be a window size parameter. Considering a
window size $w$ and $i{\in}\\{w,...,b-w\\}$, we can create a left window
$W^{l}_{w,i}{=}\\{\sigma{\in}B_{j}|i-w<j\leq i\\}$ and a right window
$W^{r}_{w,i}{=}\\{\sigma{\in}B_{j}|i<j\leq i+w\\}$.

To perform a comparative analysis, we move the sliding window over the whole
range of $\kappa$. We need at least $w$ buckets on the left window. Therefore,
we start from $i{=}w$ and each time move the sliding window for one bucket. We
repetitively continue until we reach a point where the number of buckets on
the right window is equal to the window size, i.e., $i{=}b{-}w$.

###### Definition 8

(Local Distance Function) Let $L\in\mathcal{L}$ be an event log, $\kappa$ be a
case-level indicator, $b\in\mathbb{N}^{[2,|L|]}$ be the number of buckets
given by the user, and $w\in\mathbb{N}^{[1,\frac{b}{2}]}$ be a window size
parameter. We define the local distance function
$ldist_{L,\kappa,w,b}:\\{w,...,b-w\\}\rightarrow[0,1]$ such that
$ldist_{L,\kappa,w,b}(i){=}EMD(W^{l}_{w,i},W^{r}_{w,i})$. If the context is
clear, we show $ldist_{L,\kappa,w,b}$ as $ldist$.

In Fig. 2(b), some analysis is performed based on different event logs and
different window sizes. Using the event log $L_{1}$ and the window size
$w_{2}$, from $i{=}5$ the change in behavior is detectable by calculating the
earth mover’s distance between the left and right windows. The earth mover’s
distance value is visualized with colors, i.e., darker color shows a higher
distance. After shifting the sliding window, the maximum difference between
the left and right windows is observed at $i{=}7$ and $i{=}10$. A similar
pattern in changing $ldist$ value is observed using $L_{2}$ and $w_{2}$.
However, the introduced framework does not give us any clue whether the
control flow behavior before $i{=}7$ and after $i{=}10$ are similar or
different. Using $L_{3}$ and the smaller window $w_{1}$, it is observed that
the change in behavior is only observed sharply at $i{=}7$ and $i{=}10$. Using
the larger window $w_{2}$, the change in behavior started to affect $ldist$
from $i{=}5$ but with shifting the sliding window, it cannot exactly identify
the point in which the behavior is changed. Therefore, the larger window size
is more robust against noise but may miss some important change points.

The running example introduced in Fig. 1(a) is simulated using the CPN tools
and the generated event log is used to explain how the framework works. This
event log consists of 10,000 cases and 31 trace
variants111https://github.com/aliNorouzifar/process-variants-
identification/blob/main/event%20logs/test.xes. The cases are ordered based on
their risk score value. In Fig. 3(a), the results using $b=100$, i.e., 1 % of
the cases in each bucket and different window sizes $w\in\\{2,5,10,15\\}$ are
shown. The numbers in the parenthesis show the raw risk score values. The
experiment is repeated for different window sizes. The colors show how high
the earth mover’s distance is between the left and right windows.

(a) Results of applying the introduced framework using the simulated event log
with $b{=}100$ and $w\in\\{2,5,10,15\\}$.

(b) Pairwise comparison between the segments.

Figure 3: Process variant identification framework for the claim handling
process.

###  4.2 Segmentation and Pairwise Comparison

Based on the visualizations shown in Fig. 2, the control flow behavior may
change several times through the range of the indicator but with each change,
we only know that $w$ buckets on the left are different from the $w$ buckets
on the right. Therefore, we extend the proposed framework to make it more
applicable to obtain global insights. The idea is to use the peaks in the
$ldist$ values based on specific $b$ and $w$ parameters to perform a
segmentation. Each time we observe a peak in $ldist$, we generate a segment
consisting of the cases from the previous change point to the current change
point. Then, it is possible to compare non-adjacent segments with the earth
mover’s distance measure.

###### Definition 9

(Change Point in Control Flow) Let $L\in\mathcal{L}$ be an event log, $\kappa$
be a case-level indicator, $b\in\mathbb{N}^{[2,|L|]}$ be the number of buckets
given by the user, and $w\in\mathbb{N}^{[1,\frac{b}{2}]}$ be a window size
parameter. $\theta\in[0,1]$ is a user-defined threshold to check whether a
high value of $ldist$ is significant. $p\in[w,b-w]$ is a change point in the
control flow behavior if $ldist(p)\geq\theta$, $ldist(p-1)\leq ldist(p)$ if
$p\in(w,b-w]$, and $ldist(p+1)\leq ldist(p)$ if $p\in[w,b-w)$.

###### Definition 10

(Segments) Let $P=\langle p_{1},...,p_{|P|}\rangle$ be the ordered sequence of
change points in $ldist$ function such that $\forall
i,j\in\\{1,...,|P|\\}:p_{i}\leq p_{j}$ iff $i<j$. Considering $|P|$ as the
number of peaks, we can generate $|P|+1$ segments which we refer to as
$seg_{1}$ to $seg_{|P|+1}$ such that $seg_{1}=\\{\sigma\in B_{x}|x\leq
p_{1}\\}$, $seg_{i}=\\{\sigma\in B_{x}|p_{i-1}<x\leq p_{i}\\}$ for
$i\in[2,|P|]$, and $seg_{|P|+1}=\\{\sigma\in B_{x}|p_{|P|}<x\\}$.

Considering $w=10$ in Fig. 3(a) and $\theta=0.1$, $ldist$ has two peaks in
$i=26$ ($risk\;score{=}3$) and $i=63$ ($risk\;score=10$). We can use heatmaps
to compare the resulting segments pairwise to check if non-adjacent segments
have similar control flows. Using the identified peaks, the whole event log is
divided into three segments, and the segments are compared in Fig. 3(b).

Next, we can hierarchically merge similar segments in case the control flow is
not significantly different with regard to the user-defined difference
threshold $\theta$. We recursively merge two segments with the minimum $ldist$
if this minimum distance is lower than $\theta$, i.e., a user-defined
threshold to check whether the distance is significant.

##  5 Case Study

The introduced framework is implemented and is publicly
available222https://github.com/aliNorouzifar/process-variants-identification.
To assess the effectiveness of our framework, we conducted a comprehensive
case study in collaboration with UWV, the employee insurance agency of the
Netherlands responsible for executing social security in case of unemployment
and disability. UWV provided real-life event data for this case study. We
worked closely with experts from the agency, who provided invaluable guidance
and insights. Their expertise enabled us to align our research with practical,
real-world scenarios, ensuring that our algorithms are usable in real business
contexts.

###  5.1 UWV event log

UWV has over 18000 employees and several branches all over the Netherlands.
One of the main processes of this governmental sector is investigated in this
paper. The duration of the cases in this process has been of particular
interest. UWV wants to know how the process is changed concerning the duration
of the cases and whether we can find the change points in the duration. The
duration of cases varies in the range of 1 day to 575 days. The event log has
144,096 cases, 1,026 variants, 29 activities, and 1,316,128 events. Among all
the cases, 5,449 cases (3.8% of the cases) are rejected and 138,647 cases
(96.2% of the cases) are accepted.

The normative BPMN model shown in Fig. 4 represents a claim-handling process
at UWV. First, a claim from a customer is received. Then, either the claim is
accepted or blocked. A blocked claim indicates either some information needs
to be checked or corrected after which the claim is accepted, or the claim is
blocked and then immediately rejected. After a rejected claim, an objection
can be received by customers if they do not agree with the decision. The
handling of this objection is out of the scope of this process model. After an
accepted claim, which has received one or at most three payments, an objection
can be received. This is due to customers who in hindsight find that they do
not need the payments. A claim withdrawal process is started that results in
repayment of the total sum of received benefits. In case the customer is still
entitled to one or more payments, the _Block Claim_ activity is executed. This
prevents any new payments from being automatically made. This normative model
has an alignment fitness of 99.3% with respect to the event log [3].

Figure 4: The normative BPMN model representing the investigated UWV claim
handling process.

###  5.2 Process variant analysis using UWV event log

The first experiment is performed using the complete event log and the results
are shown in Fig. 5. In this experiment, $b{=}100$ and the window sizes 2, 5,
10, and 15 are considered. The comparative results are illustrated in Fig.
5(a) which shows that the cases with a very short duration or very long
duration are different from other cases. Considering $\theta{=}0.1$, $w{=}5$,
and $i$ referring to the central point of the sliding window, two peaks are
observed at $i{=}3$ (17 days) and $i{=}95$ (78 days) which can be used to
generate three segments, i.e., [0,17] days, [18,77] days and [78,575] days. In
Fig. 5(b), the three generated segments are compared to each other using the
earth mover’s distance.

(a) Control flow change detection with $b{=}100$ and $w{\in}\\{2,5,10,15\\}$.

(b) Segmentation and pairwise comparison.

Figure 5: Applying the introduced framework to the complete UWV event log.

The number of cases in each segment and their alignment fitness with respect
to the normative model is reported in Table. 1. All the segments have a high
fitness value implying that the observed behavior in the segments fits the
normative model. According to the replay results, Fig. 6 shows the normative
BPMN model where the parts of the model that cover each of the segments are
highlighted. The colors are proportional to the frequencies and the transition
labels are colored as gray if the frequency is lower than 5%. For segment 1 in
Fig. 6(a), the activities _Receive Claim_ , _Start Claim_ , _Block Claim_ ,
_Reject Claim_ and _Receive Objection_ are highlighted. _Segment 1_ represents
the claims that are rejected and optionally UWV receives an objection to the
decision. _Segment 2_ shows claims that are accepted, i.e., the customer
receives three payments and then the process finishes. Finally, _segment 3_
represents again accepted cases. However, after the payments an objection is
received, and optionally a block is executed. For these claims, the customer
withdraws the claim. The customer then pays the received sum back to UWV.
Blocking is done to prevent new payments from being made while the claim
withdrawal process has not finished yet.

Table 1: Using alignment fitness as a conformance checking metric to check whether the event logs fit the process model or not. Event log | Number of traces | Alignment fitness
---|---|---
UWV complete log | segment 1: [0,17] days | 144096 | 4323 | 99.3% | 99.6%
segment 2: [18,77] days | 132572 | 99.7%
segment 3: [78,575] days | 7201 | 93.4%
UWV rejected claims | segment 1: [0,15] days | 5449 | 3888 | 98.4% | 99.7%
segment 2: [16,83] days | 1350 | 97.3%
segment 3: [84,550] days | 211 | 81.7%

(a) Segment 1 in the complete event log with duration in the range [0,17]
days.

(b) Segment 2 in the complete event log with duration in the range [18,77]
days.

(c) Segment 3 in the complete event log with duration in the range [78,575]
days.

Figure 6: The normative BPMN model is highlighted based on the frequency of
the transitions in replaying the segments from the complete event log
experiment.

###  5.3 A deeper analysis considering rejected cases

The extracted segments are highly correlated with the process outcome, i.e.,
the rejection or acceptance of a claim. However, the correlation between the
extracted segments with specific duration is less clear. Another experiment
using the 5,449 rejected cases is performed to get more understanding of the
relation between duration ranges and specific process variants333The analysis
of accepted cases is explained in the supplementary material
https://github.com/aliNorouzifar/process-variants-
identification/blob/main/supplementary%20material/supplementary%20material.pdf..
The alignment fitness of the rejected cases is 98.04% with respect to the
normative model.

An overview of the results for the rejected cases is shown in Fig. 7. Based on
Fig. 7(a) considering $w=2$ and $\theta{=}0.1$, four segments are found, with
duration periods of [0,13] days, [14,15] days, [16,83] days, and [84,550] days
respectively. Segments 1 and 2 are merged into one segment, with a duration
period of [0,15] days, since the distance between them is lower than
$\theta{=}0.1$. This can be observed in the heatmap in Fig. 7(b). Segments 1
and 2 have very similar colors when compared to each other in comparison to
the other segments.

(a) Control flow change detection with $b{=}100$ and $w{\in}\\{2,5,10,15\\}$.

(b) Segmentation and pairwise comparison.

Figure 7: Applying our framework to the rejected cases in the UWV event log.

The number of cases in each segment and alignment fitness values of the
identified segments are shown in Table 1. These results show that segment 3
does not fit the normative model well with an alignment fitness value of
81.7%. Fig. 8 contains the highlighted normative model for each of the three
segments. Fig. 8(a) shows the first segment with a duration period of [0,15]
days and contains cases that are rejected and no objection is received. This
segment represents customers who file a claim even though they know that they
most likely will not be entitled. In the second segment, with a duration
period [16,83] days, in Fig. 8(b) cases are described that are rejected and
some also have an objection. Finally, the third segment, with a duration
period of [84,550] days, in Fig. 8(c) consists of claims that are first
accepted and end as rejection with a full repayment, or claims that are first
rejected and are accepted after an objection is received and granted.

(a) Segment 1 in the rejected cases event log with duration in the range
[0,15] days.

(b) Segment 2 in the rejected cases event log with duration in the range
[16,83] days.

(c) Segment 3 in the rejected cases event log with duration in the range
[84,550] days.

Figure 8: The normative BPMN model highlighted based on the frequency of the
transitions in replaying the segments from the rejected event log experiment.

##  6 Conclusion

In this study, we delved into the concept of segmenting a continuous dimension
concerning changes in control flow. While our framework primarily focuses on
identifying segments with differing control flows, it may not directly imply
the desirability or undesirability of individual cases. For instance, with a
dimension like duration, cases within segments featuring either very short or
very long duration could be associated with undesirability or efficiency. This
approach holds promise for extension, particularly in identifying and labeling
cases as undesirable, taking the broader process context into account. The
interconnections between different dimensions may also play a role in the
changes in behavior which is highly relevant for future investigations.
Considering them may lead to some interesting analyses. For instance, the
duration of cases could correlate with the workload of the process,
potentially generating delayed yet normal cases during high workload periods.
Considering only one dimension may not show whether cases are normal or
problematic. The output of our framework can be used as input for various
process mining tasks like process comparison, outcome prediction, and process
discovery using desirable and undesirable cases.

## References

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footnote

11institutetext: Maastricht University, Maastricht, Netherlands
22institutetext: Delft University of Technology, Delft, Netherlands
22email<EMAIL_ADDRESS>
<EMAIL_ADDRESS>
<EMAIL_ADDRESS>
# Explaining Search Result Stances to Opinionated People

Zhangyi Wu 11 0009-0001-2540-9967 Tim Draws 22 0000-0001-5053-4674 Federico
Cau 11 0000-0002-8261-3200 Francesco Barile 11 0000-0003-4083-8222 Alisa
Rieger 22 0000-0002-2274-1606 Nava Tintarev 11 0000-0003-1663-1627

###### Abstract

People use web search engines to find information before forming opinions,
which can lead to practical decisions with different levels of impact. The
cognitive effort of search can leave opinionated users vulnerable to cognitive
biases, e.g., the confirmation bias. In this paper, we investigate whether
stance labels and their explanations can help users consume more diverse
search results. We automatically classify and label search results on three
topics (i.e., intellectual property rights, school uniforms, and atheism) as
against, neutral, and in favor, and generate explanations for these labels. In
a user study ($N$=203), we then investigate whether search result stance bias
(balanced vs biased) and the level of explanation (plain text, label only,
label and explanation) influence the diversity of search results clicked. We
find that stance labels and explanations lead to a more diverse search result
consumption. However, we do not find evidence for systematic opinion change
among users in this context. We believe these results can help designers of
search engines to make more informed design decisions.

###### Keywords:

Explainable Search Confirmation Bias User Study.

##  1 Introduction

Web search that can lead to consequential decision-making frequently concerns
debated topics, topics that different people and groups disagree on, such as
whether to vaccinate a child or whether nuclear energy should be used as a
power source. Prior research has shown that the interplay between search
engine biases and users’ cognitive biases can lead to noteworthy behavioral
patterns. For instance, when search result stance biases interact with
cognitive user biases, information seekers may experience the search engine
manipulation effect (SEME): the tendency to adopt the stance expressed by the
majority of (highly-ranked) search results [3, 9, 16, 35]. However, these
results have only been studied and found for users who are undecided, not for
users who already have strong opinions, who we refer to as opinionated users.

High cognitive demand during complex web searches can increase the risk of
cognitive biases [6]. One such bias is the confirmation bias, which involves a
preference for information that aligns with preexisting beliefs while
disregarding contradictory information during the search process [6, 33].
Interventions to mitigate confirmation bias during web search have aimed at
decreasing engagement with attitude-confirming and increasing engagement with
attitude-opposing information [40], i.e., reducing interaction with search
results that confirm a user’s attitude and increasing interaction with search
results that challenge a user’s attitude. Interventions to reduce interaction
with particular items have also been investigated in the context of
misinformation. One effective method for reducing interaction with misleading
content involves labels to flag certain items [10, 25, 31].

The core issue with confirmation bias during web search, similar to the
related issues of misinformation and SEME, is that users consume biased
content. This motivated us to investigate interventions to increase the
diversity of consumption and specifically whether labels indicating stance,
and their explanations, are likewise successful for confirmation bias
mitigation during search on debated topics. Therefore the goal of these
interventions is to promote unbiased web search and mitigate the effects of
users’ confirmation bias and underlying (stance) biases in a search engine
result page (SERP). Consequently, this paper aims to address the following
question: Do automatically generated stance labels and explanations of the
labels for search results increase the diversity of viewpoints users engage
with, even if search results are biased?

To address this question, we test three hypotheses. Previous work has found
that undecided users are likely to change their opinion when exposed to biased
search results, since they select more search results reflecting a certain
opinion [3, 15, 17, 35]. However, in this study we restrict participants to
opinionated users, having strong existing opinions on a topic, and investigate
whether H1a): Users who are exposed to viewpoint-biased search results
interact with less diverse results than users who are exposed to balanced
search results.

Second, informative labels have been shown to mitigate confirmation bias in
search results [40]. Therefore, in this study, we investigate whether simple
stance labels (against, neutral, and in favor), and stance labels with
explanations (importance of keywords) are effective for mitigating
confirmation bias. This leads to H1b): Users who are exposed to search results
with (1) stance labels or (2) stance labels with explanations for each search
result interact with more diverse content than users who are exposed to
regular search results.

Third, if the labels are effective in reducing confirmation bias, we would
expect an interaction effect between the bias in search results and
explanation level (plain, label, label plus explanation):  H1c) Users who are
exposed to search results with (1) stance labels or (2) stance labels with
explanations are less susceptible to the effect of viewpoint biases in search
results on clicking diversity.

We investigate these hypotheses in a pre-registered between-subjects user
study ($N$=203) simulating an open search task.111The pre-registration is
openly available at https://osf.io/3nxak. Our results show that both stance
labels and explanations, led to a more diverse search result consumption
compared to plain (unlabeled) search result pages. However, we did not find
evidence that the explanations influenced opinion change. We believe these
results can help designers of search engines to make more informed design
decisions.

##  2 Related Work

Explainable Artificial Intelligence (XAI) aims to help people understand the
decisions and predictions AI systems make. In this paper, we investigate
specifically how XAI can support users in searching for disputed topics.
Search for debated topics is highly subjective: when users search the web to
seek advice or form opinions on these kinds of topics, not just search result
relevance but also the stance of content is influential [3, 15, 16, 35, 40].
To mitigate undesired effects such as biased opinion change, earlier work has
measured and increased the fairness [20, 52, 55] and viewpoint diversity in
search results [13, 36, 49].

On the user interface side, it could be fruitful to label and explain the
stance represented on a search engine results page (or SERP). These labels are
related to the task known as stance detection, which is predominantly applied
in a target-specific fashion. That is, detecting not just a sentiment, but how
it is referred to in relation to a specific topic or claim (often referred to
as the target, e.g., “people should wear school uniforms”) [2]. Stance
detection is a multi-class classification task (i.e., typically classifying
documents into against, neutral, and in favor, so predictive performances are
most commonly reported in terms of macro F1 scores [26]. Furthermore, web
search interventions targeting the mitigation of undesired effects, such as
SEME, require cross-target stance detection models to quickly respond to the
large variety of debated topics users may search for. Here, stance detection
models are applied to data sets where each document may be relevant to one of
many potential topics [2, 26]. Constructing models that classify documents
into stances related to any topic in such a way may lead to weaker predictive
accuracy compared to target-specific methods, but makes stance detection more
generalizable and scalable. Cross-target ternary stance detection by previous
work (e.g., on news articles or tweets) have ranged roughly from macro F1
scores of $.450$ to $.750$ [1, 4, 5, 23, 38, 51].

Also comparable are the cross-topic stance detection models evaluated using
the Emergent data set (and its follow-up version, the 2017 Fake News Challenge
data set) which have achieved macro F1 scores of up to $.756$ [22, 41, 43].
While the main contribution of this paper is not to improve on the state of
the art for stance detection, the stance detection method (DistilBERT) used
here is comparable to this state of the art (macro F1 of 0.72). DistilBERT is
much smaller than other pre-trained models, and handles small datasets well
[44, 46, 48].

What XAI methods are suitable for explaining stance detection to users? Stance
detection can be seen as a text classification task. For text classification,
explanations containing input features have been found to be highly adaptable
and often meaningful to humans [12, 30].

The way in which explanations can be visualized depends on the data type,
purpose, and audience. Many current methods indicate input features as feature
importance using a saliency map [39, 47]. When the features are readable
texts, saliency information is shown by highlighting the most significant
input at word or token level [24]. There have been some instances where
researchers used text-based saliency maps to demonstrate their findings [21].
To the best of our knowledge, no previous work has explored whether
highlighting salient words in search results would mitigate people’s clicking
bias. One of the most closely related works found that feature-based
explanations could help users simulate model predictions for search results
[12]. Another similar study involves a verbal saliency map using a model-
agnostic explainer and a human evaluation of explanation representations of
news topic classifier and sentiment analysis [19]. Their finding is that the
saliency map makes explanations more understandable and less cognitively
challenging for humans than heatmap visualization. However, our work differs
from theirs in several ways: we study explanations in the context of search
engines, and we have conducted a full-fledged user study while they only
performed a pilot study.

Contribution to Knowledge for XAI. Previous XAI literature has contributed to
explaining information retrieval systems, focusing on the interpretability of
document-retrieval mechanisms [27, 28, 54]. For example, the authors of [54]
propose a listwise explanation generator, which provides an explanation that
covers all the documents contained in the page (e.g., by describing which
query aspects were covered by each document). These explanations were not
evaluated by people. Another paper studied how well explanations of individual
search results helped people anticipate model predictions [12], but did not
consider cognitive or stance bias. In contrast to previous work, this paper
examines how explanations affect users, considering the potential mitigation
of their cognitive biases on search engine manipulation effects (SEME). In
doing so, we see a need to address both potential (stance) bias within a
search result page, and the bias of users consuming these results. To the best
of our knowledge, this is also the first work to conduct empirical experiments
on users’ behavior in response to explanations in the context of information
retrieval.

##  3 Methodology

This section describes the materials we used for organizing the user study
(e.g., data set, stance detection model, explanation generation, and search
interface).

###  3.1 Data Preparation

|  | Stance Distribution
---|---|---
Topic | N | Against – Neutral – In Favor
Intellectual property rights | 378 | 10.5% – 17.7% – 71.7%
School uniforms | 395 | 21.5% – 36.7% – 41.8%
Atheism | 352 | 19.8% – 46.3% – 33.8%
Total | 1125 | 17.3% – 33.3% – 49.3%
Table 1: Topic and stance distribution in the used data.

To train, test, and explain the stance detection model, we considered a public
data set containing search results related to three debated topics (i.e.,
atheism, intellectual property rights, and school uniforms) [14].222The data
set is available at https://osf.io/yghr2. [14] motivate the selection of these
three topics because they offer valid arguments for both supporting and
opposing viewpoints. Additionally, they argue that opinions on these topics
have diverse impacts, ranging from concerning mainly the user (atheism) to
businesses (intellectual property rights) and society (school uniforms). These
data include URLs, titles, snippets, and stance labels for a total of 1475
search results, which had been retrieved via API or web crawling from two
popular web search engines.

Stance labels had been assigned (by experts) on seven-point Likert scales
(i.e., including three degrees of opposing or supporting a topic), which we
mapped into the three categories against, neutral, and in favor (i.e., which
is what most current stance detection methods handle). Using the provided
URLs, we crawled the full web page text bodies (stripped of any HTML tags) for
all search results. We here dropped 347 search results from the data as their
text bodies could not be retrieved (e.g., because of 404 errors), leaving 1125
search results accompanied by their respective text bodies. Finally, we
processed the retrieved contents by truncating each document’s middle section,
retaining only its head and tail, then concatenating each search result’s
title, snippet, and the head section and tail section, while ensuring that the
result is exactly 510 tokens long. We removed all other information from the
data aside from the documents’ stance labels. Table 1 shows the stance
distribution per topic in our final data set.

###  3.2 Stance Detection Model

Figure 1: Distributions of Attributes Across the Training, Validation and Test
Set

After pre-processing (tokenization), we developed the model for classifying
search results into against, neutral, and in favor. The dataset was split into
training (75%) and validation/test (25%) sets. We fine-tuned the model for
different hyperparameters. After every epoch the validation set were evaluated
to monitor its learning progress. Once the model’s evaluation loss stops to
decrease for 5 epochs the training is terminated and is evaluated based on the
unseen test set, and the result is considered the general performance that we
report. The best-performing model and learned parameters were used as the
predictor for identifying labels and generating explanations.

####  3.2.1 Tokenization.

Before training the stance classification model, we needed further
preprocessing of the raw search results. Specifically, we had to tokenize each
word before feeding the search results into the model. The tokenization
process performs tasks such as handling subwords, adding special tokens to pad
a sequence of words to a max length, or indicating the beginning or end of a
sequence. In our work, we instantiated the DistilBERT tokenizer using the
AutoTokenizer class provided by the transformers [50]. We set the length of
the tokenizer to a size of 512.

####  3.2.2 Training Details.

Similar to previous work performing search result stance classification [12],
we built one cross-topic model using our entire data set. First, we split the
dataset in a stratified manner, which preserves the percentage of the topic
and labels in each subset. Figure 1 shows the final split across the training,
validation, and test sets on both topics and labels. Then, we classified
search results into stance categories (i.e., in favor, against, or neutral),
using a pre-trained version of the uncased DistilBERT base model [42] from
HuggingFace. Specifically, we fine-tuned DistilBERT using 75% (843) of the
documents in our data and split the remaining 25% (282) equally for validation
and testing. Due to the relatively small size of our dataset, we tried to
avoid over-fitting by using neural network dropout and stopping training when
evaluation loss stops improving for 5 epochs [45, 53]. We trained using the
same dataset split and experimented with learning rates ranging from
$5e\textendash 6$ to $1e\textendash 4$. Regarding the remain hyper-parameters,
the models were optimized using the Adam optimizer with a learning rate of
$1e-5$, the batch size was set to 8 and we set the dropout rate of both
attention layers and fully connected layers to 0.4.

####  3.2.3 Metric.

In our stance detection task, we have three labels to be classified, and their
distribution is uneven. To take performance on all stances into account, we
considered the macro F1 score, defined as the average of the F1 scores for
each of the three stances:

$\small
macro_{F1}=({F1}(stance=favor)+{F1}(stance=neutral)+{F1}(stance=against))/3$
(1)

####  3.2.4 Model performance.

We obtained a stance detection model by fine-tuning the DistilBERT model for
our downstream classification task. The fine-tuning process was completed
through HuggingFace’s Trainer interface. The progress of each run was tracked
using Weights & Biases.333https://wandb.ai We observed that the learning rate
of $1e\textendash 5$ gave the best performance with a macro F1 score of
0.72.444Due to a minor error in evaluation, a slightly higher macro F1 score
was reported in the pre-registration. However, this erroneous score did not
influence the training process or affect our user study.

###  3.3 Creating Explanations

####  3.3.1 Instance Selection.

For the user study, we selected search results correctly predicted by the
model, picking them mainly from the test set and some from the validation set.
As mentioned before, the train, validation, and test sets were split in a
stratified way, which preserves the frequencies of our target classes. To
assemble search results for our study, we randomly drew 21 correctly predicted
search results per topic from our test and validation data (i.e., seven
against, seven neutral toward, and seven in favor of the topic). The SERPs
later displayed 10 results per page.

####  3.3.2 LIME Parameter Tuning.

After we fine-tuned and evaluated the model on the search result corpus, we
used LIME (Local Interpretable Model-Agnostic Explanations) to explain the
model’s predictions [39].555https://github.com/marcotcr/lime For text data,
LIME outputs a list of features (tokens) from the input sentence, ranked by
their importance w.r.t. a model’s specific prediction. We generated the
explanations by setting the neighborhood size to learn the linear model to
5000, kernel_width to 50,666We tried multiple kernel sizes (10, 25, 50, and
75) and chose a value of 50 since we got a slight increase in the R2 scores
for each LIME local prediction on the test set of about 3-4% on average
compared to the other sizes. and showing the top 20 important tokens (or less
based on the text length) belonging to the model’s predicted class.

###  3.4 Search Engine

####  3.4.1 Architecture.

We implemented our web-based search interface using the SearchX platform as a
basis [37]. The web application has both a front-end and a back-end. The
front-end is built on NodeJS using React and Flux frameworks and manages
users’ data by sending logs to the back-end, which mainly handles the
retrieval of search results from the database and stores the logs from the
front-end.

####  3.4.2 Interface.

Figure 2: Different conditions for the SERP display. a) Text-only Search
Result Page. b) Predicted stance labels. c) Predicted stance labels and
Explanations.

We designed the search interface as follows. As soon as users open the
homepage of the study, saw a search bar positioned at the top of the page,
where they may enter their queries. The search engine showed results only when
the input query included one or more keywords referring to the user’s assigned
topic (among atheism, intellectual property rights, and school uniforms).
Otherwise, showed a message informing users that no results were found for
their query. We provided users with the topic and the specific keyword to
include in the query with a sentence below the search bar. For the Atheism
topic, the keywords are “atheist” or “atheism”, for the intellectual property
right topic, the keywords are “property right”, “intellectual right” or
“intellectual property right”, while for the school uniform topic, keywords
are “uniform” or “school uniform”.

After the users inserted a query including the above mentioned keywords, the
interface displayed a list of matched results. This set of search results had
a different arrangement based on the random condition a user was assigned to.
We set up three SERP display conditions (see Section 4.2): 1) text-only SERP,
2) SERP with labels; and 3) SERP with labels and explanations. The text-only
SERP showed ten search results without any extra information. Each search
result had two parts: a clickable title redirecting the user to the
corresponding webpage and a snippet showing some of its content. Figure 2 a)
shows the layout of this type of interface. The SERP with labels introduces
two new features: the labels indicating the original content’s stance and an
overview of all the search results labels on the right side of the webpage. As
shown in Figure 2 b), we assigned a different color representing each
viewpoint. We also added extra information to each label, indicating whether
the document is against, neutral, or in favor of the topic. The third type of
SERP interface makes use of a mono-chromatic saliency map (see Figure 2 c),
highlighting the top 20 words that best contribute to the prediction in the
search result snippet. Due to the space limitation of the interface, not all
the feature would appear in the snippet. The color of the saliency map aligns
with the color of the label. We use the same color for all 20 feature words
regardless of their significance to make it less complicated for users to
understand.

####  3.4.3 Biased Versus Balanced Results.

Table 2: Templates for the first SERP (i.e., the top 10-ranked search results) in each SERP ranking bias condition. Rank |  Biased Opp. (T1) |  Biased Supp. (T2) |  Balanced (T3)
---|---|---|---
1 |  Against |  Favor |  Neutral
2 |  Against |  Favor |  Neutral
3 |  Against |  Favor |  Neutral
4 |  Against |  Favor |  Neutral
5 |  Favor |  Against |  Against
6 |  Neutral |  Neutral |  Neutral
7 |  Against |  Favor |  Favor
8 |  Favor |  Against |  Against
9 |  Neutral |  Neutral |  Neutral
10 |  Against |  Favor |  Favor

We ranked search results depending on the ranking bias condition randomly
assigned to each user. Specifically, we created biased and balanced top 10
search result ranking templates, according to which we would later display
search results to users. Biased search results were biased either in the
against or in favor direction and thus contain only results of one of these
two stance classes in the top four ranking spots (the remaining six results
were balanced across stance classes). Users who were assigned to the biased
condition would see search results biased in the opposite direction compared
to their pre-search opinion (e.g., if they were in favor of school uniforms,
they would see results biased against school uniforms). Users who were
assigned to the balanced condition would see a similar search result page, but
with neutral search results in the top four spots. Neutral results either do
not contain any arguments on the debated topic or equally many arguments in
both directions.777We chose this setup to make the conditions as comparable as
possible, e.g., rather than displaying results in alternating fashion in the
balanced condition. Table 2 shows an overview of the ranking templates.

##  4 User Study Setup

###  4.1 Research Ethics and pre-registration.

We deployed the web application on servers owned by the Faculty of Maastricht
University (UM server) and secured the connection through HTTPS protocol with
SSL certificates. The study was reviewed and accepted by our review board. The
data collected from users, ranging from demographical information to user’s
clicking behaviours, are all stored anonymously in the server. Prior to the
launch of the user study, the research question, hypotheses, methodology,
measurements, etc. were pre-registered on the Open Science Framework. Only
minor changes were made, including balancing the number of participants in the
different conditions, change the way we measure users’ attitude change, and
correcting a computation error in the macro F1 score.

###  4.2 Variables

In our study, each subject could look at search results (i.e., 10 search
results per page) accompanied by different features. We analyzed the
participants’ attitudes and interaction behavior (with a focus on the
proportion of clicks on attitude-confirming search results).

Independent variables.

  * •

Topic (between-subjects, categorical). Participants were assigned to one topic
(i.e., atheism, intellectual property rights, or school uniforms) for which
they have a strong pre-search attitude (i.e., strongly opposing or strongly
supporting). If a participant had no strong attitude on any topic, they ended
the study. If a participant had multiple strong attitudes, they were assigned
to the topic that has the fewest participants at that point in the study
(i.e., to move toward a balanced topic distribution).

  * •

SERP ranking bias (between-subjects, categorical). There were two types of
ranking conditions: biased and balanced. For each of these two conditions, we
preset a ranking template (see Table 2). Participants would see a search
result page with ten items which were ranked in accordance with the template.
If a user was assigned to the biased condition, they would see opposing-biased
search results if their pre-search attitude was in favor (i.e., $3$) and
supporting-biased search results if their pre-search attitude was against
(i.e., $-3$).

  * •

SERP display (between-subjects, categorical). Each participant saw search
results accompanied by one of these features: (1) plain text results without
stance labels, (2) results with predicted stance labels (Figure 2 b), or (3)
results with predicted stance labels and highlighted explanations (Figure 2
c).

Dependent variable.

  * •

Shannon Index (numerical). The Shannon Index was applied to measure the
diversity of users’ clicks. Let $N$ be the total number of clicks made in one
session, $n_{0},n_{1},n_{2}$ be the number of clicks of ”against”, ”neutral”
and ”favor” items respectively. The formula for computing clicking diversity
is: $-\sum_{i=0}^{2}\frac{n_{i}}{N}\ln(\frac{n_{i}}{N})$ . The convention for
no occurrence of a class is to ignore it as $\ln(0)$ is undefined [29]. For
instance, the Shannon Index for (0, 1, 3) is $0+0.34+0.21=0.55$. The minimum
value of the Shannon Index is 0, which indicates that there is no diversity
and only one viewpoint was clicked on. When each class is equal the Shannon
entropy has the highest value (for three classes, this would be
$3*(-\frac{1}{3})\ln(\frac{1}{3})=1.1$).

Descriptive and exploratory measurements. We used these variables to describe
our sample and for exploratory analyses, but we did not conduct any conclusive
hypothesis tests on them.

  * •

Demographics (categorical). We asked participants to state their gender, age
group, and level of education from multiple choices. Each of these items
includes a ”prefer not to say” option.

  * •

Clicks on Neutral Items (numerical). In a balanced SERP, the majority of items
were neutral. We were specifically interested in whether participants’
engagement with search results with a neutral stance is affected by the SERP
display condition.

  * •

Clicking Diversity (numerical). We logged the clicking behavior of
participants during the survey and computed the ratio of pre-search attitude-
confirming vs. attitude-opposing search results among the results a user has
clicked on. Clicks on neutral search results were not regarded for this
variable.

  * •

Attitude Change (numerical). In line with previous research [15, 40, 16], we
asked participants to select their attitudes on debated topics before and
after the experiments using a seven-point Likert scale ranging from “strongly
opposing” to “strongly supporting”. The difference between their two answers
is then assessed in the analysis.

  * •

Textual feedback (free text). We asked participants to provide feedback on the
explanations and the task.

####  4.2.1 Procedure.

Participants completed the study in three steps as described below. The survey
was conducted on Qualtrics888https://www.qualtrics.com/, while the interaction
with search results occurred on our own server.

##### Step 1.

After agreeing to an informed consent, participants were asked to report their
gender, age group, and level of education. Participants were first asked to
imagine the following scenario:

> You and your friend were having a dinner together. Your friend is very
> passionate about a debated topic and couldn’t help sharing his views and
> ideas with you. After the dinner, you decide to further inform yourself on
> the topic by conducting a web search.

Furthermore, participants were asked to state their attitudes concerning each
debated topic (see Section 3.1; including one attention check for which we
specifically instruct participants on what option to select from a Likert
scale).

##### Step 2.

We introduced participants to the task and subsequently assigned them to one
of the three debated topics (i.e., atheism, intellectual property rights, and
school uniforms) depending on their pre-search attitudes and randomly assigned
them to one SERP ranking bias condition and one SERP display condition (see
Section 4.2). Participants were then asked to click on a link leading them to
our search platform (i.e., SEPP; see Section 3.4). Here, participants could
enter as many queries as they want, as long as those queries include their
assigned topic term (e.g., school uniforms pros and cons for the topic school
uniforms). Regardless of what or how many queries participants enter, they
always received search results from the same pool of 21 available search
results relevant to their assigned topic (i.e., seven against, seven neutral,
and seven in favor; see Section 3.1). With every query that participants
entered, they received those search results ranked according to the ranking
template associated to their assigned SERP ranking bias condition (see Section
3.4) for the first SERP and randomly drawn search results (following the
template) for consequent searches.999Whenever a user enters a new query, the
first SERP (i.e., displaying the top 10 results) will always show search
results according to the template, whereas pages 2 and 3 will show the 21
search results relevant to the topic in random order. Depending on the SERP
display condition participants were assigned to, they could see either plain
search results, search results accompanied by stance labels, or search results
accompanied by stance labels with additional explanations. Participants were
made aware that the search results they were seeing might be biased and that
there were limited results. After entering a query, participants were free to
explore search results as long as they wish and click on links that lead to
the presented web pages, or enter new queries. Users were instructed to return
to the Qualtrics survey when they were done searching.

##### Step 3.

Finally, in the questionnaire, we asked participants to report their post-
search attitude (towards their assigned topic). Further, we asked them to
provide textual feedback on the explanations and the task. We also included
another attention check to filter out low-quality data in this post-
interaction questionnaire. The attention checks consisted of one
straightforward question with suggested response options. We excluded the data
of participants who failed one or more of the attention checks from data
analysis.

####  4.2.2 Recruitment Methods.

In this study, we used Prolific101010https://prolific.co and
Qualtrics111111https://www.qualtrics.com/ to manage the participants and
design survey workflow, respectively. The workflow of our study, including the
informed consent, screening questions, and link to the survey, were all
completed on Qualtrics. In the recruitment platform Prolific, we only selected
participants with a minimum age of 18 (in compliance with research ethics),
and fluent in English, as our dataset only contains English results.

####  4.2.3 Sample Details.

We anticipated to observe medium effects for SERP display and SERP ranking
bias on clicking diversity (Cohen’s $f=0.25$). Thus, we determined in an a
priori power analysis for a between-subjects ANOVA (see Section 4.2.4) a
required sample size of 205 participants, assuming a significance threshold of
= $\frac{0.05}{3}$ = 0.017 (testing three hypotheses), a desired power of (1-
) = 0.8 and considering that we tested, depending on the hypothesis, six
groups (i.e., three SERP display conditions: without stance labels, with
stance labels, with stance labels and explanation; and 2 SERP ranking bias
conditions: biased towards the attitude-opposing viewpoint, balanced) using
the software G*Power [18]. We aimed for a balanced distribution across topics
and conditions.

Participants were required to be older than 18 and with a high proficiency of
English (i.e., as reported by Prolific). Participants could only participate
in our study once. As mentioned above, we excluded participants from data
analysis if they did not pass one or more attention checks. We also excluded
participants from data analysis if they did not access our search platform at
all or if they did not click on any links during their search.

####  4.2.4 Statistical Analyses.

To test our three hypotheses, we conducted Analysis of Variance (ANOVA),
looking at the main and interaction effects of the three independent variables
(1) the topic, (2) SERP display (without stance labels, withstance labels,
with stance labels and explanation) and (3) SERP ranking bias (biased towards
the attitude-opposing viewpoint, balanced) on the shannon index (H1a, H1b,
H1c). Aiming at a type 1 error probability of $\alpha=0.05$ and applying
Bonferroni correction to correct for multiple testing, we set the significance
threshold to $\frac{0.05}{3}=0.017$. We added topic as an additional
independent variable to this analysis to control for its potential role as a
confounding factor.

In addition to the analyses described above, we conducted posthoc tests (i.e.,
to analyze pairwise differences) to determine the exact differences and effect
size, Bayesian hypothesis tests (i.e., to quantify evidence in favor of null
hypotheses), and exploratory analyses (i.e., to note any unforeseen trends in
the data) to better understand our results.

##  5 Results

The overarching objective of this research is to investigate the effect of
extra visual elements such as stance labels and text explanations on users’
interaction behaviours, especially in terms of clicking diversity. In this
section, we present the final results of the study with 203 participants and
address our research question and hypotheses.

####  5.0.1 Descriptive Statistics.

Prior to analyzing the primary statistics, it is necessary to first examine
the demographic data.

Figure 3: Demographic information of participants: Bar chart showing the
distribution of users’ age, gender, and education level.

In general, young people made up most of our participants (see Figure 3). The
educational level data reveal that the majority of participants have completed
at least some level of higher education, with a smaller percentage have
completed advanced degrees.

Participants were roughly equally distributed across the three topics: 70
atheism, 66 intellectual property rights, and 67 school uniforms. Regarding
factors such as bias and interface types that were randomized by Qualtrics
workflow, their distributions are also balanced, with 102 participants
accessed the biased SERPs and 101 accessed balanced SERPs, and 72 users view
text-only SERP display, 64 viewed labelled interface and 67 viewed interface
with saliency maps. In the pre-survey attitude test, 130 people were granted
the access to our survey by expressing rather negative (against) viewpoints
towards a specific topic, while only 73 people expressed a positive stance.

Figure 4:  Shannon Index across SERP Display conditions, split by ranking bias
conditions. Error bars represent confidence intervals.

Figure 4 shows the diversity of users’ clicks as measured by the Shannon index
across conditions. For balanced SERPs, the mean Shannon diversity index starts
from 0.63 when there are explanation, then it slightly reduces to 0.55 in
labelled interface, and drastically drop to 0.24 for the text SERP. For
unbalanced SERPs, the trend is similar, from 0.64 to roughly 0.52, but the
reduction is less drastic. In short, those who interacted with balanced pages
overall scored somewhat lower in Shannon diversity.

Figure 5: Number of clicks on items of each stance made by users under
different conditions. Left: different bias type, Right: different interface

Figure 5 shows the stacked histogram representing users’ click history under
different conditions. In the left subplot, we see that neutral items attracted
more clicks than favor and aginst combined in the balanced setting. In the
biased setting, however, the number neutral clicks starts to shrink and user
start to visit polarized contents more. It is notable that users clicked more
neutral items in the text-only SERP display. The other two types of interface
seem to generate very similar amounts of clicks for each stance.

###  5.1 Hypothesis Tests

We ran an ANOVA test to examine the relationship between the variable Shannon
index and other predictor variables, including explanation condition, bias
type and assigned topic. Table 3 contains the results of the ANOVA test,
including the F-statistic, p-value, and degrees of freedom for each variable
and interaction. We also included the assigned topic in the test to control
for topic as a potential confounding factor.

Variable | Df | Sum Sq | Mean Sq | F | $p$
---|---|---|---|---|---
SERP Ranking Bias | 1 | 0.81 | 0.8146 | 4.911 | .027
SERP Display | 2 | 2.54 | 1.2712 | 7.664 |  $<$.001
Topic | 2 | 0.03 | 0.0158 | 0.095 | .909
SERP RB:SERP Display | 2 | 0.65 | 0.3256 | 1.963 | .143
Residuals | 195 | 32.34 | 0.1659 |  |
Table 3: ANOVA results for the Shannon index and independent variables
including the explanation level, bias type, the interaction between
explanation and bias type, and the topic the user holds strong opinion
towards. Under our reduced significance threshold, only the Explanation effect
is significant.

####  5.1.1 H1a: Users who are exposed to viewpoint-biased search results
interact with less diverse results than users who are exposed to balanced
search results.

To test this hypothesis, we examine the Shannon diversity of clicks made by
users who viewed viewpoint-biased search engine result pages (SERPs) to those
who did not. Figure 4 suggests that users who were exposed to more balanced
search results clicked on somewhat less diverse content. However, in the ANOVA
summary (Table 3), we see that the influence of bias type
($F=4.911,df=1,p=.027$, Cohen’s $f=0.16$) is not statistically significant,
given the significance threshold of .017. We thus do not find any conclusive
evidence for a difference in the diversity of clicked results between bias
conditions.

####  5.1.2 H1b: Users who are exposed to search results with (1) stance
labels or (2) stance labels with explanations for each search result interact
with more diverse content than users who are exposed to regular search
results.

Regarding H1b, compare users’ clicks in different SERP displays. The results
of the ANOVA shows a significant effect of explanation condition on clicking
diversity ($F=7.664,df=2,p<.001$, Cohen’s $f=0.28$). This suggests that there
is a difference in the diversity of content interacted between users who were
exposed to (1) plain search results, (2) search results with stance labels, or
(3) search results with stance labels and also explanations. We conducted a
pair-wise Tukey test to determine whether there are significant differences
between SERP display levels (text, label, explanation). We found significant
differences between text-label and text-explanation (with adjusted p-values of
.0008 and .012, respectively), suggesting that both stance labels and stance
labels with explanations led to more diverse clicks compared to the regular
SERPs. The p-value of labelled-explanation group is .723, indicating that
there may be no difference in Shannon diversity between the labelled and
explanation group.

####  5.1.3 H1c: Users who are exposed to search results with (1) stance
labels or (2) stance labels with explanations are less susceptible to the
effect of viewpoint biases in search results on clicking diversity.

This hypothesis concerns the interaction effect between SERP display and bias
types on the Shannon index. Our ANOVA (Table 3) did not reveal any evidence
for such an interaction effect ($F=1.963,df=2,p=.143$, Cohen’s $f=0.02$). In
other words, when users are looking at different types of SERP displays but
are exposed to the same level of viewpoint bias, our results do not contain
evidence that users will click more diverse items because explanations and
labels are visible.

###  5.2 Exploratory Analysis

To further understand our results, we also conducted exploratory analysis.
Note that these analyses were not preregistered.

####  5.2.1 Clicks on Neutral Items.

To better understand why the search results were more diverse in the labeled
and explanation conditions, we looked closer at the distribution across the
three viewpoints. In Figure 5, we can already see a larger number of neutral
results for the text-only condition. Furthermore, in every SERP display, users
who were exposed to a balanced page clicked on more neutral items on average.
This may be due to their tendency to click on highly-ranked neutral items
while being unaware of the stance.

We conducted an exploratory ANOVA to investigate the effects of biases and
interfaces on the number of clicks on neutral items. Table 4 lists the test
results.

Variable | Df | Sum Sq | Mean Sq | F | $p$
---|---|---|---|---|---
SERP Ranking Bias | 1 | 84.3 | 84.30 | 44.308 |  $<$.001
SERP Display | 2 | 23.1 | 11.54 | 6.067 | .003
SERP RB:SERP Display | 2 | 15.6 | 7.79 | 4.096 | .018
Residuals | 197 | 374.8 | 1.90 |  |
Table 4: Results of the ANOVA analysis on for number of neutral clicks.

We can observe that both explanation condition and bias condition had main
effects on click diversity. Also their interaction is significant. This means
that users may click on more neutral results in the balanced condition, and
especially so when SERPs do not contain any stance labels or explanations.

####  5.2.2 Attitude Change.

Previous research showed that mildly opinionated users’ attitude change can
differ across levels of ranking bias [15, 17, 35, 3, 8]. However, this effect
has, to the best of our knowledge, not yet been shown for strongly opinionated
users such as in our study. We intended to measure the attitude change by
subtracting the post-search viewpoint $t_{1}$ from the pre-search viewpoint
$t_{0}$, and thus the difference would range from $-6$ to $6$. We summarize
users’ attitude change in Figure 6 and Table 5.

Figure 6:  Histogram visualizing the difference between pre- and post-survey attitudes. Each group of bins indicates the number of people in a bias type setting who changed their viewpoints towards their assigned topic by a certain number of points. The X-axis indicates the normalized difference of attitude between the pre- and post-survey answers measured by a 7-point Likert Scale.  Bias Type | SERP display | Attitude Change | Attitude Change | Skewness
---|---|---|---|---
|  | (Mean) | (Median) |
Balanced | Text-only | 1.14 | 1.00 | 1.31
Labelled | 1.30 | 1.00 | 0.15
Explanation | 1.15 | 1.00 | 1.10
Biased | Text-only | 1.25 | 1.00 | 0.20
Labelled | 1.19 | 1.00 | 0.35
Explanation | 1.09 | 1.00 | 0.25
Table 5: Absolute values of attitude change per condition.

From Figure 6, we observe that only a few participants developed large
attitude changes (absolute value $>$ 3) after the survey. In Table 5, the mean
absolute attitude change ranges from 1.0 to 1.3. The median statistics appear
to be very stable; under all conditions, 1 is always the central value. We
performed an exploratory ANOVA on the absolute attitude change variable (Table
6) but do not find any convincing evidence for an effect of any independent
variables on attitude change in our scenario, i.e., apart from a potential
role of the topic ($F=4.132,df=2,p=.017$) that would need to be further
investigated.

Variable | Df | Sum Sq | Mean Sq | F | $p$
---|---|---|---|---|---
SERP Ranking Bias | 1 | 0.01 | 0.013 | 0.014 | .907
SERP Display | 2 | 0.56 | 0.282 | 0.296 | .744
Topic | 2 | 7.88 | 3.938 | 4.132 | .017
SERP RB:SERP Display | 2 | 0.55 | 0.277 | 0.290 | .748
Residuals | 195 | 185.88 | 0.953 |  |
Table 6: ANOVA Results: absolute value of users’ attitude change

###  5.3 Qualitative feedback

After excluding feedback with fewer than four characters like “no”, “nope”
etc, there are 59 substantial comments. Positive feedback indicated that
participants perceived the search results to be diverse: “I thought the search
engine was quite varied as it provided different types of sources”. Other
participants perceived the viewpoint labels to be accurate: “The search engine
was very useful, and the classification of the information was accurate.” At
least some participants were able to perceive the bias in the search result
pages: “the results against atheism were promoted to the top results of the
search, it was not an impartial search result”. While we aimed to mimic a real
search engine, some participants may have realized that the results were
reshuffled: “It appears the results of the search were similar or the same in
each new search. I’m not sure the words used to determine the stance of the
page were appropriate.”

##  6 Discussion and Limitations

We found a significant effect of SERP display on clicking diversity. That is,
participants who were exposed to viewpoint labels or viewpoints labels with
explanations consumed more diverse results than plain text search. While both
non-text SERP displays affect users’ click diversity over text-only SERP
displays, we cannot observe any additional effect from the SERP display with
labels and explanations over the label-only SERP display. Our results suggest
that this difference can be explained by a predominance of clicks on the
neutral stance in the text-only condition. This, alongside the qualitative
comments, suggests that participants trusted and used the labels and
explanations to inform themselves diversely. Contrary to our expectations, we
did not find evidence for a difference between biased and balanced search
results, in terms of click diversity. Furthermore, exploratory analyses
revealed that users exposed to balanced SERPs clicked on more neutral items.

We further found no exploratory evidence that intervention types (bias of
search results; explanation level) affect participants’ viewpoints. This later
result is not necessarily surprising, given that the participants in this
study held strong opinions on the topic. We also would not expect a change
because the task given to participants was formulated as low-stakes and open-
ended (inform yourself about the topic after speaking to an opinionated
friend).

Limitations and Future work. Our study has at least two important limitations.
First, each search result in the data set we considered only had one overall
viewpoint label, and this was limited to against, neutral, and in favor. This
allowed for scalability but does not reflect the full nuances of online search
results. For example, an essay or blog post could express highly diverse
perspectives but still receive a positive stance label if that is its overall
conclusion. Secondly, our study carefully aimed to balance a controlled
environment with maintaining ecological validity. Despite this, it is possible
that participants recognized that this was not a true search engine when
issuing new queries. Similarly, the selection of templates allowed for some
structured reshuffling of results. However, this also meant that strong
opinions (contrary to the active user) were always at the top of biased search
results. For the balanced condition there were also more neutral results. This
likely contributed to the higher diversity of search results selected in the
biased condition. In addition, ranking fairness metrics could have been used
for the ranked lists, which could have led to slightly different results.
Further work is required to disentangle the relationship between position bias
and confirmation bias, and to replicate the study with different templates.

##  7 Conclusion

In this paper, we studied the impact of stance labels and explanations of
these labels for search on disputed topics. We found that stance labels and
explanations led to a more diverse search result consumption. However, we
cannot conclude that explanations have an extra effect in addition to labels
on user’s click diversity. Whether consuming diverse or more neutral results
is preferable is in itself a debated topic. Backfire effects, where users
become even more invested in their pre-existing beliefs, are possible when
users consume strongly opposing views [34]. Greater diversity can further
induce wrong perceptions of present evidence: for example, portraying climate
change deniers and believers equally can give the impression that climate
change is an open issue and thus may be worse than indeed weighing the
evidence on both sides [11]. How much stance diversity is ideal can thus
depend on individual user traits [32] and may lie somewhere between the
extremes [7]. While these are domains and a context where confirmation biases
are expected to be large, similar cognitive biases are likely to occur in
other decision-making tasks where XAI is used. Further work is needed to
catalogue different scenarios of opinion formation in different domains where
confirmation bias may be present.

## Acknowledgments

This project has received funding from the European Union’s Horizon 2020
research and innovation programme under the Marie Skłodowska-Curie grant
agreement No 860621.

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