Towards Breaking the Self-imposed Filter Bubble in Argumentative
Dialogues
Daniel Kornmüller
Annalena Aicher
Wolfgang Minker
Ulm University,
Ulm University,
Germany
Germany
NAIST, Japan
annalena.aicher@uni-ulm.de
Abstract
Human users tend to selectively ignore information that contradicts their pre-existing beliefs or
opinions in their process of information seeking. These “self-imposed filter bubbles” (SFB)
pose a significant challenge for cooperative argumentative dialogue systems aiming to build
an unbiased opinion and a better understandingof the topic at hand.
To address this issue, we develop a strategy
for overcoming users’ SFB within the courseof the interaction. By continuously modelingthe user’s position in relation to the SFB, we
are able to identify the respective arguments
which maximize the probability to get outside
the SFB and present them to the user. We implemented this approach in an argumentative
dialogue system and evaluated in a laboratory
user study with 60 participants to show its validity and applicability. The findings suggestthat the strategy was successful in breaking
users’ SFBs and promoting a more reflective
and comprehensive discussion of the topic.

1

Introduction

Spoken dialogue systems are getting increasingly
popular, especially as they enable easy access torequested information from online sources, such as
search engines or social media platforms. Specifically, with regard to more complex interactions,
two important phenomena can be observed that can
result in information bias.
On the one hand, due to filter algorithms, information content is selected based on previous
online behavior, which leads to cultural/ideological bubbles, the so-called “Filter Bubbles” (Pariser,2011). On the other hand, Nickerson (1998) points
out that users who are confronted with controversial topics tend to focus on a “biased subset ofsources that repeat or strengthen an already established or convenient opinion.” This user behavior
leads to the so-called “Self-imposed Filter Bubbles”(SFB) (Ekström et al., 2022; Aicher et al., 2022b)

Stefan Ultes
University of Bamberg,
Germany

Yuki Matsuda
Keiichi Yasumoto
NAIST, Japanand “echo chambers” (Quattrociocchi et al., 2016;
Anand, 2021; Donkers and Ziegler, 2021). Both are
manifestations of “confirmation bias”, a term typically used in psychological literature. These phenomena are mutually dependent according to Lee(2019) as the SFB is reinforced and perpetuated
due to algorithmic filters delivering content alignedwith presumed interests based on search histories.
Moreover, Bakshy et al. (2015) claim that studies
have shown that individual choice has even moreof an effect on exposure to differing perspectives
than “algorithmic curation”. In this paper, we focus on the second phenomenon, namely the user’s
SFB regarding a controversial topic during the interaction with an argumentative dialogue system(ADS). Building upon the work of Aicher et al.
(2022b, 2023), we model the user’s SFB using thefollowing four main dimensions: Reflective User
Engagement (RUE), Personal Relevance (PR), True
Knowledge (TK) and False Knowledge (FK).
The concept of RUE encapsulates the user’s critical thinking, building upon the definition established in our prior work (Aicher et al., 2021a). On
the other hand, PR pertains to the individual user’s
assessment of the significance of subtopics, further
on called “clusters”, in relation to the overarching
topic of discussion. True Knowledge (TK) is characterized as the information already possessed by
the user on a particular topic. Conversely, False
Knowledge (FK) entails the user’s false beliefs andmisinformation on the respective topic. Based uponthese dimensions we have the ability to constructa model for assessing the likelihood of a user being caught within an SFB. In order to achieve this,
we ascertain the user’s position along these four
dimensions and consistently update it throughout
the course of the dialogue. Building upon SFBModel we 1) introduce a rule-based system policy
to break the user’s SFB during an ongoing interaction and 2) validate our policy in a laboratory study
by comparing it to a user-interest-driven systempolicy.
The remainder of this paper is as follows: Section 2 gives an overview of related literature, followed by a description of the underlying SFBModel and our proposed rule-based SFB-breaking
policy in Section 3. Section 4 discusses an exemplary integration of our model/policy in an ADS,
which is evaluated in a laboratory study describedin Section 5. Section 6 covers the respective study
results, followed by a discussion of the former andstudy limitations in Sections 6 and 8. We closewith a conclusion and a brief discussion of future
work in Section 9.

2

Related WorkIn the following, we provide a brief overview ofthe existing literature on the main aspects of thework presented herein, Confirmation Bias and Selfimposed Filter Bubbles and Argumentative Dialogue Systems.
2.1

Confirmation Bias and Self-imposed Filter
Bubbles

As previously pointed out, a central issue in theprocess of opinion building is the phenomenon
known as “confirmation bias”. This bias refers to
the tendency of users to seek or interpret evidence
in ways that align with their existing beliefs, expectations, or hypotheses (Nickerson, 1998). Given
our goal of achieving a well-founded and unbiased
exploration of information, we are determined tocounteract the user’s inclination to focus solely
on information that confirms their preexisting beliefs (Allahverdyan and Galstyan, 2014).
To address this challenge, Huang et al. (2012)
propose the utilization of computer-mediatedcounter-arguments within decision-making processes. Additionally, Schwind and Buder (2012)
consider preference-inconsistent recommendations
as a promising approach to stimulate critical thinking. However, given our cooperative approachand the objective of maintaining the user’s motivation to explore arguments without bias, introducing an excessive number of counter-arguments
could potentially lead to undesirable negative emotional consequences, such as annoyance and confusion (Huang et al., 2012).
In order to identify a means of mitigating these
consequences, it is crucial to consider how a genuine and profound critical reflection can be stimulated. When users engage in critical thinking ina weak sense, this implies contemplating positions
that differ from their own (Mason, 2007), but often
involves a tendency to defend their own viewpoint
without thorough introspection (Paul, 1990). Critical thinking in a strong sense involves reflecting
on one’s own opinions as well, which aligns with
our objective. However, the substantial energy andeffort (Gelter, 2003) required for this robust criticalreflection are frequently lacking due to a deficiency
in individuals’ inherent need for cognition (Maloney and Retanal, 2020). Given users’ tendency todefend their own views (Paul, 1990), a system that
confronts them with opposing viewpoints might notnecessarily foster critical reflection; on the contrary,
it could lead to a reinforcement of their existing
stance. Hence, there is a need for an intelligent
system capable of adjusting the frequency, timing,and selection of counter-arguments (Huang et al.,2012). To the best of our knowledge we are thefirst to provide such a system, which integrates amodel to determine the user’s Self-imposed Filter Bubble (SFB) and adapts its strategy accordingly. This adaptation aims to identify the mostsuitable arguments and still maintaining the user’s
interest, ensuring a well-balanced exploration of
viewpoints.
In contrast to Del Vicario et al. (2017), whostudy online social debates and try to mathematically model the related polarization dynamics, wedefine a model for this “seeking or interpreting ofevidence in ways that are partial to existing beliefs,
expectations or a hypothesis in hand” (Nickerson,1998) consisting of four dimensions building upon
our previous work (Aicher et al., 2022b, 2023).
The respective dimensions are based on a wellestablished framework in persuasion research, the “
Elaboration Likelihood Model” (ELM) (Petty et al.,2009).
2.2

Argumentative Dialogue Systems

Within this paper we define a system policy aiming
to help users overcome their SFBs in a cooperative argumentative dialogue. Argumentative dialogue systems (ADS) enable users to engage in
information-seeking and to explore pro and con
arguments on a controversial topic by accessing
large-scale argumentation structures and assist ina well-founded opinion building (Waheed et al.,2021; Aicher et al., 2021b,a, 2023). The “ability
to engage in argumentation is essential for humans
to understand new problems, to perform scientificreasoning, to express, to clarify and to defend theiropinions in their daily lives” (Palau and Moens,2009) and thus, enables to reflect controversial topics critically. A consensual dialogue is much more
likely to resolve diverging perspectives on evidence
and repair incorrect, partial, and subjective readings of evidence than a persuasive one (Villarroel
et al., 2016). Hence, it is crucial for the argumentative dialogue system, in which our SFB-Model isembedded, that it does not try to persuade or win adebate against a user.
Most approaches to human-machine argumentation utilize different models to structure the interaction and are embedded in a competitive, persuasive
scenario. For instance, Slonim et al. (2021) introduced the IBM Debater, which is an autonomous
debating system that can engage in a competitive
debate with humans via natural language. Anotherspeech-based approach was introduced by Rosenfeld and Kraus (2016), presenting a system basedon weighted Bipolar Argumentation Frameworks(wBAG). Arguing chatbots such as Debbie (Rakshit et al., 2017) and Dave (Le et al., 2018) interact
via text with the user. A menu-based frameworkthat incorporates the beliefs and concerns of the opponent was presented by Hadoux et al. (2022). In
the same line, Chalaguine and Hunter (2020) used apreviously crowd-sourced argument graph and considered the concerns of the user to persuade them.
Another introduced persuasive prototype chatbot
is tailored to convince users to vaccinate against
COVID-19 using computational models of argument (Chalaguine and Hunter, 2021). As pointed
out in Subsection 2.1 in contrast to those persuasive
approaches we chose collaborative exploration ofarguments, enabling users to express their preferences and thus providing a more suitable basis thanthe previously mentioned, competitive ADS.

3

Self-imposed Filter Bubble ModelIn the following section we will give a shortoverview on the SFB-Model we adapted to andits respective dimensions. This serves as a basis
for our system’s SFB-breaking policy introducedin Subsection 3.3.
3.1

SFB-Model Dimensions

We adapted the SFB-Model introduced by Aicher
et al. (2022b) which is motivated by the “Elaboration Likelihood Model” (ELM) (Petty et al.,2009). As already mentioned, it incorporates offour dimensions, which span a four-dimensional
space to describe the user’s SFB: Reflective User
Engagement (RUE), Personal Relevance (PR), True
Knowledge (TK) and False Knowledge (FK).
The Reflective User Engagement (RUE) describes the critical-thinking and open-mindedness
demonstrated by the user. It takes into account thepolarity and number of heard arguments. This canbe mapped onto the request for more information,either on the pro or con side of the topic of the
discussion. Thus, it measures how balanced theuser is exploring a topic. The RUE has first been
introduced by Aicher et al. (2021a), to whose work
we refer to for details of its calculation.
The Personal Relevance (PR) refers to the user’s
individual assessment of how relevant a cluster iswith regard to the topic of the discussion. The
greater the relevance a cluster holds for a user, thestronger their inclination to delve into the corresponding arguments associated with it. As this is
impossible to ascertain through implicit methods,
the Personal Relevance (PR) is explicitly queried
within the dialogue when transitioning to a newcluster, with respect to the previous cluster.
The True Knowledge (TK) serves as a measurefor the information gain and is defined as the new
information the user is provided with by talking to
the system. It can be determined by comparing thetotal information provided by the system and theinformation, which is already known to the user.
For its determination, the user is required to provide feedback on each known argument. For eachcluster, this number of known arguments is subtracted from the total number of arguments heard
within the cluster. As we want the user to explore as
much information as possible, a high TK increases
the chance to explore other aspects and viewpoints.
Thus, the bigger the TK of the users, the moreunlikely they find themselves in an SFB.
The concept of “False Knowledge (FK)1 ” pertains to inaccurate information held by a user regarding a specific topic. When a user possesses
false beliefs about specific clusters, it increases
1Regarding the terminology, please note that the term
“False Knowledge” was chosen to facilitate a simplified threedimensional representation, wherein the dimensions of “True”
and “False Knowledge” are merged into the single dimension
of “Knowledge”. This choice is intended solely for the purpose of simplified illustration as the actual calculation occurs
within a four-dimensional space. Without loss of generality
the information stored in the system’s database is defined as
factually accurate, thereby classifying information contradicting it as wrong.

the probability to be caught in an SFB and fosters reluctance toward conflicting information andviewpoints. Likewise to the “True Knowledge”,
the “False Knowledge” is determined by the user
indicating that they consider an argument to be
factually incorrect.
3.2

SFB-Model

Argumentative discussions are complex and consist of a lot of different cluster, which contain arguments referring to the same content-related aspects.
For each of these clusters, a corresponding SFB# »vector sf bk = (prk , rk , tkk , f kk )T , k ∈ N is de#»fined, contributing to the overall SFB vector SF Bk
for the entire discussion topic. It is important to differentiate between the SFB and the SFB-vector of auser (refer to Figure 1). The SFB-vector is conceptualized as a vector originating from the coordinate
system’s origin and terminating at the user’s position in the four-dimensional space. The SFB, on theother hand, constitutes the region within the fourdimensional space that signifies a specific probability of users to be caught in their SFB. Figure 1
presents an illustration2 of two positions of this vector, and the respective SFB (dark blue geometricshape) for a single cluster. As it is very difficult toestablish precise boundaries of the SFB, we establish a probability denoting a user’s position withinor outside the SFB. A short SFB-vector (dashed red
arrow) corresponds to a high probability of the user
to be caught within the SFB. Conversely, a large
SFB vector (continuous green arrow) that extends
further beyond the SFB diminishes the likelihoodof the user to be caught in the SFB. The overall SFB# »vector SF B = (P R, RU E, T K, F K)T , consistsof the overall cluster values for each dimension,
derived from a weighted mean calculation (Aicher
et al., 2023).
3.3

SFB-breaking policy

Building upon the model described in Subsection 3.2, we propose a rule-based system policy
with the objective of breaking the user’s SFB. Utilizing data from a prior crowd-sourcing user study,
we investigated how SFB dimensions changed under two distinct system policies. The first policy,
as outlined in Section 5, follows the interest-based
approach, selecting arguments based on the esti2
Please note that this illustration serves solely explanatory
purposes, and thus is reduced to a three dimensional space (bymerging TK and FK and that the actual form and structure of
the SFB may deviate.

Figure 1: Schematic sketch of a clusterwise SFB-vector
and SFB for a cluster k. The probability of an SFB
is very high in proximity to the origin and/or when adimension approaches a value close to zero. As a fourdimensional space is challenging to visualize, we consolidate the dimensions of TK and FK into the Knowledge
dimension. The red dashed vector indicates the positionof a user within the SFB. The green continuous arrow
indicates the position of a user outside the SFB.

mation of the user’s greatest interests. The second
policy involves the random presentation of arguments from the remaining set. The calculated averages across all participants were utilized as benchmark values for identifying regions where there
is a higher probability of being caught in an SFB(very high probability = interest average; medium
probability = random average).
Given that PR and FK cannot be ascertained
beforehand but only in hindsight, the rule-based
policy focuses on maximizing the RUE and TK dimension, which can be computed in advance. If thevalues for PR or fK deteriorate (become smaller) after introducing a new argument, we assign a greater
weight to the associated cluster and respective arguments to counteract this.
To ensure logical coherence, it is important that
potential argument candidates are logically connected to the requested argument, either throughsibling relationships or by sharing the highest degree of overlap in their respective cluster affiliations. Once candidates are identified, they are evaluated against the user-selected argument in terms ofthe corresponding RUE and TK dimensions. Subsequently, the argument with the maximum values
in these dimensions is presented. In cases where
the system selects an argument different from theuser’s choice, the system response includes an explanation such that the user understands the system’s choice.
Following an initialization phase (first five argu-

ment requests) aimed at detecting and rewarding
shifts in users’ exploration behaviors, the user’s
current SFB-vector is compared to the data-based
SFB-margins (interest, random) after each interaction turn. If the SFB-vector falls within the firstarea (below the interest margin), the ADS will consistently opt to select the best available argument ineach turn. When the SFB-vector is situated within
the second region (above the interest margin, below the random margin), a decision is made basedon recent changes in the SFB vector over the preceding three interaction turns. This determines
whether the system offers an “SFB-breaking” argument or the requested argument. If the SFB-vector
surpasses the random margin, the ADS presents therequested argument, contingent upon the precondition that the absolute value of the SFB-vector did
not decrease in the preceding turn.

4

SFB-Model and Policy Integration into
the ADSIn the following, the relevant components of the
ADS, namely the knowledge base and dialogue
model, focusing on the exemplary integration of
our SFB-Model. In order to combine the presented
Move

Description

SFB DimwhyprowhyconsuggestRequest pro argument
Request con argument
Suggest any argument

rk , tkk
rk , tkk
rk , tkk

pref er
rejectPrefer current argument
Reject current argument

rk
rk

know
f alseCurrent argument is already known
Current argument is incorrect

tkk,i 3f kk

exit

Terminates the conversation

Table 1: Description of potential user actions along with
their corresponding impact on SFB dimensions.

model with existing argument mining approaches,
ensuring its adaptability with respect to discussed
topics, we adhere to the bipolar argument annotation scheme introduced Stab and Gurevych (2014)4 .
This scheme encompasses argument components(nodes), structured in the form of bipolar argumentation trees. The overall topic represents the root
node in the graph. We consider two relationships
between these nodes: support or attack. Each component, excluding the root node (which has no re4
Due to the generality of the annotation scheme, the system
is not confined to the data considered herein. In general, anyargument structure that aligns with the applied scheme can beutilized.

lation), has exactly one unique relation to anothercomponent. This results in a non-cyclic tree structure, wherein each node, or “parent”, is supported
or attacked by its “children”. If no children exist,
the node is a leaf and marks the end of a branch.
Furthermore, the SFB-Model necessitates semantically clustered arguments, wherein each argument pertains to one or more clusters related tothe discussed topic. Given that an argument can encompass multiple aspects of a topic, it may belong
to several overlapping clusters (Daxenberger et al.,2020). Every argument directly addresses one ormore clusters. Since each argument component targets the preceding parent, it indirectly refers to all
preceding parents. Consequently, we stipulate thateach argument component inherits the clusters of
its preceding nodes, meaning it indirectly encompasses all clusters that its parent addresses, whether
directly or indirectly. Notably, the root node is notaffiliated with a cluster.
In this ADS, a sample debate on the topic Marriage is an outdated institution provides a suitablemanually clustered argument structure. It serves
as the knowledge base for the arguments and issourced from the Debatabase of the idebate.org5
website. It consists of a total of 72 argument components, their corresponding relations, and is encoded in an OWL ontology (Bechhofer, 2009) for
further use. In each whypro/con move, a single
supporting/attacking argument component is presented to the user. To prevent the user from being
overwhelmed by the amount of information, theavailable arguments are presented to the users incrementally upon their request. In order to integrate the SFB-Model 3.2, the dialogue model has
to provide respective user moves. The interaction
between the system and the user is separated into
turns, consisting of a user action and the corresponding natural language answer from the system.
The system’s response is based on the original textual representation of the argument components,
which is embedded in moderating utterances. Table 1 shows the required6 possible moves (actions)the user is able to choose from. This allows the user
to navigate through the argument tree and inquire5
https://idebate.org/debatabase (last accessed July 23rd , 2022). Material reproduced from www.
idebate.org with the permission of the International Debating Education Association. Copyright © 2005 International
Debate Education Association. All Rights Reserved.
6
Only moves that are relevant for the SFB-Model are
shown. Other moves are not listed due to their mere
navigational/meta-informational purposes.

for more information. The determiners show whichmoves are available depending on the position of
the current argument.
As shown in Table 1, rk , tk , and fk are directly
influenced by respective user moves and thus updated immediately. However, this does not apply
to PR, which does not directly refer to the dialogue
content but rather serves as a meta reflection. Since
prk does not directly pertain to the argument, butrather to the respective cluster, this informationis requested in a separate pop-up window during
the interaction. To avoid inconveniencing the user
(given that the cluster might remain the same overa certain number of moves), we update prk whenever the corresponding clusters change (when a newcluster k2 is addressed and the old cluster k1 is nolonger addressed). The user’s spoken input is captured through browser-based audio recording using
the Google Speech Recognition API. Subsequently,
it is processed by an NLU framework (Abro et al.,2022) that employs an intent classifier based on a
BERT Transformer Encoder (Devlin et al., 2019)
and a bidirectional LSTM classifier. After recognizing a user move, the spoken system response ispresented using speech synthesis provided by the
Google Web Speech API. An exemplary dialogue
is shown in Appendix A.1.

5

User Study

We conducted a user study from October 4th to15th , 2022, involving 60 participants. The participants were divided into two groups: one group
was presented with arguments based on their interests (referred to as the “interest” group), whereas
the other group was presented with argumentsthat might challenge their existing beliefs (referred
to as the “SFB-breaking” group). In the interest group, the system presented arguments thatprecisely matched the user’s requests. If a lossof interest was detected (modeled by an interest
model (Aicher et al., 2022a)), the system suggested
arguments that aligned best with the user’s preferences and interests. This interest policy is based on
our previously introduced interest model (Aicher
et al., 2022a) and adapted accordingly. In the SFBbreaking group, the system presented arguments
based on the system policy described in Subsection 3.3. Consequently, the arguments presented to
the SFB-breaking group might have differed in polarity and/or cluster from the original user request.
The primary objective of this study was to address

the following research questions: 1) Can the proposed system policy effectively break a user’s SFB?
2) What are the discernible differences in the overall SFB dimensions between the two participantgroups? To investigate these research questions, weformulated the following hypotheses to be tested
during the study:
H1 Participants in the SFB-breaking (interest)group exhibit a lower (higher) probability of
being caught in an SFB after the interaction.
H2 The exploration behavior of the SFB-breaking
group changed during the interaction.
These hypotheses were designed to assess the effectiveness of the system policy in breaking the users’
SFBs and to explore potential differences in SFB
dimensions between the two groups. The study was
conducted in a laboratory setting at a university, involving international participants who possessed a
sufficient level of proficiency in English. Including the introductory phase and the completion ofpre- and post-questionnaires, the entire study duration was estimated to be one hour. Participants
were compensated with a payment of 10$, whichcorresponded to an hourly rate of 10$/hour. After a brief introduction to the system, including ashort text and instructions on how to interact withit, participants were required to answer two control
questions. These questions served as a means toverify their understanding of how to interact withthe system. Only participants who successfully
passed this test were allowed to proceed to a test
interaction with the system.
During the “real” interaction, participants were
instructed to listen to at least 20 arguments7 . Participants were not informed about the underlying
SFB or Interest Model. They were only informedthat the ADS might provide suggestions on its own,
and they could return to the previous argument if
they did not approve.
Throughout the study, the following data wascollected: Self-assessment questionnaire (P.851,
2003), Calculated SFB-values: RUE, PR, TK,
and FK (for each cluster k), Participants’ opinions
and interests regarding the topic of discussion, setof heard arguments, dialogue history. Strict adherence to data protection regulations and participant
anonymity was maintained throughout the study.
Participants had the freedom to withdraw from the7This minimum ensured a sufficient amount of data wascollected to analyze the different system policies.

study at any time. The study was approved by an
Institutional Review Board (IRB) after thoroughethical review and met all internal guidelines dueto the solely cooperative, non-persuasive design of
the user study.

6

Results

The user study involved 60 participants, ranging inage from 22 to 41 years. The participants’ average
age was 28.45 (with a standard deviation (SD) of
4.11). The two participant groups each consisted of
30 individuals (SFB-breaking: 7 females, 23 males;interest: 10 females, 20 males). Both groups exhibited similar levels of experience with spoken
dialogue systems, rated on a 5-point Likert scale
where 1 represented “No experience” and 5 represented “Very much experience”: interest group at
2.40 (SD 0.89); SFB-breaking group at 2.13 (SD1.04).
On average, participants spent approximately
33.87 minutes engaged in interactions with the system (interest group: 33.99 min (SD 7.74), SFBbreaking group: 33.75 min (SD 5.96)). Throughout
the interaction, participants were presented with anaverage of 22.02 arguments (interest group: 21.73(SD 4.00), SFB-breaking group: 22.30 (SD 3.54)).
In Table 2, we present the mean values for all diInterest

SFB-breaking

Asp.

M

SD

M

SD

pcorr value

r

RUE
PR
TK
FK

0.30
0.78
0.28
0.97

0.28
0.20
0.18
0.09

0.47
0.80
0.31
0.99

0.26
0.19
0.25
0.05

<0.001<0.001<0.001
0.008

0.92
0.45
0.61
0.39

Table 2: Means and SD of all SFB dimensions over all
cluster for for both groups. Bold values indicate statically significant differences with respective Bonferroni
corrected pcorr values and effect sizes r.

mensions of both groups across all clusters. Given
the paper’s limited scope, our primary focus lies on
the weighted overall means for each SFB dimension, calculated by averaging across all clusters(subtopics). Exemplary clusterwise results are provided in Appendix A.2. Notably, the SFB-breaking
group displayed significantly larger values for all
dimensions: Reflective User Engagement (RUE),
Personal Relevance (PR), True Knowledge (TK),and False Knowledge (FK) when compared to theinterest group.
To ascertain the statistical significance of thesefindings, we employed the non-parametric Mann-

Whitney U-test for two independent samples (McKnight and Najab, 2010). This choice was made dueto the deviation of group means from normal distribution, as indicated by the Shapiro-Wilk test.
Given that we are considering four dimensions,
we applied the Bonferroni correction to accountfor multiple comparisons, thereby adjusting thep-value (represented as pcorr ). The most substantial and statistically significant distinction was observed in the dimension of Reflective User Engagement (RUE) (pcorr < 0.001), as indicated by a very
high effect size of 0.92 (0.5 < r < 1). Similarly, asignificant difference (pcorr < 0.001) with a higheffect size (0.5 < r = 0.61 < 1) was noted for True
Knowledge (TK). Concerning Personal Relevance(PR) and False Knowledge (FK), the differenceswere also found to be highly significant, exhibiting
a medium effect size (0.3 < r < 0.5).
Regarding the “pre-interest” of the participants(measured on a 5-point Likert scale before the interaction, where 1 represented "Not at all interested"
and 5 represented "Very much interested"), the difference between the two groups is insignificant(interest: 3.67 [SD 0.71], SFB-breaking: 3.47 [SD0.82]; pcorr = 0.986). Similarly, the difference intheir "pre-opinion" (rated on a scale of 1 to 5, where
1 represented "Totally disagree" and 5 represented"Totally agree") is also insignificant (interest: 3.09
[SD 0.93]; SFB-breaking: 2.78 [SD 0.83]; pcorr= 0.308). During the interaction, approximately
36.67% (11 out of 30) participants changed theiropinion (from pro to con or vice versa) in the SFBbreaking group, compared to 6.67% (2 out of 30)
in the interest group. Regarding the "post-interest"(measured after the interaction), a significant difference with pcorr = 0.024 < 0.05 = α is notable(interest: 3.20 [SD 1.16], SFB-breaking: 3.97 [SD0.89]). Similarly, the "post-opinion" also exhibits
a significant difference (interest: 3.63 [SD 0.96],
SFB-breaking: 3.07 [SD 0.87], pcorr = 0.048, r =0.29).
To determine the significance of the difference between pre- and post-measurements, we utilized the non-parametric Wilcoxon signed rank
test (Woolson, 2007) for paired samples and Bonferroni corrected p-values pcorr based on a set offour comparisons. For the SFB-breaking group,
both interest and opinion showed significant differences before and after the interaction (interest:
pcorr = 0.006, r = 0.38; opinion: pcorr = 0.036,
r = 0.30). In the interest group, the pre- andpost-interest also exhibited significant differences(pcorr = 0.006, r = 0.39).
Considering the user moves, a significant difference between both groups becomes evident. In theinterest group, a pro (con) argument was requested
297 (172) times. Only in 15% of all argument requests, interest group users asked for an argument
which did not align with their own opinion. In the
SFB-breaking group, a con (pro) argument was requested 117 (90) times. Furthermore, in 71 (82)
instances, the ADS opted to present a con (pro)argument. Particularly towards the end, the SFBbreaking group tended to request arguments without specifying polarity, and if polarity was specified, it contradicted the user’s opinion in 43% of
all requests. In the interest group, arguments wererarely rejected (3) and mostly preferred (87). In the
SFB-breaking group, suggested arguments were
rejected 65 times and explicitly preferred 71 times.
Moreover, participants in the SFB-breaking (interest) group requested to return to the previous
argument in only 8 (1) cases.

the SFB-breaking system’s tailored policy, whichaims to present arguments spanning as many clusters as possible to encompass diverse facets of the
topic. In contrast, the interest policy concentrates
on clusters aligned with the user’s interest, offeringarguments accordingly.
Significant variations in Personal Relevance(PR) are also evident, even accounting for differences between individual clusters, notably contingent on the number of arguments heard from eachcluster. Participants who explored a greater numberof clusters in a balanced manner tended to exhibit
notably higher Personal Relevance (PR) on average. Similarly, disparities are discernible among
the individual clusters concerning False Knowledge(FK). Out of the nine instances of f alse moves,
merely two were initiated by participants in the
SFB-breaking group. Hence, aligning with our hypothesis, the outcomes affirm that participants in
the SFB-breaking (interest) group demonstrated anotably lower (higher) likelihood of being caught
in an SFB after the interaction.

7

7.2

Discussion

In the following the results of our study (Section 6),
particularly regarding our two hypotheses (refer to
Section 5) are discussed.
7.1

Validation of Effectiveness of
SFB-breaking policy (H1):

The significant differences in all overall dimensions
between both groups can be attributed to the substantial disparity in polarity and the corresponding
clusters to which the heard arguments belonged, despite the nearly similar number of heard arguments.
While the interest group was exclusively exploringarguments of the requested polarity and the estimated most interesting clusters, the SFB-breaking
group encountered arguments strategically chosen
to break the SFB of the user. Consequently, participants in the interest group primarily requested
arguments aligning with their pre-existing opinions.
In contrast, the SFB-breaking group encounteredarguments of both polarities, elucidating the significant difference in the overall Reflective User
Engagement (RUE). These observations further validate the hypothesis that users tend to remain within
their SFBs while exploring contentious topics unless proactively motivated to consider opposing
viewpoints. The substantial difference in True
Knowledge (TK) across all clusters is a result of

Change of exploration behaviour (H2):

In the initial stage of the interaction, the first fivearguments presented by the ADS were selected
solely based on the user’s requests. During thisphase, both groups exhibited a tendency to seekarguments that aligned with their pre-existing opinions. However, a shift in behavior was observed
among the SFB-breaking group participants after
being repeatedly exposed to arguments of opposing
polarity. On average, after the eleventh argument,
SFB-breaking users began to request pro and con
arguments almost equally or no longer specified thepolarity. Interestingly, with the exception of one
case, participants from the SFB-breaking group
continued the interaction and did not revert to theprevious argument. This suggests that the participants appeared to be more motivated by the system’s suggestions to explore differing viewpoints
and facets. This observation is further supportedby the heightened Personal Relevance (PR) of thecorresponding clusters. Conversely, participantsin the interest group returned to the previous argument when they did not perceive the corresponding
cluster as personally relevant.
Within the SFB-breaking group, participants
expressed a preference for and rejection of the
proposed arguments almost equally, with approximately a third changing their opinion, resulting

in a relatively neutral post-opinion. In contrast,
the interest group predominantly indicated theirpreference for arguments and rarely rejected any.
The reinforcement of their pre-existing opinions
becomes particularly evident as the interest groupencountered over twice as many pro arguments as
con arguments, and only two participants altered
their stance on the topic. The comparatively diminished level of interest after the interaction in
the interest group could potentially be attributed toa saturation effect. Conversely, the SFB-breaking
group exhibited an elevated post-interest, indicativeof heightened engagement and a greater willingness to explore additional aspects.
In conclusion, it is evident that the exploration
behavior exhibited by the SFB-breaking groupdemonstrates a significant improvement in balance concerning clusters and polarity. To sum up,
our findings corroborate our initial hypotheses anddemonstrate that our SFB-breaking policy takes us
closer to achieving our goal of assisting users in
critically evaluating information on a contentious
topic.

8

Limitations

However, this work has certain limitations that
could be addressed in future research. First, thesample size of our study is relatively small, potentially affecting the generalizability of our findings. In future endeavors, a study (e.g., throughcrowdsourcing) with a larger sample size could
yield more robust data, enabling us to refine the
SFB margins and enhance the validity of our approach. Second, given that the SFB-Model is a
novel concept, it is presently constrained to four
dimensions. Subsequent research could explore
additional dimensions that may prove pertinent invarious scenarios and applications. Additionally,finding ways to implicitly estimate both PR and
TK, which can only be determined retrospectively,
would be advantageous. This could involve leveraging common sense knowledge bases and employing
fake news detection techniques. Third, while ourstudy demonstrates the proof-of-principle for theeffectiveness of a rule-based policy to break SFB,
it is limited to static, predefined rules, rendering it
relatively inflexible. In future work, we intend todelve into more advanced machine learning techniques, such as reinforcement learning. This would
enable us to personalize and adapt these strategies
based on the user’s verbal and non-verbal feedback,thereby ensuring the user’s satisfaction and sustaining their willingness to engage in the dialogue.

9

Conclusion and Future WorkIn this work, to the best of our knowledge, we introduce a novel approach to break the user’s SFB. After shortly explaining the underlying SFB-Model,
we define a rule-based system policy to break therespective user SFB during a cooperative dialogue
with an argumentative dialogue system and validate it in a laboratory user study. The study results
strongly indicate the effectiveness of the proposedsystem policy in reducing the likelihood of being
stuck in an SFB compared to a policy that prioritizes the users’ greatest interest. Moreover, thestudy revealed significant changes in users’ exploration behaviors during the interaction. In particular, the SFB-breaking participants requested arguments of both polarities almost equally often after
the ADS pointed out that the previous exploration
seemed to be one-sided. These findings emphasize
the influence of the system policy on users’ exploration behaviors and opinions, further highlighting
the success of the proposed approach in mitigating
SFB tendencies and fostering open-mindedness inan argumentative dialogue. In future research, wewill augment our system’s policy by incorporating
sophisticated techniques for perceiving and interpreting the user’s non-verbal social signals (gestures, facial expressions) in real-time during theinteraction. Building upon estimation methods for
sentiment and emotion recognition, we aim to leverage Reinforcement Learning to optimize the system’s policy, enabling it to dynamically adapt toeach individual user’s motivation and effectively
engaging the users to recognize and overcome their
SFB.
In conclusion, this paper highlights the importance of addressing SFBs in argumentative dialogues and takes us a step closer to enabling users
to build a well-founded opinion and foster critical,reflective thinking, and open-mindedness in theirinteraction with cooperative ADS.

Acknowledgements
This work has been funded by the DFG within
the project “BEA - Building Engaging Argumentation”, Grant no. 313723125, as part of the Priority
Program “Robust Argumentation Machines (RATIO)” (SPP-1999).

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A

Appendix

A.1

Example Interaction

In Table 3, we present a segment of an exemplary
dialogue with the argumentative dialogue system,
following the SFB-breaking policy. It illustrates
an artificial interaction between the system and theuser regarding the topic Marriage is an outdated
institution, sourced from the Debatabase on theidebate.org8 website.
We assume that the interaction has been ongoingfor a substantial duration, and the user’s previous
interactions suggest a likelihood of being trappedin an SFB due to consistently requesting attacking
arguments.
Throughout the illustrated interaction, the user’s
requests for con/pro arguments impact two SFB
dimensions: the Reflective User Engagement (rk )and the True Knowledge (tkk ), within the corre# »sponding SFB-vector sf bk . Additionally, the value
for False Knowledge (f kk ) is updated due to theuser’s contradictory knowledge statement.
A.2

Exemplary clusterwise Results

Due to the limited scope of the paper, we focusedon the overall SFB dimension results. Nonetheless,as we observed variations among individual clusters, we present the results of two specific clusters
below.
Table 4 displays the mean values for each SFB
dimension in both groups for two particular clusters(subtopics) of the discussed theme "Marriage is an
outdated institution." Cluster 4 (“Children”) comprises 22 arguments (one of the largest clusters),
while Cluster 7 (“Law”) includes only 6 arguments,
making it one of the smallest clusters.
Similar to the overall cluster means (refer toTable 2), we observe significant differences with
8

https://idebate.org/debatabase (last accessed 23rd July 2021). Material reproduced from www.
iedebate.org with the permission of the International Debating Education Association. Copyright © 2005 International
Debate Education Association. All Rights Reserved.

ActorUtteranceMove

SFB Dimension

System...

User

Could you please provide an attacking argument?

why_conrk

SystemRather than focusing solely on an attacking argument, I why_pro_sf b
propose we also consider the opposing view. One could
argue that religion as a whole is becoming less significant,leading to a reduced importance of marriage.

User

I understand, but could you provide additional evidence?

SystemOn the pro side, it’s worth mentioning that the number ofpeople in the UK identifying as religious has declined by
20% in the last 20 years, as indicated by the British Social
Attitudes Survey of 2007.

User

Impressive, but isn’t this statistic outdated by now? I don’t
believe it holds true anymore.

System

I understand your skepticism, but if you allow me, I’d liketo provide a more detailed explanation.

why_con

tkk

rk
tkk

f alsef kk

Table 3: Artificial dialogue between the ADS and the user on the topic “Marriage is an outdated institution”. The
table includes actor, utterance, respective moves, and updated SFB dimensions of cluster k.

Interest

SFB-breaking

Asp.

M

SD

M

SD

pcorr value

r

r4
pr4
tk4
f k4

0.35
0.71
0.25
0.93

0.20
0.21
0.14
0.13

0.66
0.80
0.47
1.00

0.12
0.12
0.64
0.00

<0.0010.007<0.001
0.044

0.63
0.23
0.73
0.21

r7
pr7
tk7
f k7

0.21
0.74
0.45
0.92

0.39
0.15
0.26
0.14

0.82
0.81
0.83
1.00

0.19
0.18
0.15
0.00

<0.001<0.001<0.001
0.021

0.73
0.35
0.77
0.32

Table 4: Means and SDs of all SFB dimensions for two
clusters (4 = “Children”, 7 = “Law”) for both groups.
Bold values indicate statically significant differenceswith respective Bonferroni corrected p values and effectsizes r.

small to high effect sizes in each dimension. Furthermore, noticeable differences are evident between individual clusters, as illustrated in Table 4.
Particularly concerning smaller clusters, we discern that our SFB-breaking policy has a moderate
to large effect on each dimension. This can be attributed to the fact that our SFB-breaking policy
aims to explore all clusters in a balanced manner,
whereas the interest policy only targets clusters ofuser interest.