# Influence of the projectile geometry on the momentum transfer from a kinetic
impactor and implications for the DART mission

S. D. Raducan  Space Research and Planetary Sciences, Physikalisches Institut,
University of Bern, Switzerland;  M. Jutzi  Space Research and Planetary
Sciences, Physikalisches Institut, University of Bern, Switzerland;  T. M.
Davison  Impacts and Astromaterials Research Centre, Department of Earth
Science and Engineering, Imperial College London, United Kingdom;  M. E.
DeCoster  The Johns Hopkins University Applied Physics Laboratory, Laurel, MD,
United States.  D. M. Graninger  The Johns Hopkins University Applied Physics
Laboratory, Laurel, MD, United States.  J. M. Owen  Lawrence Livermore
National Laboratory, Livermore, CA, United States.  A. M. Stickle  The Johns
Hopkins University Applied Physics Laboratory, Laurel, MD, United States.  G.
S. Collins  Impacts and Astromaterials Research Centre, Department of Earth
Science and Engineering, Imperial College London, United Kingdom;

###### Abstract

The DART spacecraft will impact Didymos’s secondary, Dimorphos, at the end of
2022 and cause a change in the orbital period of the secondary. For
simplicity, most previous numerical simulations of the impact used a spherical
projectile geometry to model the DART spacecraft. To investigate the effects
of alternative, simple projectile geometries on the DART impact outcome we
used the iSALE shock physics code in two and thee-dimensions to model vertical
impacts of projectiles with a mass and speed equivalent to the nominal DART
impact, into porous basalt targets. We found that the simple projectile
geometries investigated here have minimal effects on the crater morphology and
momentum enhancement. Projectile geometries modelled in two-dimensions that
have similar surface areas at the point of impact, affect the crater radius
and the crater volume by less than 5%. In the case of a more extreme
projectile geometry (i.e., a rod, modelled in three-dimensions), the crater
was elliptical and 50% shallower compared to the crater produced by a
spherical projectile of the same momentum. The momentum enhancement factor in
these test cases, commonly referred to as $\beta$, was within 7% for the 2D
simulations and within 10% for the 3D simulations, of the value obtained for a
uniform spherical projectile.

The most prominent effects of projectile geometry are seen in the ejection
velocity as a function of launch position and ejection angle of the fast
ejecta that resides in the so-called ‘coupling zone’. These results will
inform the LICIACube ejecta cone analysis.

##  Introduction

NASA’s Double Asteroid Redirection Test (DART) will impact the secondary of
the 65803 Didymos binary asteroid system, Dimorphos, at the end of 2022 (Cheng
et al., 2018). The impact will cause a measurable change in the orbital period
of Dimorphos around the primary. Several years after the DART impact, ESA’s
Hera mission will arrive at the system and will characterise the binary system
in detail, particularly Dimorphos and the crater produced by DART on its
surface (Michel et al., 2018).

When deflecting an asteroid by the means of a kinetic impactor, the amount by
which the asteroid is deflected is determined by the target properties (e.g.,
cohesion, porosity, internal friction) and impact conditions (e.g., impact
speed, projectile characteristics, impact angle). In the case of the DART
impact, the impact conditions are mostly known; the mission design dictates
the projectile shape and mass, the impact speed and to some extent the impact
angle. However, most of the target properties will remain unknown until after
the DART impact. For this reason, recent numerical simulations of the DART
impact have focused on determining the sensitivity of the ejecta momentum
transfer and the asteroid deflection efficiency to surface material properties
and asteroid structure (e.g., Jutzi and Michel, 2014; Stickle et al., 2015;
Bruck Syal et al., 2016; Raducan et al., 2019, 2020). For simplicity and
computational expediency, these studies have used either a uniform solid or
porous aluminium sphere as the impactor. However, the DART spacecraft’s
structure and mass distribution is more complex and the effects caused by the
spacecraft geometry on the crater size and morphology and on the momentum
enhancement are not yet known.

Previous laboratory studies suggest that impactor geometry can have an effect
on the ejecta generated from a high velocity impact. For example, Hermalyn et
al. (2012) conducted laboratory experiments of solid and hollow aluminium
projectiles impacting sand and pumice targets at $\approx$ 2.5 km/s and showed
that the two projectiles types produce ejecta with different velocity and
ejection angle distributions as a function of launch position. The solid
projectile produced ejecta that had a significantly steeper ejection angles
compared to the ejecta produced by the hollow projectiles.

In high velocity impacts on an asteroid the change in momentum of the
asteroid, $\Delta P$, can be amplified by the momentum of crater ejecta that
exceeds the escape velocity, which is often expressed in terms of the
parameter $\beta=\Delta P/mU$, where $mU$ is the impactor momentum (Housen and
Holsapple, 2012). Therefore, in the case of a kinetic impactor, a lower
ejection angle would imply a lower vertical momentum component, and hence a
lower $\beta$ value (Raducan et al., 2021).

Here we used numerical impact simulations in two and three dimensions to
quantify the effects of simple projectile geometries on the crater size and
morphology, and on the ejecta momentum transfer. One of the aims of this study
is to understand the role of simplified projectile geometries on the effects
of a kinetic impact. Such studies are important to determine whether simple
projectile structures are appropriate when modelling a kinetic impactor, which
is the subject of ongoing investigations.

##  Numerical model

We used the iSALE shock physics code in two (-2D) and three dimensions (-3D)
(Wünnemann et al., 2006; Elbeshausen and Wünnemann, 2011) to model DART-like
impacts on asteroid surfaces, considering different projectile geometries.
iSALE is a multimaterial, multirheology extension of the SALE hydrocode
(Amsden et al., 1980), specifically developed for simulating impact processes
and similar to the older SALEB hydrocode (Ivanov et al., 1997; Ivanov and
Artemieva, 2002). iSALE-3D (Elbeshausen et al., 2009; Elbeshausen and
Wünnemann, 2011) uses a 3D solution algorithm very similar to the SALE-2D
solver, as described by Hirt et al. (1974). The development history of
iSALE-3D is described in Elbeshausen et al. (2009). Both codes share the same
material modelling routines, including strength models suitable for impacts
into geologic targets (Collins et al., 2004) and a porosity compaction model
(Wünnemann et al., 2006).

iSALE-2D has been extensively validated against laboratory impact experiments
(Wünnemann et al., 2016), and benchmarked against other hydrocodes (Pierazzo
et al., 2008; Davison et al., 2011; Stickle et al., 2020) for simulating the
crater size and morphology. Moreover, the ejection velocities and angles
produced by vertical impacts have been shown to be in good agreement with data
from laboratory impacts into sand (Luther et al., 2018; Raducan et al., 2019)
and regolith simulant (Raducan et al., 2021). The crater sizes produced in
iSALE-3D impact simulations into aluminium targets also showed good agreement
with laboratory data (Davison et al., 2011).

This numerical study is divided into two parts that aim to quantify the
effects of projectile geometry on crater size, ejecta mass-velocity
distribution and momentum enhancement: iSALE-2D simulations, in two
dimensions, with an axially symmetric geometry; and iSALE-3D simulations, in
three dimensions, which employ Cartesian coordinates ($x-y-z$). For these
simulations, the computational domain was modelled as a half-space, with a
symmetry axis along the horizontal component of the projectile velocity, in
the $x-z$ plane.

The projectiles used in this study are made of non-porous aluminum with a
given shape and were modelled using the Tillotson equation of state (EoS)
(Tillotson, 1962) and the Johnson-Cook strength model (Johnson and Cook,
1983). The projectile input parameters are summarised in Table 3. In all
simulations, the impact speed was kept constant, at 6.5 km/s, and the
gravitational acceleration, at 5$\times 10^{-5}$ m/s2.

###  Projectile geometry in two-dimensions (2D)

Two-dimensional simulations with simple projectile structures provide a method
for building up intuition about the spacecraft geometry. Because 2D
simulations are less computationally expensive than the 3D equivalents, they
allow us to test a larger number of initial conditions and at higher initial
spatial resolution. In order to determine the final crater size and
morphology, yet capture the subtle differences in the ejecta mass-velocity
distribution, all 2D simulations presented here used regridding (Raducan et
al., 2019). A variety of simple shapes were chosen to represent the DART
spacecraft bus (Fig 1): sphere, cylinder, cylinder with a thick shell,
cylinder with a thin shell, cylinder with a thin shell and an enclosed filled
sphere and a cylinder with a thin shell and with an enclosed hollow sphere. We
note that the projectile structures considered in this study had a similar
contact surface area at impact. All the structures considered here were made
of solid aluminium and had constant mass, $m$ = 650 kg. The projectile
geometries used in this study are shown in Table 1.

Figure 1: Schematic representation of the projectile structures considered in
the iSALE-2D study. Table 1: Table of impactor input parameters from iSALE-2D
simulations (cppr = cells per projectile radius)

.  Shape Dimensions Initial resolution Mass Sphere $r$ = 38.6 cm (80 cppr)
4.82 mm/cppr 650 kg Cylinder $r$ = 33.7 cm (80 cppr) 4.21 mm/cppr 650 kg Thick
shell (cylinder) $r$ = 35.2 cm (80 cppr) 4.41 mm/cppr  650 kg $h$ = 17.6 cm
(40 cppr) Thin shell (cylinder) $r$ = 40.5 cm (80 cppr) 5.06 mm/cppr  650 kg
$h$ = 10.1 cm (20 cppr) Shell (cylinder) + filled sphere $l$ = 35.4 cm (80
cppr) 4.43 mm/cppr  650 kg $r$ = 17.7 cm (40 cppr) Shell (cylinder) + hollow
sphere $l$ = 37.3 cm (80 cppr) 4.66 mm/cppr  650 kg $r$ = 28.0 cm (60 cppr)
$h$ = 7.0 cm (15 cppr)

###  Projectile geometry in three-dimensions (3D)

The second study considered two projectile geometries modelled in three-
dimensions (3D): a $\approx$ 572 kg solid aluminium sphere and a $\approx$ 572
kg horizontal solid aluminium rod (impacting vertically). Because of the low
spatial resolution (5 cppr) employed to model the 3D impact scenarios here,
the projectile is less well resolved than the projectiles in the 2D study.
Therefore, the projectile mass was slightly lower than the theoretical,
assumed $\approx$ 600 kg mass (see Table 2). These two projectile geometries
are extreme cases intended to represent two different idealised spacecraft
geometries (i.e., the rod is a simplification of a spacecraft with solar
panels), and are chosen to maximise possible differences in crater size and
morphology and ejecta mass-velocity distribution. The iSALE-3D simulations
presented here had a much coarser spatial resolution compared to the iSALE-2D
study.

Table 2: Table of impactor input parameters from iSALE-3D simulations (cppr =
cells per projectile radius)

.

Shape |  Dimensions |  Resolution |  Mass
---|---|---|---
Sphere |  $r$ = 37.5 cm (10 cppr) | 2.5 cm/cppr | 572 kg
Rod |  $l$ = 4.42 m (200 cppr) |  2.21 cm/cppr  |  575 kg
$h$ = 11.05 cm (5 cppr)

###  Target material model

For consistency, in all impact scenarios considered here, the target was
modelled using the same material parameters and only varying the target
strength. Observational studies suggest that Didymos is an S-type silicaceous
asteroid system (e.g., de León et al., 2006), however here the target asteroid
material was considered to be made of weak porous basalt, considered to be a
good approximation of most asteroids. Similar analogue material has been used
in previous studies of asteroid impacts (e.g., Durda et al., 2007; Benavidez
et al., 2012; Jutzi and Michel, 2014; Stickle et al., 2015; Jutzi, 2019).
Therefore, the target was modelled using the Tillotson equation of state for
basalt (Tillotson, 1962; Benz and Asphaug, 1999), which is coupled to the
$\epsilon-\alpha$ porosity model (Wünnemann et al., 2006; Collins et al.,
2011), and a pressure-dependent strength model (Collins et al., 2004). The
porosity was kept constant at 20% porosity, which is the current best estimate
of the primary’s porosity (Naidu et al., 2020). To study the influence of the
projectile geometry on the cratering process, given different cratering
efficiencies, here we modelled targets with varying cohesive strength.

The cohesive strength of the target material was modelled using iSALE’s ROCK
model (Collins et al., 2004), a complex strength model in which a pressure-
dependent strength is reduced as strain accumulates, and the yield strength is
defined as:

$Y=Y_{d}D+Y_{i}\left(1-D\right)$ (1)

where $D$ is a scalar measure of damage, which is itself a function of
accumulated plastic strain. The strength of the damaged material, $Y_{d}$ is
defined by a Drucker-Prager relationship:

$Y_{d}=\min\left(Y_{d0}+f_{d}P,Y_{dm}\right),$ (2)

and the intact material strength, $Y_{i}$, is defined by the Lundborg
relationship:

$Y_{i}=Y_{i0}+\frac{f_{i}P}{1+f_{i}P/(Y_{im}-Y_{i0})},$ (3)

where $f_{i}$ is the coefficient of internal friction for intact material and
$f_{d}$ is the coefficient of internal friction for damaged material. The
assumption used in the ROCK strength model is that the strength of the intact
and damaged material are the same above the brittle-ductile transition. For
this reason, here the limiting strength at high pressure for intact material
and the limiting strength for damaged material at high pressure are chosen to
have the same value: $Y_{im}=Y_{dm}=Y_{inf}$. The plastic strain was related
to the damage, $D$, using the damage model described by Ivanov et al. (2010).

In our iSALE-2D impact simulations, we considered two targets that were intact
before the impact ($D$ = 0) and a weaker target, that was fully-damaged before
the impact ($D=1$). In the intact target scenarios, the intact material
strength, $Y_{i0}$, was varied between 1 MPa and 100 MPa, and the cohesive
strength of the damaged material, $Y_{d0}$, was varied between 100 kPa and 10
MPa, respectively. The fully-damaged target had $Y_{d0}$ = 0.1 kPa. In our
iSALE-3D impact simulations, the target was intact before the impact and had
$Y_{i0}$ = 90 MPa and $Y_{d0}$ = 1 MPa. The target input parameters are
summarised in Table 3.

As described in Raducan et al. (2019), it is the post-shock, damaged strength
of the asteroid surface, rather than the intact strength of the material that
exerts most influence on the crater ejecta behaviour. Therefore, to simplify
the notation throughout this paper, we refer to the different target cohesion
configurations described above by their damaged strength, as follows: $Y_{d0}$
= 1 kPa, 0.1 MPa and 10 MPa for the iSALE-2D simulations and $Y_{d0}$ = 1 MPa
for the iSALE-3D simulations.

To be able to simulate a large number of projectile geometries with high
spatial resolution, here we consider relatively strong targets, that have a
low cratering efficiency. Even though the cohesions used here are higher than
the values recently measured on asteroid surfaces (e.g., the recently visited
asteroids Ryugu and Bennu have surfaces with very low cohesions (Arakawa et
al., 2020)), these targets are still suitable to inform possible projectile
geometry effects on the cratering process. While there will be an interplay
between projectile geometry effects and target property effects, projectile
geometry effects will be most evident in impacts with low cratering
efficiency, where the final crater is not much larger than the projectile
size. Conversely, projectile geometry effects are expected to be greatly
diminished in higher cratering efficiency impacts where the crater grows to
many times larger than the projectile. Projectile geometry effects will also
be most evident in the fastest ejecta, produced at very early times close to
the contact zone when target strength effects are negligible because the
driving stresses are so large. Hence, projectile geometry effects on fast
ejecta are expected to be independent of target strength. As $\beta$ is
dominated by the slowest ejecta, projectile effects on deflection efficiency
are also expected to diminish with decreasing target strength. Here we focus
on relatively high cohesion targets to maximise the potential influence of
projectile geometry. Our results should therefore provide an upper bound on
the potential influence of projectile geometry effects.

Table 3: Material model parameters for DART-like impact. For all simulated materials we used the thermal parameters from Ivanov et al. (2010).  Description | Impactor | iSALE-2D target | iSALE-3D target
---|---|---|---
Material | Aluminium | Basalt | Basalt
Equation of state |  Tillotsona |  Tillotsonb |  Tillotsonb
Strength model | Johnson-Cook |  ROCKc |  ROCKc
Poisson ratio, $\nu$ | 0.33 | 0.25 | 0.25
ROCK strength parametersc |  |  |
Intact strength at zero pressure, Yi0 (MPa) | – | -/1/100 | 90
Internal friction coefficient (intact), $f_{i}$ | – | 1.2 | 1.2
Damage strength at zero pressure, Yd0 (kPa) | – |  1/100/104 | 100
Strength at infinite pressure, Yinf (GPa) | – | 3.5 | 3.5
Internal friction coefficient (damaged), $f_{d}$ | – | 0.6 | 0.6
Johnson-Cook strength parametersd |  |  |
Strain coefficient A (MPa), $A$ | 244 | – | –
Strain coefficient B (MPa), $B$ | 488 | – | –
Strain exponent, $n$ | 0.50 | – | –
Strain rate coefficient, $C$ | 0.02 | – | –
Thermal softening, $m$ | 1.7 | – | –
Porosity model parameters ($\epsilon-\alpha$)e |  |  |
Initial porosity, $\phi_{0}$ | - | 20% | 20%
Initial distension, $\alpha_{0}$ | - | 1.25 | 1.25
Distension at transition to power-law, $\alpha_{x}$ | - | 1.00 | 1.00
Elastic volumetric strain threshold, $\epsilon_{e0}$ | - |  -1.88$\times 10^{-4}$ |  -1.88$\times 10^{-4}$
Exponential compaction rate, $\kappa$ | - | 0.90 | 0.90
aTillotson (1962); bBenz and Asphaug (1999); cCollins et al. (2004); dPierazzo
et al. (2008);
eWünnemann et al. (2006). |  |  |

###  Ejecta measurement and resolution tests

In iSALE, tracer particles are placed in the high resolution domain and their
mass, velocity and radial distance are measured at a fixed altitude above the
surface, here set to one impactor diameter, to exclude excavated material
forming in the crater rim that never detaches from the target (Luther et al.,
2018; Raducan et al., 2019). To quantify the effects of spatial resolution
when resolving the impactor and the resulting ejecta, we first conducted a
series of resolution tests.

Figure 2a shows the crater volume as a function of time from iSALE-2D for a
solid aluminium sphere impacting a basalt target (20% porous and $Y_{i0}$ = 10
MPa), resolved with 5, 10, 20, 40 and 80 cells per projectile radius (cppr).
At 0.01 s after the impact, the simulation with the lower, 5 cppr, spatial
resolution underpredicts the crater volume by about 12%, compared to a
simulation with 80 cppr resolution. When comparing the momentum enhancement
from these simulations (Fig. 2b), the difference between the 5 cppr run and
the 80 cppr run was almost 20%. As discussed in Raducan et al. (2019), when
measuring the ejecta, a high spatial resolution is required in order to
capture the high velocity particles. Though these particles have a low mass,
they carry a significant amount of the total ejected momentum. In order to
maintain the high spatial resolution needed to record the fast ejecta at the
beginning of the cratering process, here we used the regridding option
described in Raducan et al. (2019). The regridding option allowed us to
coarsen the simulation domain by a factor of two during the crater formation
process, starting from 80 cppr at the beginning of the simulation and ending
with 10 cppr by the end of the simulation. When regridding was used, the
crater volume and the $\beta$ value recorded was within 2% from the 80 cppr
run (Fig. 2). The small difference in $\beta$ between the 80 cppr and the
simulation that used regridding is due to less fast ejecta being recorded in
the regridding scenario.

Figure 2: a) Crater volume as a function of time for a solid aluminium sphere
impacting a $Y_{i0}$ = 10 MPa target, at resolutions between 80 and 5 cppr,
compared to a simulation where regridding was used. b) Momentum enhancement,
$\beta$ as a function of time, for varying resolution.

###  Fate of projectile

In the impact scenarios considered here, the projectiles experience extreme
shock pressures and temperatures. To record the thermodynamic history of the
impactor material we used Lagrangian tracer particles, which were placed in
every cell across the impactor, at the beginning of the simulation (80 cppr
resolution). This allowed us to record the peak pressures and temperatures,
which were then analysed in post-processing (Potter and Collins, 2013).

The projectile material was modelled using the Tillotson EoS for aluminium
(Tillotson, 1962), which is designed to fit experimentally derived linear
shock velocity–particle velocity relations and is extrapolated to the Thomas-
Fermi limit at high pressures (Pierazzo et al., 1997). While the Tillotson EoS
is a relatively simple model, it is not always thermodynamically consistent
and it is especially inaccurate in the partial vaporisation regime (i.e., when
gas and liquid coexist) (Melosh, 1989). However, the Tillotson EoS for
Aluminium approximates well the material behaviour at low pressures
($\lesssim$ 100 GPa) and a comparison with experimental shock data from LASL
shock Hugoniot data for Al6061 (Marsh and Los Alamos Scientific Laboratory,
1980) shows a very good agreement for these pressures (Fig. 3a), which
indicates that the EoS accurately represents the target material behaviour at
pressures lower than $P\lesssim$ 100 GPa. For pressures higher than $P\gtrsim$
100 GPa, not enough experimental data is available and therefore behavior in
this regime may not be well represented by the EoS. Figure 3b shows the
particle velocity as a function of shock velocity from the EoS, compared to
shock Hugoniot data for AL6061 (Marsh and Los Alamos Scientific Laboratory,
1980).

Figure 3: Tillotson Hugoniot for an aluminium projectile in the a) pressure-
volume (P-V) space and in the b) particle velocity - shock velocity
($v_{p}-v_{s}$) space, compared to shock data from LASL shock Hugoniot data
for Al6061 (Marsh and Los Alamos Scientific Laboratory, 1980).

Figure 4 shows the cumulative mass fraction of impactor material that
experiences peak shock pressures less than a given pressure, for different
shaped projectiles impacting a 0.1 MPa basalt target, at 6.5 km/s. All
projectiles considered here experience similar peak pressures, with only small
differences due to the projectile shape. In the case of the spherical
projectile, all the material experiences pressures of less than $\approx$ 40
GPa, while in the other five projectile scenarios, only 80% of the projectile
material experienced peak pressures of less than 40 GPa. This result indicates
that there is a subtle effect of projectile geometry on the impact effects,
however differences in the spatial distribution of the shock pressures are not
significant enough to play a role on crater morphologies or ejected material.

The peak shock pressures required for incipient and complete melting of the
projectile are given from the intersection of the Hugoniot with the aluminium
isentropes on a phase diagram. The incipient and impressive melting as
measured at 1 bar are $P_{im}$ = 73 GPa, or the complete melting pressure,
$P_{cm}$ = 106 GPa (Pierazzo et al., 1997) and are plotted on Figure 4 for
reference. Our simulation results suggests that regardless of the projectile
geometry, only a small fraction of the projectile material experiences peak
shock pressures higher than the incipient melting pressure, $P_{im}$, or the
complete melting pressure, $P_{cm}$.

In the impact scenarios considered here, most of the projectile material
experiences pressures of less than $P\lesssim$ 100 GPa. However, while in this
region the shock compression behaviour is well represented by the Tillotson
EoS, a more sophisticated EoS (e.g., ANEOS) is required for a more detailed
and more accurate analysis of the thermodynamic effects.

Figure 4: Cumulative mass fraction of impactor material that experiences a
peak shock pressure less than a given pressure, for different projectiles
impacting a 10 MPa basalt target. The grey shaded areas represent the pressure
and temperature thresholds (at 1 bar) for aluminium incipient and complete
melting (Pierazzo et al., 1997).

##  Results from iSALE-2D simulations

###  Crater size and morphology

Firstly, we investigated the influence of the projectile shape on the crater
size. Here we modelled projectiles with six different geometries (Fig. 1),
impacting a 20% porous basalt target. In order to determine whether the target
cohesion plays a role when quantifying the effects of the projectile geometry,
here we modelled impacts into targets with three different cohesions, $Y_{d0}$
= 1 kPa, 0.1 MPa and 10 MPa (see Section 2 for details on the notation), at
6.5 km/s. Figure 5a, b and c show the crater radius, measured at the pre-
impact level, as a function of time for the six different projectile
geometries. Figure 5d, e and f show the crater volume, as a function of time.
The crater growth is shown for the three target cohesions, $Y_{d0}$ = 1 kPa,
0.1 MPa and 10 MPa and the crater growth time decreases with decreasing
cratering efficiency, which is lower for higher target strength. For a 1 kPa
target, the crater stops growing at about 5 s after the impact, for a 0.1 MPa
target, at about 0.3 s after the impact and for a 10 MPa target, at about
0.003 s after the impact. These results are consistent with crater scaling
laws (e.g., Housen et al., 1983; Holsapple and Housen, 2007). For all three
target cohesions considered here, the impacts of different projectile
structures produced crater radii that were within 5% and crater volumes that
were within 3% of each other.

Figure 5: Crater radius and crater volume as a function of time for different
shaped impactors into a 20% porous basalt target and a cohesion of a), d) 1
kPa, b), e) 0.1 MPa and c, f) 10 MPa. The small discontinuities in a) and c)
are due to time undersampling.

Figure 6 shows the crater profiles from impacts of different projectile
structures into a $Y_{d0}$ = 0.1 MPa target, as recorded at 1 s after the
impact. In all cases, the impact produces a bowl-shaped crater, with no
significant effects of the projectile geometry.

Figure 6: Crater profile at t = 1 s after the impact. Target cohesion is
$Y_{d0}$ = 0.1 MPa.

###  Ejecta mass-velocity distribution

Figure 7a shows the ejecta speed normalised by the impact velocity, $v/U$ as a
function of radial distance, $x$, normalised by the final crater radius
(measured at pre-impact level), $R$, produced by impacts with six different
shaped projectiles, into a $Y_{d0}$ = 1 kPa target. For a given target
strength, the ejecta distributions from the six impact scenarios follows very
similar trends, with only minor differences (Fig. 7a). Most notable
differences are seen for the fast ejecta, in the ‘coupling zone’ (Housen and
Holsapple, 2011), while ‘power-law’ and ‘near-rim’ regimes, the ejecta follows
very similar mass-velocity distributions, regardless of the projectile
geometry. In the case of the spherical projectile, the first ejecta to leave
the crater displays larger ejection velocities compared to ejecta from the
other five impact scenarios. A very similar trend in the ejecta speed-radial
distance distribution can be seen in the case of the $Y_{d0}$ = 1 MPa (Fig.
7b) and the $Y_{d0}$ = 10 MPa target (Fig. 7c). While there is an interplay
between projectile geometry effects and target property effects, projectile
effects are more evident in impacts with low cratering efficiencies, where the
final crater size is not much larger than the projectile size. Due to the
higher target cohesion, the 10 MPa and the 1 MPa target scenarios have a
smaller cratering efficiency compared to the 1 kPa target, which in turns
results in a smaller relative duration of the power-law regime. However, the
duration of the ’coupling-zone’ ejecta is comparable in all target scenarios.
Considering that the involved pressures are much higher than the target
strength, the target cohesion is not expected to affect the early time ejecta
behaviour. However, the cohesion is expected to influence the late-time ejecta
behaviour and the total amount of ejected mass.

Figure 7: a, b, c) Normalised speed of ejecta and d, e, f) ejection angle as a
function of radial distance for different impactor structures. The shaded
areas represent the different stages of the ejecta distribution: coupling
zone, power-law regime and rear-rim (see Housen and Holsapple (2011) and
Raducan et al. (2019) for details).

Figures 7d, e and f show the ejection angle as a function of normalised radial
distance, for (d) $Y_{d0}$ = 1 kPa, (e) $Y_{d0}$ = 0.1 MPa and (f) $Y_{d0}$ =
10 MPa targets. In the case of the spherical projectile, the material ejected
close to the impact point has lower ejection angles compared to the other
impact scenarios. In this case, the first material to leave the crater is
ejected at angles of $\approx 45^{\circ}$, then the ejection angle increases
to $\approx 60^{\circ}$. In all the other five impact scenarios the material
ejected close to the impact point has an ejection angle of about $\approx
60^{\circ}$. This difference in the ejecta velocity and ejection angle for the
spherical impactor scenario compared to the other projectile geometry
scenarios is most likely caused by jetting (Johnson et al., 2014).

In all impact scenarios, the ejection angle slowly decreases from about
$\approx 60^{\circ}$ at 0.1 $x/R$ to $\approx 40^{\circ}$ close to the crater
rim. Similar ejection angles have been noted by Luther et al. (2018) for
spherical projectiles. The total mass of ejected material, normalised by the
projectile mass, $M_{ej}/m$, was $\approx$ 15 for the 10 MPa target, $\approx$
300 for the 0.1 MPa target, $\approx$ 1100 for the 1 kPa target, and it was
not influenced by the projectile shape. The simulation results presented here
suggest that the impactor geometry only has an effect on the ejecta close to
the impact point, in the ‘coupling zone’ of the ejecta distribution (see
Housen and Holsapple (2011) for details). As shown in Raducan et al. (2019),
the ejected momentum originating from the coupling zone represents an
increasingly larger proportion of the total ejecta momentum, with increasing
cohesion.

###  Momentum enhancement

An important metric for the DART mission is the amount of momentum transferred
to the target by the impact. Figure 8 shows the momentum enhancement, $\beta$,
as a function of time, from projectiles with different geometries impacting
(a) $Y_{d0}$ = 1 kPa, (b) $Y_{d0}$ = 0.1 MPa and (c) $Y_{d0}$ = 10 MPa
targets. In the 1 kPa target scenario, the spherical projectile produces a
$\beta$ value that is about 2% larger than in the other projectile scenarios.
The total spread in $\beta$ value for this target scenario was about 3%. On
the other hand, in the case of the 0.1 MPa and 1 MPa target scenarios, the
sphere, thick shell and shell + hollow sphere projectiles produced very
similar $\beta$ values, while the cylinder and the shell + filled sphere
projectiles produced an amplification in $\beta$ of about 3%, compared to the
spherical projectile case. The thin shell impactor was less efficient by 2 to
3% compared to the spherical projectile, in all three target scenarios. For
the 1 kPa target scenario, the impactor geometry causes a spread in $\beta$
values of $\approx 3\%$, while for the 0.1 MPa target scenario, the spread in
$\beta$ was about 7% and in the 1 MPa target scenario, the spread in $\beta$
was about 4%. In terms of $\beta-1$, which is the momentum carried away by the
ejecta, the spread in values was between 5% (1 kPa target) and 15% (10 MPa
target). In summary, the 2D impact simulations show slight variations in the
peak pressures experienced by the projectiles and ejection angle
distributions, for the simplified projectile geometries studied here, however
their effect on the momentum enhancement factor is minimal.

Figure 8: Momentum enhancement, $\beta$, from six different impactor shapes,
impacting a basalt target with (a) $Y_{d0}$ = 1 kPa, (b) $Y_{d0}$ = 0.1 MPa,
(c) $Y_{d0}$ = 10 MPa.

##  Results from iSALE-3D simulations

The second study considered vertical impacts with two different projectiles, a
sphere and a horizontal rod (aligned in the $x$-direction), modelled in three
dimensions. The two scenarios represent simplifications of the spacecraft bus
(spherical projectile) and spacecraft bus with solar panels (rod projectile).

###  Crater size and morphology

Figure 9 shows plan view of crater and ejecta curtain evolution (a, b) and the
crater profile (c, d) from a solid aluminium sphere and solid aluminium rod
impacting a $Y_{d0}$ = 1 MPa basalt target. The spherical projectile produces
a bowl-shaped spherical crater, with a depth-to-diameter ratio of $d/D\approx$
0.33. The rod projectile produces an elliptical crater, that is wider in the
$x$-direction (the direction parallel to the long axis of the rod) but
narrower in the $y$-direction and shallower than the spherical projectile
scenario. In this case, the depth-to-diameter ratio is $d/D\approx$ 0.13 for
semi-major axes and $d/D\approx$ 0.20 for the semi-minor axes.

Figure 9: (a, b) Plan view of crater and ejecta curtain evolution at the time
when the transient crater is reached ($T$ = 1 s) and (c, d) crater profiles of
the transient craters from iSALE-3D simulations of a sphere and a rod
projectile impacting a porous, $Y_{d0}$ = 1 MPa target.

Figure 10 shows the growth of the crater (a) radius and (b) volume, as a
function of time, for the two impact scenarios. The spherical projectile
produces a circular crater with a radius of $R\approx$ 3 m and a volume of
$\approx$ 20 $m^{3}$. The rod projectile produces an elliptical crater with a
semi-major axis, $R_{a}\approx$ 3.5 m and a semi-minor axis, $R_{b}\approx$
2.7 m. The volume of the elliptical crater was $\approx$ 10 $m^{3}$, twice as
small as the spherical projectile crater.

Figure 10: Crater growth from iSALE-3D simulations of a sphere and a rod
projectile. a) Crater radius. The spherical projectile produces a circular
crater of radius $R$, while the Rod projectile produces an elliptical crater
of semi-major axis $R_{a}$ and semi-minor axes $R_{b}$. b) Crater volume.

###  Ejecta mass-velocity distribution

Figure 11a shows the mass-velocity distribution from iSALE-3D simulations of a
solid aluminium sphere and of a long aluminium rod impacting a porous, 1 MPa,
basalt target. The rod projectile ejects more mass at high velocities
($v/U>0.1$) compared to the spherical projectile. On the other hand, the
spherical projectile has a larger efficiency and there is more mass being
ejected overall, thus at lower ejection velocities.

Figure 11b shows the normalised cumulative ejecta momentum as a function of
vertical ejection velocity. In the case of the rod impactor, the larger amount
of ejected mass at high velocities (compared to the spherical projectile
scenario) translates to a larger amount momentum being imparted by the ejected
particles at high velocities. However, the spherical projectile produced an
overall cumulative ejecta momentum that is about 10% larger than in the rod
impactor scenario.

Figure 11: a) Cumulative mass of ejected particles at speeds grater than $v$,
as a function of normalised ejection speed $v/U$, for two projectile shapes:
sphere and rod, modelled in three-dimensions. b) Cumulative vertical ejecta
momentum, normalised by the momentum of the projectile, $mU$, as a function of
vertical ejection velocity, normalised by the impactor velocity, $v_{z}/U$.
The value where the ejecta distribution intersects the $y-$ axis represents
the total momentum carried away by the ejecta, that contributes to the impact
momentum transfer, $\beta-1$.

##  Discussion

Here we first modelled simple projectile structures with axial symmetry,
impacting targets with three different cohesion configurations, here referred
to based on the damaged cohesion, $Y_{d0}$ = 1 kPa, 0.1 MPa and 10 MPa. For
all three target scenarios, the projectile geometry has minimal effects on the
crater radius and crater volume (less than 5%). These results suggest that
projectiles with similar footprint surface areas do not influence the crater
size significantly, even in the case of strong targets, where the crater
efficiency is much lower.

When projectiles with different footprint surface areas were considered, i.e.,
spherical and rod projectiles modelled in 3D, the differences in crater
dimensions were clearer. However, our results suggest that for projectile
geometries with similar surface areas at the point of impact there are subtle
differences in the ejecta speed-radial distance distribution and in the angle
of the ejecta generated from these impacts. These differences cause subtle
amplifications and reductions in the momentum enhancement, compared to a
spherical projectile scenario. Here we did we did not see a clear trend in the
change in $\beta$ values with projectile geometry, and the spread in $\beta$
values was less than 5%.

The projectile effects are most evident in the fastest ejecta, produced at
very early times close to the contact zone when target strength effects are
negligible. Hence, these effects are expected to be independent of target
strength. As $\beta$ is dominated by the slowest ejecta, projectile effects on
deflection efficiency are expected to diminish with decreasing target
strength.

This behaviour of the fast ejecta caused by the projectile geometry might be
visible in the early crater ejecta plume. Information about the DART crater
ejecta plume will be available and will be provided by LICIACube (Cheng et
al., 2020; Dotto et al., 2021). LICIA (Light Italian Cubesat for Imaging of
Asteroids) is the Italian Space Agency (ASI) contribution to the DART mission.
The CubeSat will be carried by the DART spacecraft and will be released in the
vicinity of the Didymos system before the impact. The main aim of the CubeSat
is to take images of the ejecta plume, over a range of angle phases, at
136–163 s after the impact.

The timescale of the ‘coupling zone’ ejecta produced in our simulations is too
short and would be too transparent to be observed at $\approx$ 150 s after the
impact. However, the transition between the ‘coupling zone’ and the power-law
regime is gradual and the duration of each stage is dependent on the
projectile and target properties. Previous experimental and numerical studies
(e.g., Schultz, 1988; Hermalyn and Schultz, 2010; Raducan et al., 2019) of
vertical impacts showed that while a very small proportion of the crater
growth occurs in the ‘coupling zone’, its duration increases relative to the
total crater formation time as the impactor increases in size and velocity.
Here we can also make an analogy with impact cratering experiments with
projectiles with different densities (Hermalyn and Schultz, 2011): dense
projectiles (or projectiles with less voids) penetrate deeper into the target
and the coupling of the energy and momentum occurs later in the cratering
process, while an under-dense projectile (a projectile with more voids)
remains closer to the surface and the coupling occurs quicker.

Moreover, previous numerical simulations (i.e., Raducan et al., 2019) showed
that target porosity (or more precisely the ratio of the projectile and target
porosities) also affects the size of the ‘coupling zone’. However, target
strength parameters such as the cohesion and the coefficient of internal
friction do not. While a weaker target translates to longer transient crater
growth times (i.e., ranging from $\approx$ few minutes for a 50 Pa target, up
to $\approx$ 2 h for a cohesionless target (Raducan and Jutzi, 2021)),
nonetheless it is important to note that this does not translate to a longer
‘coupling time’. Therefore, the strength of the target is not expected to
influence the projectile geometry’s effect on the ejecta cone.

However, due to the projectile to target density ratio, which is not yet
known, differences might still be visible at the times of the LICIACube images
the ejecta plume. In other words, the observed ejecta plume produced by a
kinetic impactor with a complex shape (e.g., a spacecraft) may appear
different to what would be expected for ejecta produced by a homogeneous
spherical projectile. A complex projectile is expected to exhibit crater
ejecta with lower ejection speeds and higher ejection angles compared to a
simple sphere.

The results presented here are only the first step towards understanding the
effects of projectile geometry on crater morphology and momentum enhancement
from a kinetic impactor. In the case of the DART spacecraft, the projectile
geometry (i.e., the spacecraft geometry) is much more complex, and full 3D
models of the entire spacecraft, including solar panels, need to be
considered. In this aspect, several works are in progress (e.g., Stickle et
al. (2018)), however such simulations are very computationally demanding and
are typically restricted to very specific scenarios (e.g., only to impacts
into targets with low cratering efficiency, such as very strong targets).

##  Conclusions

The DART mission will impact Didymos’s secondary, Dimorphos, at the end of
2022 and cause a change in the orbital period of the Dimorphos around the
primary. For simplicity, previous studies aimed at quantifying the
consequences of the DART impact have used a spherical projectile. However, the
geometry of a kinetic impactor is more complex. Here we conducted a systematic
study to determine the effects of simple projectile geometries to the crater
size and morphology and to the ejecta mass-velocity distribution.

We found that for geometries with similar footprint surface area, the
projectile geometry affects crater radius and crater volume by less than 5%.
Moreover, the projectile geometry can both amplify and reduce $\beta$, but by
less than 5% compared to a spherical projectile. The more prominent
differences produced by the different projectiles are seen in the ejected
mass-radial distance and ejection angle distribution of the fast ejecta.
LICIACube, which will image the DART impact ejecta plume, $\approx$ 150 s
after the impact, might record crater ejecta that has higher ejection angles
compared to what is expected from a spherical projectile. However, the exact
differences will depend on the target porosity and on the imaging conditions.

We also considered the difference between a sphere and a horizontal rod
impactor geometry (here considered a simplification of a spacecraft with solar
panels) that used a full 3D impact simulation. The rod projectile, which had a
very different footprint surface area and aspect ratio to the spherical
impactor, produced an elliptical crater that was about 50% shallower than the
crater produced by the spherical impactor. The momentum enhancement from the
rod impact scenario was also $\approx$ 10% lower than in the spherical
projectile scenario.

Our results suggest that for simple projectile geometries, differences in
projectile geometry have only a very small effect on the momentum transfer
from a kinetic impactor, however further studies, with more realistic
projectile geometries (i.e., a spacecraft) are needed to study this in more
detail.

##  Acknowledgements

We gratefully acknowledge the developers of iSALE (www.isale-code.de),
including Kai Wünnemann, Dirk Elbeshausen, Boris Ivanov and Jay Melosh. This
work has received funding from the European Union’s Horizon 2020 research and
innovation programme, grant agreement No. 870377 (project NEO-MAPP) and the
UK’s Science and Technology Facilities Council (STFC) (Grant ST/N000803/1). J.
M. Owen’s work was performed under the auspices of the U.S. Department of
Energy by Lawrence Livermore National Laboratory under Contract DE-
AC52-07NA27344; LLNL-JRNL-824958.

##  Data availability

Additional supporting information (tables, model outcomes) will be archived on
GitHub and provided at the time of the publication as a DOI.

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  * Wünnemann et al. (2016) Wünnemann, K., Zhu, M.-H., and Stöffler, D. (2016).  Impacts into quartz sand: Crater formation, shock metamorphism, and ejecta distribution in laboratory experiments and numerical models.  Meteoritics & Planetary Science, 51(10):1762–1794.


Copyright for this paper by its authors. Use permitted under Creative Commons
License Attribution 4.0 International (CC BY 4.0).

Fire 20: Forum for Information Retrieval Evaluation, December 16–20, 2020,
Hyderabad, India

<EMAIL_ADDRESS>url=https://nlp.cic.ipn.mx/maazamjad/, ]

<EMAIL_ADDRESS>]

<EMAIL_ADDRESS>]

<EMAIL_ADDRESS>]

<EMAIL_ADDRESS>]

# Overview of the Shared Task on Fake News Detection in Urdu at FIRE 2020

Maaz Amjad  Center for Computing Research (CIC), Instituto Politécnico
Nacional (IPN), Mexico  Grigori Sidorov  Alisa Zhila  Independent Researcher,
United States  Alexander Gelbukh  Paolo Rosso  Universitat Politècnica de
València, Spain

(2020)

###### Abstract

This overview paper describes the first shared task on fake news detection in
Urdu language. The task was posed as a binary classification task, in which
the goal is to differentiate between real and fake news. We provided a dataset
divided into 900 annotated news articles for training and 400 news articles
for testing. The dataset contained news in five domains: (i) Health, (ii)
Sports, (iii) Showbiz, (iv) Technology, and (v) Business. 42 teams from 6
different countries (India, China, Egypt, Germany, Pakistan, and the UK)
registered for the task. 9 teams submitted their experimental results. The
participants used various machine learning methods ranging from feature-based
traditional machine learning to neural networks techniques. The best
performing system achieved an F-score value of 0.90, showing that the BERT-
based approach outperforms other machine learning techniques.

###### keywords:

Natural Language Processing Urdu language fake news detection low resource
language

##  1 Introduction

Fake news dissemination has been an important issue starting in the
15th111https://www.blackbird.ai/history-of-misinformation/ century. While
meant to be objective, not all news articles follow the rigor of conveying
fair facts chasing the fast-paced readers’ attention by screaming headlines
and sensational content. The spread of fake news brought many technical and
social challenges. For example, it was a dispersion of fake news, which
ignited the origin of antisemitism222
https://libguides.ncl.ac.uk/fakenews/history in 1475, when a Franciscan
preacher on the occasion of Easter Sunday claimed that Jewish community killed
a toddler. Moreover, to celebrate the child’s pass-over, some Jewish drained
the child’s blood and drank. The fake news spread fast and as a revenge,
Trent’s whole Jewish community was arrested, tortured, and fifteen Jewish were
found guilty and burned at the stake. This story inspired surrounding local
communities to commit similar atrocities. Thus, propagation of fake news
brought terrifying results and inflamed social conflict.

Automatic fake news identification is difficult, because we deal with very
high level semantic phenomenon and at the first glance fake news look like
real news. There are several types of news that are considered fake news. The
researchers [1] classified fake news into six types: (i) fabrication, (ii)
news satire, (iii) manipulation (e.g., editing pictures), (iv) advertising
(e.g., ads are depicted as professional journalism), (v) propaganda, and (vi)
news parody. Fabricated news can be defined as a deliberately fabricated news
article manipulated to deliver a particular narrative, such as to create
confusion, to be prominent in news headlines, or to make money.

The term “`fake news`” is not a simple concept. Publishers have been spreading
false and misleading information even before the availability of the Internet.
Different studies proposed various definitions of fake news [2, 1, 3]. For
example, a recent study [3] defined fake news as a factually incorrect news
article, which intentionally misleads a reader to believe that the conveyed
information is true. There is a related term clickbait, which is defined as a
snippet of a news article that is used to attract the reader’s attention, and
upon clicking, it redirects the reader to a different page. Clickbaits are
used to generate revenues by online advertisements. Notably, the term
misinformation – spreading untruths – emerged in the late 16th
century333https://www.theguardian.com/books/2019/nov/22/factitious-taradiddle-
dictionary-real-history-fake-news . Further, the more specific term
disinformation came from Russian
word444https://www.theguardian.com/books/2019/jun/19/dominic-raab-
disinformation “dezinformacija”. Disinformation is used when there is the
intent to harm. The Guardian showed that this term was used during the cold
war by all participants, being its meaning sowing falsehoods to confuse
enemies.

Automatic detection of fake news is crucial to prevent the devastating and
havoc impact that the fake news phenomenon causes worldwide. Throughout
history, humans have always been prejudiced and intolerant to different views.
For example, in 1620, Francis Bacon555
https://www.theguardian.com/technology/2016/nov/29/fake-news-echo-chamber-
ethics-infosphere-internet-digital  emphasized the consequences of inaccurate
language in his book Novum Organum, “The ill and unfit choice of words
wonderfully obstructs the understanding.” To this point, fake news detection
is a means to eliminate the vast and disastrous effects produced by
misinformation. Further, the ever increasing pace and scale of fake news
propagation can be mitigated only by automating the solutions and increasing
their effectiveness.

Fortunately, fake news detection attracted many researchers, in particular
after the US 2016 presidential election. The importance of the task visibly
increased for the Natural language processing (NLP) community as well as the
demand for the solutions from the industry. Given the topicality of fake news
detection and the urgency of coming up with an effective solution, this
competition aims to gain the attention of a larger research community and to
incentivize development of different solutions to combat the propagation of
fake news on the Web.

##  2 Importance of Fake News Detection in Urdu

A large number of existing studies in the literature examined automatic fake
news detection in multiple languages, such as English, Spanish, German,
Chinese, and Arabic. However, a very limited work was done in Urdu language to
automatically identify fake news content on the web. Urdu is a widely spoken
language having more than 100 million native speakers worldwide666
http://www.bbc.co.uk/voices/multilingual/urdu.shtml. However, according to the
best of our knowledge, there are no automatic web sources to verify the
authenticity of news articles in Urdu language. This situation requires the
attention of researchers working in NLP to develop tools and solutions in
verifying the authenticity of news articles written in Urdu language. Note
that Urdu is a low resource language, i.e., it does not have many NLP tools
and corpora data.

Urdu is spoken mainly in Pakistan. The fake news phenomenon had bad effects in
Pakistan’s social, politics and economical situation. For example, a Pakistani
TV anchor Dr. Shahid Masood777 https://www.globalvillagespace.com/dr-shahid-
masoods-claims-about-zainabs-murderer-prove-false/ , was sent to jail and
barred from hosting the TV show due to deceptive claims and spreading fake
news on the rape case of a teenage girl during a television show. Similarly,
according to the Washington
Post888https://www.washingtonpost.com/world/asia_pacific/as-mob-lynchings-
fueled-by-whatsapp-sweep-india-authorities-struggle-to-combat-fake-
news/2018/07/02/683a1578-7bba-11e8-ac4e-421ef7165923_story.html, fake news
about child trafficking led to many deaths of innocent people in India.

In addition to this, BBC reported that some Indian sites claimed a civil
war999 https://www.bbc.com/news/world-asia-54649302  had broken out in one of
the cities of Pakistan. The report mentioned that some Indian websites
described the situation in Pakistan as dangerous and the civil war resulted in
the deaths of many city police officers. Moreover, the websites claimed that
tanks had been seen on the streets, which eventually proved to be fake news.
Therefore, this urge to conduct studies to combat the dissemination of fake
content in Urdu language.

##  3 Literature Review and Task Overview

###  3.1 Related Work

A number of approaches for automatic fake news detection were proposed. These
approaches are based on statistical text analysis to tackle fake news
detection. Previous studies used various datasets, which comprised mainstream
media news articles and news published on social media. Notably, the majority
of work focused on English [4, 5], with some efforts in other languages such
as Spanish [4, 6], German [7], Arabic [8, 9], Persian [10], Indonasian [11],
Bangla [12], Portuguese [13], Dutch [14], Italian [15], and Hindi [16].

Correspondingly, challenges101010 http://www.fakenewschallenge.org/  and
shared tasks on automatic fake news detection were proposed, such as SemEval
2017 task 8 RumourEval for English [17], SemEval-2019 task 7 RumourEval for
English [18], and PAN 2020[4], the latter focused on the author profiling of
Fake News Spreaders on Twitter. Moreover, previous studies [19, 20] have shown
that emotional information can be helpful to identify fake news on social
media effectively. To the best of our knowledge, this is the first shared task
on fake news detection for the Urdu language. This task incentivizes the
development of fake news detection in Urdu as well as provides an opportunity
to compare the system performances with the recent shared tasks in other
languages.

Several studies [5, 6] reported that linguistic features can be helpful to
identify fake news. For example, a recent study [5] demonstrated that
linguistic features proved to be helpful to capture the differences of writing
styles between fake and real news. Another study [6] exploited some linguistic
features and showed that these features provided cues for differentiating
between fake and real news.

###  3.2 Task Description

The task of fake news detection is to solve a binary classification problem,
in which the input is a news article and the output is the assigned label
(real or fake). Built around the idea reported in recent studies that the
textual content can be helpful to identify fake news [5, 6], this shared task
is aimed to explore the efficiency of models to detect fake news, in
particular, for the news articles written in Urdu language.

The task was made publically
available111111https://www.urdufake2020.cicling.org/home  on June 30, 2020,
and the same day the training dataset was released as well. The training
dataset was splitted into two subfolders, (i) real news subfolder, and (ii)
fake news subfolder. The real news subfolder contained 500 real news articles,
and the fake news subfolder contained 400 fake news articles. We released the
test dataset on August 31, 2020. Like for the training dataset, the testing
dataset was also splitted into two subfolders, (i) real news, and (ii) fake
news. The real news subfolder contained 250 real news articles, and the fake
news subfolder contained 150 fake news articles. The participating teams
submitted their system until September 10, 2020. Each team could submit up to
3 different runs.

##  4 Dataset Collection and Annotation

This section provides an overview of the dataset developed for the shared
task. A smaller version of the dataset, named “Bend The Truth”, along with the
detailed information about the collection and annotation description was
presented in the recent study [3]. For this task, we performed additional data
collection and annotation following the exactly same procedure. As a result,
we obtained the final dataset of the annotated fake and real news in Urdu that
was 1.5 times the size of the original "Bend-The-Truth" dataset. It is
publicly available for academic research121212
https://github.com/UrduFake/urdufake2020eval.git.

The fake news articles were intentionally written by hired professional
journalists under specific instructions. The domains of the news present in
our dataset are: (i) Business, (ii) Health, (iii) Showbiz (entertainment),
(iv) Sports, and (v) Technology. They are similar to the dataset [5] used to
identify fake news in English language. Nonetheless, only one domain of news,
namely, related to education, is not available in our dataset because it was
difficult to find enough verifiable news in Urdu related to the education
domain.

For the training and development purposes, we offered 900 news articles from
the previously published “Bend The Truth” dataset. The 400 news articles from
the previously unseen and unpublished newly collected part were held out as a
test dataset.

###  4.1 Dataset Annotation Procedure

For the dataset annotation, rigorous guidelines and annotation procedures were
defined. The news articles were annotated into two types of news: (i) real
news article, and (ii) fake news article. The dataset can be used as a corpus
for supervised machine learning. It is possible to use the knowledge of the
dataset annotation procedure for applying to the underlying characteristics of
fake news in addition to linguistics features, but we do not recommend it,
because in real life there is no such information. We followed different
strategies for real and fake news annotation.

####  4.1.1 Real News Annotation

For the annotation of real news articles, initially numerous news articles
from different mainstream were collected. Table 1 shows news agencies used to
collect news articles for annotation. To annotate a news article as a real
news, the major points in the real news data collection and annotation were:

  1. 1.

The data collection and annotation procedure were performed manually.

  2. 2.

The news article was labeled as real news if the news meets the following
criteria:

     * •

A reliable newspaper or a prominent news agency published that news article.

     * •

Other authentic and credible newspaper agencies published the same news
article and the veracity of the news article can be easily verified using
information such as place of the event, image, date, etc. We also performed
manual source verification from where the news are originated. We further
compared and cross checked different sources (mainstream news agencies) to
verify the information present in the news article.

     * •

We also confirmed that a news article has a correlation between its title and
its content. We read the complete news articles to find out the correlation
between the title and the content.

If a news article does not follow one of these criteria, we simply discard
that news article.

Note that the length of all the news articles is heterogeneous. The reason is
that each news agency has a different style of news articles. For example, BBC
Urdu contained on average more than 1,500 words in a news article. Thus, we
selected real news articles carefully following the described procedure.

####  4.1.2 Professional Crowdsourcing of Fake News

To obtain fake news in this dataset, we used professional journalist services
from various news agencies in Pakistan: Express news, Dawn news, etc., who
were asked to write fake news stories that correspond to the original real
news articles. This is a peculiar attribute of this dataset, because it
ensured that the fake news articles realistically imitated real life approach
to fake news creation. In real life, it is journalists who are responsible for
writing fake news articles. Obviously, not all journalists do it. Still,
people of this profession have a better understanding of how to write an
article (real or fake) and make it interesting to hook and, in case of fake
news, trick the reader.

The reasons to use professional “crowdsourcing” to collect fake news are the
following:

  1. 1.

Finding and verifying the falsehood of fake news in the same domain as the
available real news articles is a challenging task that requires a huge amount
of time and resources unavailable to a small group of organizers. Thus, manual
analysis of hundreds of thousands of news articles for verification through
web scraping approach was unfeasible.

  2. 2.

Unlike the case of the English language, most of the news verification in Urdu
language is done manually due to the absence of web services that offer news
validation.

We should mention that the news articles style and language characteristics
vary depending on the news domain. Our dataset contains news in five major
domains: sports, business, education, technology, and showbiz. Thus, we
assigned news articles according to the journalists expertise in the
corresponding domain. Moreover, all the journalists were given instructions to
minimize the possibilities of introducing defined patterns that can provide
undesirable clues in the classification task. Also, some technical guidelines,
such as the requirement that the lengths of fake news should be in the range
of those of the original news, were provided. Finally, all fake news articles
were prepared using journalists’ expertise.

Table 1: Legitimate websites Name | URL | Origin
---|---|---
BBC News | www.bbc.com/urdu | England
CNN Urdu | cnnurdu.us | USA
Dawn news | www.dawnnews.tv | Pakistan
Daily Pakistan | dailypakistan.com.pk | Pakistan
Eteemad News | www.etemaaddaily.com | India
Express-News | www.express.pk | Pakistan
Hamariweb | hamariweb.com | Pakistan
Jung News | jang.com.pk | Pakistan
Mashriq News | www.mashriqtv.pk | Pakistan
Nawaiwaqt News | www.nawaiwaqt.com.pk | Pakistan
Roznama Dunya | dunya.com.pk | Pakistan
The daily siasat | urdu.siasat.com | India
Urdu news room | www.urdunewsroom.com | USA
Urdupoint | www.urdupoint.com | Pakistan
Voice of America | www.urduvoa.com | USA
Waqt news | waqtnews.tv | Pakistan

###  4.2 Training and Testing Datasets

####  4.2.1 Training and Validation Dataset

The training set was made available to the participants to develop their
approaches to identify fake news. It contained 900 news articles, annotated in
a binary manner as real or fake. 500 news articles were annotated as real, and
400 articles were annotated as fake. The real news part of the dataset was
retrieved from January 2018 to December 2018, which is different from the test
set.

All five topic domains, i.e., (i) Business, (ii) Health, (iii) Showbiz
(entertainment), (iv) Sports, and (v) Technology were present in the training
dataset. That is, we did not hold out any domain from the training set to make
it “unseen” for the participants.

The use of the training set for validation, development, and parameter tuning
purposes was at the participants’ discretion.

####  4.2.2 Test dataset

The test dataset was used to evaluate the performance of the submitted
classifiers. It was provided to all the participants without the ground truth
labels. The truth labels were only used by the organizers to evaluate and
compare the performance of participants’ approaches.

To create the testing dataset, news articles were retrieved from January 2019
to June 2020. It also has all five types of news as the training set. The test
dataset is composed of 400 news articles. The ground truth distribution among
these 400 news articles was 250 real news articles and 150 fake news articles.
We emphasize again that this information, along with labels, was not made
available to the participants.

###  4.3 Dataset Statistics

To prepare the data for the experiments, the corpus was split into train and
test sets. In the first stage of the shared task, the training dataset was
released which contained 900 news samples (500 real and 400 fake). In the
second stage, we released the test dataset which contained 400 news articles
(250 real and 150 fake). Table 2 describes the corpus distribution of the news
articles by topics for the training and testing sets.

Table 2: Domain Distribution in Train and Test subsets Domain | Train | Test
---|---|---
real | fake | real | fake
Business | 100 | 50 | 50 | 30
Health | 100 | 100 | 50 | 30
Showbiz | 100 | 100 | 50 | 30
Sports | 100 | 50 | 50 | 30
Technology | 100 | 100 | 50 | 30
Totals | 500 | 400 | 250 | 150

##  5 Evaluation Metrics

The task consists in classifying a news article as fake or real news. First,
the training dataset was released for the participants to develop and train
their systems, and subsequently, the test dataset was released. Each
participant team had an option to submit only 3 different runs. The
participants’ submissions were evaluated by comparing the labels predicted by
the participants’ classifiers and the ground truth labels. To quantify the
classification performance, we employed the commonly used evaluation metrics:
Precision (P), Recall (R), Accuracy, and two F1-scores, namely, F1real for the
prediction of label “real” and F1fake for the prediction of label “fake” out
of all news. Additionally, to accommodate the skew towards the real class,
which dominates (it has more samples than the fake news class), we used the
macro-averaged F1-macro which is the average of F1real and F1fake.

As this is a binary classification problem and the dataset contains two
equally important classes, we measured both of them, evaluating the quality of
predicting whether a news article is real, i.e., treating the “real” label as
a positive, or target, class, and the quality of predicting whether the news
article is fake, i.e., considering the label “fake” as a positive class. It is
important to mention that since the dataset is not balanced, this is why these
metrics are used.

The final ranking is based on the F1-macro score. It can be observed that
F1-macro and accuracy are correlated, as it is expected.

##  6 Baselines

We provided three baseline systems with the goal that their performance could
serve as reference points for qualitative evaluation of the submissions’
placement in the ranking. First, we provided the Random Baseline as the most
basic and trivial baseline, which is expected to be ranked at the bottom with
a more massive gap from the participating systems. Second, we provided the
most traditional baseline: bag of words (BoW) model. It uses words as features
and then apply a machine learning classifier. In this baseline, we used binary
weighting scheme (i.e., a feature is present or not) with Logistic Regression
classifier. For the third baseline, we provided the results of character bi-
gram with tf-idf weighting scheme using Logistic Regression classifier, which
achieved surprisingly good results. Overall, we tried five weighting schemes
(tf-idf, logent, norm, binary, relative frequency) [11] along with various
classifiers such as Logistic Regression, SVM, Adaboost, Decision Tree, Random
Forest, and Naive Bayes, but we got the best results with Logistic Regression,
which we are reporting.

##  7 Overview of the Submitted Approaches

This section gives a brief overview of the systems submitted to this
competition. 42 teams registered for participation, 9 teams submitted their
runs. Registered participants were from 6 different countries (India,
Pakistan, China, Egypt, Germany, and the UK). This wide range of the regions
where the interested participants were located confirms the importance of this
task. The team members came from various types of organizations: universities,
research centers, and industry.

As the initial step of the experimental setup, the majority of participating
teams performed data cleaning and preprocessing such as stop words
elimination. In particular, the system submitted by the team MUCS removed the
stopwords, whereas the systems submitted by teams BERT 4EVER, Chanchal_Suman,
CNLP-NITS, SSNCSE_NLP and NITP_AI_NLP decided to leave them.

Further, all the news articles were represented with different text
representation techniques. The team MUCS used traditional bags of words
representation. Similarly, the teams MUCS, NITP_AI_NLP, and SSNCSE_NLP used
the n-gram (words or characters) representation weighted with tf-idf. The
teams BERT 4EVER, CNLP-NITS, and Chanchal_Suman represented news article texts
using word embeddings. In particular, only one team, SSNCSE_NLP, represented
texts using Word2Vec embeddings, while the team SSNCSE_NLP employed FastText
embeddings. Furthermore, the team Chanchal_Suman used Urdu word embedding and
only one team, BERT 4EVER, used the contextual representation using BERT [21],
which is one of the most recent and advanced manners of text representation.

To implement their classifiers, some participating teams used the traditional,
i.e., non-neural machine learning algorithms, while some teams submissions
were based on various neural network architectures. The team MUCS used two
classical machine learning classifiers such as Multinomial Naive Bayes and
Logistic Regression with default parameters, and the same team also used LSTM
in the experimental setup. Similarly, the team SSNCSE_NLP used machine
learning classifiers such as Multi-Layer Perceptron (MLP), AdaBoost (AB),
ExtraTrees (ET), Random Forest (RF), Support Vector Machine (SVM), and
Gradient Boosting (GB). Another team, NITP_AI_NLP, used ensemble models by
combining Random Forest, Decision Tree and AdaBoost classifiers. The team
NITP_AI_NLP also used a multi-layer dense neural network. In contrast, one
team, Chanchal_Suman, used Gated Recurrent Unit (GRU). All the participating
teams, except one team (NITP_AI_NLP), used Transformers. Description of the
approaches is presented in Table 3.

Table 3: Approaches used by the participating systems.

System/Team Name | Feature Type | Feature Weighting Scheme | Classifying algorithm | NN-based
---|---|---|---|---
BERT 4EVER | context embedding BERT | BERT | CharCNN-Roberta | Yes
Character bi-gram (baseline) | char bi-grams | TF-IDF | Logistic Regression | No
BoW (baseline) | word uni-grams | Binary | Logistic Regression | No
CNLP-NITS | $N/A$ | embedding | XLNet pre-trained model | Yes
NITP_AI_NLP | char 1-3 grams | TF-IDF | Dense Neural Network | Yes
Chanchal_Suman | $N/A$ | embeddings | Bi-directional GRU model | Yes
MUCS | mix of char and word n-grams | embeddings | ULMFiT model | Yes
SSNCSE_NLP | char n-gram | TFIDF, fastText, word2vec | RF, Adaboost, MLP, SVM | Yes

##  8 Results and Discussion

Among all the submitted runs, the results of the best run (among up to three
submitted runs) are presented in Table LABEL:tab:overlapping2. The systems are
ranked by the F1-macro score. Table 5 provides the aggregated statistics about
the performance of all non-trivial systems, including the baselines, that is,
all of the systems apart from the random baseline.

We observe that except one system, all the other participating teams’ systems
outperformed the random baseline in terms of F1-macro score. The BERT 4EVER
system achieved the best F1-macro, Accuracy, as well as Rfake (recall),
F1fake, and Preal (precision) scores. However, the baseline approach with
character bi-grams and Logistic Regression achieved the second position in the
shared task with just 1.1% difference in F1-macro from BERT 4EVER, which is
quite an unexpected result. The explanation of this fact is a question for
further research.

Table 4 presents the best results of the submitted systems.

Over 50% of the systems obtained F1-macro and Accuracy of more than 0.8 (Table
LABEL:tab:overlapping2), which is a reasonably high result. In Table 3, we
observe that most of these high performing systems achieve better Recall for
fake news, and better Precision for the real news detection. This tells us
that these systems tend to “mistrust” the news articles and tag more news as
fake than there are in reality. This can be considered as an overly secure
approach.

At this moment, it is hard to judge whether any of these approaches is ready
to be applied “in the wild”. While the results of F1real and F1fake over 0.9
shown by the winning BERT 4EVER system are impressively high, the modest size
of the provided training and testing datasets cannot guarantee the same
performance on an arbitrary text input. To ensure the scalability of the
presented approaches, more multifaceted research at a larger scale is needed.
We see that one of the paths is a community-driven effort towards the increase
of available resources and datasets in the Urdu language.

Table 4 presents statistics of the submitted systems.

Table 4: Statistics of the submitted systems and the baselines (excluding the
random baseline).

Stat. metrics | Preal | Rreal | F1real | Pfake | Rfake | F1fake | F1-macro | Acc. |
---|---|---|---|---|---|---|---|---|---
mean | 0.746 | 0.649 | 0.748 | 0.795 | 0.843 | 0.755 | 0.767 | 0.770 |
std | 0.193 | 0.127 | 0.131 | 0.091 | 0.161 | 0.138 | 0.130 | 0.134 |
min | 0.377 | 0.433 | 0.490 | 0.628 | 0.472 | 0.490 | 0.502 | 0.495 |
percentile 10% | 0.418 | 0.523 | 0.534 | 0.652 | 0.627 | 0.534 | 0.560 | 0.558 |
percentile 25% | 0.725 | 0.593 | 0.762 | 0.792 | 0.838 | 0.762 | 0.781 | 0.786 |
percentile 50% | 0.810 | 0.617 | 0.781 | 0.798 | 0.906 | 0.781 | 0.799 | 0.806 |
percentile 75% | 0.887 | 0.728 | 0.815 | 0.841 | 0.945 | 0.815 | 0.830 | 0.835 |
percentile 80% | 0.890 | 0.745 | 0.828 | 0.850 | 0.949 | 0.839 | 0.846 | 0.850s |
percentile 90% | 0.891 | 0.800 | 0.850 | 0.888 | 0.952 | 0.902 | 0.882 | 0.892 |
max | 0.890 | 0.860 | 0.874 | 0.918 | 0.936 | 0.926 | 0.900 | 0.908 |

##  9 Conclusion

This paper describes the first competition on automatic fake news detection in
Urdu, the UrduFake 2020 track at FIRE 2020. We provided the training and
testing parts of the dataset that included news articles in five domains
(business, health, sports, showbiz, and technology). The news articles in the
dataset were manually annotated with labels “fake” and “real” with a slightly
imbalanced ratio of approximately 60% real news and 40% fake news.

Forty two teams from six different countries registered for this task. Nine
teams submitted their experimental results (runs). The approaches employed by
the submitted systems varied from the traditional feature-crafting and
application of traditional ML algorithms to word representation through pre-
trained embeddings to contextual representation and end-to-end neural network
based methods. In particular, ensemble methods were used in the traditional ML
case. LSTM, and Transformers (BERT) were used in neural network based
solutions.

Among all the submissions, only the best submitted model, BERT 4EVER,
outperformed the character bi-grams with Logistic Regression baseline
achieving F1-macro score of 0.90. This confirms that contextual representation
and large neural network techniques outperform classical features-based
models, which has been shown in many recent studies in all branches of natural
language processing.

This competition aimed to encourage researchers working in different NLP
domains to attempt to tackle the proliferation of fake content on the web. It
also provided an opportunity to fully explore the sufficiency of textual
content modality and effectiveness of fusion methods. And last but not the
least, this track provides a useful resource in the form of an annotated
dataset for other researchers working in automatic fake news detection in
Urdu.

###### Acknowledgements.

This competition was organized with partial support of National Council for
Science and Technology (CONACYT) A1-S-47854, SIP-IPN 20200797 and 20200859 and
CICLing conference. The work of the last author was partially funded by MICINN
under the research project MISMIS-FAKEnHATE on MISinformation and
MIScommunication in social media: FAKE news and HATE speech
(PGC2018-096212-B-C31).

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