# Quantum correlations by four–wave mixing in an atomic vapor in a non-
amplifying regime: a quantum beam splitter for photons.

Quentin Glorieux<EMAIL_ADDRESS>Luca Guidoni  Samuel
Guibal  Jean-Pierre Likforman  Thomas Coudreau  Univ Paris Diderot, Sorbonne
Paris Cité, Laboratoire Matériaux et Phénomènes Quantiques, UMR 7162 CNRS,
F-75205 Paris, France

###### Abstract

We study the generation of intensity quantum correlations using four–wave
mixing in a rubidium vapor. The absence of cavity in these experiments allows
to deal with several spatial modes simultaneously. In the standard,
amplifying, configuration, we measure relative intensity squeezing up to 9.2
dB below the standard quantum limit. We also theoretically identify and
experimentally demonstrate an original regime where, despite no overall
amplification, quantum correlations are generated. In this regime a four–wave
mixing set–up can therefore play the role of a photonic beam splitter with
non–classical properties, i.e. a device that splits a coherent state input
into two quantum correlated beams.

###### pacs:

42.50.Dv, 42.50.Lc, 42.50.Nn

Non–classical ”intense” beam have been widely studied in a large variety of
contexts including potential applications to quantum information protocols
Braunstein05 ; Sangouard10 , fundamental issues in quantum mechanics such as
entanglement and non–locality Reid09 , quantum imaging QuantumImaging  or
enhancement of the sensitivity of gravitational wave interferometers Caves81 .
Quantum correlated beams are usually obtained through optical non–linear
effects described as $\chi^{(2)}$ or $\chi^{(3)}$ non linearities present in a
variety of media (see BachorBook  for a review). In this paper, we study the
generation of quantum correlation by using four–wave mixing (4WM) in a hot
atomic vapor.

Based on $\chi^{(3)}$ non linearity, 4WM is well known to generate intense non
classical beams Yuen79 ; Slusher1985 ; Shelby86 ; Maeda87 . However, over the
last 20 years, attention has been focused mainly on $\chi^{(2)}$ media Wu87 ;
Heidmann87 ; Laurat05 ; Mehmet10  mainly because of their low losses
(availability of high quality optical crystals). On the contrary, in hot
vapors the presence of an atomic resonance enhances the non-linearity but also
usually increases the losses. Yet, recently, it was shown that non–degenerate
4WM in atomic vapors can produce very large amounts of quantum correlations
between intense beams McCormick2007 ; McCormick08 ; GlorieuxSPIE10 . Such a
set–up has a significant advantage over $\chi^{(2)}$ media in that it does not
require an optical cavity to enhance the nonlinearity and the related quantum
effects. This is particularly important in the case of quantum imaging where
spatially multimode quantum effects are involved QuantumImaging ; Boyer08 .
Furthermore, the generated beams directly match the atomic resonance frequency
of an atom–based quantum memory, a key requirement for quantum communications
Sangouard10 .

As noted above, the large nonlinear and quantum effects observed in 4WM
originate from the presence of an atomic resonance. This resonance also
induces incoherent effects, most notably absorption and spontaneous emission,
which, in general, decrease the degree of quantum correlations. These possible
drawbacks are often reduced by increasing the detuning from resonance,
resulting in an overall amplification of the probe and conjugate beams.
However, as we show, there exists a regime where quantum correlations can be
observed despite the fact that the probe beam is de-amplified by propagation
through the atomic vapor. In this regime, a 4WM setup then behaves as a
beam–splitter separating an incoming beam in two different beams, without
overall amplification.

However, when the input beam is in a coherent state, the two output states are
quantum correlated : we thus call this new device a quantum beam–splitter for
photons. On the two input ports of the device are respectively sent the vacuum
state and a coherent state and through the two output ports are emitted the
quantum correlated states. This denomination omits the role of the pump which
is crucial in this scheme as naturally no classical beam splitter can generate
non classical states starting from coherent states. The simplest way to model
theoretically such a device is to chain an ideal linear phase–insensitive
amplifier with a partially transmitting medium. Despite the introduction of
large losses, up to a level that cancels the gain, we show that quantum
correlated beams can be generated in such a configuration. We then introduce
the gemellity Treps05 , a criterion well adapted to describe experiments with
non balanced beams.

Figure 1: (Color Online) a) 4WM in hot atomic vapor schematic setup. b)
Relevant levels of the Rb D1 line described as a double-$\Lambda$ system.
$\Delta$ is the so called one–photon detuning and $\delta$ the two–photon
detuning.

We demonstrate, using a microscopic modelGlorieuxPRA10 , that 4WM in a hot
atomic vapor can efficiently implement a quantum beam–splitter and we show
that the limit for the maximum gemellity predicted in the linear amplifier
model can be theoretically exceeded in this new regime. Finally we test these
predictions experimentally.

##  I 4WM in the amplifying regime

The experiment is based on McCormick2007  and is described in detail in
GlorieuxSPIE10  so that we only recall here its main features. A linearly
polarized intense pump beam, frequency locked near the 85Rb $D_{1}$ line, is
mixed with an orthogonally polarized weak probe beam inside an isotopically
pure cell of length $L$. The relevant levels are shown in Fig. 1, a. At the
output of the cell, due to 4WM, the probe beam is amplified and a conjugate
beam is generated (see Fig. 1, b). After filtering out the pump beam with a
polarizing beam splitter, intensity correlations between the probe and
conjugate beams are measured by a pair of high quantum-efficiency photodiodes
coupled to a spectrum analyzer.

A high gain can be observed for a relatively large set of experimental
parameters. The use of a heated cell yields a large number of atoms: for a
temperature $T$ ranging from 100∘C to 150∘C, the atomic density $\mathcal{N}$
calculated from the Clausius–Clapeyron formula Alcock84  varies from 6$\times
10^{12}~{}$cm-3 to $10^{14}~{}$cm-3. Thus, the equivalent optical depth,
$\mathcal{N}\sigma L$ varies between $5\times 10^{3}$ and $10^{5}$ where
$\sigma$ is the atomic cross section for the 5$S^{1/2}$ $\to$ 5$P^{1/2}$
transition in 85Rb. These atoms interact with beams close to resonance : the
single photon detuning $\Delta$ is typically 1 GHz (on the order of the
Doppler broadening) while the two–photon detuning $\delta$ is smaller than 10
MHz.

Within these domains of parameters, explored systematically GlorieuxSPIE10 ,
we have identified an optimal noise reduction regime. For $\Delta=+750$ MHz,
$\delta=+6$ MHz, $T=118^{\circ}$C, $P_{pump}=1200$ mW (corresponding to a Rabi
frequency $\Omega=1$ GHz), gain on the incoming probe beam up to 20 can be
observed. In these conditions, Fig. 2 shows the noise power of the intensity
difference of the probe and conjugate as a function of the analysis frequency
after correcting for the electronic noise:significant noise reduction is
observed in the range 500 kHz to 5 MHz and with a maximal noise reduction of
9.2 dB$\pm$0.5 dB below the SQL is observed between 1 and 2 MHz. This value is
slightly larger than the best results obtained to date with 4WM McCormick08
and very close to those obtained with OPOs Laurat05 . The matching of the
atomic resonance of Rb turns this setup into an ideal source of non-classical
light to interact with Rb vapor quantum memory PL1 ; PL2 .

Figure 2: (Color Online) Noise power of the intensity difference between the
probe and conjugate beams as a function of the frequency after correcting for
the electronic noise. A reduction of 9.2 dB$\pm$0.5 dB below the SQL is
reached at 1 MHz analysis frequency.

##  II Quantum beam splitter regime

In the previously described regime,the larger was the gain, the larger were
the quantum correlations. However, we will show here that this not a necessary
condition and that one can, somewhat counter-intuitively, observe significant
quantum correlations in the absence of overall gain.

###  II.1 Ideal linear amplifier model

In an ideal phase–insensitive amplifier, an input probe beam is amplified
while a conjugate beam is generated. At the output of the amplifier,
neglecting the contribution of the noise to the average number of photons, the
probe beam has an intensity $GI_{0}$ and the conjugate beam has an intensity
$(G-1)I_{0}$ where $G$ denotes the gain and $I_{0}$ the input probe beam
intensity. Taking into account the ideal character of the amplifier, no noise
is added and the intensity difference at the output has a noise ratio
$1/(2G-1)$ with respect to the input. For probe and conjugate at the input
respectively coherent and vacuum states, this noise ratio is equal to the
quantum correlations at the output of the amplifier. If we now extend this
model by including losses at the output of the medium on the probe and/or
conjugate beams, one would expect a reduction in these correlations as it is
well known that losses are detrimental to squeezing. Let us recall that this
is not always the case as the beams intensity are not balanced: a small amount
of extra losses on the probe beam will tend to make the two beams more
balanced and thus improve the noise reduction on the intensity difference as
noted e.g. in Jasperse11 .

Contrary to the case of Optical Parametric Oscillators above threshold
Laurat05 , 4WM naturally generates unbalanced beams. Unbalanced beams may
exhibit strong quantum correlations but the measurement of the noise on the
intensity difference is not an ideal criterion in this case. It is useful to
introduce the gemellity $\mathcal{G}$ Laurat03 ; Laurat04 ; Treps05 ;
Laurat05b  defined by :

$\mathcal{G}=\frac{F_{a}+F_{b}}{2}-\sqrt{C_{ab}^{2}F_{a}F_{b}+\left(\frac{F_{a}-Fb}{2}\right)^{2}},$
(1)

where $F_{i}=\langle\hat{X}_{i}\hat{X}_{i}\rangle$ with $i$ used for $a$
(probe) and $b$ (conjugate),
$C_{ab}=\frac{\langle\hat{X}_{a}\hat{X}_{b}\rangle}{\sqrt{F_{a}F_{b}}}$ and
$\hat{X}_{i}$ is the amplitude quadrature of the related field as defined in
Treps05 . In case of balanced beams the gemellity is equal to the normalized
noise on the difference between the fluctuations of the two measurements :
$\mathcal{G}=\frac{\langle(\hat{X}_{i}-\hat{X}_{j})^{2}\rangle}{2}$, which is
the quantitative measure of the maximal “non–classicality” that can be
extracted from the correlated beams Treps05 . For balanced beams, such as the
ones produced in the limit of infinite gain, this value is equal to the
standard criterion, namely the intensity noise difference. In the conditions
above where the intensity difference noise is -9.2 dB, the noise on the
individual beams are $F_{a}=F_{b}$ = +12 dB at 1 MHz yielding a gemellity
$\mathcal{G}$ = -9.8 dB $\pm$ 0.5 dB. This value is comparable with record
values measured with an OPO above threshold Laurat05 and moreover a large
number of spatial modes (estimated to 100 in this particular configuration)
are squeezed simultaneously Boyer08  .

Using this criteria and introducing losses on the probe ($T_{a}$) and
conjugate ($T_{b}$) beams so that the overall transmission is equal to one
($T_{a}G+T_{b}(G-1)=1$), it is straightforward to show that there always
exists a region in the parameter space where a gemellity lower that one is
expected. To our knowledge, this phenomenon, albeit simple, has neither been
discussed nor observed. The larger quantum correlations reachable with no
overall amplification corresponds to the situation of a gain $G=1.23$, a
transmission of 0.62 on the probe beam and perfect transmission on the
conjugate beam. This configuration gives the limit for the gemellity reachable
by this simple model : $\mathcal{G}=-2.8$ dB.

###  II.2 Microscopic model

To investigate further this effect, we have studied the 4WM process using a
microscopic model based on the cold–atom model described extensively in
GlorieuxPRA10 . This model assumes the simplified double–$\Lambda$ level
structure of Fig. 1, right. The Heisenberg–Langevin approach is used to obtain
the relevant classical quantities (probe gain $G_{a}$, conjugate gain $G_{b}$
defined with respect to $I_{0}$) as well as the quantum properties of the
output beams. In particular, it is possible to calculate noise spectra that
allow for quantifying quantum correlations both in terms of
intensity–difference noise $S_{N_{-}}$ and for the unbalanced case in terms of
gemellity $\mathcal{G}$. In the regime of high amplification previously
described, this model is in good quantitative agreement with the measured
correlations GlorieuxPhD . Exploring the parameters space in this model, we
have found a new region where the 4WM process generates quantum correlations
in the absence of overall amplification. This regime is therefore very similar
to the linear amplifier model followed by a lossy medium described above.
Nevertheless, the microscopic model predicts that in this regime, the
gemellity can be significantly enhanced in contrast to the linear model and
exceeds the -2.8 dB limit discussed previously.

Let us start by presenting the classical behavior of the probe and conjugate
beams in the region of interest of parameter space (theoretical data are
compared to the experimental results). We plot in Fig. 3, the gain for the two
fields as a function the two–photon detuning $\delta$. The main difference
with respect to the high gain parameter region is the choice of the atomic
density (experimentally driven by the temperature). The large gain results of
Fig. 2 were obtained for a temperature of 118 ∘C while the curves in Fig. 3
are obtained for $T=95^{\circ}$C. The approximately one order of magnitude
lower optical density, together with the different choice of $\delta$ and
$\Delta$, explain the drastic reduction of $G_{a}$ and $G_{b}$.
”Beam–splitter” regime is obtained near the two-photon resonance, where
$G_{a}$ goes to zero due to a Raman process involving a probe and a pump
photonGlorieuxPRA10 . Due to the pump–induced AC-Stark shift, this two–photon
resonance is shifted to negative values of $\delta$ and its exact position
depends on the one-photon detuning $\Delta$ and on the pump Rabi frequency
$\Omega$. Within a very narrow region of parameter space, the sum of the two
beams output intensities becomes slightly smaller or almost equal to the input
probe intensity. It is interesting to note that for potential applications
this very narrow feature could be considered as a limitation. Notwithstanding
we have verified numerically, by changing simultaneously $\Delta$, $\Omega$
and the optical depth $\mathcal{N}\alpha L$, that the detuning for which this
system exhibits the behavior of a quantum beam splitter can be tuned over more
than 100 MHz. As already remarked in GlorieuxPRA10 , we note that, despite the
fact that the model is based on a cold atom sample, it yields without any
adjustable parameter a qualitative agreement with the experimental data
obtained in a hot vapor.

Figure 3: (Color Online) Theoretically predicted (left) and experimentally
measured (right) gain for the probe beam ($G_{a}$, ) and conjugate beam
($G_{b}$, black) as a function of the two–photon detuning $\delta$. The
parameters used in the simulations are : , optical depth $\mathcal{N}\alpha
L=500$, Pump Rabi frequency $\Omega$ = 0.42 GHz, single photon detuning
$\Delta/2\pi$=0.8 GHz. Measured parameters are : pump power $P$=0.6 W
($\Omega/2\pi=0.4$ GHz), T=95∘C, single photon detuning $\Delta/2\pi$=0.8 GHz.
Figure 4: (Color Online) Quantum intensity correlations between the probe and
conjugate beams as a function of the analysis frequency with the same
parameters as above and $\delta/2\pi$=-52 MHz.

###  II.3 Demonstration of the quantum beam splitter

Motivated by these theoretical predictions, we have experimentally
investigated this original regime. We plot in Fig. 4 the experimentally
measured intensity difference noise as a function of the analysis frequency
$\omega$. We observe significant quantum correlations, down to 1.0$\pm 0.2$ dB
below the SQL around an analysis frequency of 1 MHz. At the same time, the
power of the two beams normalized to the probe input power is measured to be
0.65 and 0.35 for the probe and conjugate respectively. This demonstrates
clearly the behavior of a quantum beam splitter for photons where one laser
beam is split into two beams without gain but generating quantum correlations.
We note that the measured noise reduction is slightly smaller than the one
predicted theoretically (Fig. 4): this discrepancy can be attributed to the
fact that the model is based on a cold atomic sample, far from the
experimental regime.

In this situation, $\mathcal{G}$ can be calculated to compare it to the
theoretical limit of the linear amplifier model. By measuring the noise on the
two individuals beams respectively equal to + 3 and + 2 dB for probe and
conjugate, we obtain a value of the gemellity equal to $\mathcal{G}=-1.8\pm
0.5$ dB. This value does not exceed the maximum limit of -2.8 dB predicted by
the linear amplifier model. As previously noted, the theoretical model does
not take into account the velocity distribution of the atoms, thus time
transit effects and Doppler broadening which are expected to play a
detrimental role. This can explain why the linear amplifier model limit cannot
be reached in this configuration whereas the microscopic model predicts that
gemellities better than $\mathcal{G}$=-3.2 dB can be obtained with the above
parameters and an optical depth, $\mathcal{N}\sigma L=1500$.

We have thus shown firstly that generating quantum correlations does not
require overall amplification and, secondly, that the ideal linear amplifier
is not the ideal device to perform this operation but that 4WM in atomic
vapours presents an interesting avenue in this context. This setup could also
be used as the input beam splitter introducing quantum correlations for an
original version of the Mach-Zender interferometer as well as in a so-called
SU(1,1) interferometer Yurke86 .

##  III Conclusion

We have studied the production of quantum correlated beams in four–wave mixing
in a 85Rb cell. First, we have identified and experimentally realized an
optimal regime in the high gain region where intensity–difference noise down
to -9.2 dB below the standard quantum limit (gemellity
$\mathcal{G}=-9.8~{}dB$) have been measured. This result is important in the
domain of quantum communications where both large non–classical effects and
the availability of an atom–based storage media form strong requirements
Sangouard10 ; PL1 ; PL2 .

We have also predicted and observed an original regime where quantum
correlations are present despite significant losses on the probe beam. This
regime is of particular interest, because it can occur in a situation in which
the sum of the two output beam intensities is smaller or equal to the input
probe intensity. Therefore the atomic medium controlled by the pump laser acts
like a beam splitter device that creates quantum correlations (quantum beam
splitter). Although this effect could in principle be observed with an ideal
amplifier, it is to our knowledge the first demonstration of it. In this
context, we have discussed the use of the gemellity criterion more appropriate
in the case of unbalanced beams produced by four–wave mixing. Finally, a
microscopic model allowed us to demonstrate that 4WM in the quantum beam
splitter regime can beat theoretically the limit of quantum correlations
predicted by the model of a linear amplifier followed by a lossy medium.

In our experiment, with a hot atomic vapor, a value of $\mathcal{G}=-1.8\pm
0.5$ dB has been reported. Although the parameter values required to beat the
linear amplifier model limit are presently beyond reach of experiments
performed with cold atoms, our model provides an interesting avenue to surpass
this limit using hot or cold atoms.

## Acknowledgements

We thank E. Arimondo, R. Dubessy and P. Lett for fruitful discussions. We
thank N. Treps for the loan of high–efficiency photodiodes.

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2022

[1]Ivandré Paraboni

[1]School of Arts, Sciences and Humanities, University of São Paulo, Av
Arlindo Bettio 1000, São Paulo, 03828000, SP, Brazil

# UstanceBR: a multimodal language resource for stance prediction

Camila Pereira<EMAIL_ADDRESS>Matheus Pavan<EMAIL_ADDRESS>Sungwon
Yoon<EMAIL_ADDRESS>Ricelli Ramos<EMAIL_ADDRESS>Pablo Costa
<EMAIL_ADDRESS>Laís Cavalheiro<EMAIL_ADDRESS><EMAIL_ADDRESS>*

###### Abstract

This work introduces UstanceBR, a novel multimodal corpus in the Brazilian
Portuguese Twitter domain for target-based stance prediction. The corpus
comprises 86.8 k labelled stances towards selected target topics, and
extensive network information about the users who published these stances on
social media. In this article we describe the corpus multimodal data, and a
number of usage examples in both in-domain and zero-shot stance prediction
based on text- and network-related information, which are intended to provide
initial baseline results for future studies in the field.

###### keywords:

Corpora, stance prediction, social media, Portuguese NLP

##  1 Introduction

Stance prediction (Kucuk Can, 2020; Aldayel Magdy, 2021) is the task of
automatically determining for/against attitudes towards a given target (e.g.,
a politician, climate change etc.) from text and, in some cases, from
multimodal data (Darwish ., 2017; Lai ., 2019; Sakketou ., 2022). The task is
analogous to sentiment analysis, but distinct in that sentiment and stance do
not necessarily correlate (Kucuk Can, 2020). Thus, for instance, an attitude
in favour of the target ‘World Cup’ may express a negative sentiment, as in ‘I
hate having to work on match days’.

Computational models of stance prediction often rely on supervised machine
learning methods based on annotated corpora, often in in-domain (Mohammad .,
2016) or more recently in zero-shot settings (Allaway McKeown, 2020).
Accordingly, purpose-built corpora have become increasingly common since the
SemEval-2016 English shared task in Mohammad . (2016), and are now available
in multiple languages and domains (Taulé ., 2017; Cignarella ., 2020; Alhindi
., 2021; N. Chen ., 2022).

Most of the existing resources focus on a well-defined set of target entities
(often of a political or moral nature, as in Mohammad . (2016); Pavan .
(2020)), in so-called target-based stance prediction, but others have
addressed the issue of stance towards propositions in quoted text (WF. Chen
Ku, 2018; Alhindi ., 2021). Our own work focuses on the former, that is, on
the development of a novel language resource for target-based stance
prediction, and which is intended to fill a number of gaps in the field. First
of all, we notice that our target language - Brazilian Portuguese, which is
spoken by some 214 million individuals - still lacks resources for target-
based stance prediction. This represents a resource gap not only at the
language level, but also at the domain level since many of the target topics
addressed in existing work (e.g., the Catalonia independence in Taulé .
(2017), Danish immigration policies in Lehmann Derczynski (2019) etc.) would
be unfamiliar among Portuguese speakers.

We notice also that most of the existing resources usually provide text-level
labelling (e.g., individual tweet instances may be labelled as being
for/against a given target etc.), and that few (e.g., Geiss . (2022)) have
addressed the issue of user-level stance prediction (Flores ., 2022) by
providing user- or timeline-level stance labels. Although instance- and user-
level prediction arguably represent distinct task definitions, we notice that
the ubiquity of social media data allows us to build a collection of
individually-labelled instances accompanied by additional user-generated
contents - including their full public timelines, which would support both
kinds of investigation at once.

Finally, as pointed out in Kucuk Can (2020) and others, stance prediction does
not need to rely solely on text data and, at least when applied to social
media settings, models of this kind may greatly benefit from the well-known
principle of homophily, that is, the notion that connection is motivated by
similarity. This suggests that purely text-based resources may be enriched
with a wide range of multimodal information (e.g., friends connections,
interactions etc.) as indeed a number of studies have recently considered
(e.g., Darwish . (2017); Lai . (2019); Cignarella . (2020).)

Based on these observations, we envisage creating a novel multimodal corpus in
the Brazilian Portuguese Twitter domain, hereby called UstanceBR. The corpus
follows the SemEval-2016 tradition of using a pre-defined set of targets
(Mohammad ., 2016), but pays regard to both explicit and implicit stance as in
Gohring . (2021) with a focus on heavily polarised topic pairs as in
Grimminger Klinger (2021).

Although not the first Twitter stance corpus for Portuguese (dos Santos
Paraboni, 2019), UstanceBR is, to the best of our knowledge, the first
resource of its kind to include extensive network information covering not
only the individuals who produce every stance, but also their entire social
media friends and followers lists111Some limited information of this kind has
been made available for other languages in Cignarella . (2020) and Lai,
Cignarella . (2020).. This may potentially support future studies on the role
of polarisation in cross-target and zero-shot scenarios, among many other
possibilities, and which are not necessarily limited to the use of text data.

In addition to describing the corpus itself, in this article we report a
number of usage examples in both in-domain and zero-shot stance prediction
from UstanceBR multimodal data. The main intended contributions are as
follows.

  * •

Large corpus of explicit and implicit stances towards polarised targets in the
Portuguese Twitter domain.

  * •

Accompanying timeline text data and network information for both user-level
and multimodal stance prediction.

  * •

Reference results for in-domain stance prediction based on text and network
data alike, and zero-shot stance prediction from text.

##  2 Background

Table 1 shows recent studies that produced labelled data for stance
prediction, categorised according to target type (a pre-defined set of
entities or quoted text), domain (e=essays, f=facebook, n=news, o=opinions,
p=political discourse, t=Twitter, r=reddit), target language (Ar=Arabic,
Ca=Catalan, Ch=Chinese, Da=Danish, De=German, Du=Dutch, En=English, Fr=French,
It=Italian, Pt=Portuguese, Sp=Spanish, *=others), the availability of non-text
data (net=network features, dem=demographics, or dom=domain information such
as political affiliation etc.), and corpus size in number of instances.

Table 1: Corpora for stance prediction. Corpus | Stance | Domain | Language | Non-textual | Size
---|---|---|---|---|---
Mohammad . (2016) | set | t | En |  | 4,163
Magdy . (2016) | set | t | En | net | 336,294
Persing Ng (2016) | quote | e | En |  | 826
Taulé . (2017) | set | t | Ca,Sp | dem | 10,800
Darwish . (2017) | set | t | Ar | net | 33,024
Sobhani . (2017) | set | t | En |  | 4,455
WF. Chen Ku (2018) | quote | f | Ch | net | 12,828
Lehmann Derczynski (2019) | set | p | Da | dem,dom | 898
Lai . (2019) | set | t | It | net | 968
Conforti . (2020) | set | t | En |  | 51,284
Pavan . (2023) | set | e | Pt | dem | 4,080
Evrard . (2020) | quote | t | Fr |  | 5,803
Vamvas Sennrich (2020) | quote | p | Fr,It,De |  | 67,281
Hosseinia . (2020) | quote | o | En |  | 6,094
Mutlu . (2020) | set | t | En |  | 14,374
Cignarella . (2020) | set | t | It | net | 3,242
Allaway McKeown (2020) | set | o | En |  | 23,525
Lai, Cignarella . (2020) | set | t | En,Fr,It,Sp,Ca | net,dom | 14,440
Lai, Patti . (2020) | set | t | En | net,dom | 1,760
Alhindi . (2021) | quote | n | Ar |  | 4,063
Grimminger Klinger (2021) | set | t | En |  | 3,000
Li . (2021) | set | t | En |  | 21,574
Glandt . (2021) | set | t | En |  | 6,133
Jaziriyan . (2021) | set | t | Ar |  | 9,566
Gohring . (2021) | quote | p | De |  | 1,000
Won Fernandes (2022) | quote | t | Pt |  | 16,655
Geiss . (2022) | set | r | En | net | 2,716,998
Sakketou . (2022) | set | r | En | net | 3,526
N. Chen . (2022) | set | t | Fr,De,Du,Sp,En,* |  | 17,934

Existing corpora for stance prediction fall into two broad categories
according to the kind of target under consideration, hereby identified as set
and quote stance definitions. In the more standard, SemEval-like task
definition, a pre-defined set of targets is considered. These may consist of a
single subject, as the Catalonia independence movement in the IberEval-2017
corpus (Taulé ., 2017), a small and well-defined set of topics (often
following some of the SemEval topics of political or moral nature, such as
Trump, Hillary, abortion legislation etc.), or ultimately any topic covered in
the corpus regardless of their actual number of instances (e.g., Allaway
McKeown (2020); Jaziriyan . (2021)).

In quote-based stance prediction, by contrast, the intended target consists of
a quoted piece of text (e.g., a previous tweet or idea under discussion, a
proposition, a question etc.) In these cases, the notion of a well-defined
target may be secondary or even absent from the data, and different instances
may be simply regarded as referring to different targets. As a result, the
number of possible targets is usually large (Evrard ., 2020; Vamvas Sennrich,
2020; Alhindi ., 2021; Gohring ., 2021; Won Fernandes, 2022), and although the
goals remain essentially the same as in target-based stance prediction,
modelling the intended target as a piece of quoted text may suggest a
different computational approach based on, e.g., text similarity between
target and stance texts.

Regarding the text genre adopted by existing resources, we notice that the use
of data in the Twitter domain (and to a lesser extent other social media) is
common and, as expected, there is a significant presence of corpora in the
English language. Most resources convey purely text data, with a number of
exceptions including mainly network-related information such as interaction
features (e.g., replies, retweets, discussion contexts etc.) or user
demographics, as in Taulé . (2017); Lehmann Derczynski (2019).

Most recent resources are considerably larger than the original SemEval
corpus, but the largest are created either by using label propagation (Magdy
., 2016), by using existing labels available from the data such as response
scores (Vamvas Sennrich, 2020), or are simply labelled at the user level
rather than at the instance level (Geiss ., 2022).

Finally, we notice that most existing corpora use a three- or four-class label
definition (e.g., ‘for’ and ‘against’, accompanied by ‘neutral’, ‘neither’, or
both). An exception is the corpus in Vamvas Sennrich (2020), which focuses on
the for/against extremes only, and which has been partially extended in
Gohring . (2021) to include more fine-grained labels representing either
explicit or implicit stance information as in Sakketou . (2022).

##  3 UstanceBR

The existing work described in the previous section suggests that our target
language - Brazilian Portuguese - remains underrepresented in the field, the
exceptions being the quote-based corpus of European Portuguese tweets in Won
Fernandes (2022), and Brazilian Portuguese essays corpus BRmoral (Pavan .,
2023). As a result, target topics covered by previous work do not generally
match our local interests (e.g., Brazilian politics). An exception is the
corpus of stances towards the use of Hydroxychloroquine as a bogus Covid-19
treatment in Mutlu . (2020), available in the English language. None of these
resources, however, comprises multimodal data.

Based on these observations we envisage creating a novel multimodal corpus of
labelled tweets and accompanying network information in the Brazilian
Portuguese language. The corpus, called UstanceBR, focuses on six targets
organised in polarised pairs (two Brazilian presidents, two kinds of
medication, and two institutions) mostly favoured by either conservative or
liberal individuals. The target pairs consist of two politicians of left/right
leaning, two medications that were widely debated during the Covid-19 pandemic
(a vaccine generally favoured by liberals, and a drug promoted by
conservatives as alternative treatment), and two institutions (a TV channel
often attacked by the conservatives, and the church).

The corpus data is organised around three related datasets: a stance corpus
conveying 86.8 k tweets (1.7 million words) labelled as for/against/other
towards the six targets under consideration, a timelines corpus conveying 34.8
million public tweets (511.8 million words) published by every individual who
authored a for/against stance, and additional network data conveying (4.5
million unique friend and follower identifiers, and additional user mentions.)

###  3.1 Data collection and annotation

Data collection started by searching publicly available Portuguese-speaking
Twitter data for keywords representing the six targets of interest. These were
the Brazilian politicians ‘Lula’ and ‘Bolsonaro’, ‘Cloroquina’
(Hydroxychloroquine), ‘Coronavac’ (Sinovac vaccine), ‘Globo’ (a popular TV
channel), and ‘igreja’ (church). About 30k matching tweets from the 2018-2020
period were retrieved for each target.

Duplicates were removed from the raw data, and each target was annotated by
two independent judges over a six-month period. As a means to focus on the
more clear cases of for/against stances, we decided to keep in the final
dataset only those instances for which there was agreement between the two
judges. As for the ‘other’ category, however, we kept all instances that were
marked as ‘other’ by at least one of the two annotators, the underlying
assumption being that these cases were not sufficiently clear to qualify as a
proper stance towards the target.

Annotation was carried out until each annotator obtained at least 4000
positive (for) and further 4000 negative (against) instances for each target
regardless of the number of ‘other’ instances that they came across during the
process. For most classes, however, the final corpus size turns out to be
below 4000 instances as we only kept the instances in which there was total
agreement between judges.

Annotation followed a protocol similar to Mohammad . (2016) and others, in
which judges were requested to decide whether a given tweet expressed a stance
towards the target or not and, if so, whether the stance would be in favour or
against it. Thus, all tweets mentioning a keyword with a different meaning, or
different context, were marked as ‘others’. These include, for instance, the
use of homonyms (e.g., the word ‘church’ referring to a physical building
rather than a religious institution), the expression of factual information
(e.g., ‘Lula was the president at that time’) and others.

One significant difference between the present protocol and those followed in
Mohammad . (2016) and similar studies is the definition of what actually
counts as a stance. More specifically, unlike annotation tasks in which
linguistic topic may coincide with the stance target, annotators were
instructed to take a broad perspective and consider how the tweet reflected
upon the target even if the target was not the main topic under discussion.
Thus, for instance, a corruption scandal in the government of Lula or
Bolsonaro was to be interpreted as a stance against them even if the main
topic under discussion was the scandal, and not the person of the president
himself. As a result, UstanceBR may be seen as a collection of more explicit
(e.g., ‘TV Globo has done a great job’) and less explicit (‘I love the film
that TV Globo is broadcasting right now’) stances towards the selected
targets. Similar distinctions have been addressed in Gohring . (2021).

Table 2 presents average agreement and Cohen’s Kappa results for the six
target topics.

Table 2: Annotation agreement. Target | % Agreement | Cohen’s Kappa
---|---|---
Bolsonaro | 89.9 | 0.79
Lula | 57.7 | 0.24
Hydrox. | 91.7 | 0.82
Sinovac | 89.2 | 0.78
Globo TV | 88.2 | 0.76
Church | 63.3 | 0.28

From these results we notice that inter-annotator agreement was very high or
substantial for four targets (Hydroxychloroquine, Bolsonaro, Sinovac and Globo
TV), and fair for the other two (Lula and Church). Upon manual inspection,
lower agreement was found to be largely due to a more conservative view on so-
called implicit stances (e.g., ‘The film Globo TV showed last night was
great’, which reflects only indirectly upon the TV network), which were often
dismissed by one of the annotators as ‘others’ instead of ‘for’ or ‘against’.

For the set of agreed for/against stances, we also collected the entire public
timeline of every user, and their lists of friends and followers. Finally, the
stance dataset was expanded to include those tweets marked as ‘other’ as
discussed above, bearing in mind that these are not accompanied by user
timelines.

Each of the six subsets in the stance corpus was subject to a random 75-25
train-test split as a means to provide reference results for future work, as
reported in Section 4. As Twitter privacy policies forbids the reproduction of
tweet contents, the corpus is provided as a set of labelled tweet
identifiers222https://drive.google.com/drive/folders/1qThfcIe0HjwVbsDVgkot-
AqgnXoJdB0K.

###  3.2 Descriptive statistics

UstanceBR main component, the stance corpus proper, is a collection of
labelled tweets in the Portuguese language not unlike the SemEval corpus in
Mohammad . (2016). Descriptive statistics are summarised in Table 3.

Table 3: Stance corpus descriptive statistics. Target | Against | For | Other | All | Words | W/tweets
---|---|---|---|---|---|---
Bolsonaro | 5,565 | 3,849 | 1,668 | 11,082 | 259,521 | 23.4
Lula | 4,514 | 3,806 | 13,934 | 22,254 | 422,064 | 19.0
Hydrox. | 3,978 | 4,017 | 1,785 | 9,780 | 277,824 | 28.4
Sinovac | 4,058 | 3,915 | 932 | 8,905 | 252,663 | 28.4
Globo TV | 3,341 | 2,672 | 15,253 | 21,266 | 214,876 | 10.1
Church | 3,539 | 3,598 | 6,329 | 13,466 | 322,289 | 23.9
Overall | 24,995 | 21,857 | 39,901 | 86,753 | 1,749,237 | 20.7

We notice that the smaller sizes of some subsets (e.g., Sinovac) are due to
the higher proportion of stances (for/against) on social media. In other
words, most mentions to Sinovac do represent a stance for/against vaccination,
whereas factual mentions to targets such as Lula or Globo TV are more common.
These limitations not withstanding, we notice that the final dataset -
comprising 86,753 labelled tweets - is considerably larger than existing work
in the field, with the exception of the aforementioned studies that use label
propagation (e.g., Magdy . (2016)) or user-level labelling (e.g., Geiss .
(2022)).

UstanceBR timelines corpus is a collection of unlabelled tweets comprising the
entire public timeline of the above individuals who provided for/against
stances, from which the stances that appeared in the above stance corpus were
removed. This dataset is intended to support future studies in user-level
stance prediction (Geiss ., 2022). Descriptive statistics are summarised in
Table 4.

Table 4: Timelines corpus descriptive statistics. Target | Users | Male % | Tweets(mi) | T/users | Words(mi) | W/tweets | W/users
---|---|---|---|---|---|---|---
Bolsonaro | 756 | 42.2 | 2,939 | 3,887.3 | 35,756 | 12.2 | 47,296.5
Lula | 1,097 | 59.1 | 4,160 | 3,792.5 | 58,152 | 14.0 | 53,010.1
Hydrox. | 2,298 | 73.9 | 4,904 | 2,134.1 | 94,845 | 19.3 | 41,272.7
Sinovac | 3,099 | 70.5 | 6,562 | 2,117.6 | 120,828 | 18.4 | 38,989.3
Globo TV | 1,661 | 41.8 | 6,632 | 3,992.7 | 81,180 | 12.2 | 48,874.4
Church | 2,404 | 39.7 | 9,627 | 4,004.4 | 121,072 | 12.6 | 50,362.9
Overall | 11,315 | 57.4 | 34,824 | 3,077.7 | 511,834 | 14.7 | 45,235.0

Finally, UstanceBR network data consists of anonymised identifiers
representing the lists of friends, followers and user mentions (the latter
being computed from the user’s timeline texts) of the individuals who provided
for/against stances. Descriptive statistics are summarised in Table 5.

Table 5: Network data descriptive statistics. Target | Friends avg. | Followers avg. | Mentions avg.
---|---|---|---
Bolsonaro | 774 | 1277 | 106
Lula | 919 | 1687 | 160
Hydrox. | 1579 | 7665 | 723
Sinovac | 1588 | 11490 | 598
Globo TV | 944 | 2270 | 112
Church | 931 | 2684 | 119

##  4 In-domain stance prediction from text and network data

In-domain stance prediction represents the standard task in which both train
and test data refer to the same intended target. In what follows we describe
initial results for in-domain stance prediction towards each of the six
UstanceBR targets independently, and using both text and network data (namely,
lists of friends, followers and mentions to other users) as an input.

For text-based prediction, we use a standard neural architecture with a number
of parameters selected through grid search. The input consists of a token
embedding layer provided by BERTabaporu (da Costa ., 2023), a BERT language
model that has been pre-trained on 237 million tweets in
Portuguese333https://huggingface.co/pablocosta/bertabaporu-large-uncased, from
which either the last one or the last four BERT layers are taken. In the
latter, a 3072-dimensional vector is taken to represent the embedding for each
token. The embeddings layer is followed by 1 or 2 recurrent layers of
bidirectional long short-term memory (BiLSTM) cells of 16 or 128 hidden
dimensions, a 32- or 64-density multi-head self-attention mechanism of 1 or 16
attention heads, and a dense layer activated by a sigmoid function to output
the final prediction. All layers use dropout regularisation. The output is
represented by the hidden state of the last 1 or 2 layers, and when there are
multiple layers for a single token, these are concatenated. Results appeared
in da Costa . (2023), and are presently reported for comparison with the
network-based approach.

For network-based prediction, we build three separate models based on friends,
followers and user mention lists, all of which following a bag-of-users
approach based on TF-IDf counts (that is, creating bag-of-friends, bag-of-
followers, and bag-of-mentions vectors.) Each of these vectors is taken as an
input to a logistic regression classifier using liblinear solver and balanced
class weights with optimal penalty (L1 or L2), $C$ (0.1 to 1.9) and tol (0.01
to 1e-05) parameter values obtained through grid search.

Table 6 report results for in-domain stance prediction based on text- and
network-related features. Text results (left side of the table) appeared in da
Costa . (2023).

Table 6:  In-domain stance prediction results using text- and network-related features. Best F1 scores for each target are highlighted. | Text | Friends | Followers | Mentions
---|---|---|---|---
Target | P | R | F1 | P | R | F1 | P | R | F1 | P | R | F1
Bolsonaro | 0.88 | 0.87 | 0.87 | 0.95 | 0.94 | 0.94 | 0.94 | 0.92 | 0.93 | 0.95 | 0.94 | 0.95
Lula | 0.85 | 0.84 | 0.85 | 0.91 | 0.91 | 0.91 | 0.90 | 0.87 | 0.88 | 0.90 | 0.90 | 0.90
Hydrox. | 0.85 | 0.85 | 0.85 | 0.89 | 0.88 | 0.88 | 0.87 | 0.85 | 0.85 | 0.93 | 0.93 | 0.93
Sinovac | 0.86 | 0.86 | 0.86 | 0.86 | 0.85 | 0.84 | 0.83 | 0.80 | 0.79 | 0.90 | 0.90 | 0.90
Globo TV | 0.90 | 0.90 | 0.90 | 0.75 | 0.74 | 0.72 | 0.77 | 0.71 | 0.67 | 0.77 | 0.77 | 0.75
Church | 0.87 | 0.87 | 0.87 | 0.77 | 0.76 | 0.75 | 0.76 | 0.59 | 0.51 | 0.78 | 0.78 | 0.78

F1 scores from Table 6 are generally high for most targets and models but,
perhaps surprisingly, the content-based Text model obtains best results only
for two targets (Globo TV and Church). For the others, network-based models
are generally superior and, in particular, the bag-of-mentions model stands
out. This outcome reinforces the view that social media interaction may play a
significant role in stance prediction (Magdy ., 2016; Darwish ., 2017; WF.
Chen Ku, 2018; Lai ., 2019; Cignarella ., 2020; Lai, Cignarella ., 2020; Lai,
Patti ., 2020; Geiss ., 2022; Sakketou ., 2022).

##  5 Zero-shot stance prediction from text data

From a machine learning perspective, in-domain classification will arguably
obtain optimal results as long as suitably labelled training data is available
for every target of interest. However, as the number of possible targets is
unlimited, more adaptable approaches are called-for. Among these, a common
strategy is the use of zero-shot methods (Allaway McKeown, 2020), that is,
building a model from a source domain that happens to be available (i.e., from
labelled stances towards some other topic), and then using this model to
predict, possibly with some accuracy loss, stances towards the intended
target.

An initial investigating of zero-shot methods for stance detection, based on a
preliminary version of the present corpus data, appeared in Pavan Paraboni
(2022). Using now the entire dataset, we conducted two sets of pairwise
experiments. In the first setting, source data is obtained from the more
closely-related target in the corpus, that is, each target is predicted from
stances towards its own counterpart (i.e., the president, medication, or
institution that is not the target itself.) In the second setting, we attempt
to use every possible target other than the target itself as source data, and
report results from the best alternative for each scenario.

The zero-shot experiments use the same in-domain classifiers described in the
previous section, varying only the source data. Results from pairwise zero-
shot stance prediction considering both closely-related target-source domain
pairs, and only the best source domain for each target, are shown in Table 7.

Table 7:  Pairwise zero-shot stance prediction results.  | Closely-related source domain | Best source domain
---|---|---
Target | Source | P | R | F1 | Source | P | R | F1
Bolsonaro | Lula | 0.59 | 0.57 | 0.56 | Lula | 0.59 | 0.57 | 0.56
Lula | Bolsonaro | 0.53 | 0.54 | 0.53 | Sinovac | 0.73 | 0.72 | 0.70
Hydrox. | Sinovac | 0.72 | 0.71 | 0.71 | Sinovac | 0.72 | 0.71 | 0.71
Sinovac | Hydrox. | 0.66 | 0.66 | 0.65 | Lula | 0.74 | 0.74 | 0.74
Globo TV | Church | 0.31 | 0.35 | 0.30 | Lula | 0.72 | 0.72 | 0.72
Church | Globo TV | 0.31 | 0.34 | 0.31 | Hydrox. | 0.66 | 0.65 | 0.64

Zero-shot stance prediction results are below those obtain by text-based in-
domain prediction (left side of Table 6), particularly when using the more
closely-related source domain for each target (on the left side of Table 7).
This suggests that semantic relatedness may not be a major (or at least not
the only) factor at play in our current setting, and indeed there seems to be
a better source domain for most targets, as shown on the right side of the
table.

##  6 Final remarks

This work has introduced UstanceBR, a novel multimodal corpus of labelled
stance for the Brazilian Portuguese language. The corpus has been made
publicly available for reuse, and it is suitable for both in-domain and zero-
shot stance prediction based on text- and network-related data alike.

In the case of in-domain prediction, we notice that despite the high accuracy
obtained by some of our current models (particularly when using interaction-
related features), we may ask whether combining text- and network-related
features into a single model may improve these results even further. This may
be accomplished, for instance, by making use of an ensemble architecture.
Moreover, although the corpus data include the full timelines of every
individual represented in the corpus, we notice that the related issue of
user-level prediction (Geiss ., 2022) still remains to be investigated.

We notice also that stance prediction is well-known to rely heavily on
supervised methods, and that creating large labelled corpora may be costly or
even unrealistic when the number of targets under consideration becomes overly
large. As a means to overcome these difficulties, zero-shot models are in
demand. However, our own experiments in zero-shot stance prediction are
presently limited to pairwise settings, leaving a number of research questions
to be addressed as future work. These include the issues of how semantic
similarity and polarisation may impact the task outcome, and whether the use
of larger models that combine all the available domain sources may outperform
the current baseline.

Finally, we notice that even when relying on zero-shot prediction, the
availability of labelled data may be still insufficient for practical
applications. One possible alternative in these cases may be the use of data
augmentation methods (Magdy ., 2016) as a means to label large datasets in
semi-supervised fashion. We believe that the models introduced in the present
work may be taken a starting point for studies of this kind.

##  7 Declarations

Funding. This work has been supported by FAPESP grant # 2021/08213-0. This
work was carried out at the Center for Artificial Intelligence (C4AI-USP),
with support by the São Paulo Research Foundation (FAPESP grant #2019/07665-4)
and by the IBM Corporation.
Authors’ contribution. Conceptualisation, Methodology, Investigation, Writing,
Supervision: I. Paraboni. Corpus annotation: C. Pereira, M. Pavan, S. Yoon, R.
Ramos, P. Costa. Validation, Formal analysis: C. Pereira. Text-based
experiments: M. Pavan. Network-based experiments: L. Cavalheiro. Language
models: P. Costa. Manuscript revision: all.
Conflicts of interest. The authors declare none.

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  * Hosseinia . (2020) corp-contemporaryHosseinia, M., Dragut, E. Mukherjee, A.  2020\.  Stance Prediction for Contemporary Issues: Data and Experiments Stance prediction for contemporary issues: Data and experiments.  8th International Workshop on Natural Language Processing for Social Media 8th international workshop on natural language processing for social media ( 32–40).  OnlineAssoc. for Computational Linguistics.  10.18653/v1/2020.socialnlp-1.5
  * Jaziriyan . (2021) corp-exascJaziriyan, M.M., Akbari, A. Karbasi, H.  2021\.  ExaASC: A General Target-Based Stance Detection Corpus in Arabic Language ExaASC: A General Target-Based Stance Detection Corpus in Arabic Language.  11th International Conference on Computer Engineering and Knowledge (ICCKE) 11th international conference on computer engineering and knowledge (iccke) ( 424–429).  Mashhad, IranIEEE.  10.1109/ICCKE54056.2021.9721486
  * Kucuk Can (2020) st-survey2Kucuk, D. Can, F.  2020\.  Stance Detection: A Survey Stance detection: A survey.  ACM Computing Surveys5311–37.
  * Lai, Cignarella . (2020) corp-lai-muti2020Lai, M., Cignarella, A.T., Farias, D.I.H., Bosco, C., Patti, V. Rosso, P.  2020\.  Multilingual stance detection in social media political debates Multilingual stance detection in social media political debates.  Computer Speech and Language63.
  * Lai, Patti . (2020) corp-brexitLai, M., Patti, V., Ruffo, G. Rosso, P.  2020\.  #Brexit: Leave or remain? the role of user’s community and diachronic evolution on stance detection #Brexit: Leave or remain? the role of user’s community and diachronic evolution on stance detection.  Journal of Intelligent & Fuzzy Systems3922341–2352.
  * Lai . (2019) corp-homophilyLai, M., Tambuscio, M., Patti, V., Ruffo, G. Rosso, P.  2019\.  Stance polarity in political debates: A diachronic perspective of network homophily and conversations on Twitter Stance polarity in political debates: A diachronic perspective of network homophily and conversations on twitter.  Data & Knowledge Engineering124C1–20.
  * Lehmann Derczynski (2019) corp-danishLehmann, R. Derczynski, L.  2019\.  Political Stance in Danish Political stance in Danish.  22nd Nordic Conference on Computational Linguistics 22nd nordic conference on computational linguistics ( 197–207).  Turku, FinlandLinköping University Electronic Press.
  * Li . (2021) corp-pstanceLi, Y., Sosea, T., Sawant, A., Nair, A.J., Inkpen, D. Caragea, C.  2021\.  P-Stance: A Large Dataset for Stance Detection in Political Domain P-stance: A large dataset for stance detection in political domain.  Findings of ACL-IJCNLP-2021 Findings of ACL-IJCNLP-2021 ( 2355–2365).  OnlineAssoc. for Computational Linguistics.  10.18653/v1/2021.findings-acl.208
  * Magdy . (2016) corp-islamMagdy, W., Darwish, K., Abokhodair, N., Rahimi, A. Baldwin, T.  2016\.  #ISISisNotIslam or #DeportAllMuslims? Predicting Unspoken Views #ISISisNotIslam or #deportallmuslims? predicting unspoken views.  8th ACM Conference on Web Science 8th ACM conference on web science ( 95–106).  New York, NY, USAAssoc. for Computing Machinery.  10.1145/2908131.2908150
  * Mohammad . (2016) semeval2016Mohammad, S., Kiritchenko, S., Sobhani, P., Zhu, X. Cherry, C.  201606\.  SemEval-2016 Task 6: Detecting Stance in Tweets SemEval-2016 task 6: Detecting stance in tweets.  10th International Workshop on Semantic Evaluation (SemEval-2016) 10th international workshop on semantic evaluation (SemEval-2016) ( 31–41).  San Diego, CaliforniaAssoc. for Computational Linguistics.  10.18653/v1/S16-1003
  * Mutlu . (2020) corp-cloroqMutlu, E., Oghaz, T., Jasser, J., Tutunculer, E., Rajabi, A., Tayebi, A.Garibay, I.  2020\.  A stance data set on polarized conversations on Twitter about the efficacy of hydroxychloroquine as a treatment for COVID-19 A stance data set on polarized conversations on twitter about the efficacy of hydroxychloroquine as a treatment for covid-19.  Data in brief33106401.
  * Pavan . (2023) brmoralPavan, M.C., dos Santos, V.G., Lan, A.G.J., ao Trevisan Martins, J., dos Santos, W.R., Deutsch, C.Paraboni, I.  2023\.  Morality Classification in Natural Language Text Morality classification in natural language text.  IEEE transactions on Affective Computing141857–863.  10.1109/TAFFC.2020.3034050
  * Pavan . (2020) bracis-pavanPavan, M.C., dos Santos, W.R. Paraboni, I.  2020\.  Twitter Moral Stance Classification using Long Short-Term Memory Networks Twitter Moral Stance Classification using Long Short-Term Memory Networks.  BRACIS-2020 proceedings LNAI 12319 BRACIS-2020 proceedings LNAI 12319 ( 636–647).  Springer.  10.1007/978-3-030-61377-8_45
  * Pavan Paraboni (2022) ustancebr1Pavan, M.C. Paraboni, I.  2022\.  Cross-target Stance Classification as Domain Adaptation Cross-target stance classification as domain adaptation.  O. Pichardo Lagunas, J. Martínez-Miranda B. Martínez Seis (), Advances in Computational Intelligence - MICAI 2022 - Lecture Notes in Artificial Intelligence vol 13612 Advances in Computational Intelligence - MICAI 2022 - Lecture Notes in Artificial Intelligence vol 13612 ( 15–25).  ChamSpringer Nature Switzerland.  10.1007/978-3-031-19493-1_2
  * Persing Ng (2016) corp-essaysPersing, I. Ng, V.  2016\.  Modeling Stance in Student Essays Modeling stance in student essays.  ACL-2016 proceedings ACL-2016 proceedings ( 2174–2184).  Berlin, GermanyAssoc. for Computational Linguistics.  10.18653/v1/P16-1205
  * Sakketou . (2022) corp-radicalSakketou, F., Lahnala, A., Vogel, L. Flek, L.  2022\.  Investigating User Radicalization: A Novel Dataset for Identifying Fine-Grained Temporal Shifts in Opinion Investigating user radicalization: A novel dataset for identifying fine-grained temporal shifts in opinion.  LREC-2022 proceedings LREC-2022 proceedings ( 3798–3808).  Marseille, FranceELRA.
  * Sobhani . (2017) corp-multitargetSobhani, P., Inkpen, D. Zhu, X.  2017\.  A Dataset for Multi-Target Stance Detection A dataset for multi-target stance detection.  EACL-2017 proceedings EACL-2017 proceedings ( 551–557).  Valencia, SpainAssoc. for Computational Linguistics.
  * Taulé . (2017) corp-iberaval2017Taulé, M., Martí, M.A., Pardo, F.M.R., Rosso, P., Bosco, C. Patti, V.  2017\.  Overview of the Task on Stance and Gender Detection in Tweets on Catalan Independence at IberEval 2017 Overview of the task on stance and gender detection in tweets on catalan independence at IberEval 2017\.  2nd Workshop on Evaluation of Human Language Technologies for Iberian Languages (IberEval 2017) 2nd workshop on evaluation of human language technologies for iberian languages (IberEval 2017) ( 157–177).  Murcia, SpainCEUR-WS.org.
  * Vamvas Sennrich (2020) corp-xstanceVamvas, J. Sennrich, R.  2020\.  X-stance: A Multilingual Multi-Target Dataset for Stance Detection X-stance: A Multilingual Multi-Target Dataset for Stance Detection.  5th Swiss Text Analytics Conference (SwissText) 5th swiss text analytics conference (SwissText) ( 1–12).  Zurich, SwitzerlandCEUR-WS.org.
  * Won Fernandes (2022) corp-portugueseWon, M. Fernandes, J.  2022\.  SS-PT: A Stance and Sentiment Data Set from Portuguese Quoted Tweets SS-PT: A stance and sentiment data set from portuguese quoted tweets.  PROPOR-2022 proceedings PROPOR-2022 proceedings ( 110–121).  Fortaleza, BrazilSpringer.  10.1007/978-3-030-98305-5_11