11institutetext: Centre for Computational Imaging and Simulation Technologies
in Biomedicine (CISTIB), School of Computing and School of Medicine,
University of Leeds, Leeds, UK 22institutetext: NIHR Leeds Biomedical Research
Centre (BRC), Leeds, UK 33institutetext: Alan Turing Institute, London, UK
44institutetext: Medical Imaging Research Center (MIRC), Electrical
Engineering and Cardiovascular Sciences Departments, KU Leuven, Leuven,
Belgium 55institutetext: Division of Informatics, Imaging and Data Science,
Schools of Computer Science and Health Sciences, University of Manchester,
Manchester, UK

# Learned Local Attention Maps for Synthesising Vessel Segmentations from T2
MRI

Yash Deo 11 Rodrigo Bonazzola 11 Haoran Dou 11 Yan Xia 11 Tianyou Wei 11
Nishant Ravikumar 1122 Alejandro F. Frangi 1122334455 Toni Lassila 1122

###### Abstract

Magnetic resonance angiography (MRA) is an imaging modality for visualising
blood vessels. It is useful for several diagnostic applications and for
assessing the risk of adverse events such as haemorrhagic stroke (resulting
from the rupture of aneurysms in blood vessels). However, MRAs are not
acquired routinely, hence, an approach to synthesise blood vessel
segmentations from more routinely acquired MR contrasts such as T1 and T2,
would be useful. We present an encoder-decoder model for synthesising
segmentations of the main cerebral arteries in the circle of Willis (CoW) from
only T2 MRI. We propose a two-phase multi-objective learning approach, which
captures both global and local features. It uses learned local attention maps
generated by dilating the segmentation labels, which forces the network to
only extract information from the T2 MRI relevant to synthesising the CoW. Our
synthetic vessel segmentations generated from only T2 MRI achieved a mean Dice
score of $0.79\pm 0.03$ in testing, compared to state-of-the-art segmentation
networks such as transformer U-Net ($0.71\pm 0.04$) and nnU-net($0.68\pm
0.05$), while using only a fraction of the parameters. The main qualitative
difference between our synthetic vessel segmentations and the comparative
models was in the sharper resolution of the CoW vessel segments, especially in
the posterior circulation.

###### Keywords:

Image Synthesis Deep Learning Brain Vasculature Vessel Segmentation Multi-
modal Imaging

##  1 Introduction

A magnetic resonance angiogram (MRA) contains vital information for
visualising the brain vasculature, which includes an anastomotic ring of
arteries located at the base of the brain called the circle of Willis (CoW).
Multiple different topological variants of the CoW exist in the general
population, and certain variants of the CoW can lead to worse outcomes
following a stroke [12]. To that end, it would be useful to visualise the main
cerebral blood vessels in large imaging datasets and identify them by CoW
phenotype to understand their relevance to stroke in the general population.
Vessel segmentation from MRA is a well-studied problem with state-of-the-art
methods achieving high quality vessel segmentation results [13] with Dice
scores as high as 0.91 [20]. However, as MRA acquisition may require the
injection of contrast agents and has longer acquisition times, it is not
commonly available in population imaging studies. T1- and T2-weighted MRI
scans are the most common MR imaging modalities available and are used to
study the presence of lesions or other abnormal structures in the brain. While
the blood vessels are not explicitly visible in these modalities, they contain
latent information that can be used to synthesise the major vessels in the
brain.

Generative adversarial neural networks [4] (GANNs) have seen remarkable
success in the field of image synthesis, with networks like pix2pix [9]
achieving impressive results in paired image-to-image synthesis. GANNs have
also been widely used in medical image synthesis in various use cases such as
generating T1, T2, and FLAIR images of the brain using Wasserstein-GANNs [5].
Progressively growing GANNs [1] have been used for the generation of retinal
fundus and brain images. Previous works on brain MRA synthesis used SGAN [17]
to generate MRA from paired T1 and T2 images, or used starGAN [19] to
synthesise MRA given T1, T2 and/or a PD-weighted MRI as input. GANN-based
approaches such as vox2vox [3] have been used to synthesise segmentations of
brain tumour directly from T1, T2, Gadolinium-enhanced T1, and T2 FLAIR
modalities. Most GANN based approaches synthesise MRA from multiple other MR
modalities, and then require the use of a separate segmentation algorithm,
such as U-net (which is popularly accepted as baseline), to segment the brain
vascular structures from the synthesised MRA. As the brain vessels form a very
small portion of the MRA image, attention mechanisms were introduced to the
segmentation algorithms to more accurately capture the small vessels. This has
been achieved in networks such as Attention U-Net [16] or more recently
transformer based networks such as TransU-Net [2].

In spite of their successes, GANs and transformers are complex models with
tens or hundreds of millions of parameters that can be notoriously hard to
train. On top of that, GANNs tend to produce phantoms (non-existent image
features), especially when dealing with very high-resolution images with
intrinsic detail arising from medical imaging [21]. To alleviate these issues,
we propose multi-task learnable localised attention maps to directly generate
vessel segmentations based on a U-Net architecture, which can capture both
global and local features from the input domain. Our method requires only the
T2 modality as in input, which eliminates the need of multiple input
modalities. The learned local attention maps enable the trained model to only
look for vessels in specific parts of the image, which drastically decreases
the number of parameters required to train the synthesis network. Our model
consequently synthesises more accurate CoW segmentations with fewer parameters
than competing GANN-based approaches.

##  2 Methodology

We propose a deep convolutional encoder-decoder model, which is trained with
two-phase multi-task learning. At training time, paired T2 images and ground-
truth MRA segmentations are available. Our encoder-decoder network captures
both global information (by encoding input images into a latent space) and
local information (by learning soft attention maps for brain vessels based on
MRA segmentations) from the given input images. We train the model using
multi-task learning in two phases, where a learned local attention map learns
where on the T2 image the vessels are most likely located to improve the
synthesised vessel segmentation masks. At run-time, the model efficiently
synthesises brain vessel segmentation masks from only T2 images.

###  2.1 Data and Pre-processing

The model was trained on the IXI dataset [7] using the 3T scans acquired at
Hammersmith Hospital, and includes paired T2 and MRA scans of 181 patients.
The T2 and MRA images were first registered using rigid registration. The
images were centered, cropped from $512\times 512$ to $400\times 400$, and
intensity-normalised. Ground-truth segmentations were then generated from the
MRA images for each corresponding T2 slice using a residual U-Net [11]. The
segmentations were then dilated to form a binary mask and multiplied pixelwise
with the corresponding T2 slice to create the ground truth local attention map
(see Fig. 1)

Figure 1: Process for the generation of the local attention masks. Vessel
segmentations are generated from the MRA and dilated. We then multiply this
dilation with the corresponding T2 slice to create the mask.

###  2.2 Network Architecture

The proposed model follows the general architecture of the pix2pix-model [9]
with one encoder branch and two output branches (Fig. 3). The encoder branch
combines U-net and Resnet [6] architectures with a latent space consisting of
three consecutive residual blocks, similar to the vox2vox-model [3]. The
encoder has four convolution + max-pooling -blocks, where each block consists
of three strided convolution layers followed by a max-pooling layer. Each
convolution layer is followed by an instance-normalisation -layer. The latent
space branches out into two output branches: the decoding branch and the
synthesis branch. In case of multiple input modalities (eg. T1 + T2) we have a
separate decoding branch for each modality. The output branches have the same
structure as the encoding branch with the max-pooling layers replaced by up-
sampling layers and with skip connections from corresponding encoding blocks.
The first convolution block of the synthesis branch receives a skip connection
from both the corresponding encoder branch and the decoder branch.

#### Local Attention Mask

The output of the segmentation branch consists of fine vessel information. The
small dimensions of the vessels make the segmentation masks unsuitable for
generating the local attention maps. For this reason, we dilate these vessel
segments to 10 pixels in each direction to create a local attention mask. The
optimal dilation width was found through experimentation as shown in Table 1.
We then perform pixel-wise multiplication of this local attention mask with
the output of the decoder to generate a local attention map as shown in Fig.
1. This local attention map is compared to the ground truth local attention
maps during model training to calculate loss. This dependency between these
two tasks adds a collaborative element between what would otherwise be two
contrastive tasks. The use of a local attention mask forces the network to
learn from a very small portion of the input image, which contains information
about the blood vessels and ignore the rest of the image. This property allows
us to greatly reduce the number of parameters required to train the model.

###  2.3 Training and Losses

The network is trained in two phases to effectively capture both the global
and local features required to synthesise the vessels from T2 images.

Figure 2: Overview of our network architecture. The encoder takes T2-weighted
MRI as input and compresses it into a latent space. The latent space branches
out into the decoding branch, which reconstructs the input, and the synthesis
branch, which generates the segmentation.

#### Phase 1:

We pre-train the network on T2 images by freezing the synthesis branch and
only training the decoder branch, effectively training an autoencoder for T2
images. The network is trained with an early stopping criteria based on the
loss slope. The only loss calculated in this stage is the T2 reconstruction
loss from the decoder branch.The loss function used is L1 and is specified
below where $X_{T_{2}}$ is the ground truth T2 image and $\hat{X}_{T_{2}}$ is
the generated T2 image:

$\mathcal{L}_{\textrm{phase}\,1}=\textrm{MAE}(X_{T_{2}},\hat{X}_{T_{2}})$ (1)

#### Phase 2:

After we finish the pre-training step, we unfreeze the synthesis branch and
train it in conjunction with the decoder branch. Although the decoder branch
is being trained in this step, the loss calculated for this branch is not the
reconstruction loss but local loss, which is calculated over the dot product
of the output of the decoder branch and the dilated segmentation obtained from
the output of the synthesis branch.

In order to train these two contrasting branches together, we tested our model
with various multi-task learning (MTL) approaches: Nash-MTL [15] (average Dice
after evaluation 0.76), CAGrad [14] (average Dice after evaluation 0.74), and
uncertainty-based MTL [10] (average Dice after evaluation 0.79). The best
performing version was the uncertainty-based MTL, where both the losses are
weighted based on the assumption of homoscedastic uncertainty for each task.
The loss function for our multi-output model is described in (2), where $W$
are the model parameters and we interpret minimising the loss with respect to
$\sigma_{1}$ and $\sigma_{2}$ as learning the relative weights for the losses
$\mathcal{L}_{\textrm{seg}}$ and $\mathcal{L}_{\textrm{loc}}$ adaptively. We
used Dice score as the loss for $\mathcal{L}_{\textrm{seg}}$ and MAE as the
loss for $\mathcal{L}_{\textrm{loc}}$

$\mathcal{L}_{\textrm{phase}\,2}=\frac{1}{2\sigma_{1}^{2}}\mathcal{L}_{\textrm{seg}}(\mathbf{W})+\frac{1}{2\sigma_{2}^{2}}\mathcal{L}_{\textrm{loc}}(\mathbf{W})+\log\sigma_{1}\sigma_{2}$
(2)

##  3 Experiments and results

###  3.1 Implementation Details

All the models were implemented in TensorFlow 2.8 and Pytorch (for nnU-Net)
and Python 3. Out of the 181 cases in the dataset we used 150 for training and
31 for testing and validation. All the models were pre-trained on T2 images
and grid search was used to optimise the following hyperparameters: (1) batch
size, (2) learning rate, (3) number of epochs, and (4) momentum. To train the
transformer network, we first used the parameters recommended in [2] and
applied further fine-tuning of the parameters to achieve comparative
performance in the segmentation task.

Figure 3: Local attention maps learned by the network compared against the
ground truth local attention maps.

To evaluate the results of our model against other methods, we used the
segmentation metrics of Dice score and Hausdorff distance (hd95). The results
were averaged over the 3D volumes of the 11 leave-out cases and are shown in
Table 2. Our method clearly outperforms conventional GANN-based synthesis
methods, such as vox2vox, and also performs slightly better than state-of-the-
art segmentation models like transformer U-Net [2] and nnU-net [8], while also
being easier to train with fewer trainable parameters. We experimented with
training our model with different input modalities, which showed that using
only T1 as an input had the worst performance (average dice 0.64 $\pm 0.04$)
while the performance of using only T2 (average dice 0.79 $\pm 0.04$) and both
T1 + T2 (average dice 0.78 $\pm 0.05$) was essentially the same, with T1 + T2
requiring additional parameters (33.4 million) compared to using just T2 (26.7
million) as we would need an additional decoding branch for the T1 decoder. A
crucial hyperparameter in our model is the dilation width of the segmentations
to generate the local attention maps, which was optimised in a separate
experiment. (Table 1).

Table 1: Difference in loss with different values of dilation for the local attention mask Attention mechanism used | Dice (95% CI) | Area covered by mask
---|---|---
No local attention mask | 0.62 $\pm 0.04$ | NA
Mask with no dilation | 0.59 $\pm 0.04$ | 1.5%
Mask with dilation by 5 pixels | 0.74 $\pm 0.03$ | 8.5%
Mask with dilation by 10 pixels | 0.79 $\pm 0.03$ | 18%
Mask with dilation by 15 pixels | 0.75 $\pm 0.02$ | 28%
Mask with dilation by 20 pixels | 0.75 $\pm 0.03$ | 37%
Table 2: Accuracy of synthesised vessel segmentation masks in a test set of $11$ leave-out cases Model | Model | Dice | HD95 | Model Type
---|---|---|---|---
| params. ($\times 10^{6}$) | (95% CI) | (95% CI) |
Our model | $26.7$ | 0.79 $\pm 0.03$ | 9.1 $\pm 0.5$ | Segmentation/synthesis
Transformer U-Net [2] | $105.8$ | 0.71 $\pm 0.04$ | 10.4 $\pm 0.5$ | Segmentation
nnU-Net [8] | $127.8$ | 0.68 $\pm 0.03$ | 9.3 $\pm 0.4$ | Segmentation
Vox2vox [3] | $78.8$ | 0.67 $\pm 0.05$ | 17.2 $\pm 1.4$ | Segmentation/synthesis
Pix2pix [9] | $36.9$ | 0.55 $\pm 0.04$ | 23.1 $\pm 3.0$ | Synthesis
U-Net [18] (base) | $9.1$ | 0.57 $\pm 0.05$ | 42.6 $\pm 4.2$ | Segmentation

###  3.2 Qualitative Results

Figure 4: CoW synthesis results compared between models. Pix2pix and U-Net are
able to capture the overall structure of the Cow but with a lot of noise.
Vox2vox performs comparatively better, but still suffers from noise in the
outputs. NnU-Net, Transformer U-Net and our method show good results with our
method capturing more details and dealing better with noise.

Figure 5: CoW synthesis results for the average case, the best case, and the
worst case in our unseen test set.

Fig. 5 shows a qualitative comparison of our method against pix2pix, vox2vox,
U-Net, nnU-net, and transformer U-Net for two samples from the unseen test
set. It can be observed that pix2pix and the base U-Net are only able to
capture the overall structure of the CoW with a lot of noise. The vox2vox
model synthesises the vessels slightly better, but is still unable to capture
the finer details and suffers from noise. The nnU-net and transformer U-Net
are able to synthesise the vessels with high quality, but struggle to
synthesise smaller vessels such as the posterior communicating arteries
(PComA) in the first case. An interesting observation can be made in the
second case, where the ground truth has faults in the segmentation (especially
in the posterior circulation). The Transformer U-Net, nnU-net, and our model
attempt to fix these faults by synthesising a continuous PCA, but our model
does better in restoring vessel continuity. Fig. 5 shows the CoW synthesis
results for the best case, worst case, and median case scenarios. It can be
observed that in the worst case the model struggles to synthesise the smaller
vessels towards the end of the posterior cerebral circulation, whereas in the
median case scenario most of the major vessels are synthesised with only the
small PComA artery missing. The best case is that all the major arteries of
the CoW are synthesised while also removing noise from the input image.

###  3.3 Limitations

While our method outperforms state-of-the-art approaches with a much smaller
number of trainable parameters and is able to generated the complete structure
of the CoW, it can be seen that in come cases the model can struggle to
generate some of the finer vessels branching from the main arteries
(especially the posterior communicating arteries). This could be either
because the input data is of insufficient resolution (T2 images were acquired
at 3T) or because the T2 modality does not contain information that could be
used to synthesise the anterior circulation. It is possible that additional MR
modalities, such as multi-view T1, or a fully-3D neural network architecture
could add more information about the posterior and anterior vessels and
recover a complete CoW.

##  4 Conclusion

We proposed a multi-output encoder-decoder -based network that learned to
effectively synthesise vessels from only T2-weighted MRI using local attention
maps and multi-task learning. The qualitative and quantitative results show
that our method outperformed both the state-of-the-art and conventional
segmentation/synthesis algorithms, while at the same time being easier to
train with fewer parameters. In future work, we are extending our model to a
fully 3D synthesis model to achieve better connectivity of the CoW structure.

## Acknowledgement

This research was partially supported by the National Institute for Health and
Care Research (NIHR) Leeds Biomedical Research Centre (BRC) and the Royal
Academy of Engineering Chair in Emerging Technologies (CiET1919/19).

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#  Unraveling the Italian and English Telegram Conspiracy Spheres through
Message Forwarding

Lorenzo Alvisi  IMT School for Advanced Studies
Lucca, Italy
<EMAIL_ADDRESS>Institute of Informatics and Telematics
National Research Council (IIT-CNR)
Pisa, Italy
<EMAIL_ADDRESS>Serena Tardelli  Institute of Informatics and
Telematics
National Research Council (IIT-CNR)
Pisa, Italy
<EMAIL_ADDRESS>
Corresponding author  Maurizio Tesconi  Institute of Informatics and
Telematics
National Research Council (IIT-CNR)
Pisa, Italy
<EMAIL_ADDRESS>
###### Abstract

Telegram has grown into a significant platform for news and information
sharing, favored for its anonymity and minimal moderation. This openness,
however, makes it vulnerable to misinformation and conspiracy theories. In
this study, we explore the dynamics of conspiratorial narrative dissemination
within Telegram, focusing on Italian and English landscapes. In particular, we
leverage the mechanism of message forwarding within Telegram and collect two
extensive datasets through snowball strategy. We adopt a network-based
approach and build the Italian and English Telegram networks to reveal their
respective communities. By employing topic modeling, we uncover distinct
narratives and dynamics of misinformation spread. Results highlight
differences between Italian and English conspiracy landscapes, with Italian
discourse involving assorted conspiracy theories and alternative news sources
intertwined with legitimate news sources, whereas English discourse is
characterized by a more focused approach on specific narratives. Finally, we
show that our methodology exhibits robustness across initial seed selections,
suggesting broader applicability. This study contributes to understanding
information and misinformation spread on Italian and English Telegram
ecosystems through the mechanism of message forwarding.

###### Index Terms:

telegram, message forwarding, linked chats, conspiracy, network, communities

##  I Introduction

Telegram has grown popular thanks to its commitment to anonymity, low
moderation, and privacy, establishing itself as a significant hub for news and
information. Yet, the very features that attract users also open doors for
misinformation to spread. In fact, Telegram’s minimal content moderation
serves as a double-edged sword. On the one hand, it fosters valuable
information exchange on sensitive issues. On the other hand, this freedom
creates fertile ground for the proliferation of conspiracy theories and
misleading information to large audiences. For example, Telegram has emerged
as hotspot for misinformation during critical political events, including
elections in countries like Spain [1] Brazil [2, 3], and the United States
[4], challenging election integrity and promoting divisive ideologies.
Similarly, the platform has served as a fertile environment for the spread of
misinformation on topics such as the infodemic, pandemic, and other societal
issues [5, 6, 7, 8]. Additionally, Telegram has been exploited by crypto
investors to orchestrate large-scale market manipulations, including pump and
dump schemes [9, 10]. The platform has also facilitated ideology
radicalization [11], coordination of attacks, including those on Capitol Hill
[12], mobilizing protests [13], and the promotion of other conspiratorial
narratives [14, 8], thus playing a crucial role in influencing public
discourse and impacting democratic processes. Discussing how these phenomena
organize and characterize themselves is crucial for understanding the
direction and evolution of public discourse and the factors influencing it.
This understanding is vital not only for making online environments safer but
also for grasping potential offline developments. This involves examining the
dynamics within these platforms to identify how misinformation spreads, the
community structures that support such narratives, and the implications for
broader societal issues. This analysis can inform strategies to mitigate the
spread of harmful content and foster a healthier public dialogue. In this
study, we focus on understanding the spread of conspiratorial narratives
within Telegram communities through message forwarding, specifically within
Italian and English language landscapes. Message forwarding on Telegram
involves sharing a message from one chat directly into another, serving as a
critical mechanism for distributing content across different user groups. We
hypothesize that forwarded messages not only distribute content but also
signal homophily, that is shared interests and beliefs, among community
members, similar to how the diffusion of invite links has been studied in the
past [15, 10].

Contributions. We first collect data from Telegram by leveraging message
forwarding. Starting from selected initial chats as seeds, we perform
iterative, snowball sampling and expand the data by iteratively identifying
and retrieving new chats and messages. We collect two large datasets: the
Italian dataset covers the period from January 1, 2024, to February 13, 2024,
and comprises more than 1K chats and 3.4M messages. Meanwhile, the English
dataset spans from January 1, 2024, to February 20, 2024, and consists of more
than 600 chats and 5M messages. We build two Telegram networks based on
message forwarding, identify key communities and employ topic modeling to
characterize their discussions and understand the specific narratives. We show
that the Italian landscape of conspiracy theories forms a network involving
religious groups, Russian influences, anti-vaccination proponents, and news
source of varying reliability. In contrast, the English landscape appears more
tied to structured conspiracies, involving ties with cryptocurrency scams.
Finally, we validate our method by showing that our findings does not depend
on the initial seeds, offering a new lens through which to examine the flow of
information and misinformation.

We summarize our main contributions in the following:

  * •

We leverage message forwarding to collect two extensive Telegram conspiracy-
related datasets, including channels, groups – often overlooked in existing
literature, and messages. For the first time, we also incorporate linked
chats, which are two-tiered structures consisting of channels linked to their
respective groups.

  * •

We characterize conspiratorial narratives within Telegram communities,
focusing on both English and Italian spheres, shedding light on Italian
Telegram dynamics not extensively explored in existing literature.

  * •

We highlight differences in conspiracy theory landscapes between Italian and
English-speaking communities, revealing the presence of diverse news sources
playing varied roles in shaping discourse, and exploring the connections among
various conspiracy theories within these groups.

  * •

We show that forwarded messages serve for content distribution and signal
community homophily, and that our insights do not overly depend on the initial
selection of seeds, suggesting the robustness and broad applicability of our
methodology.

##  II Related Works

###  II-A Overview of Telegram data collection methods

Several studies relied on message forwarding to collect data from Telegram.
For example, the authors in [16] aimed to create the largest collection of
English Telegram channels, spanning a wide range of diverse topics, with their
analysis primarily centered on dataset statistics. In contrast, research in
[17] analyzed communities by building user networks from forwarded messages,
and exploring the narratives within. Similarly, research in [18] and [19]
followed a snowball characterized specific English-speaking Telegram
communities of channels. Our study, however, expands on this foundation by
incorporating not just channels but also groups into our analysis.
Specifically, we uniquely consider the linked chat feature on Telegram, where
a channel is directly connected to a group. To the best of our knowledge, this
is the first research effort to include this duality feature in literature.
Other studies adopted snowball approaches on Telegram, focusing on different
elements like mentions [20] or invite links – special URLs that allow users to
join channels [21, 10]. For example, the research in [10] analyzed how
fraudsters used these invite links in scam channels to attract large
audiences, highlighting the significance of invite link diffusion patterns for
identifying homophily and shared interests within online communities. In a
similar way, the study in [18] explored the concept within far-right
communities, proposing that Telegram groups act as echo chambers and that the
sharing of forward links suggests a level of homophily. Building on this, our
research seeks to further explore the utility of forward links in content
distribution and their ability to reveal homophily among users. Lastly, other
studies employed different data collection strategies, such as gathering
messages from an initial set of seeds without employing a snowballing approach
[9, 22]. These studies primarily aim to illustrate the unfolding of specific
events, like instances of toxicity or fraud schemes.

###  II-B Studies of conspiracy in Italian and English Telegram discussions

Conspiracy theories have been identified and analyzed across various
platforms, thriving in numerous online environments [23, 24, 25, 26],
including Telegram. The majority of the research on Telegram has focused on
conspiracy theories within English-speaking discussions, including studies on
the pandemic [27], the far-right [28, 14], and the QAnon movements [12].
Notably, the QAnon conspiracy, in particular, has been linked to a wide range
of conspiratorial narratives, highlighting its broad influence [17, 29, 30].
Building upon this works, our study extends the examination of conspiracy
discourse in English-speaking communities, especially QAnon and its current
connections with other narratives. On the other hand, the realm of conspiracy
theories within Italian-speaking Telegram communities remains largely
unexplored. The Italian conspiracy ecosystem on Telegram came to the spotlight
during the COVID-19 pandemic [27], as protest movements gained significant
social momentum, leading to widespread protests [31], movements having ties
with Italian alt-right, a phenomenon observed also in other European countries
[8]. Other studies focused into the Italian QAnon disinformation
infrastructure [32], highlighting the closed nature of these communities
within the Italian sphere, similarly to English-speaking environments [17].
Despite these insights, a comprehensive understanding of the broader
conspiracy landscape in Italy remains unexplored. Our study seeks to fill this
gap by examining the connections between various conspiracy narratives in
Italian-speaking Telegram communities, and comparing them with English-
speaking communities.

##  III Methodology

###  III-A Designing and collecting the dataset

(a) IT

(b) EN

Figure 1: Retrieved chats by iteration

#### Telegram terminology

Telegram offers a variety of chat types. Channels are unidirectional chats
where typically only administrators broadcast content to an audience that
cannot interact directly. Groups are chat rooms where all members have
permission to share contents by default and interact with each other.
Supergroups are a variation of groups, differentiated mainly by administrative
powers and member limits. However, for our study, we treat them as equivalent
to regular groups since these differences are not relevant to our analysis. A
notable feature in Telegram is the ability for channel admins to link a
channel to a corresponding group, creating a two-tiered structure known as
linked chat. In this structure, a channel enables any user, whether a follower
or not, to reply directly to each post. Simultaneously, the associated group
houses these conversational threads and operates as a standard group. This
composite structure allows unrestricted interaction on the channel’s posts and
fosters broader discussion within the group. For the scope of our paper, we
consider public channels, groups, and linked chats. We use the term chat
interchangeably to refer to all three types. As mentioned, we highlight a key
Telegram feature, that is the ability for users to share posts and messages
from one chat to another via message forwarding. This feature preserves the
original chat’s information, effectively creating a bridge between chats and
facilitating the discovery and retrieval of connected content.

#### Data collection approach

We retrieve two distinct Telegram datasets pertaining to conspiracy
discussions in Italian and English using the following approach. We employ a
snowball technique focused on message forwarding, a method previously used in
several papers for channel retrieval [20, 16]. For the first time, we expand
this technique to include groups and linked chats. We begin by selecting seed
chats known for conspiracy content. For the Italian discussions, we select
seeds through keyword on tgstats.com, a platform that provides a categorized
catalog of existing Telegram chats. We focus on terms associated with pandemic
conspiracy theories, identifying 43 Italian chats related to conspiracies as
seeds. Similarly, for the English seeds, we use tgstats and search for
keywords associated with the QAnon conspiracy, resulting in 20 seed chats. We
start from two different conspiracy theories to anchor our study in the
specific cultural and linguistic contexts, ensuring a focus on the conspiracy
sphere and exploring how these conspiracies expand and evolve in these
settings. We leverage Telegram APIs to collect messages. Starting with seed
chats at iteration 0, we parse messages to identify forwarded messages,
following them to retrieve new chats and their messages in subsequent
iterations. We only add new chats that meet our language criteria, either
Italian or English, determined by the most frequently detected language in
their messages. Our data collection concludes after iteration 2.

#### Datasets overview

(a) Users

(b) Messages

Figure 2: Distribution of users and messages per chat

Using the aforementioned approach, we collect two large datasets: the Italian
dataset, covering the period from January 1, 2024, to February 13, 2024,
includes $1,346$ chats, containing a total of 3.4M messages. Meanwhile, the
English dataset, spanning from January 1, 2024, to February 20, 2024,
comprises $634$ chats, including a total of 5M messages. Figure 1 shows the
number of chats per type collected at each iteration of our snowball crawling
strategy. Predominantly, linked chats are more prevalent at each stage, while
standalone groups are less used in these contexts. We analyze the distribution
for both the number of users and the number of comments per chat. Linked chats
required a specialized approach for analysis. For message counts, we aggregate
the total number of messages across both linked chats. For user counts, we
consider the higher number of subscribers, whether from the channel or its
linked group. As shown in Figure 2, we observe that the log-number of users
and messages within the chats exhibit a gaussian distribution, contrasting
with the typical heavy-tailed distribution of conversational trees documented
in prior research [22]. This variation could imply that linked chats and
groups, being more similar to chat rooms than traditional social media feeds,
might exhibit different behaviors. Alternatively, it could suggest that our
snowballing techniques could miss smaller chats, thus filtering out less
influential ones.

###  III-B Building the networks/uncovering communities

The message forwarding mechanism enables us to construct a directed weighted
graph $\mathcal{G}=(\mathcal{N},\mathcal{E})$, where $\mathcal{N}$ represents
the set of nodes and $\mathcal{E}$ the set of edges. In this graph, nodes
correspond to chats, which include unlinked channels, unlinked groups, and
linked chats. For any two nodes $u,v\in\mathcal{N}$, the weight of the edge
$w_{e_{u,v}}\in\mathcal{E}$ is determined by the number of messages forwarded
from chat $u$ to chat $v$. To prevent loops, forwards from a chat to itself,
including within linked chats, are excluded. This exclusion is crucial as, in
linked chats, each message from the channel is automatically forwarded to the
associated group to form conversational trees. The Italian network consists of
$1,346$ nodes and $35,802$ edges, and the English network comprises $634$
nodes and $24,546$ edges. We employed community detection within our graph
using the Louvain algorithm tailored for directed graphs [33], focusing only
on communities with more than 10 chats to ensure the robustness of our
findings.

##  IV Results

The application of our methodology brought to the detection of multiple
communities within the Italian and English Telegram conspiracy landscapes. In
the following we shed light on the activity and dissemination patterns of the
communities.

Italian communities | Size |  | Top words in ranked topics
---|---|---|---
Freedom | 297 |  | liberademocrazia, dissenso, geopolitica, democrazia, anonimato, governo, imporre, controllare
Warfare | 261 |  | ucraino, yemen, internazionale, biden, geopolitica, gaza, russia
ConspiracyMix | 249 |  | governo, pandemia, salute, genocidio, storia, agricoltore, protesta, biden, trump
ConspiracyMix2 | 188 |  | trump, epstein, agricoltore, alimentare, guerra, vaccino, covid, sicurezza, dissenso, diritto
NewsSource | 102 |  | warrealtime, internazionale, informazione, media, ministero, affermare
Politics | 73 |  | politica, economico, governo, presidente, italia, europeo, ministro, pubblico, carabiniere
AltNews | 52 |  | bankers, informazione, censura, globalista, società, imporre, morte, libertà, controllare
Fight | 43 |  | popolare, lotta, civile, collegare, verità, libertà, importante, agire
Novax | 37 |  | dissenso, vaccinazione, bambino, studio, mortalità, controinformazione, salute, rischiare
Religious | 14 |  | gesù, valore, sacramento, pentire, rinascere, invidia, esorcista, miracolosamente, guarigione
Spiritual | 12 |  | awakening, riflessione, luce, conscience, inspirations, meditation
English communities |  |  |
QAnonCrypto | 119 |  | trump, god, control, chadgptcoin, coin, btc, pump, dump, money, official
Warfare | 117 |  | ukrainian, attack, military, israel, defense, missile, rhetoric, soldier
QAnonHealth | 103 |  | trump, god, child, food, cancer, parasite, health, weapon, medical, water
CHScams | 89 |  | transfer, money, deposit, click, payment, win-win, card, finance
QAnon | 73 |  | endhumantrafficking, minor, abuse, police, evil, control, trump, god
ConspiracyMix | 52 |  | kaplan, dogedesigner, elon, war, trump, heaven, biden, border, ballot, court
Covid | 30 |  | vaccine, covid, health, body, food, cancer, doctor, government
OldSchoolConsp | 22 |  | weird, shit, ufo, alien, paranormal, time, experience, consciousness
TABLE I: Topics identified by Corex models. For each community, The words
listed in each row correspond to the top-ranked terms associated with that
community, as determined by the Corex algorithm. This highlights the main
terms and topics prevalent within each community.

###  IV-A Uncovering narratives

Here, we present summary information for each community, alongside their main
narratives. We uncover the main topics of discussion within each community
through a comprehensive analysis approach. This involves utilizing topic
modeling techniques, channel information, and examining TF-IDF weighted
hashtags used by each community. By leveraging these diverse methods, we aim
to offer valuable insights into the unique themes and narratives that shape
the discourse within each community. To perform topic modeling, we adopted a
state-of-the-art algorithm known as Anchored Correlation Explanation (CorEx)
[34]. Unlike traditional methods like Latent Dirichlet Allocation (LDA), CorEx
identifies hidden topics within a collection of documents without assuming any
particular data generating model. Instead, it leverages the dependencies of
words in documents through latent topics, by maximizing the total correlation
between groups of words and the respective topic, ensuring greater flexibility
[34]. We applied unsupervised CorEx, in order to discover topics spontaneously
emerging from our data. Given that our network consists of chat platforms,
with each chat having a one-month history, we trained separate models for each
community. We utilized the chat messages as corpora to capture the full
spectrum of topics discussed within each community. This approach allows us to
comprehensively explore the range of topics present in each community’s
discourse. After experimenting with different configurations, we set the
expected number of topics to 10, since additional topics were adding
negligible correlation to the learned models. Finally, we ranked the obtained
topics according to the fraction of the total correlation that they explain.
Results are presented in Table I and discussed as follows.

Italian Narratives. The Italian-speaking communities are presented as follows
and presented in Table I, ordered by decreasing number of members:

  * •

Freedom: This community is centered around concepts of liberal democracy and
dissent, discussing geopolitical topics, democracy, anonymity in governance,
and control-related issues.

  * •

Warfare: A community concerned with international warfare, particularly
focusing on the Ukrainian conflict and Russian propaganda.

  * •

ConspiracyMix: A community that discusses various conspiracy theories
involving government actions, health-related topics such as the pandemic, and
foreign political figures.

  * •

ConspiracyMix2: Similar to ConspiracyMix, this community spans across
conspiracy theories, touching on warfare, vaccines, COVID-19, farmers’
protests, and QAnon.

  * •

NewsSource: A community that encompasses a spectrum of information sources
ranging from conspiracy theory-driven outlets to reputable journalistic
sources (e.g., “IlSole24Ore,” “IlFattoQuotidiano”). This convergence reflects
the dynamics of conspiratorial contexts, where genuine information is often
filtered through a conspiratorial lens, shared, and discussed alongside news
from international sources, with an emphasis on media scrutiny and critique
[35, 36].

  * •

Politics: A political community discussing economic issues, government
policies and European affairs.

  * •

AltNews: A community focused on counter-information and alternative news
sources, focusing on issues of censorship, globalism, and societal control.

  * •

Fight: A community engaged in civil struggles, emphasizing the importance of
truth, freedom, and action in the face of societal challenges.

  * •

Novax: A community characterized by dissent against vaccinations, health
studies, health risks, and mortality rates.

  * •

Religious: A community centered on Italian religious values, discussing Jesus,
sacraments, and other themes of rebirth, envy, exorcism, and miraculous
healing.

  * •

Spiritual: A community centered on spiritual topics, such as spiritual
awakening and meditation.

These communities all circle around conspiracy theories, each one with its own
angle, with alternative information challenging mainstream narratives to news
source offering more traditional views. In addition, conspiracy narrative ties
to religiosity, alternative health, and conspiratorial thinking, as observed
in literature for English-speaking groups [17, 29, 30]. Exploring these groups
gives us insight into the Italian conspiracy ecosystem on Telegram, a subject
that is relatively unexplored in existing literature.

English Narratives. While our focus thus far has centered on Italian-speaking
communities, here we present the English ones. Examining English-speaking
communities allows us to provide valuable comparative insights into conspiracy
theories in different cultural contexts. The English-speaking communities are
presented as follows:

  * •

QAnonCrypto: A community where conspiracy discussions are hijacked by the
cryptocurrency world, featuring themes of various coins and fraudulent schemes
like pump and dump [9]. Indeed, prior research has explored the involvement of
cryptocurrency in discussions, noting the frequent presence of cryptocurrency
and finance-related tags within QAnon-related themes [37]. In fact, belief in
conspiracy theories plays a role in people’s decisions to invest in
cryptocurrency, as people exhibiting cunning traits and a distrustful stance
toward government are more likely to favor cryptocurrency as an investment
option [38].

  * •

Warfare: A community similar to its Italian counterpart, focusing on the
Ukrainian conflict, military issues, and other war rhetoric.

  * •

QAnonHealth: A community where QAnon conspiracy theories intersect with health
concerns, discussing food, cancer, and parasites, along with other medical
aspects.

  * •

CHScams: A community that relies on conspiracy theory discussions to promote
financial scams and fraudulent activities in Chinese language. The terms
listed in the table are translated from Chinese to English.

  * •

QAnon: This community focuses on pure QAnon conspiracy theories, involving
topics such as child abuse, government control, and political figures.

  * •

ConspiracyMix: This community discusses various conspiracy theories, with a
focus on legal issues as seen in terms like “court,” while also touching the
cryptocurrency sphere (e.g., “DodgeCoin,” “Elon”). Discussions also involve
Judge Lewis Kaplan, who presided over both Trump’s federal defamation
trial111https://www.nytimes.com/2023/04/27/nyregion/who-is-lewis-kaplan-judge-
in-carroll-case-against-trump.html and Sam Bankman-Fried’s cryptocurrency
fraud trial222https://www.bloomberg.com/news/articles/2022-12-27/bankman-
fried-case-reassigned-to-us-judge-lewis-kaplan-in-ny.

  * •

Covid: A community centered around discussions of COVID-19, vaccine
skepticism, and related health and governmental issues.

  * •

OldSchoolConsp: A community focused on traditional conspiracy topics such as
UFOs, aliens, the paranormal, and discussions of time and consciousness.

Figure 3: t-SNE representation of message distribution by topic in the EN
Dataset

(a) QAnon

(b) QAnonCrypto

(c) Warfare

(d) OldSchool

Figure 4: KDE of message topics for different EN communities

(e) NewsSource

(f) AltNews

(g) Warfare

(h) Novax

Figure 5: KDE of message topics for different IT communities

The English-speaking communities exhibit a marked tendency towards insularity,
as QAnon is a very closed community [39, 40]. Indeed, many communities,
although primarily connected with QAnon themes, show a distinct emphasis on
topics such as cryptocurrency, health, or governmental affairs, unified by an
underling QAnon narrative. This phenomenon of thematic variations within a
singular ideological framework is indicative of the QAnon community’s
cohesiveness. Indeed, prior work has observed an increasing association of
QAnon with religiosity, alternative health, and wellness philosophies, as well
as affective states that promote conspiratorial thinking [17, 29, 30] – trends
also observed in the Italian-speaking communities.

###  IV-B t-SNE for context analysis

To provide a comprehensive visual representation of the topics discussed
within our datasets, we represent all messages using t-Distributed Stochastic
Neighbor Embedding (t-SNE) [41], a dimensionality reduction technique used for
visualizing high-dimensional data through visual clustering. In this way,
spatial proximity in the t-SNE map can suggest how topics fit into the larger
conversation on conspiracies. We build the t-SNE visualization on topics
identified by the CorEx algorithm. In particular, we developed two distinct
models, one for Italian and one for English to analyze the entire corpus of
messages. We opted to identify 50 topics to further our understanding of the
context dynamics inside the clusters. By representing each message as a
50-dimensional vector corresponding to these topics, we can highlight the
diverse contexts within each community. This is particularly important because
Telegram chats often cover a broad range of topics rather than focusing on a
single subject [12]. We obtain and $n\times m$ matrix where $n$ and $m$ are
respectively the number of messages and the number of topics we wanted to
detect. Each value $v_{i,j}$ represents the correlation between the
$i\textsuperscript{th}$ message and the $j\textsuperscript{th}$ topic. We
lower the dimensionality of our matrix using the tSNE and plot all messages in
a two-dimensional space, coloring them according to the community of origin to
show how clusters are closely related or share similar discussions. Figure 5
presents the results on the English dataset. The varying distributions of the
messages across communities highlight the differences in discussion in terms
of quantity, focus, and framework, even among similar communities. This
spatial arrangement underlines the nuanced interactions between these
communities. For example, we can observe the proximity of the QAnonCrypto
community to the QAnon and the QAnonHealth communities, suggesting that crypto
topics tend to piggyback engage with QAnon-related discussions. Figure 5
better presents the differences in distributions through Kernel Density
Estimation (KDE) of the messages, where areas of higher density indicate a
higher likelihood of encountering messages related to specific topics. For
instance, in Figure 5a, the distribution of messages in chats of the QAnon
community is notably widespread, suggesting correlations with many different
topics, similarly to QAnonCrypto (Figure 5b) and Warfare (Figure 5c)
communities. This suggests that some communities on Telegram tend to discuss a
broad array of topics, they each enrich the discourse with their unique
frameworks and worldviews. In contrast, more specialized communities like
OldSchoolConsp (Figure 5d) are localized to very specific areas. We conduct
the same analysis for the Italian dataset. Due to space constraints, we
highlight only some notable patterns. We observe distinct patterns between the
NewsSource (Figure 5a) and AltNews (Figure 5b) communities, which both cover
alternative news topics. However, NewsSource also includes legitimate news
sources, resulting in messages that show dual density peaks, possibly
indicating interdependence, whereas AltNews messages display a single density
peak, reflecting a more homogeneous topic focus.

##  V Validation

Here, we show that the insights derived from our network analysis are not
overly dependent on the initial seeds used to construct the dataset. This
robustness check highlights the applicability of our methodology across
different settings and its potential for broader research applications in the
study of online discourse and information diffusion. To assess the robustness
of our findings, we aim to determine if starting from different seeds results
in the same chat composition in our dataset. We focus on the Italian dataset
and create a counterpart validation dataset using the snowballing process,
this time starting from a distinct set of 28 seeds that were not among the
original 43 Italian seeds used in the initial data collection. These new seeds
are sourced from the Butac blacklist333https://www.butac.it/the-black-list/, a
list of Italian disinformation Telegram channels. The collected dataset
includes 1,591 chats active from February 1, 2024 to March 20, 2024. We
stopped the collection after two iterations of the process to maintain
consistency with the original methodological framework. We then examine the
overlap between the Italian datasets and the validation dataset to determine
if the chats retrieved in the validation dataset match those in our original
dataset. We find that $80\%$ of the chats in the validation dataset are also
present in our original dataset, suggesting that our results would remain
robust even with a different set of seeds. To further validate this finding,
in Figure 6 we compare the size, in-degree, and out-degree distributions
between chats included in our original dataset and those in the validation
dataset that are not included in the original. The results indicate that the
chats excluded from the original dataset have lower averages in size, in-
degree, and out-degree, suggesting that the missing chats have less influence
within the dataset.

Figure 6: Difference in distribution of in-degree, out-degree, and size
between the chats in the validation dataset included in the initial Italian
dataset and those that are not.

##  VI Discussions and limitations

Leveraging the Telegram message forwarding mechanism has unveiled distinct
trends and dynamics within conspiracy theory discussions across cultural
contexts. In Italian-speaking communities, the diversity in handling
conspiracy theories – from challenging mainstream narratives with alternative
information to sharing views from more traditional news sources – enriches our
understanding of the Italian conspiracy ecosystem on Telegram, a relatively
uncharted territory in existing literature. The presence of news sources and
alternative news outlets shows a dynamic interplay in the dissemination and
legitimization of conspiracy theories, highlighting the intricate balance
between mainstream credibility and the counter-narratives that thrive on
Telegram. We also show trends of thematic diversity within a cohesive
ideological framework, with conspiracy narrative ties to religiosity,
alternative health, and conspiratorial thinking, trends similarly observed in
literature for English-speaking groups [17, 29, 30]. The English-speaking
communities span various topics like cryptocurrency, health, and governmental
affairs, yet are tightly woven around the QAnon narrative [39, 40] with no
presence of legitimate news sources, suggesting a significant echo chamber
effect where misinformation may circulate more freely without the
counterbalance of accredited information. Our methodological robustness check
suggests a relative independence from the choice of initial seeds used for the
dataset construction. This implies that, despite starting from different
seeds, we would likely have mapped out similar networks, suggesting that the
communities identified through message forwarding – encompassing both channels
and groups – tend to stay focused on conspiracy themes, thus remaining within
this thematic bubble and fostering community homophily. These observations
align with previous studies indicating that channel communities engaged in
forwarding tend to form echo chambers with varying structures [18].

However, the diffusion of misinformation, a process inherently temporal and
complex, cannot be fully captured through this static analysis alone. Future
work should incorporate temporal network analyses to fully capture the actual
journey of misinformation through the network or to uncover dynamic
coordinated communities on Telegram [42]. Despite this limitation, the
insights and robustness check highlight the applicability of our methodology
across different settings and its potential for broader research applications
in the study of online discourse and information diffusion.

##  VII Conclusions

In this study, we analyzed online Italian and English conspiracy-related
Telegram communities through the lens of message forwarding, aiming to uncover
the dynamics of conspiracy theory discussions in different speaking contexts.
Using snowball sampling, we collected two extensive datasets encompassing
Telegram channels, groups, linked chats, and messages shared over a month in
2024. We built the Italian and English networks, revealing key communities,
and characterize their narratives through topic modeling. We uncovered trends
of thematic diversity within a cohesive ideological framework, linking
conspiracy narratives to religiosity, alternative health, and conspiratorial
thinking, and uncovered the interplay of news sources and alternative news
outlets in disseminating and legitimizing conspiracy theories. Our analysis
also shed light on the thematic relationships between communities and the role
of forwarded messages in fostering content distribution and community
homophily. Finally, we tested our methodology’s robustness against variations
in initial dataset seeds, showing the reliability of our insights and broader
applicability. This research contributes new perspectives on misinformation
spread, paving the way for further exploration of conspiracy discourse,
especially in the under-explored Italian context, and misinformation diffusion
on Telegram.

## Acknowledgments

This work was partly supported by SoBigData.it which receives funding from
European Union – NextGenerationEU – National Recovery and Resilience Plan
(Piano Nazionale di Ripresa e Resilienza, PNRR) – Project: “SoBigData.it –
Strengthening the Italian RI for Social Mining and Big Data Analytics” – Prot.
IR0000013 – Avviso n. 3264 del 28/12/2021.; and by project SERICS (PE00000014)
under the NRRP MUR program funded by the EU – NGEU.

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