2022

[1]Francisco de Arriba-Pérez These authors contributed equally to this work.

These authors contributed equally to this work.

These authors contributed equally to this work.

These authors contributed equally to this work.

These authors contributed equally to this work.

1]atlanTTic, University of Vigo, Information Technologies Group, Campus
Universitario de Vigo, Lagoas-Marcosende, 36310, Vigo, Spain

2]REMIT, Universidade Portucalense, Rua Dr. António Bernardino de Almeida,
4200-072 Porto, Portugal

3]ISEP, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 4249-015
Porto, Portugal

4]INESC TEC, Campus da Faculdade de Engenharia da Universidade do Porto, Rua
Dr. Roberto Frias, 4200-465 Porto, Portugal

# Exposing and Explaining Fake News On-the-fly

<EMAIL_ADDRESS>Silvia García-Méndez<EMAIL_ADDRESS>Fátima Leal
<EMAIL_ADDRESS>Benedita Malheiro<EMAIL_ADDRESS>Juan Carlos Burguillo
<EMAIL_ADDRESS>[  [  [  [

###### Abstract

Social media platforms enable the rapid dissemination and consumption of
information. However, users instantly consume such content regardless of the
reliability of the shared data. Consequently, the latter crowdsourcing model
is exposed to manipulation. This work contributes with an explainable and
online classification method to recognize fake news in real-time. The proposed
method combines both unsupervised and supervised Machine Learning approaches
with online created lexica. The profiling is built using creator-, content-
and context-based features using Natural Language Processing techniques. The
explainable classification mechanism displays in a dashboard the features
selected for classification and the prediction confidence. The performance of
the proposed solution has been validated with real data sets from Twitter and
the results attain $80\text{\,}\mathrm{\char 37\relax}$ accuracy and macro
F-measure. This proposal is the first to jointly provide data stream
processing, profiling, classification and explainability. Ultimately, the
proposed early detection, isolation and explanation of fake news contribute to
increase the quality and trustworthiness of social media contents.

###### keywords:

Artificial Intelligence, Data Stream Architecture, Machine Learning, Natural
Language Processing, Reliability and Transparency, Social Networking.

##  1 Introduction

In social media, information is shared collaboratively through platforms like
Facebook111Available at https://www.facebook.com, June 2023.,
Twitter222Available at https://twitter.com, June 2023., or
Wikinews333Available at https://www.wikinews.org, June 2023.. Such platforms
enable the rapid dissemination of information regardless of its
trustworthiness, leading to instant consumption of non-curated news. The
negative consequence of this openness of social media platforms is the spread
of false information disguised as truth, i.e., fake news. Fake news can be
defined as deceptive posts with an intention to mislead consumers in their
purchase or approaching the context of misinformation and disinformation (Xiao
et al, 2020). Specifically, while misinformation is an inadvertent action,
disinformation is a deliberate creation/sharing of false information. The
authenticity and intention can be distinguished as: (i) non-factual and
mislead, i.e., deceptive news and disinformation; (ii) factual and mislead
(cherry-picking); (iii) undefined and mislead (click-bait); and (iv) non-
factual and undefined, i.e., misinformation.

Misinformation and fake news are characterized by their big volume,
uncertainty, and short-lived nature. Furthermore, they disseminate faster and
further on social media sites causing serious impact on politics and economics
(Tandoc, 2019). Accordingly, the report on digital transformation of media and
the rise of disinformation/fake news of the European Union (EU) (Martens et
al, 2018) reinforces the need to strengthen trust in digital media.

This work contributes with a real-time explainable classification method to
recognize fake news, promoting trust in digital media as suggested by the
SocialTruth project444Available at
http://www.socialtruth.eu/index.php/documentation, June 2023.. In fact, the
early discarding of fake news has a positive impact on both information
quality and reliability. The proposed method employs stream processing,
updating the profiling and classification models on each incoming event. The
profiling is built using side-based (related to the creator user and
propagation context) and content-based features (extracted from the news text
through Natural Language Processing (nlp) techniques), together with
unsupervised methods, to create clusters of representative features. The
classification relies on stream Machine Learning (ml) algorithms to classify
in real-time the nature of each cluster. Finally, the proposed method includes
an explanation mechanism to detail why an event has been classified as fake or
non-fake. The explanations are presented visually and in natural language on
the user dashboard.

The rest of this paper is organized as follows. Section 2 overviews the
relevant work on fake news concerning the profiling, classification and
detection tasks. Section 3 introduces the proposed method, detailing the data
processing and stream-based classification procedures along with the online
explainability. Section 4 describes the experimental set-up and the empirical
evaluation results considering the online classification and explanation.
Finally, Section 5 concludes and highlights the achievements and future work.

##  2 Related work

Social media plays a crucial role in news consumption due to its low cost,
easy access, variety, and rapid dissemination (Hu et al, 2014). Indeed, social
media is becoming an increasing source of breaking news. However, the fake
news problem indicates that social platforms suffer from lack of transparency,
reliability, and real-time modeling. In this context, fake news
(misinformation/disinformation, such as rumor, deception, hoaxes, spam
opinion, click-bait and cherry-picking) are false information created with the
dishonest intention to mislead consumers (Xiao et al, 2020; Choraś et al,
2021). To characterize the nature of fake news and understand whether they
result from inadvertent or deliberate action, it is necessary to establish
their authenticity and the intention of the creator (Shu et al, 2017). In
addition, social media streams are subject to feature variation over time
(Bondielli and Marcelloni, 2019; Choraś et al, 2021). Thus, the accurate
detection of fake news in real time requires proper profiling and
classification techniques. However, according to Shu (2022), the current
detection techniques are based on opaque models, leaving users clueless about
classification outcomes. Consequently, the current work addresses transparency
through explanations, reliability through fake news detection, and real-time
modeling through incremental content profiling.

The following discussion compares existing works in terms of: (i) stream-based
profile modeling for fake detection; (ii) stream-based classification
mechanisms; and (iii) transparency and credibility in detection tasks.

###  2.1 Profiling

Profiling methods model the stakeholders according to their contributions and
interactions. Due to information sparsity, it is frequent to represent
profiles using side and content information. In addition, in stream-based
modeling, profiles are continuously updated and refined. To model fake news
stakeholders, the literature contemplates multiple types of profiling methods:
(i) creator-based; (ii) content-based; and (iii) context-based.

Creator-based

profiling focuses on both demographic and behavioral characteristics of the
creator. Specifically, the literature contemplates account name, anomaly
score555It is computed by the number of the user’s interaction in a time
window divided by the user’s monthly average., credibility score, geolocation
information, ratio between friends and followers, total number of
tweets/posts, etc. (Castillo et al, 2011; Zubiaga et al, 2017; Goindani and
Neville, 2019; Vicario et al, 2019; Liu and Wu, 2020; Jang et al, 2021; Li et
al, 2021; Silva et al, 2021a; Jain et al, 2022; Mosallanezhad et al, 2022).

Content-based

profiling explores textual features extracted from the post aiming to identify
the meaning of the content. It can be obtained using linguistic and semantic
knowledge, or style analysis via nlp approaches together with fact-checking
resources666E.g., Classify.news, FackCheck.org, Factmata.com,
Hoaxy.iuni.iu.edu, Hoax-Slayer.com, PolitiFact.com, Snopes.com,
TruthOrFiction.com.. Most of the revised works exploit this type of features.
Therefore, content-based profiling encompasses:

  * •

Lexical and syntactical features are properties related to the syntax, e.g.,
sentence-level features, such as bag-of-words approaches, $n$-grams, and part-
of-speech. These features are exploited by Dong et al (2020); Zhou et al
(2020). In addition, Vicario et al (2019); Jang et al (2021) compute the
overall sentiment score of sentences.

  * •

Stylistic features provide emphasis and clarity to the text. Tweet-writing
styles can be determined through: (i) physical style analysis (e.g., number of
adjectives, nouns, hashtags and mentions as well as emotion words and casual
words); and (ii) non-physical style analysis (e.g., complexity and readability
of the text). The work by Jang et al (2021) is a representative example of the
physical style analysis.

  * •

Visual features describe the properties of images or videos used to ascertain
the credibility of multimedia content. Visual features can: (i) be purely
statistic (e.g., number of images/videos); (ii) represent distribution
patterns; or (iii) describe user accounts (e.g., background images). While
Jang et al (2021) compute statistic visual features, Liu and Wu (2020)
consider information from the user account. Li et al (2021) verify if the
image has been tampered, integrating this information as visual content, and
Ying et al (2021) combine textual with visual content to generate multi-level
semantic features.

Context-based

profiling analyses both the surrounding environment and the creator
engagements around the piece of information posted (Castillo et al, 2011;
Goindani and Neville, 2019; Shu et al, 2019b; Liu and Wu, 2020; Zhao et al,
2020; Jang et al, 2021; Li et al, 2021; Puraivan et al, 2021; Silva et al,
2021a; Song et al, 2021; Jain et al, 2022). Specifically, it applies user-
network analysis and distribution pattern analysis to obtain:

  * •

Network-based features which aggregate similar online users in terms of
location, education background, and habits (Shu et al, 2019b; Liu and Wu,
2020; Silva et al, 2021a).

  * •

Propagation-based features that describe the dissemination of fake news based
on the propagation graph as in the work by Mosallanezhad et al (2022). These
may include, for an online account, the root degree, sub-trees number, the
maximum/average degree and depth tree depth (Castillo et al, 2011; Jang et al,
2021) or the number of retweets/re-posts for the original tweet/post, the
fraction of tweets/posts retweeted (Zhao et al, 2020; Li et al, 2021).

  * •

Temporal-based features which detail how two posts/tweets relate in time. They
may comprise the posting frequency, the day of the week of the post (Jang et
al, 2021; Silva et al, 2021a), the interval between two posts or even a
complete temporal graph (Song et al, 2021).

###  2.2 Classification

Fake news detection is a classification task. The main news classification
techniques in the literature encompass supervised, semi-supervised,
unsupervised, deep learning, and reinforcement learning approaches. Deep
learning, depending on the problem, can fall into the supervised or
unsupervised classification scope (Mathew et al, 2021). Moreover, its high
computational cost requires more computational resources than the
corresponding traditional approaches, motivating a separate discussion.

Supervised classification is a widely used technique to map objects to classes
based on numeric features or inputs (see Table 1). The most frequently used
supervised fake news detectors are Bayes, Probabilistic, Neighbor-based,
Decision Trees, and Ensemble classifiers.

Semi-supervised classification algorithms learn from both labeled and
unlabeled samples. They are employed when it is difficult to annotate manually
or automatically the samples. The works by Shu et al (2019b) and Dong et al
(2020) use supervised learning for fake news detection.

Unsupervised classification techniques group statistically similar unlabeled
data based on underlying hidden features, using clustering algorithms or
neural network approaches. The most commonly used cluster algorithms include
k-means, Iterative Self-Organizing Data Analysis Technique, and Agglomerative
Hierarchical. Li et al (2021); Puraivan et al (2021) are representative
examples of this approach.

Deep Learning classification relies essentially on neural networks with three
or more layers. In terms of fake news, deep learning has been employed mainly
for text classification using Convolutional Neural Networks (cnn), Long Short
Term Memory (lstm), and Recurrent Neural Networks (rnn) as in the works by
Akinyemi et al (2020); Nasir et al (2021).

Reinforcement Learning classification works with unlabeled data (Sutton and
Barto, 2018), but tends to be slow when applied to real-world classification
problems (Dulac-Arnold et al, 2021). While Goindani and Neville (2019); Wang
et al (2020); Mosallanezhad et al (2022) perform fake news detection through
reinforcement learning, the most used technique is the Multivariate Hawkes
Process (mhp) by Goindani and Neville (2019).

Classification can be performed offline or online. Offline or batch processes
build static models from pre-existing data sets, whereas online or stream-
based processes compute incremental models from live data streams in real-
time.

Offline

classification divides the data set into training – used to create the model –
and testing – to assess the quality of the model – partitions. The model
remains static throughout the testing stage. This is the most popular fake
news detection approach found in the literature.

Online

classification mines data streams in real-time. Fake news, being dynamic
sequences of data originated from multiple sources, i.e., the crowd, demand
real-time processing. Typically, whenever new data arrive, the models are
incrementally updated, enabling the generation of up-to-date classifications.
To the best of the authors’ knowledge, only Ksieniewicz et al (2020) perform
online fake news detection, processing samples as a data stream and
considering concept drifts, i.e., that sample classification may naturally
change over time.

Classification models can be interpretable and opaque. While opaque models
behave as black boxes (e.g., standalone deep neural networks), interpretable
models are self-explainable (e.g., trees- or neighbor-based algorithms).
Interpretable classifiers explain classification outcomes (Škrlj et al, 2021),
clarifying why a given content is false or misleading. More in detail, the
explainable fake news detection framework by Shu et al (2019a) integrates a
news content encoder, a user comment encoder, and a sentence-comment co-
attention network. The latter captures the correlation between news contents
and comments and chooses the top-$k$ sentences and comments to explain the
classification outcome. Zhou et al (2020) explore lexicon-, syntax-,
semantic-, and discourse-level features to enhance the interpretablity of the
models. Mahajan et al (2021) and Kozik et al (2022) adopt model agnostic
interpretability techniques, such as Local Interpretable Model-agnostic
Explanations (lime) (Ribeiro et al, 2016) and the Shapley Additive
Explanations (shap) (Lundberg and Lee, 2017), respectively. Finally, Silva et
al (2021a) provide explanations based on feature weights assigned to
tweet/retweet nodes in the propagation patterns.

Table 1 provides an overview of the above works considering profiling
(creator-, content-, and context-based), classification (supervised, semi-
supervised, unsupervised, and reinforcement learning), processing (offline and
online) and explainability. Summing up, this literature review shows that
existing explainable fake news detectors explore creator-, content-, and
context-based profiles, essentially adopt supervised classification and mostly
implement offline processing.

Table 1: Comparison of fake news detection approaches considering: (i) profiling (creator, content, context), (ii) classification (supervised, semi-supervised, unsupervised, reinforcement learning), (iii) execution (offline, online), and (iv) explainability (Ex.). Proposal | Profiling | Classification | Execution | Ex.
---|---|---|---|---
Castillo et al (2011) |  Creator Content Context | Supervised | Offline | No
Liu and Wu (2020)
Jang et al (2021)
Jain et al (2022)
Zubiaga et al (2017) | Creator | Supervised | Offline | No
Vicario et al (2019) | Content
Song et al (2021) | Content | Supervised | Offline | No
Context
Akinyemi et al (2020) | Content | Supervised | Offline | No
Silva et al (2020)
Nasir et al (2021)
Ying et al (2021)
Galli et al (2022)
Zhao et al (2020) | Context | Supervised | Offline | No
Dong et al (2020) | Content | Semi-supervised | Offline | No
Shu et al (2019b) | Context | Semi-supervised | Offline | No
Puraivan et al (2021) | Content | Unsupervised | Offline | No
Context | & supervised
Li et al (2021) | Creator | Unsupervised | Offline | No
Content
Context
Mosallanezhad et al (2022) | Creator |  Reinforcement Learning | Offline | No
Content
Goindani and Neville (2019) | Creator |  Reinforcement Learning | Offline | No
Context
Wang et al (2020) | Content |  Reinforcement Learning | Offline | No
Silva et al (2021a) | Creator | Supervised | Offline | Yes
Content
Context
Shu et al (2019a) | Content | Supervised | Offline | Yes
Zhou et al (2020)
Mahajan et al (2021)
Kozik et al (2022)
Ksieniewicz et al (2020) | Content | Supervised | Online | No
Current | Creator |  Unsupervised & Supervised | Online | Yes
Content
Context

The most closely related works from the literature, considering the pheme
experimental data used for design and evaluation, are the fake news
classification solutions proposed by Zubiaga et al (2017), Akinyemi et al
(2020), Ying et al (2021), and Jain et al (2022). Firstly, Zubiaga et al
(2017) experimented with sequential (Conditional Random Fields, Maximum
Entropy and Enquiry-based) and non-sequential (Naive Bayes, Support Vector
Machines (svm) and Random Forests (rf)) classifiers. Secondly, Akinyemi et al
(2020) applied a rf model as the meta classifier trained with a stack-ensemble
of svm, rf, and rnn models as base learners. Thirdly, Ying et al (2021)
presented a Multi-level Multi-modal Cross-attention Network for batch fake
detection. Furthermore, Jain et al (2022) employed a Hierarchical Attention
Network (han) and a Multi-Layer Perceptron (mlp) trained with creator-,
content-, and context-based features. The final prediction (fake or non-fake)
combines both classifier outputs through a logical or. Nonetheless, all these
solutions work offline without explaining the outcomes. In contrast, our work
exploits a wide variety of profiling features (creator, content, and context),
operates online and is able to explain the classification outcomes.

Similarly to our research, Puraivan et al (2021) combineed both unsupervised
and supervised techniques, for feature extraction (Principal Component
Analysis and t-Distributed Stochastic Neighbor Embedding) and classification
(optimized distributed gradient boosting), respectively. However, this offline
work disregards the textual content of the news and lacks transparency.

Finally, the sole online system found explores fake news detection with
Gaussian Naive Bayes, mlp, and Hoeffding Tree base learners independently and
in ensembles (Ksieniewicz et al, 2020). Unfortunately, this work uses another
data set collected by the authors and automatically labeled by BS Detector
Chrome Extension. Profiles are exclusively based on content features and the
outcomes are not explained.

###  2.3 Research contribution

As previously stated, this work contributes with an explainable classification
method to recognize in real-time fake news and, thus, promote trust in digital
media. Particularly, the method implements online processing, updating
profiles and classification models on each incoming event. First, user
profiles are built using creator-, content- and context-based features
engineered through nlp. Then, unsupervised methods are exploited to create
clusters of representative features. Finally, interpretable stream-based ml
classifiers establish the trustworthiness of tweets in real-time. As a result,
the proposed method provides the user with a dashboard, combining visual data
and natural language knowledge, to make tweet classification transparent.

##  3 Proposed method

The proposed online and explainable fake news detection system is described in
Figure 1. It is composed of three main modules: (i) the stream-based data
processing module (Section 3.1) which comprises feature engineering (Section
3.1.1), and analysis and selection tasks (Section 3.1.2); (ii) the stream-
based classification module (Section 3.2) composed of lexicon-based (Section
3.2.1), unsupervised and supervised (Section 3.2.2) classifiers; and (iii) the
stream-based explainability module (Section 3.3). The explored data comprises
two collections of tweets related to breaking news events released in 2016
(pheme) and augmented in 2018 (pheme-r).

Figure 1: System diagram composed of: (i) stream-based data processing, (ii)
online classification, and (iii) stream-based explainability.

###  3.1 Stream-based data processing

This module exploits nlp techniques to take full advantage of the ml models.
Firstly, the feature engineering process generates new knowledge from the
experimental data. Then, it analyses the resulting feature set to finally
select the most relevant features for the classification.

####  3.1.1 Feature engineering

The proposed system computes features from a wide spectrum: (i) creator-, (ii)
content- (lexical and syntactical features, stylistic features, and visual
features), and (iii) context-based (network, distribution and temporal)
features.

The creator-based features specify whether the user has an account
description, a profile image and if the account has been protected and/or
verified, the timezone, the number of followers and friends, the ratio between
friends and followers, as well as the number of favourite tags received by the
user. In the end, the time span in days between user registration and tweet
post is calculated along with the weekly post frequency of the user777These
last two features may be considered as context-based temporal..

The linguistic and syntactic content-based features include the word $n$-grams
from the processed tweet and whether the content is duplicated in the
experimental data set. The physical style features comprise the adjective,
auxiliary, bad word, determiner, difficult word, hashtag, link (also
repeated), noun, pronoun, punctuation, uppercase word and word counters. The
sentiment-related features comprise emotion (anger, fear, happiness, sadness
and surprise) and polarity (negative, neutral and positive). The non-physical
style-based features are based on the Flesch reading ease metric (see Table
2), the McAlpine eflaw readability score for English foreign speakers888It is
recommended to be equal or lower than 25 points. and the reading time in
seconds. Concerning visual-based features, the system verifies if the tweet
contains links to images and videos.

The generated context-based features consider whether the tweet has been
retweeted and/or favourited, the depth of the retweet distribution network and
the number of first-level retweets. Finally, the distribution pattern is
analysed through the retweet and favourite counters.

Table 2: Flesch reading ease score and difficulty. Score | Difficulty
---|---
90-100 | Very Easy
80-89 | Easy
70-79 | Fairly Easy
60-69 | Standard
50-59 | Fairly Difficult
30-49 | Difficult
0-29 | Very Confusing

The specific techniques applied to compute the aforementioned features will be
described in Section 4.2.1 along with the data processing details.

####  3.1.2 Feature analysis and selection

Prior to feature selection, the system computes the variance of the features
to establish their relative importance and, finally, discard those with low
variance. Thus, the feature space dimension is reduced to minimize the
computational load and time needed by ml models to classify tweets.

###  3.2 Stream-based classification

The proposed method involves lexicon-based (Section 3.2.1), unsupervised and
supervised classification (3.2.2) in both the predict and train steps of each
incoming tweet.

####  3.2.1 Frequency-based lexicon

The adopted frequency-based lexicon is applied to the content of each incoming
tweet. Algorithm 1 provides the corresponding pseudo-code. The lexica allow
swift prediction followed by updating (training) based on the tweet content.
The training stage considers the target class the $n$-grams represent and
their frequency. More in detail, it defines three thresholds: (i) the $n$-gram
range to extract the words; (ii) the number of elements to be included in the
resulting lexica; and (iii) the frequency used as insert condition.

Algorithm 1 Frequency-based lexicon generation

1:Content-related data of the incoming tweet: tweet_processed, ngram_range,
num_elements, threshold, frequency_lexicon.

2:The algorithm returns the fake_lexicon and non-fake_lexicon disjoint sets.

3:tweet_ngrams=$generate\\_ngrams($tweet_processed, ngram_range); // Holds the
$n$-gram representation (within the given $n$-gram_range of the tweet content)
and frequency.

4:frequency_lexicon=$update\\_frequency\\_lexicon($frequency_lexicon,
tweet_ngrams);

5:fake_lexicon=$frequency\\_lexicon$[$class=$ fake, $frequency$ >
threshold][0:num_elements];

6:non-fake_lexicon=$frequency\\_lexicon$[$class=$ non-fake, $frequency$ >
threshold][0:num_elements]; return $frequency\\_lexicon$, $fake\\_lexicon$,
$non$-$fake\\_lexicon$. // Returns the updated frequency_lexicon plus the fake
and non-fake lexica.

####  3.2.2 Unsupervised and supervised classification

First, the unsupervised classification creates clusters of comparable spatial
extent, by splitting the input data based on their proximity. It applies
k-means clustering (Sinaga and Yang, 2020; Vouros et al, 2021) to minimise
within-cluster variances, also known as squared Euclidean distances. Then, for
each discovered cluster, one supervised classifier is trained.

The method involves several well-known stream-based ml models, selected
according to their good performance in similar classification problems
(Aphiwongsophon and Chongstitvatana, 2018; Xiao et al, 2020; Silva et al,
2021b).

  * •

Adaptive Random Forest Classifier (arfc) (Gomes et al, 2017). It induces
diversity using re-sampling, random feature subsets for node splits and drift
detectors per base tree.

  * •

Hoeffding Adaptive Tree Classifier (hatc) (Bifet and Gavaldà, 2009). It uses a
drift detector to monitor branch performance. Moreover, it presents a more
efficient and effective bootstrap sampling strategy compared to the original
Hoeffding Tree classifier.

  * •

Hoeffding Tree Classifier (htc) (Pham et al, 2017). It is an incremental
decision tree algorithm which quantifies the number of samples needed to
estimate the statistics while guarantying the prescribed performance.

  * •

Gaussian Naive Bayes (gnb) (Xue et al, 2021). It enhances the original Naive
Bayes algorithm by exploiting a Gaussian distribution per feature and class.

Algorithmic performance is determined with the help of classification
accuracy, F-measure (macro and micro-averaging) and run-time metrics,
following the prequential evaluation protocol (Gama et al, 2013).

###  3.3 Stream-based explainability module

Transparency is essential to make results both understandable and trustworthy
for the end users. This means that outcomes need to be accompanied by
explanatory descriptions. The designed fake news classification solution
relies on interpretable models to obtain and present the relevant data in an
explainability dashboard. The explanation of each prediction includes:

  * •

Relevant user, content and context features selected by the supervised ml
models.

  * •

Predicted class (fake and non-fake) together with confidence.

  * •

$K$ disjoint elements ordered by their appearance frequency extracted from the
fake and non-fake lexica.

  * •

$K$ features that surround the centroid of the cluster to which the entry
belongs.

The latter is completed with natural language descriptions of the
corresponding tree decision path.

##  4 Experimental results

All experiments were performed using a server with the following hardware
specifications:

  * •

Operating System: Ubuntu 18.04.2 LTS 64 bits

  * •

Processor: IntelCore i9-10900K $2.80\text{\,}\mathrm{GHz}$

  * •

RAM: $96\text{\,}\mathrm{GB}$ DDR4

  * •

Disk: $480\text{\,}\mathrm{GB}$ NVME + $500\text{\,}\mathrm{GB}$ SSD

###  4.1 Experimental data sets

The experiments were performed with temporally ordered data streams created
from the pheme and pheme-r data sets999Available at
https://figshare.com/articles/dataset/PHEME_dataset_for_Rumour_Detection_and_Veracity_Classification/6392078
and https://figshare.com/articles/dataset/PHEME_dataset_of_rumours_and_non-
rumours/4010619, June 2023. and, for additional testing, from the Nikiforos et
al (2020) data set101010Available at https://hilab.di.ionio.gr/wp-
content/uploads/2020/02/HILab-Fake_News_Detection_For_Hong_Kong_Tweets.xlsx,
June 2023.. The pheme collections comprise $6424$ tweets created by $2893$
users between August 2014 and March 2015. All tweets were manually labeled as
fake and non-fake. The data set from Nikiforos et al (2020) contains $2366$
tweets posted by $51$ users between April 2013 and December 2019. This data
set was exclusively used to confirm the performance of the proposed method
(see Section 4.3.3). Table 3 details the number of users and tweets per class
in each experimental data set.

Table 3: Classes, number of users and tweets of the experimental data sets. Data set | Class | Users | Tweets
---|---|---|---
pheme | Fake | $1023$ | $2402$
Non-fake | $2204$ | $4022$
Total | $2893$ | $6424$
Nikiforos et al (2020) | Fake | $42$ | $272$
Non-fake | $9$ | $2094$
Total | $51$ | $2366$

###  4.2 Stream-based data processing

As previously mentioned, data processing applies nlp techniques to ensure the
competing performance of the ml models. The procedures used for online feature
engineering, analysis and selection are presented below.

####  4.2.1 Feature engineering

Firstly, tweet content is purged from url, redundant blank spaces, special
characters (non-alphanumerical items, like accents and punctuation marks) and
stop-words from the list provided by the Natural Language Toolkit
(nltk)111111Available at https://gist.github.com/sebleier/554280, June 2023..
The remaining content is lemmatised with the English en_core_web_md
model121212Available at https://spacy.io/models/en, June 2023. of the spaCy
library131313Available at https://spacy.io, June 2023. and content polarity is
established with TextBlob141414Available at
https://pypi.org/project/spacytextblob, June 2023., a sentiment analysis
component for spaCy. The tweet emotion is calculated using Text2emotion Python
library151515Available at https://pypi.org/project/text2emotion, June 2023..

The creation of non-physical style features relies on the
TextDescriptives161616Available at
https://spacy.io/universe/project/textdescriptives, June 2023. spaCy module
(features 13, 14, 17, 26, 28 and 29 in Table 4) and on the
Textstat171717Available at https://pypi.org/project/textstat, June 2023.
Python library (features 18, 20, 25 and 30 in Table 4). The bad word count
(feature 15 in Table 4) depends on the list provided by Wikimedia Meta-
wiki181818Available at
https://meta.wikimedia.org/wiki/Research:Revision_scoring_as_a_service/Word_lists/en,
June 2023..

Given the importance of hashtags within tweets, hashtags are decomposed into
their elementary constituents, i.e., words. This is applied to the cases where
the hashtag is not represented in title format191919The first letter of each
of the words which compose the hashtag capitalised. This splitter uses a
freely available English corpus, the Alpha lexicon202020Available at
https://github.com/dwyl/english-words, June 2023., along with the English
corpus by García-Méndez et al (2019). It employs a recursive and reentrant
algorithm to minimise the number of splits needed to decompose the hashtag
into correct English words. As an example, the proposed text decomposition
solution splits hatecannotdriveouthate as hate cannot drive out hate.

The word $n$-grams are extracted from the accumulated tweet textual data using
CountVectorizer212121Available at https://scikit-
learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html,
June 2023. Python library. Listing 1 shows the ranges and best values for the
CountVectorizer configuration parameters based on iterative experimental tests
with GridSearch222222Available at https://scikit-
learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html,
June 2023. meta transformer wrapper for the hatc classifier.

Listing 1: Parameter ranges for the generation of $n$-grams (best values in
bold).

⬇

1maxdf = [0.7, 0.5, 0.3]

2mindf = [0.1, 0.01, 0.001]

3ngramrange = [(1, 2), (1, 3), (1,4)]

Table 4 shows the creator-, content- and context-based features selected for
the detection of fake news. An additional pair of features is created for each
user and numerical feature in Table 4 (features 6-9, 13-18, 21-24, 26, 28, 29,
31-33, 39 and 40): the user incremental feature average and latest feature
trend, a Boolean feature that compares the last user feature value with the
current user feature average232323True if the feature value is equal or higher
than the user feature average; otherwise is false..

Table 4: Features considered for the classification by profile (creator, content, context) and data type (Boolean, categorical, numerical, textual). Profiling | Data type | Number | Name
---|---|---|---
Creator-based | Boolean | 1 | Has profile description
2 | Has profile image
3 | Protected
4 | Verified
Categorical | 5 | Timezone
Numerical | 6 | Follower count
7 | Friend count
8 | Friends-followers ratio
9 | User favourite count
10 |  Tweet-registration time spam (in days)
| 11 | Weekly tweet frequency
Boolean | 12 | Text duplicated
Content-based | Numerical | 13 | Adjective count
14 | Auxiliary count
15 | Bad word count
16 | Char count
17 | Determiner count
18 | Difficult word count
19 |  Emotion (anger, fear, happiness, sadness, surprise)
20 | Flesch reading ease
21 | Hashtag count
22 | Image count
23 | Link count
24 | Link repeated count
25 |  McAlpine eflaw readability
26 | Noun count
27 | Polarity
28 | Pronoun count
29 | Punctuation count
30 | Reading time
31 | Uppercase word count
32 | Video count
| 33 | Word count
Textual | 34 |  Word $n$-grams
Context-based | Boolean | 35 | Retweeted
36 | Tweet favourited
Numerical | 37 | Distribution depth
38 | First level retweet
39 | Retweet count
40 | Tweet favourite count

####  4.2.2 Feature analysis and selection

The method analyses the variance of features in Table 4 to compute their
relative importance. Those features with low variance are discarded.
Particularly, feature selection is performed at each incoming event using the
VarianceThreshold242424Available at https://riverml.xyz/0.11.1/api/feature-
selection/VarianceThreshold, June 2023. algorithm from River252525Available at
https://riverml.xyz/0.11.1, June 2023. library to improve the fake class
recall metric.

###  4.3 Stream-based classification

Online classification involves prediction and training for each incoming
sample. This section presents the results obtained by the lexicon-based,
unsupervised and supervised classification procedures.

####  4.3.1 Frequency-based lexicon

The building of dynamic frequency-based lexicon starts after accumulating
$5\text{\,}\mathrm{\char 37\relax}$ of the samples. More in detail, the system
extracts $700$ from 2- to 4-word-length unique elements for each target class
(fake and non-fake). Listing 2 provides the configuration parameter ranges.
Best values were obtained once again from iterative experimental tests and
using the hatc classifier.

Listing 2: Parameter ranges for the generation of the frequency-based lexicon
(best values in bold).

⬇

1ngrams = [(1,4),(2,4),(3,4)]

2numberwords = [800,700,600,500, 400, 300, 200]

3minfreqvalue = [1, 2, 5, 8, 10, 15, 20, 30]

####  4.3.2 Unsupervised and supervised classification results

As described in Section 3.2, the first step applies unsupervised clustering.
The latter uses the widely known k-means model262626Available at
https://riverml.xyz/dev/api/cluster/KMeans, June 2023.. Then, for each of the
discovered clusters, one supervised classifier is trained using the following
implementations:

  * •

arfc272727Available at
https://riverml.xyz/0.11.1/api/ensemble/AdaptiveRandomForestClassifier, June
2023.

  * •

hatc282828Available at
https://riverml.xyz/0.11.1/api/tree/HoeffdingAdaptiveTreeClassifier, June
2023.

  * •

htc292929Available at
https://riverml.xyz/0.11.1/api/tree/HoeffdingTreeClassifier, June 2023.

  * •

gnb303030Available at https://riverml.xyz/0.11.1/api/naive-bayes/GaussianNB,
June 2023.

Hyperparameter optimisation is performed for the aforementioned ml algorithms.
Listings 3, 4, 5 and 6 show the configuration ranges and best values (in bold)
for each algorithm.

Listing 3: Hyperparameter ranges for the arfc model (best values in bold).

⬇

1clusters = [10, 20, 30]

2models = [50, 100, 200]

3features = [50, 100, 200]

4lambda = [50, 100, 200]

Listing 4: Hyperparameter ranges for the hatc model (best values in bold).

⬇

1clusters = [10, 20, 30]

2depth = [50, 100, 200]

3tiethreshold = [0.5, 0.05, 0.005]

4maxsize = [50, 100, 200]

Listing 5: Hyperparameter ranges for the htc model (best values in bold).

⬇

1clusters = [10, 20, 30]

2depth = [50, 100, 200]

3tiethreshold = [0.5, 0.05, 0.005]

4maxsize = [50, 100, 200]

Listing 6: Hyperparameter ranges for the gnb model (best value in bold).

⬇

1clusters = [10, 20, 30]

Table 5 shows the performance of the ml models. Set a of features includes
those in Table 4 except for word $n$-grams, whereas, set b includes set a plus
the latter textual features. Finally, set c is composed of set b plus the
frequency-based lexicon. The proposed solution exhibits a processing time of
$0.42\text{\,}\mathrm{s}\mathrm{/}\mathrm{s}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{e}$
in the worst scenario (arfc model and the set of features a), which can be
considered real time.

In light of the results, arfc exhibits the best performance with all feature
sets and for all evaluation metrics. The use of word $n$-grams results in
significant improvement across all algorithms. The highest boost occurs for
the gnb model (+$12\text{\,}\mathrm{\char 37\relax}$ percent points in
accuracy and micro F-measure for the fake class). Despite the promising
results, micro F-measure values for the target fake class remain under the
$70\text{\,}\mathrm{\char 37\relax}$ threshold with feature sets a and b.
Finally, the solution reaches accuracy and macro F-measure about
$80\text{\,}\mathrm{\char 37\relax}$ with all engineered features (set c).

Table 5: Online fake detection results in terms of accuracy, macro and micro F-measure (best values in bold) and run-time for the arfc, hatc, htc and gnb models by feature set. Set | Classifier | Accuracy |  F-measure | Time
---|---|---|---|---
|  |  | Macro | #non-fake | #fake | (s)
A |  arfc | 73.09 | 70.62 | 79.14 | 62.10 | $2677.82$
hatc | 64.95 | 63.29 | 71.10 | 55.49 |  $8.91$
htc | 64.76 | 62.79 | 71.36 | 54.21 |  $7.45$
gnb | 52.95 | 49.29 | 62.91 | 35.68 |  $6.36$
B |  arfc | 75.43 | 73.17 | 80.96 | 65.38 | $1644.25$
hatc | 70.11 | 66.28 | 77.65 | 54.90 |  $29.88$
htc | 69.46 | 64.59 | 77.72 | 51.47 |  $23.25$
gnb | 64.09 | 60.21 | 72.64 | 47.79 |  $20.44$
C |  arfc | 80.26 | 78.97 | 84.18 | 73.77 | $1910.07$
hatc | 78.20 | 76.42 | 82.91 | 69.92 |  $299.35$
htc | 77.94 | 76.11 | 82.72 | 69.51 |  $293.74$
gnb | 74.66 | 73.45 | 79.13 | 67.76 |  $286.24$

####  4.3.3 Discussion

Since the majority of the competing works implement batch rather than stream
processing and use different data sets, result comparison may not be
straightforward. Batch and stream results are only directly comparable if
obtained with the same data samples. This means that, ideally, the comparison
should be made with a chronologically ordered data set, and the evaluation
should consider only the test partition samples. In the case of stream
processing, this is achieved by setting the dimension of the sliding window to
the number of samples of the test partition and then processing the data set
as a stream.

The batch classification works by Zubiaga et al (2017), Akinyemi et al (2020)
and Ying et al (2021) explore the same pheme data set with cross-folded
validation, using $80\text{\,}\mathrm{\char 37\relax}$ of the samples for
training and $20\text{\,}\mathrm{\char 37\relax}$ for testing. The related
online fake news classification system of Ksieniewicz et al (2020) employs
another data set, preventing direct comparison.

Table 6 provides the theoretical comparison results of the most related works
together with those of the proposed solution with a sliding window holding
$20\text{\,}\mathrm{\char 37\relax}$ of the data (for offline comparison) and
a sliding window comprising all data (for online comparison)313131na is used
to indicate when the competing works did not provide results for specific
metrics.. The proposed solution with a sliding window of
$20\text{\,}\mathrm{\char 37\relax}$ of the data achieves an improvement in
macro F-measure of $20.12$ and $17.42$ percent points with respect to the work
of Zubiaga et al (2017) and Jain et al (2022), respectively. Moreover, it
attains $+4.62$ percent points in fake F-measure compared to Akinyemi et al
(2020). When compared with the batch and online deep learning approaches of
Ying et al (2021) and Ksieniewicz et al (2020), the proposed solution exhibits
slightly lower performance but grants algorithmic transparency with lesser
memory and computation time. Finally, for a fair comparison with the most
related work by Ksieniewicz et al (2020), due to the fact the authors provided
the implementation of the solution, we were able to run the experiments with
the pheme data set and the accuracy obtained in this regard is
$74.10\text{\,}\mathrm{\char 37\relax}$ ($-6.16$ percent points than our
proposal).

Table 6: Fake detection theoretical comparison in terms of accuracy, macro and
micro F-measure between related works and the proposed solution.

Authorship | Processing | Accuracy |  F-measure
---|---|---|---
|  |  | Macro | #non-fake | #fake
Zubiaga et al (2017) | Offline | na1 | 60.70 | na | na
Akinyemi et al (2020) | Offline | 81.90 | 78.00 | 87.00 | 70.00
Ying et al (2021) | Offline | 87.20 | na | 90.40 | 80.70
Jain et al (2022) | Offline | na | 63.40 | na | na
Ksieniewicz et al (2020) | Online | 81.90 | na | na | na
Proposed solution |  Online2 | 82.82 | 80.82 | 87.02 | 74.62
Online3 | 80.26 | 78.97 | 84.18 | 73.77
|  Offline4 | 99.14 | 97.54 | 99.52 | 95.56

  * 1

Not available.

  * 2

Sliding window holds $20\text{\,}\mathrm{\char 37\relax}$ of data.

  * 3

Sliding window holds the full data.

  * 4

Sliding window holds $20\text{\,}\mathrm{\char 37\relax}$ of data for the data
set provided by Nikiforos et al (2020).

Originally, Nikiforos et al (2020) achieved an accuracy of
$99.79\text{\,}\mathrm{\char 37\relax}$ and $99.37\text{\,}\mathrm{\char
37\relax}$ with Naive Bayes and rf offline classifiers, respectively. Both
models were trained with a synthetic minority over-sampled set generated from
$80\text{\,}\mathrm{\char 37\relax}$ of the original data (to overcome the
class imbalance of the original data) and tested with the
$20\text{\,}\mathrm{\char 37\relax}$ of the original data. To compare with
these results, the experiment was repeated with a sliding window comprising
$20\text{\,}\mathrm{\char 37\relax}$ of the total number of samples and the
best arfc model. In this case, the current solution attained
$99.14\text{\,}\mathrm{\char 37\relax}$ accuracy, macro F-measure of
$97.54\text{\,}\mathrm{\char 37\relax}$, and micro F-measure of
$99.52\text{\,}\mathrm{\char 37\relax}$ and $95.56\text{\,}\mathrm{\char
37\relax}$ for non-fake and fake classes, respectively. This means that the
proposed online method achieves, without oversampling and in real time, a
comparable accuracy.

###  4.4 Stream-based explainability module

Figure 2 shows the user explainability dashboard, which aims to make the model
outcome comprehensible. In the upper part, it displays the classification of
the tweet sample. The user name is Zone 6 Combatives and the timezone
Canadian. The top center displays the tweet content and the center presents
the creator-, content- and context-related features selected by the ml
classifier. Feature warnings are shown when a feature deviates from the user
average as is the case of reading ease & time feature. Otherwise, the features
include an ok symbol as in the case of the 5-years post-registration span
feature. The classifier singled out the word pilot as relevant. The tweet was
classified as fake with an $81\text{\,}\mathrm{\char 37\relax}$ of confidence,
according to the Predict_Proba_One323232Available at
https://riverml.xyz/0.11.1/api/base/Classifier, June 2023. from River ml
library. In the end, the most representative features for both the frequency-
based lexicon and the clustering procedure are provided333333The sample
belongs to cluster 5..

The bottom part of the dashboard displays the decision tree path (obtained
using debug one and draw343434Available at
https://riverml.xyz/0.11.1/api/tree/HoeffdingAdaptiveTreeRegressor, June 2023.
libraries) and the corresponding natural language description. Particularly,
the first decision is based on the surprise feature (see feature 19 in Table
4). If its value is lower or equal to 0.55, the reasoning continues through
the left branch. Otherwise it goes to the right branch.

Figure 2: Explainability dashboard comprising: (i) selected features from the
content, context, and creator, (ii) the prediction, (iii) representative
entries of the frequency-based lexicon and the clustering procedure, and (iv)
the decision path and its natural language transcription.

##  5 Conclusion

Social media is becoming an increasing source of breaking news. In these
platforms, information is shared regardless of the context and reliability of
the content and creator of the posted information. This instant news
dissemination and consumption model easily propagates fake news, constituting
a challenge in terms of transparency, reliability, and real-time processing.
Accordingly, the proposed solution addresses transparency through
explanations, reliability through fake news detection, and real-time
processing through incremental profiling and learning. The motivation for the
current work relies on the early detection, isolation and explanation of
misinformation, all of them crucial procedures to increase the quality and
trust in digital media social platforms.

More in detail, this work contributes with an explainable classification
method to recognise fake news in real-time. The proposed method combines both
unsupervised and supervised approaches with online created lexica.
Specifically, it comprises (i) stream-based data processing (through feature
engineering, analysis and selection), (ii) stream-based classification
(lexicon-based, unsupervised and supervised classification), and (iii) stream-
based explainability (prediction confidence and interpretable classification).
Furthermore, the profiles are built using creator-, content- and context-based
features with the help of nlp techniques. The experimental classification
results of $80\text{\,}\mathrm{\char 37\relax}$ accuracy and macro F-measure,
obtained with a real data set manually annotated, endorse the promising
performance of the designed explainable real-time fake news detection method.

Analyzing the related work, this proposal is the first to jointly provide
stream-based data processing, profiling, classification and explainability.
Future work will attempt to mitigate further the impact of fake news within
social media by automatically identifying and isolating potential malicious
accounts as well as extend the research to related tasks like stance
detection, by exploiting new creator-, content- and context-based features.

## Declarations

### Funding

This work was partially supported by: (i) Xunta de Galicia grants
ED481B-2021-118 and ED481B-2022-093, Spain; (ii) Portuguese national funds
through FCT –- Fundação para a Ciência e a Tecnologia (Portuguese Foundation
for Science and Technology) – as part of project UIDP/50014/2020 (DOI:
10.54499/UIDP/50014/2020 $\mid$ https://doi.org/10.54499/UIDP/50014/2020); and
(iii) University of Vigo/CISUG for open access charge.

### Competing interests

The authors have no competing interests to declare that are relevant to the
content of this article.

### Ethics approval

Not applicable

### Consent to participate

Not Applicable

### Consent for publication

Not Applicable

### Availability of data and material

The used data is openly available.

### Code availability

The code will become available at Github upon acceptance of the manuscript.

### Authors’ contributions

Francisco de Arriba-Pérez: Conceptualization, Methodology, Software,
Resources, Data Curation, Writing - Original Draft, Writing - Review &
Editing. Silvia García-Méndez: Conceptualization, Methodology, Resources, Data
Curation, Writing - Original Draft, Writing - Review & Editing. Fátima Leal:
Methodology, Data Curation, Writing - Original Draft, Writing - Review &
Editing. Benedita Malheiro: Conceptualization, Methodology, Validation,
Writing - Review & Editing, Supervision. Juan Carlos Burguillo-Rial:
Conceptualization, Methodology, Validation, Writing - Review & Editing,
Supervision.

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