11institutetext: University of Minnesota, USA
11email<EMAIL_ADDRESS><EMAIL_ADDRESS>Harvard College
22email<EMAIL_ADDRESS>
# SCARLET: Explainable Attention based Graph Neural Network for Fake News
spreader prediction

Bhavtosh Rath 11 Xavier Morales 22 Jaideep Srivastava 11

###### Abstract

False information and true information fact checking it, often co-exist in
social networks, each competing to influence people in their spread paths. An
efficient strategy here to contain false information is to proactively
identify if nodes in the spread path are likely to endorse false information
(i.e. further spread it) or refutation information (thereby help contain false
information spreading). In this paper, we propose SCARLET (truSt and
Credibility bAsed gRaph neuraL nEtwork model using aTtention) to predict
likely action of nodes in the spread path. We aggregate trust and credibility
features from a node’s neighborhood using historical behavioral data and
network structure and explain how features of a spreader’s neighborhood vary.
Using real world Twitter datasets, we show that the model is able to predict
false information spreaders with an accuracy of over 87%.

##  1 Introduction

Social network platforms like Twitter, Facebook and Whatsapp are used by
millions around the world to share information and opinions. Often, the
veracity of content shared on these platforms is not confirmed. This gives
rise to scenarios where information having conflicting veracity, i.e. false
information and its refutation, co-exist. Refutation can be defined as true
information which fact checks claims made by a false information. A typical
scenario is that false information originates at time $t_{1}$, and starts
propagating. Once it is identified, its refutation information is created at
time $t_{2}$ ($t_{1}<t_{2}$). Both pieces of information propagate
simultaneously, with many nodes lying in their common spreading paths.

While detecting false information is an important and widely researched
problem, an equally important problem is that of preventing the impact of
false information spreading. Techniques involve containment/suppression of
false information, as well as accelerating the spread of its refutation. Being
able to predict the likely action of such users before they are exposed to
false information is an important aspect of such a strategy. Nodes identified
as vulnerable to believing false information can thus 1) be cautioned about
the presence of the false information so that they do not propagate it, and 2)
be urged to propagate its refutation. While optimization models based on
information diffusion theories have been proposed in the past for
misinformation containment, recent advancements in deep learning on graphs
serve as the motivation to explore false information control models which use
components that exist even before false information starts spreading, namely
the underlying network structure and people’s historical behavioral data.

Trust and Credibility are important psychological and sociological concepts
respectively, that have subtle differences in their meanings. While trust
represents the confidence one person has in another person, credibility
represents generalized confidence in a person based on their perceived
performance record [14]. Thus, in a graph representation of a social network,
trust is a property of a (directed) edge, while credibility is a property of
an individual node. Metzger et al. [7] showed that the interpretation of a
neighbor’s credibility by a node relies on its perception of the neighbor
based on their trust dynamics. Motivated with this idea, we propose a graph
neural network model that integrates people’s credibility and interpersonal
trust features in a social network to predict whether a node is likely to
spread false information or not. We make the following contributions in this
paper:
1) We propose $SCARLET$, a novel user-centric model using graph neural network
with attention mechanism to predict whether a node will most likely spread
false information, its refutation or be a non-spreader.
2) We demonstrate that a person’s decision to spread a false information is
sensitive to its perception of neighbor’s credibility, and this perception is
a function of trust dynamics with the neighbor.
3) To the best of our knowledge, this is the first model being evaluated on
real world Twitter datasets of co-existing false and refutation information.

Related Work: Social science research in the past has explored the aspects of
people’s behavior that cause false information spreading. Jaeger et al. [5]
was one of the first to study what makes rumors believable when told by peers
instead of authority figures. While it focused on modelling people’s anxiety,
it served as motivation to explore other sociological features that are
relevant to information spreading. Petty and Cacioppo [10] found credibility
perception to be an important factor for believing false information. Rosnow
et al. [15] proposed that interpersonal trust also played an important role in
rumor transmission. The idea was further enforced by Morris et al. [8] where
they claimed that people assess credibility based on trust relationships with
their neighbors in a social network. Motivated by these ideas, there has been
much interest in computational models for false information spreader detection
using trust, which has shown promising results [12, 13]. Many computational
techniques to combat false information spreading have been explored over the
past decade, as summarized by Sharma et al. [17]. Most models rely on
generating relevant features from the information that help distinguish false
information from true. Our proposed model is based on recent advances in graph
neural networks [22]. In addition, our work proposes an explainable attention
based model, inspired from recent work [23, 24]. Qui et al. [11] focuses on
influence in general, while our model integrates people’s psychological and
sociological features to identify false information spreaders.

Models inspired by information diffusion models for false information
mitigation have also been proposed. Budak et al. [1] proposed an optimization
strategy to identify false information spreaders in a network who, when
convinced by its refutation, would minimize the number of people receiving the
false information. Nguyen et al. [9] proposed greedy approaches to a similar
problem of limiting the spread of false information in social networks. More
recently, Tong et al. [19] studied the problem as a multiple cascade diffusion
problem.

##  2 Interpersonal Trust and User Credibility features

###  2.1 Trust-based features

1\. Global Trust ($Tr^{G}$) : Global trust are trust scores that are computed
on the directed follower-followee network around information spreaders. It is
called global because an individual’s trust score is sensitive to changes in
the network structure. Using the Trust in Social Media (TSM) algorithm [16],
we quantify the likelihood of trusting others and being trusted by others. The
TSM algorithm uses a directed graph $\mathcal{G(\mathcal{V},\mathcal{E})}$ as
input, together with a specified convergence criteria, and computes
trustingness and trustworthiness scores using the equations:
$ti(v)=\sum_{\forall x\in out(v)}\left(\frac{w(v,x)}{1+(tw(x))^{s}}\right)$
and $tw(u)=\sum_{\forall x\in in(u)}\left(\frac{w(x,u)}{1+(ti(x))^{s}}\right)$
where $u,v,x\in\mathcal{V}$ are nodes, $ti(v)$ and $tw(u)$ are the
trustingness and trustworthiness scores of $v$ and $u$, respectively, $w(v,x)$
is the weight of edge from $v$ to $x$, $out(v)$ is the set of out-edges of
$v$, $in(u)$ is the set of in-edges of $u$, and $s$ is the involvement score
of the network. The involvement score is basically the potential risk an actor
takes when creating a link in the network. Details of the algorithm are
excluded due to space constraints and can be found in [16].
2\. Local Trust ($Tr^{L}$) : Local trust is computed based on the retweeting
behavior of an individual. It is termed local because the trust score depends
on node’s behavior, and not on the network structure. We consider the proxy
for trusting others as the fraction of tweets of $x$ that are retweets
($RT_{x}$) denoted by $\sum_{\forall i\in t}\\{1$ if $i=RT_{x}$ else
$0\\}/n(t)$. Meanwhile, we consider the proxy for trusted by others as the
average number of times $x$’s tweets are retweeted ($n(RT)$) denoted by
$\sum_{\forall i\in t}{i_{n(RT_{x})}/n(t)}$. ($t$ represents the most recent
tweets posted in $x$’s timeline).

###  2.2 Credibility-based features

Credibility of users is generalized based on features extracted from
information posted on their timeline, empirically studied by Castillo et al.
[2]. We generate relevant credibility features for nodes in the network, which
can be categorized into two types: user-based and content-based.
1\. User-based Credibility ($Cr^{U}$) : User credibility features are
extracted from user metadata of nodes in the network. Features used in our
model are summarized below:

A. Registration age (U1): Registration age denotes the time that has
transpired since a user created their account. Older accounts tend to be
associated with more credible users.

B. Overall activity count (U2): Activity or statuses count is the number of
tweets issued by a user. Low credibility is associated with users who have
less activity on their timeline.

C. Is verified (U3): This label suggests whether a user account is marked as
authentic or not by Twitter. Verified accounts are more likely to be credible.
2\. Content-based Credibility ($Cr^{C}$) : These features are obtained by
aggregating a user’s timeline activity. It is important to note that, unlike
Castillo’s assumption, we do not make a distinction between information that
is specifically related to news or not, as that process would require manually
assessing newsworthiness of the tweets. The following relevant features are
extracted:

A. Emotions conveyed by user (M1): Emotions represent positive or negative
sentiments associated with a tweet. Strong sentiments are usually associated
with non-credible users.

B. Level of uncertainty (M2): Level of uncertainty is quantified as the
fraction of user’s tweets that are questioning in nature. Tweets with a high
level of uncertainty tend to be less credible.

C. External source citation (M3): External source citation is quantified as
the fraction of user’s tweets that cite an external URL. Tweets which cite
URLs tend to be more credible.

##  3 Proposed approach

This section explains how we integrate both credibility and trust features in
an attention based graph neural network model to predict whether a person
would likely be a spreader of false information or its refutation. The problem
formulation is as follows:
Problem formulation: Let $\mathcal{G(\mathcal{V},\mathcal{E})}$ be a directed
social network containing false information spreaders ($\mathcal{V}_{F}$),
refutation information spreaders ($\mathcal{V}_{T}$) and non-spreaders
($\mathcal{V}_{\hat{Sp}}$) at a time instance t
($\\{\mathcal{V}_{F}\cup\mathcal{V}_{T}\cup\mathcal{V}_{\hat{Sp}}\\}\subset\mathcal{V}$).
By assigning importance score using global ($Tr^{G}$) and local ($Tr^{L}$)
trust features ($Tr=Tr^{G}||Tr^{L}$), and aggregating user-based ($Cr^{U}$)
and content-based ($Cr^{C}$) credibility features ($Cr=Cr^{U}||Cr^{C}$) of
node i and its neighborhood nodes ($\mathcal{N}_{i}{{}^{K}}$) sampled till
depth K, we predict whether i is more likely to spread false information,
refutation information or be non-spreader at future time $t+\Delta t$.

The proposed graph neural network framework can be broadly divided into two
steps:

1. We assign an importance score to neighborhood nodes ($\mathcal{N}_{i}{{}^{K}}$) sampled till depth $K$ based on trust ($Tr$) features. This is done using an attention mechanism.

2. We learn representations using Graph Convolutional Networks by aggregating credibility ($Cr$) features proportional to the importance scores assigned for the neighborhood nodes based on step 1.

An overview of the proposed model architecture is shown in Figure 1. The
following subsections explain the framework in detail.

Figure 1: Architecture overview. a) Node neighborhood is fed into the graph
neural network. b) Interpersonal trust dynamics is evaluated using ($Tr$)
features. c) Importance score $e$ is assigned to neighbors using graph
attention mechanism. d) Credibility ($Cr$) features are aggregated
proportional to neighbors’ importance scores using Graph Convolution Networks.
e) Node is identified as either false information spreader (red), refutation
spreader (green), or a non-spreader (black).

3.1 Importance score using attention: We apply a graph attention mechanism
[21] which attends over the neighborhood of $i$ and, based on their trust
features, assigns an importance score to every $j$ ($j\in\mathcal{N}_{i}$).
First, every node is assigned a parameterized weight matrix (W) to perform
linear transformation. Then, self-attention is performed using a shared
attention mechanism $a$ (a single layer feed-forward neural network) which
computes trust-based importance scores. The unnormalized trust score between
$i$,$j$ is represented as:

$\vspace*{-.2cm}e_{ij}=a(\textbf{W}_{Tr_{i}},\textbf{W}_{Tr_{j}})\\\ $ (1)

where $e_{ij}$ quantifies $j$’s importance to $i$ in the context of
interpersonal trust. We perform masked attention by only considering nodes in
$\mathcal{N}_{i}$. This way we aggregate features based only on the
neighborhood’s structure. To make the importance scores comparable across all
neighbors we normalize them using the softmax function:

$\vspace*{-.2cm}\alpha_{ij}=softmax(e_{ij})=\frac{exp(e_{ij})}{\sum_{k\in\mathcal{N}_{i}}exp(e_{ik})}\\\
$ (2)

The attention layer $a$ is parameterized by weight vector $\vec{\textbf{a}}$
and applied using LeakyReLU nonlinearity. Normalized neighborhood edge weights
can be represented as:

$\vspace*{-.2cm}\alpha_{ij}=\frac{exp(LeakyReLU(\vec{\textbf{a}}^{T}[\textbf{W}_{Tr_{i}}||\textbf{W}_{Tr_{j}}]))}{\sum_{k\in\mathcal{N}_{i}}exp(LeakyReLU(\vec{\textbf{a}}^{T}[\textbf{W}_{Tr_{i}}||\textbf{W}_{Tr_{k}}]))}$
(3)

$\alpha_{ij}$ thus represents trust between $i$ and $j$ with respect to all
nodes in $\mathcal{N}_{i}$. Each $\alpha_{ij}$ obtained for the edges is used
to create an attention-based adjacency matrix
$\hat{A}_{atn}=[\alpha_{ij}]_{|\mathcal{V}|\times|\mathcal{V}|}$ which is
later used to aggregate credibility features.
3.2 Feature aggregation: The Graph Convolution Network [6] is a graph neural
network model that efficiently aggregates features from a node’s neighborhood.
It consists of multiple neural network layers where the information
propagation between layers can be generalized by Equation 4. Here, $H$
represents the hidden layer and $A$ represents the adjacency matrix
representation of the subgraph ($A=\hat{A}_{atn}\prime$). $H^{(0)}=Cr$ and
$H^{(L)}=Z$, where $Z$ denotes node-level output during transformation.

$\vspace*{-.3cm}H^{(l+1)}=f(H^{(l)},A)\\\ $ (4)

We implement a Graph Convolution Network with two hidden layers using a
propagation rule as explained in [6].

$\vspace*{-.3cm}H^{(l+1)}=\sigma(\hat{D}^{-1/2}\hat{A}\hat{D}^{-1/2}H^{(l)}W^{(l)})\\\
$ (5)

Here, $\hat{A}=A+I$, where $I$ is the identity matrix of the neighborhood
subgraph. This operation ensures that we include self-features during
aggregation of neighbor’s credibility features. $\hat{D}$ is the diagonal
matrix of node degrees for $\hat{A}$, where
$\hat{D}_{ii}=\sum_{j}\hat{A}_{ij}$. $W^{(l)}$ is the layer weight matrix, and
$\sigma$ denotes the activation function. Symmetric normalization of $\hat{D}$
ensures our model is not sensitive to varying scale of the features being
aggregated.

3.3 Node classification: Using credibility features and network structure for
nodes in $i$’s neighborhood, node representations are learned from the graph
using a symmetric adjacency matrix with attention-based edge weights
($\hat{A}=\hat{D}^{-1/2}\hat{A}_{atn}\prime\hat{D}^{-1/2}$). Following forward
propagation model is applied:

$\vspace*{-.2cm}Z=f(X,\hat{A}_{atn}\prime)=softmax(\hat{A}ReLU(\hat{A}X{W^{(0)}})W^{(1)})\\\
$ (6)

$X$ represents the credibility features. $W^{(0)}$ and $W^{(1)}$ are input-to-
hidden and hidden-to-output weight matrices respectively, and are learnt using
gradient descent learning. Classification is performed using the following
cross entropy loss function:

$\vspace*{-.2cm}\mathcal{L}={\sum_{l\in\mathcal{Y}_{L}}\sum_{f\in
Cr}}Y_{lf}lnZ_{lf}\\\ $ (7)

where $\mathcal{Y}_{L}$ represents indices of labeled vertices, $f$ represents
each of the credibilty features being used in the model, and $Y\in
R^{|\mathcal{Y}_{L}|\times|Cr|}$ is the label indicator matrix.

Table 1: Network dataset statistics for news events N1-N10.

| N1 | N2 | N3 | N4 | N5
---|---|---|---|---|---
| $|\mathcal{V}|$ | $|\mathcal{E}|$ | $|Sp|$ | $|\mathcal{V}|$ | $|\mathcal{E}|$ | $|Sp|$ | $|\mathcal{V}|$ | $|\mathcal{E}|$ | $|Sp|$ | $|\mathcal{V}|$ | $|\mathcal{E}|$ | $|Sp|$ | $|\mathcal{V}|$ | $|\mathcal{E}|$ | $|Sp|$
F | 1,797,059 | 5,316,114 | 2,584 | 885,598 | 1,824,585 | 943 | 1,228,479 | 2,477,986 | 1,313 | 2,607,629 | 7,146,454 | 4,552 | 2,150,820 | 5,215,120 | 3,344
T | 1,164,162 | 2,283,160 | 437 | 453,537 | 879,854 | 403 | 1,169,681 | 1,988,576 | 425 | 433,616 | 773,778 | 467 | 1,168,820 | 1,543,513 | 305
F $\cup$ T | 2,677,924 | 7,562,503 | 3,017 | 1,230,559 | 2,641,513 | 1,337 | 2,198,524 | 4,458,228 | 1,738 | 2,900,925 | 7,882,019 | 5,015 | 3,019,066 | 6,631,032 | 3,627
F $\cap$ T | 283,297 | 8,956 | 4 | 108,576 | 59,912 | 9 | 199,636 | 376 | 0 | 140,320 | 3,273 | 5 | 300,574 | 112,098 | 22
| N6 | N7 | N8 | N9 | N10
| $|\mathcal{V}|$ | $|\mathcal{E}|$ | $|Sp|$ | $|\mathcal{V}|$ | $|\mathcal{E}|$ | $|Sp|$ | $|\mathcal{V}|$ | $|\mathcal{E}|$ | $|Sp|$ | $|\mathcal{V}|$ | $|\mathcal{E}|$ | $|Sp|$ | $|\mathcal{V}|$ | $|\mathcal{E}|$ | $|Sp|$
F | 2,387,610 | 5,356,288 | 3,498 | 627,147 | 1,071,120 | 696 | 2,036,162 | 2,876,783 | 894 | 1,197,935 | 2,139,912 | 2,317 | 2,174,023 | 4,280,962 | 2,323
T | 1,297,371 | 1,727,503 | 481 | 1,166,528 | 2,524,907 | 847 | 1,058,482 | 1,513,404 | 489 | 2,999,865 | 6,317,032 | 1,833 | 704,006 | 1,314,996 | 741
F $\cup$ T | 2,449,434 | 5,691,728 | 3,769 | 1,606,924 | 3,577,449 | 1,534 | 2,663,392 | 4,082,373 | 1,365 | 4,064,545 | 8,443,888 | 4,151 | 2,729,312 | 5,584,915 | 3,063
F $\cap$ T | 1,235,547 | 1,379,510 | 212 | 186,751 | 11,131 | 9 | 431,252 | 305,358 | 20 | 133,255 | 722 | 1 | 148,717 | 699 | 1

##  4 Experimental Analysis

4.1 Data collection: We evaluate our proposed model using real world Twitter
datasets. The ground truth of false information and the refuting true
information was obtained from www.altnews.in, a popular fact checking website
based in India. The source tweet related to the information was obtained
directly as a tweet embedded in the website. From that source tweet, we used
the Twitter API to determine the source tweeter and retweeters (proxy for
spreaders), the follower-following network of the spreaders (proxy for social
network), and user activity data (100 most recent tweets) for all nodes in the
network. Trust and credibility features extracted from the activity data are
summarized in Figure 2. Besides evaluating our model on the false information
(F) and true information (T) spreading networks separately, we also evaluated
our model on the combined information spreading networks (F $\cup$ T). Details
regarding the number of nodes ($|\mathcal{V}|$), edges ($|\mathcal{E}|$), and
spreaders ($|Sp|$) for the networks of 10 different news events (N1-N10) is
detailed in Table 1.

Figure 2: Trust and credibility feature analysis from networks N1-N10.

4.2 Analysis of F $\cap$ T: F $\cap$ T in Table 1 denotes the section of the
network that was exposed to both the false and its refutation information. An
interesting observation is the spreaders who decided to spread both types of
information. Figure 3 (a) denotes the distribution of spreaders in F $\cap$ T
who spread false information followed by its refutation (FT) and those whose
spread refutation followed by the false information (TF). N1 and N9 is
excluded from the analysis as our dataset as we did not have the spreaders’
timestamp information. An interesting observation is that the majority of
spreaders belong to FT. Intuitively, these are spreaders who trusted the
endorser without verifying the information and later corrected their position,
thereby implying that they did not intentionally want to spread false
information. Consequently, the proposed model can help identify such people
proactively in order to take measures to prevent them from endorsing false
information in the first place. While spreaders belonging to TF are
comparatively fewer (whose intentions are not certain) the proposed model can
help identify them and effective containment strategies can be adopted. Figure
3 (b) shows the time that transpired between spreading refutation and false
information for FT spreaders. Once the false information is endorsed, large
portions of the network must have already been exposed to false information
before the endorser corrected themselves after a significant amount of time
($\sim$ 1 day). This serves as a strong motivation to have a spreader
prediction model which proactively identifies likely future spreaders.

Figure 3: Analysis of spreaders in F $\cap$ T.

4.3 Models and metrics: We compare our proposed attention based model with 10
baseline models. Among the baselines, 3 models use node features only
($SVM_{Tr}$, $SVM_{Cr}$, $SVM_{Tr,Cr}$), 1 model uses network structure only
($LINE$) and 6 models integrate both node features and the network structure (
$SAGE_{Tr}$, $SAGE_{Cr}$, $SAGE_{Tr,Cr}$, $GCN_{Tr}$, $GCN_{Cr}$,
$GCN_{Tr,Cr}$).
1\. Node feature-based models:
i). $SVM_{Tr}$: This model applies Support Vector Machines (SVM) [3] on node’s
trust based features $Tr$ to find an optimal classification threshold.
ii). $SVM_{Cr}$: This model applies SVM on node’s credibility based features
$Cr$.
iii). $SVM_{Tr,Cr}$: This model applies SVM by combining node’s trust based
and credibility based features.
2\. Network structure-based models:
iv). $LINE$: Applies the Large-scale Information Network Embedding [18] as a
transductive representation learning baseline, where node embeddings are
generated after optimization is performed on the entire graph structure.
3\. Network structure + node feature-based models:
v). $SAGE_{Tr}$: GraphSAGE [4] serves as the inductive learning baseline where
node embeddings are generated by aggregating $Tr$ features from neighborhoods.
vi). $SAGE_{Cr}$: This inductive representation learning baseline generates
node embeddings by aggregating $Cr$ features from neighborhoods.
vii). $SAGE_{Tr,Cr}$: This inductive representation learning baseline
generates node embeddings by aggregating both $Tr$ and $Cr$ features from
neighborhoods.
viii). $GCN_{Tr}$: This model applies Graph Convolution Networks [6] to learn
node embeddings by aggregating $Tr$ features from neighborhoods.
ix). $GCN_{Cr}$: This model applies Graph Convolution Networks by aggregating
$Cr$ features from neighborhoods.
x). $GCN_{Tr,Cr}$: This model applies Graph Convolution Networks by
aggregating both $Tr$ and $Cr$ features from neighborhoods.

$SCARLET$ is the proposed model in this paper, which aggregates a node
neighborhood’s $Cr$ features based on attention based importance scores
assigned using $Tr$. For evaluation, we did an 80-10-10 train-validation-test
split of the dataset. We used 5-fold cross validation and four common metrics:
Accuracy, Precision, Recall, and F1 score.

4.4 Implementation details: We obtained Global Trust features by running the
TSM algorithm on the follower-following network of the spreaders. We used the
generic settings for TSM parameters (number of iterations = 100, involvement
score = 0.391) based on [16]. The size of sampled neighborhood was set to 50
and depth was set to 1. We considered neighbors with higher degrees in order
to generate denser adjacency matrices. The number of epochs, batch size,
learning rate and dropout rate were set to 200, 64, 0.001 and 0.2,
respectively. The code implementation is also
available111https://github.com/BhavtoshRath/GAT-GCN-SpreaderPrediction.

Table 2: Model performance evaluation ($\mathcal{V}_{F}$): False information
spreader, ($\mathcal{V}_{T}$): Refutation spreader.

| F ($\mathcal{V}_{F}$) | T ($\mathcal{V}_{T}$) | F $\cup$ T ($\mathcal{V}_{F}$)
---|---|---|---
| Accu. | Prec. | Rec. | F1 | Accu. | Prec. | Rec. | F1 | Accu. | Prec. | Rec. | F1
$SVM_{Tr}$ | 0.497 | 0.512 | 0.468 | 0.478 | 0.473 | 0.472 | 0.452 | 0.445 | 0.398 | 0.19 | 0.465 | 0.229
$SVM_{Cr}$ | 0.508 | 0.517 | 0.517 | 0.509 | 0.501 | 0.477 | 0.565 | 0.509 | 0.408 | 0.196 | 0.542 | 0.272
$SVM_{Tr,Cr}$ | 0.516 | 0.514 | 0.579 | 0.53 | 0.52 | 0.513 | 0.598 | 0.545 | 0.444 | 0.193 | 0.489 | 0.267
$LINE$ | 0.686 | 0.626 | 0.896 | 0.733 | 0.635 | 0.608 | 0.881 | 0.717 | 0.688 | 0.71 | 0.896 | 0.786
$SAGE_{Tr}$ | 0.734 | 0.762 | 0.691 | 0.722 | 0.680 | 0.698 | 0.719 | 0.705 | 0.752 | 0.743 | 0.859 | 0.793
$SAGE_{Cr}$ | 0.747 | 0.772 | 0.710 | 0.736 | 0.714 | 0.692 | 0.764 | 0.725 | 0.764 | 0.747 | 0.881 | 0.805
$SAGE_{Tr,Cr}$ | 0.779 | 0.831 | 0.720 | 0.763 | 0.755 | 0.787 | 0.732 | 0.755 | 0.785 | 0.764 | 0.878 | 0.814
$GCN_{Tr}$ | 0.784 | 0.726 | 0.947 | 0.821 | 0.718 | 0.675 | 0.916 | 0.767 | 0.753 | 0.783 | 0.930 | 0.845
$GCN_{Cr}$ | 0.800 | 0.742 | 0.953 | 0.834 | 0.731 | 0.697 | 0.906 | 0.773 | 0.762 | 0.786 | 0.940 | 0.851
$GCN_{Tr,Cr}$ | 0.824 | 0.774 | 0.942 | 0.848 | 0.743 | 0.702 | 0.916 | 0.783 | 0.776 | 0.788 | 0.954 | 0.861
$SCARLET$ | 0.876 | 0.834 | 0.966 | 0.893 | 0.734 | 0.674 | 0.981 | 0.794 | 0.789 | 0.785 | 0.972 | 0.866

4.5 Performance evaluation: Classification results of the baselines and
proposed model are summarized in Table 2. The results are averaged over the 10
news events. We report the precision, recall, and F1 scores of the false
information spreaders class ($\mathcal{V}_{F}$) in F and F $\cup$ T networks,
and of the refutation spreaders class ($\mathcal{V}_{T}$) in T network. Due to
class imbalance, we undersample the majority class to obtain balanced class
distribution. We observe that structure only baseline performs better than
feature only baselines, and models that combine both node features and network
structure show further improvement in performance. Additionally, we observe
that $Cr$ features perform better than $Tr$ features (because there are more
number of $Cr$ features than $Tr$ features) and the model performance
increases when we use $Tr$ and $Cr$ features together. $LINE$, the structure
only baseline, performs better than feature only baselines by a substantial
margin, which suggests that network structure plays an important role in
identifying false information spreaders. In terms of accuracy, the $LINE$
model shows an increase of 32.9%, 22.1% and 54.9% for F, T and F $\cup$ T
networks, respectively, over $SVM_{Tr,Cr}$. Graph neural network baselines
that combine both network structure and node features show a significant
improvement in performance. $GCN$ models perform better than $GraphSAGE$
models on all metrics for F networks, while that is not the case for T and F
$\cup$ T networks. This is because $Tr$ and $Cr$ features for neighborhood of
refutation information spreaders and non-spreaders do not differ much from
each other. Our proposed model $SCARLET$ shows an increase in performance for
all three networks. However, $SAGE_{Tr,Cr}$ shows better accuracy and
precision on T networks because the specific news events on which it performed
better involved religious tones, and so decision to refute them is more
sensitive to neighborhood’s $Cr$ than $Tr$. Precision on F $\cup$ T networks
is highest for $GCN_{Tr,Cr}$, though it is still comparable to the proposed
model’s performance. More importantly, in the F $\cup$ T network we observe
highest accuracy and F1 scores of 78.9% and 86.6% , thus supporting our
hypothesis that false information spreading is very sensitive to trust and
credibility.

Figure 4: Sensitivity analysis: Neighborhood size (Neighbors) and features
(Tweets).

4.6 Sensitivity analysis: Figure 4 shows the sensitivity analysis of F1 scores
of the proposed model on two important parameters: the size of neighborhoods
(Neighbors), and the number of recent tweets from user timeline (Tweets).
Neighbors: We evaluated our model on n-neighbors, where n = 10, 20, 30, 40,
50. Figure 4 a), b), and c) show results on F, T and F $\cup$ T networks,
respectively. We observe that model performance is not very sensitive to
varying neighborhood size, which could be attributed to the fact that since we
have only the immediate follower-following network (sampling depth=1) we are
not able to entirely capture meaningful dynamics (i.e. the decision to retweet
might depend less on the immediate neighbors, and more on the source tweeter).
Tweets: We also evaluated our model on the n-most recent timeline tweets,
where n = 20, 40, 60, 80, 100. Figure 4 d), e), and f) shows results on F, T
and F $\cup$ T networks, respectively. We observe that for all three networks,
prediction performance tends to increase as the number of timeline tweets used
to aggregate features increases. This is probably because using more
behavioral data helps us estimate trust and credibility features better.
4.7 Explainability analysis of trust and credibility: Figure 5 shows
importance scores that false ($\mathcal{V}_{F}$) and refutation
($\mathcal{V}_{T}$) spreader’s neighbors (size=10) assign each other based on
trust dynamics (softmax attention score) and credibility score (euclidean norm
of normalized feature vector) for neighbors with both high and low modularity.
Node 0 is the neighbor that the spreader endorses. We observe that
$\mathcal{V}_{T}$’s neighbors have higher credibility than $\mathcal{V}_{F}$’s
neighbors because of network homophily. Also low magnitude of importance
scores for neighbors of node 0 of $\mathcal{V}_{F}$ suggest that it’s
neighbors trust each other less compared to $\mathcal{V}_{T}$’s neighbors. We
observe in Figure 5 a) and b) that node 0 in $\mathcal{V}_{F}$’s neighbor has
strong trust dynamics with its followers (i.e. incoming edges) because it has
more incoming edges than outgoing edges and also retweets and gets retweeted
substantially more by the neighbors, unlike who $\mathcal{V}_{T}$ endorses in
Figure 5 c) and d), because $\mathcal{V}_{T}$’s decision to endorse depends
more on information source, which is usually a fact checker.

Figure 5: Explaining trust and credibility of spreader’s neighbors.

##  5 Conclusions and Future work

We propose $SCARLET$, an attention-based explainable graph neural network
model to predict whether a node is likely to spread false information or not.
The model learns node embeddings by first assigning trust-based importance
scores and then aggregating its neighborhood’s credibility features
proportionally. What makes this model different from most existing research is
that it does not rely on features extracted from the information itself. Thus
it can be used to predict spreaders even before information spreading begins.
As part of future work, we would like to analyze our model on more news events
comprising larger networks in order to sample and aggregate features at
greater sampling depths.

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