From Arguments and Reviewers to their Simulation
Reproducing a Case-Study
Simone Gabbriellini
and Francesco Santini
Dipartimento di Economia e Management, Universit
a di Brescia, Brescia, Italy
Dipartimento di Matematica e Informatica, Universtit
a di Perugia, Perugia, Italy
Argumentation, Social Simulation, Review-based Systems.
We propose an exploratory study on arguments in reviews. Firstly, we extract positive (in favour
of purchase) and negative (against it) arguments from each review concerning a selected product. We ac-
complish this information extraction manually, scanning all the related reviews. Secondly, we link extracted
arguments to the rating score, to the length, and to the date of reviews, in order to undertand how they are
connected. As a result, we show that negative arguments are quite sparse in the beginning of such social
review-process, while positive arguments are more equally distributed along the timeline. As a final step, we
replicate the behaviour of reviewers as agents, by simulating how they assemble reviews in the form of argu-
ments. In such a way, we show we are able to mirror the measured experiment through a simulation that takes
into account both positive and negative arguments.
Recent surveys have reported that 50% of on-line
shoppers spend at least ten minutes reading reviews
before making a decision about a purchase, and 26%
of on-line shoppers read reviews on Amazon prior to
making a purchase.
This paper reports an exploratory study of how
customers use arguments in writing such reviews. We
start from a well acknowledged result in the literature
on on-line reviews: the more reviews a product gets,
the more the rating tends to decrease (Rogers, 2003).
Such rating is, in many case, a simple scale from 1
to 5, where 1 is a low rating and 5 is the maximum
possible rating.
This fact can be explained easily considering that
first customers are more likely to be enthusiast of the
product, then as the product gets momentum, more
people have a chance to review it and inevitably the
average rating tends to stabilise on some values lower
than 5. Such process, with a few enthusiast early
adopters then followed by a majority of innovators,
ultimately followed by late adopters that end the hype
of an innovation, is a typical pattern in diffusion stud-
ies (Rogers, 2003). In on-line reviews however, when
more people get involved in reviewing a product, we
observe a lower level of satisfaction among them.
More data is needed to assess the shape of diffusion
of products through on-line reviews, but our initial in-
vestigation points in this direction.
However, the level of disagreement in product re-
views remains a challenge: does it influence what
other customers will do? In particular, what does it
happen, on a micro level, that justifies such diminish-
ing trend in ratings? Since reviewing a product is a
communication process, and since we use arguments
to communicate our opinions to others, and possibly
convince them (Mercier and Sperger, 2011), it is evi-
dent that late reviews should contain enough negative
arguments to explain such a negative trend in ratings -
or that we are more susceptible to negative arguments.
The presence of extreme opinions on-line is a
well-known issue grounded on the reporting bias and
the purchasing bias of online customers - we will
deepen this argument in the next section.
We limited the horizon of our study to a “micro”
dimension (Gabbriellini and Santini, 2015) due to
the constraint imposed by the argument-mining field,
which is still at its first steps: no well-established tool
seems already to exist to handle this task in our ap-
plication, except for some emerging approach (Lippi
and Torroni, 2015).
Gabbriellini, S. and Santini, F.
From Arguments and Reviewers to their Simulation - Reproducing a Case-Study.
DOI: 10.5220/0005816200740083
In Proceedings of the 8th International Conference on Agents and Artificial Intelligence (ICAART 2016) - Volume 1, pages 74-83
ISBN: 978-989-758-172-4
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Our present study can be considered as “micro”
because we focus on a single product only, even if
with a quite large number of reviews (i.e., 253). Un-
fortunately, due to the lack of well-established tools
for the automated extraction of arguments and attacks,
we cannot extend our study “in the large” and draw
more general considerations.
We extracted by hand, for each review about the
selected product, both positive and negative argu-
ments expressed, the associated rating (from one to
five stars), and the time when the review has been
posted. Afterwords, we analyse our data in terms of:
how positive/negative arguments are posted
through time.
how many positive/negative arguments a review
has (through time).
In particular, we argue that the reason why aver-
age ratings tend to decrease as a function of time de-
pends not only on the fact that the number of negative
reviews increases, but also on the fact that negative
arguments tend to permeate positive reviews, decreas-
ing de facto the average rating of these reviews.
Finally, we propose three different core mecha-
nisms to understand the two main stylised facts ob-
served in the data: a) the tendency for average re-
view rating to decrease with time, and b the pres-
ence of negative arguments in reviews with positive
ratings. The goal of this step is to evaluate the sim-
ilarity between empirical and simulated data as per
the correlations and distribution outlined in Section 4.
In addition, reviews, reviewers and products could be
mapped as in (Bal
azs, 2014):
reviewers and products are represented as two sets
of nodes in a bipartite network;
reviews are represented as links that connect con-
sumers and products, where the weight of the link
represents the rating of the review.
Different strategies are thus possible to check how
much empirical and simulated networks share a com-
mon topology and to validate the realism of the mech-
anisms proposed. An interesting approach is to use as
more statistics as possible, coupling for example de-
scriptive statistics and GOF statistics (Manzo, 2013;
Gabbriellini, 2014).
To run our simulation on all such three mech-
anisms we use NetLogo, which is a programmable
modelling environment for simulating natural and so-
cial phenomena.
The rest of the paper is structured as follows. Sec-
tion 2 sets the scene where we settle our work: we in-
troduce related proposals that aggregate
reviews in order to produce an easy-to-understand
summary of them. Afterwards, in Sec. 3 we de-
scribe the dataset from where we se-
lect our case-study. Section 4 plots how both positive
and negative arguments dynamically change through
time, zooming inside reviews with a more granular
approach. Section 5 reproduce the observed phe-
nomenon through a simulation of different mecha-
nisms. Finally, Sec. 6 wraps up the paper and hints
direction for future work.
Electronic Word-of-Mouth (e-WoM) is the passing of
information from person to person, mediated through
any electronic means. Over the years it has gained
growing attention from scholars, as more and more
customers started sharing their experience online
(Anderson, 1998; Stokes and Lomax, 2002; Zhu
and Zhang, 2006; Goldenberg et al., 2001; Chat-
terjee, 2001). Since e-WoM somewhat influences
consumers’ decision-making processes, many review
systems have been implemented on a number of
popular Web 2.0-based e-commerce websites (e.g.,
), product comparison
websites (e.g.,
and news websites (e.g.,
and Slash-
Unlike recommendation systems, which seek to
personalise each user’s Web experience by exploit-
ing item-to-item and user-to-user correlations, review
systems give access to others’ opinions as well as an
average rating for an item based on the reviews re-
ceived so far. Two key facts have been assessed so
Reporting Bias: customers with more extreme
opinions have a higher than normal likelihood of
reporting their opinion (Anderson, 1998);
Purchasing Bias: customers who like a product
have a greater chance to buy it and leave a review
on the positive side of the spectrum (Chevalier and
Mayzlin, 2006).
These conditions produce a J-shaped curve of rat-
ings, with extreme ratings and positive ratings being
more present. Thus a customer who wants to buy
a product is not exposed to a fair and unbiased set
From Arguments and Reviewers to their Simulation - Reproducing a Case-Study
of opinions. Scholars have started investigating the
relation between reviews, ratings, and disagreement
among customers (Moe and Schweidel, 2012; Del-
larocas, 2003). In particular, one challenging question
is: does the disagreement about the quality of a prod-
uct in previous reviews influence what new reviewers
will post?
A common approach to measure disagreement in
reviews is to compute the standard deviation of rat-
ings per product, but more refined indexes are pos-
sible (Nagle and Riedl, 2014). The next step is to
detect correlations among disagreement as a function
of time (Dellarocas, 2003; Nagle and Riedl, 2014).
We aim, however, at modelling a lower level, micro-
founded mechanism that could account for how cus-
tomers’ reviewing behaviour evolves over time. We
want to analyse reviews not only in terms of rating and
length, but also in terms of what really constitutes the
review itself, i.e., the arguments used by customers.
We aim at explaining disagreement as a consequence
of customers’ behaviour, not only at describing it as a
correlation among variables; an analytical and micro-
founded modelling of social phenomena is well de-
tailed in some works (Manzo, 2013; Hedstrom, 2005;
Squazzoni, 2012), and applied to on-line contexts as
well (Gabbriellini, 2014).
However, before automatically reasoning on ar-
guments, we have first to extract them from a text
corpora of on-line reviews. On this side, research
is still dawning, even if already promising (Villalba
and Saint-Dizier, 2012; Wyner et al., 2012). In ad-
dition, we would like to mention other approaches
that can be used to summarise the bulk of unstruc-
tured information (in natural language) provided by
customer reviews. Some authors (Hu and Liu, 2004)
summarise reviews by i) mining product features that
have been commented on by customers, ii) identify-
ing opinion sentences in each review and deciding
whether each opinion sentence is positive or nega-
tive, and, finally, iii) summarising the results. Sev-
eral different techniques have been advanced to this,
e.g., sentiment classification, frequent and infrequent
features identification, or predicting the orientation of
opinions (positive or negative).
3 DATASET allows users to submit their reviews to
the web page of each product, and the reviews can be
accessed by all users. Each review consists of the re-
viewer’s name (either the real name or a nickname),
several lines of comments, a rating score (ranging
from one to ve stars), and the time-stamp of the re-
view. All reviews are archived in the system, and
the aggregated result, derived by averaging all the re-
ceived ratings, is reported on the Web-page of each
product. It has been shown that such reviews pro-
vide basic ideas about the popularity and dependabil-
ity of corresponding items; hence, they have a sub-
stantial impact on cyber-shoppers’ behaviour (Cheva-
lier and Mayzlin, 2006). It is well known that the
current reviewing system has some no-
ticeable limits (Wang et al., 2008). For instance, i)
the review results have the tendency to be skewed to-
ward high scores, ii) the ageing issue of reviews is not
considered, and iii) it has no means to assess reviews’
helpfulness if the reviews are not evaluated by a suf-
ficiently large number of users.
For our purposes, we retrieved the “Clothing,
Shoes and Jeweller” products section of Amazon
The dataset contains approximately 110k products
and spans from 1999 to July 2014, for a total of more
than one million reviews. The whole dataset contains
143.7 millions reviews.
We summarise here a quick description of such
the distribution of reviews per product is highly
the disagreement in ratings tends to rise with the
number of reviews until a point after which it
starts to decay. Interestingly, for some highly re-
viewed products, the disagreement remains high:
this means that only for specific products opin-
ions polarise while, on average, reviewers tend to
more recent reviews tend to get shorter, irrespec-
tively of the number of reviews received, which
is pretty much expectable: new reviewers might
realise that some of what they wanted to say has
already been stated in previous reviews;
more recent ratings tend to be lower, irrespec-
tively of the number of reviews received.
To sum up, it seems that the disagreement in pre-
vious reviews does not affect much latest ratings -
except for some cases which might correspond to
products with polarised opinions. This result has al-
ready been found in the literature (Moe and Schwei-
Courtesy of Julian McAuley and SNAP project (source: and https:
Space constraints prevented us to show more detailed re-
sults here, but additional plots are available in the form of
research notes at
Polarisation only on specific issues has already been ob-
served in many off-line contexts, see (Baldassarri and
Bearman, 2007).
ICAART 2016 - 8th International Conference on Agents and Artificial Intelligence
del, 2012). However, it has also already been chal-
lenged by Nagle and Riedl (Nagle and Riedl, 2014),
who found that a higher disagreement among prior
reviews does lead to lower ratings. They ascribe their
new finding to their more accurate way of measuring
the disagreement in such J-shaped distributions of rat-
One of the main aims of this work is to under-
stand how it is that new reviews tend to get lower rat-
ings. Our hypothesis is that this phenomenon can be
explained if we look at the level of arguments, i.e., if
we consider the dynamics of the arguments used by
customers, more than aggregate ratings.
Since techniques to mine arguments from a text
corpora are yet in an early development stage, we
focus on a single product and extract arguments by
hand. We randomly select a product, which happens
to be a ballet tutu for kids, and we examine all the 253
reviews that this product received between 2009 and
July 2014. From the reviews, we collect a total of 24
positive arguments and 20 negative arguments, whose
absolute frequencies are reported in Tab. 1.
There are of course many issues that arise when
such a process is done by hand. First of all, an ar-
gument might seem positive to a reader and nega-
tive to another. For the purpose of this small exam-
ple, we coded arguments together and, for each argu-
ment, tried to achieve the highest possible agreement
on its polarity. A better routine, for larger studies,
would be to have many coders operate autonomously
and then check the consistency of their results. How-
ever, we didn’t find case where an argument could be
considered both positive and negative, maybe because
the product itself didn’t allow for complex reasoning.
When we encountered a review with both positive and
negative arguments, like ”the kid loved it, but it is
not sewed properly”, we split the review counting one
positive argument and one negative argument. The
most interesting thing emerging from this study is the
fact that, as reviews accumulate, they tend to contain
more negative bits, even if the ratings remain high.
In Fig. 2, the first plot on the left shows the monthly
absolute frequencies of positive arguments in the
specified time range. As it is easy to see, the num-
ber of positive arguments increases as time goes by,
which can be a consequence of a success in sales:
more happy consumers are reviewing the product. At
the same time, the first plot on the right shows a sim-
ilar trend for negative arguments, which is a signal
that, as more customers purchase the product, some
Table 1: Positive and negative arguments, with their number
of appearances in reviews between 2009 and July 2014.
ID Positive arguments #App.
A the kid loved it 78
B it fits well 65
C it has a good quality/price ratio 52
D it has a good quality 44
E it is durable 31
F it is shipped fast 25
G the kid looks adorable 23
H it has a good price 21
I it has great colors 21
J it is full 18
K it did its job 11
L it is good for playing 11
M it is as advertised 9
N it can be used in real dance classes 7
O it is aesthetically appealing 7
P it has a good envelope 2
Q it is a great first tutu 2
R it is easier than build your own 2
S it is sewed properly 2
T it has a good customer service 1
U it is secure 1
V it is simple but elegant 1
W you can customize it 1
X you cannot see through it 1
ID Negative arguments #App.
a it has a bad quality 18
b it is not sewed properly 17
c it does not fit 12
d it is not full 11
e it is not as advertised 8
f it is not durable 7
g it has a bad customer service 4
h it is shipped slow 3
i it smells chemically 3
j you can see through it 3
k it cannot be used in real dance class 2
l it has a bad quality/price ratio 2
m it has a bad envelope 1
n it has a bad waistband 1
o it has bad colours 1
p it has high shipping rates 1
q it has no cleaning instructions 1
r it is not lined 1
s it never arrived 1
t it was damaged 1
of them are not satisfied with it. According to what
we expect from the literature (see Sec. 2), the higher
volume of positive arguments is a consequence of the
J-shaped curve in ratings, i.e., a consequence of re-
porting and selection biases. What is interesting to
note though, is that the average review rating tends to
decrease with time, as shown by the second row of
plots in Fig. 2. This holds both for reviews contain-
ing positive arguments as well as for those containing
negative arguments. In particular, the second plot on
the right shows that, starting from 2012, negative ar-
guments start to infiltrate “positive” reviews, that is
reviews with a rating of 3 and above. Finally, the last
row of plots in Fig. 2 shows that the average length of
From Arguments and Reviewers to their Simulation - Reproducing a Case-Study
reviews decreases as time passes; this happens both
for reviews with positive arguments and for reviews
with negative arguments. However, such a decrease is
much more steep for negative ones than for positive
In Fig. 3 we can observe the distribution of posi-
tive and negative arguments.
Regarding positive ar-
guments, we cannot exclude a power-law model for
the distribution tail with x-min = 18 and α = 2.56
(pvalue = 0.54)
. We also tested a log-normal model
with x-min = 9, µ = 3.01 and σ = 0.81 (pvalue =
0.68). We then searched a common x-min value to
compare the two fitted distributions: for x min =
4, both the log-normal (µ = 3.03 and σ = 0.78) and
the power-law (α = 1.55) models still cannot be ruled
out, with p value = 0.57 and pvalue = 0.54 re-
spectively. However, a comparison between the two
leads to a two-sided pvalue = 0.001, which implies
that one model is closer to the true distribution - in
this case, the log-normal model performs better. For
negative arguments, we replicated the distribution fit-
ting: for xmin = 2, a power law model cannot be
ruled out (α = 1.78 and p-value = 0.22) as well as
a log-normal model (µ = 1.48 and σ = 0.96, pvalue =
0.32). Again, after comparing the fitted distributions,
we cannot drop the hypotheses that both the distribu-
tions are equally far from the true distribution (two-
sided pvalue = 0.49). In this case, too few data are
present to make a wise choice.
Among the positive arguments (plot on the left),
there are four arguments that represent, taken to-
gether, almost 44% of customers’ opinions. These ar-
guments are: i) good because the kid loved it, ii) good
because it fits well, iii) good because it has a good
quality/price ratio, iv) good because it has a good
quality. Negative arguments represent, all together,
less than 20% of opinions.
We have a clear view where the pros and cons of
this product are stated as arguments: not surprisingly,
the overall quality is the main reason why customers
consider the product as a good or bad deal. Even
among detractors, this product is not considered ex-
pensive, but quality still is an issue for most of them.
The plots in Fig. 1 show the cumulative frequen-
cies and the rate at which new arguments are added
as a function of time. In the left plot, it is interesting
to note that, despite the difference in volume (positive
arguments are more cited than negative ones), the cu-
mulative frequencies at which positive and negative
arguments are added are almost identical. Positive ar-
We used the R poweRlaw package for heavy tailed distri-
butions (developed by Colin Gillespie (Gillespie, 2015)).
We used the relatively conservative choice that the power
law is ruled out if pvalue 0.1 (Clauset et al., 2009).
guments start being posted earlier than negative ones,
consistently with the fact that enthusiast customers
are the first that review the product. Moreover, it is
interesting to note that no new positive argument is
added in the 2011-2013 interval, while some negative
ones arise in the reviews. Since 2013, positive and
negative arguments follow a similar trajectory. How-
ever, as can be noted in the second plot on the right,
new arguments are not added at the same pace. If we
consider the total amount of added arguments, posi-
tive ones are repeated more often than negatives, and
the rate at which a new positive argument is added is
considerably lower than its counterpart. This infor-
mation sheds a light on customers’ behaviour: dissat-
isfied customers tend to post new reasons why they
dislike the product, more than just repeating what
other dissatisfied customers have already said.
In this section we propose an agent-based simulation
to replicate empirical data about customers, reviews,
and arguments as described in Section 4. The aim of
this step is to translate the theoretical mechanism used
to write reviews into its computational counterpart.
Following J. Moody (Moody, 2008), our aim is to
specify a substance-specific model that can shed light
on how customers behave when they have to review
a product, thus to identify properties that make real-
world data and simulated data differ, without quanti-
fying these differences with a statistical significance.
We opt for the Agent-Based Modelling (ABM)
computational approach (Macy and Willer, 2002) to
simulate arguments networks of online reviews from
user behaviour. There is a growing literature that uses
ABM in network studies (Macy and Skvoretz, 1998;
Flache and Macy, 2011). ABMs are a straightfor-
ward way to detail and implement substance-specific
mechanisms in the form of computational models,
i.e., software that generates entities with attributes and
decision-making rules, and that is goal-oriented.
Despite the specific solution implemented, the
main logic would be to test different specifications
of the mechanism against empirical data and to re-
fine such implementations until a satisfactory match is
found or, alternatively, to get back to the blackboard
and think again about the hypotheses - agent-based
modelling is, ultimately, a tool to aid theory building.
An interesting analytical strategy to understand
the robustness of an ABM is to compare its results
against empirical data (Manzo, 2007) in order to as-
sess how realistic the model behaves - thus how sound
is the theory behind it. We will also compare the re-
ICAART 2016 - 8th International Conference on Agents and Artificial Intelligence
2010 2011 2012 2013 2014
5 10 15 20
2010 2011 2012 2013 2014
0.002 0.005 0.020 0.050 0.200 0.500
Figure 1: Left plot: cumulative frequencies of new positive and negative arguments per month. Right plot: rate of new positive
and negative arguments over total arguments per month.
2010 2011 2012 2013 2014
0 10 20 30 40 50
2010 2011 2012 2013 2014
0 10 20 30 40 50
2010 2011 2012 2013 2014
1 2 3 4 5
RATING (Positive Arguments)
2010 2011 2012 2013 2014
1 2 3 4 5
RATING (Negative Arguments)
2010 2011 2012 2013 2014
50 100 150 200 250 300 350 400
REVIEW LENGTH (Positive Arguments)
2010 2011 2012 2013 2014
50 100 150 200 250 300 350 400
REVIEW LENGTH (Negative Arguments)
Figure 2: Argument trends: (row1) absolute frequency of arguments per month, (row2) average rating of reviews per month,
(row3) average review-length per month.
From Arguments and Reviewers to their Simulation - Reproducing a Case-Study
1 2 5 10 20 50
0.05 0.10 0.20 0.50 1.00
1 2 5 10 20 50
0.05 0.10 0.20 0.50 1.00
Figure 3: Arguments distribution: probability of observing an argument repeated x times.
sults of our ABM against a random baseline in order
to assess whether a simpler model can suffice to deal
with the complexity of what we observed empirically.
The idea is that our ABM should outperform the base-
line model in approximating empirical data.
We adopt NetLogo
, a programmable modelling
environment in Scala for simulating natural and so-
cial phenomena, to implement our model. NetLogo
is particularly well suited for modelling complex sys-
tems developing over time. Modellers can give in-
structions to hundreds or thousands of agents all oper-
ating independently. This makes it possible to explore
the connection between the micro-level behaviour of
individuals and the macro-level patterns that emerge
from their interaction. Figure 4 shows our simulation
running in NetLogo.
Our simulation model assumes a few constraints
from empirical data:
1. the size of simulated and empirical populations
coincide and it is equal to 198;
2. reviewers decide to review with a probability pro-
portional to observing a review in empirical data:
the frequency of reviews is thus mimicked real-
istically, but each time reviewers are chosen ran-
domly to avoid artefacts (i.e. reproducing the
same order in which physical reviewers reviewed
the product);
3. the percentages of happy and unhappy reviewers
coincide in real and simulated scenarios (around
80% are happy about the product);
4. the average number of arguments per review is 2,
with a minimum of 1 argument and a max of 4;
5. the number and distribution of both positive and
negative arguments is held constant (24 positive
arguments and 20 negative arguments) and possi-
bly similar to the empirical one (we use a Poisson
generator to assign to every reviewers positive and
negative arguments among the 44 possible argu-
We then propose three different core mechanisms
to understand the two main stylized facts observed in
the data: (a) the tendency for average review rating
to decrease with time; (b) the presence of negative
arguments in reviews with positive ratings.
The first mechanism, Mechanism 1, is used as a
random baseline where arguments and ratings are not
related: we start assigning to reviewers a rating for
their reviews (a value between 1 and 5) and then we
randomly assign positive or negative arguments, irre-
spective of the rating value.
With Mechanism 2, we assume that a strict corre-
lation is in place between ratings and arguments, thus
reviews with positive ratings contain only positive ar-
guments and vice versa.
With Mechanism 3 we relax Mechanism 2 a bit,
assuming that positive reviews can contain also neg-
ative arguments. In this case, for a certain positive
rating (3, 4 or 5) the probability to contain a positive
arguments is given by:
1/1 + exp(α β x)
As in Mechanism 2, however, negative reviews
contain only negative arguments.
We have a very simple scheduling: at each time
step, reviewers examine their probability to review
the product. If this is the case, then they “write” a
review with their rating and all the arguments they
know. Each reviewer can review just once. The result
of this process is simply a list of lists, where every
inner list represents an agent’s review.
We simulate each of the three mechanisms 100
times and we record, for each outcome, the distribu-
ICAART 2016 - 8th International Conference on Agents and Artificial Intelligence
Figure 4: Our simulation running in NetLogo.
tion of positive and negative arguments, as well as the
corresponding ratings. We then compare each simu-
lated result against empirical data using the euclidean
distance between the two curves, and report the dis-
tributions of distances as box-plots in Figure 5.
Figure 5 (a) shows, for each mechanism, the dis-
tribution of distances from the cumulative frequency
curve of positive arguments. It is evident that all
mechanisms can produce equally distant curves from
the empirical one. When it comes to negative argu-
ments, however, things are different. Figure 5 (b)
shows the distribution of distances from the cumula-
tive frequency curve of negative arguments: it is ev-
ident that Mechanisms 2 and 3 do a statistically bet-
ter job. Figure 5 (c) shows, for positive arguments,
the distribution of distances from the curve of ratings
over time. While it looks like Mechanism 3 is per-
forming slightly better than the others, we can say
that the three mechanisms are doing pretty much the
same job. When it comes to the same measure, but for
negative arguments, Figure 5 (d) shows clearly that
Mechanism 3 performs better than the others, produc-
ing curves of ratings versus time that are statistically
more close to the empirical one w.r.t. the other two
In this paper we have proposed a first exploratory
study on how to use Abstract Argumentation to under-
stand how it can improve our knowledge about social
trends in product reviews.
More in particular, we “enter” into an Ama- review and we achieve a more granular view
of it by considering the different arguments expressed
in each of the 253 reviews about the randomly se-
lected product (a ballerina tutu). What we observe is
that the frequency of negative arguments (against pur-
chasing the tutu) increases after some time, while the
distribution of positive arguments (in favour of pur-
chasing the tutu) is more balanced between the con-
sidered period. Moreover, while positive arguments
are always associated with high ratings (i.e., 4 or 5),
negative arguments are associated with low (as ex-
pected) but also high ratings. In addition, negative ar-
guments are more frequently associated with shorter
reviews, while enthusiasts tend to be less concise. To
summarise, the aim is to “explode” reviews into argu-
ments and then try to understand how the behaviour
of reviewers changes through time, from the point of
view of arguments.
In the second part of the paper (Sec. 5) we ded-
icate ourselves to the replication of the social phe-
nomenon measured in the first sections: we propose
three different core mechanisms to understand the two
main stylised facts observed in the data: a) the ten-
dency for average review rating to decrease with time,
and b the presence of negative arguments in reviews
with positive ratings. By modelling both positive and
negative arguments in reviews rather than either pos-
itive or negative ones, it is possible to get closer to
the experimented empirical curves concerning the fi-
nal rating (from 1 to 5 stars).
Our aim is to detail a work flow to model cus-
tomers’ behaviour when it comes to review prod-
ucts. Our idea is that, by understanding arguments,
we could better understand why people do things in
a particular context, in this case buy or not a product.
We are full aware of the little explanatory power of
our study due to our limited empirical investigation
conducted by hand. We nevertheless think that pro-
gresses in argument mining will help us to overcome
this constraint. One of the most interesting outcome
From Arguments and Reviewers to their Simulation - Reproducing a Case-Study
Figure 5: Simulation results: each of the plots shows the distribution of euclidean distances between simulated curves and
empirical ones over 100 replications. For Mechanism 3, α = 4.8 and β = 1.8.. From left to right in each figure, Mechanisms
from 1 to 3.
of our approach is to being able to couple our analy-
sis with products selling data: this would open a new
research approach for correlating what customers say
and what customers do in on-line marketing.
In the future, we will widen our investigation by
taking advantage of mining-techniques, e.g., (Wyner
et al., 2012; Villalba and Saint-Dizier, 2012). In ad-
dition, we plan to consider computational approaches
based on Abstract Argumentation; for instance, if tol-
erating a given low amount of inconsistency (i.e., at-
tacks) in extensions (Bistarelli and Santini, 2010) can
help softening the impact of weak arguments (i.e.,
rarely repeated ones). Due to the possible partitioning
of arguments into clusters related to different aspects
of a product (e.g., either its quality or appearance),
we also intend to apply coalition-oriented semantics,
as proposed in (Bistarelli and Santini, 2013).
Following (Gabbriellini and Torroni, 2014), we
also plan to implement an Agent-Based Model with
Argumentative Agents to explore the possible mech-
anisms, from a user’s perspective, that give raise to
such trends and correlations among positive and neg-
ative arguments.
With our model are in the position to offer a pos-
sible explanation of reviewers’ behaviour, but we still
do not know much about why some opinions are in
place among reviewers nor how they engage in dis-
cussions when they disagree. In other words, we still
know nothing about the arguments used by review-
ers. Much research is at stake in computational ar-
gumentation and some frameworks for agent-based
modelling with argumentative agents have been pro-
posed. It would be interesting to mine the dataset for
arguments and then model how argumentative frame-
works evolve when disagreement is strong: a closer
examinations of such exchanges should lead to more
insightful conclusions.
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From Arguments and Reviewers to their Simulation - Reproducing a Case-Study