# Automated Fact-Checking Using Discrete Event System Specification Based on Concept of Concurrent Simulation

Quy Thanh Le, Maamar El Amine Hamri, Aznam Yacoub, Ismail Badache

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Quy Thanh Le, Maamar El Amine Hamri, Aznam Yacoub, Ismail Badache. Automated Fact-Checking Using Discrete Event System Specification Based on Concept of Concurrent Simulation. Annual Modeling and Simulation Conference (ANNSIM'25), Universidad Complutense de Madrid, May 2025, Madrid, Spain. hal-05206908

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Distributed under a Creative Commons Attribution 4.0 International License
Automated Fact-Checking using Discrete Event
System Specification based on Concept of
Concurrent Simulation

Quy Thanh Le¹, Maamar El Amine Hamri¹, Aznam Yacoub², and Ismail
Badache¹

¹ Aix Marseille Université, CNRS, LIS, Marseille, France
quy-thanh.le@lis-lab.fr amine.hamri@lis-lab.fr
ismail.badache@lis-lab.fr

² University of Windsor, Canada
aznam.yacoub@uwindsor.ca

**Abstract.** The rapid growth of social networks presents significant challenges in combating fake news. With the massive volume of daily content, automatic detection has become essential. Various approaches have been explored from different perspectives, utilizing diverse feature extraction techniques and classification models. Among these approaches, knowledge-based fact-checking using Large Language Models (LLMs) shows promise due to their reasoning capabilities. However, this technique involves multiple complex phases, each of which can employ various methods, making the optimization of technique combinations a challenging task. This task can be addressed by developing a framework that allows simultaneous comparison of different experimental setups corresponding to these technique combinations. Discrete Event System Specification (DEVS) based concept of concurrent simulation (CCS) is an extension to DEVS that can be used as a framework to solve this problem. This study explores the application of DEVS as a potential method to optimize model configurations for improved performance.

**Keywords:** Fake news detection, Fact-checking, Large Language Model, Discrete Event System Specification, Concept of Concurrent Simulation.

# 1 Introduction

The extensive use of social media has enhanced users' ability to access and distribute information. However, the presence of fake news is deeply embedded, creating confusion and leading to misinformation [8]. Its rapid dissemination resembles the spread of an "epidemic" [12]. There has been a notable increase in scientific research on fake news detection in recent years [10]. In particular, after the 2016 U.S. presidential election, researchers intensified their focus on developing automated methods to identify and counteract fake news [5].

Fake news refers to media content containing false information designed to mislead individuals. It follows a structure similar to traditional news articles,
incorporating elements such as headlines, body text (content), creators, publish-
ers, feedback (comments), likes, and shares. Based on this structure, research [26]
categorizes fake news detection into four main approaches: (1) Knowledge-based
methods, which assess whether the content aligns with established facts; (2)
Style-based methods, which examine writing characteristics, such as the pres-
ence of extreme emotions or sensational language; (3) Propagation-based meth-
ods, which analyze the spread of fake news across online platforms; and (4)
Source-based methods, which evaluate the credibility of news sources through-
out various phases, including creation, publication, and dissemination on social
media.

Knowledge-based fake news detection verifies the authenticity of news content
by comparing it against established knowledge sources. This approach relies
on fact-checking techniques, a process developed in journalism to assess the
truthfulness of news content by comparing claims or statements with established
facts. Fact-checking can be performed manually or automatically. Manual fact-
checking often involves expert verification, which provides high accuracy but is
limited in scalability, while crowd-sourced verification is more scalable but less
reliable. However, verifying the content circulating on online platforms is a time-
consuming task that if done manually can only encompass a small portion of the
available information, which demands the development of methods to enable
automated fact-checking, a process that starts off by identifying information
needing verification and ends up by verifying whether a claim is supported or
refuted by a reputable piece of evidence, or occasions with a verdict that there
is no sufficient evidence to determine its accuracy [14].

Automated fact-checking consists of three main phases [7]: claim detection, evidence retrieval, and claim verification. Claim verification can be further divided into verdict prediction, where truthfulness labels are assigned, and justification production, where explanations for the verdicts are generated.

**Claim detection** is the first phase in automated fact-checking. Given an input text (e.g., news content), it may contain multiple statements, but not all of them are claims. A claim detection module is designed to identify statements containing a factual assertion. For example, the statement “Cubist art is beautiful” is an opinion, whereas “Over six million Americans had COVID-19 in January” is a factual claim [11].

**Evidence retrieval** involves collecting relevant information from reliable sources to evaluate a claim's accuracy. Evidence may appear in different formats, such as text, tables, or structured data. The selection of appropriate knowledge sources is crucial, as open-web and Wikipedia-based methods have limitations, whereas scientific reports [23] provide a more robust alternative. Some previous approaches did not use external evidence and relied solely on claim-based patterns [21] [4]. However, this method struggles to detect well-presented misinformation, including machine-generated falsehoods [18]. A predominant approach for evidence retrieval leverages large language models (LLMs) [9] [16]. However, LLMs often suffer from hallucinations when handling knowledge-intensive tasks. Retrieval-Augmented Generation (RAG) models have been developed to mit-
igate this issue and have shown success in fact verification [6]. Nevertheless, RAG models struggle with long or complex claims, leading to incorrect veracity labels or misattributed evidence. To address the challenge of complex claims, several studies have introduced fine-grained retrieval techniques based on claim decomposition. Claims are used to generate sub-questions, which document retrieval systems utilize to fetch relevant sources. This detailed retrieval method collects the top-k textual segments as evidence using a sliding window and BM25 ranking [1]. Claims are broken into sub-claims, and corresponding verification questions are generated, retrieving external knowledge to validate them and assign final veracity labels [25]. A programmatic framework is proposed, in which claims are decomposed into sub-tasks, and the final label is an aggregation of individual task executions. Fact-checking sub-tasks rely on external knowledge to retrieve relevant evidence [13].

**Verdict prediction** is the process of determining a claim's truthfulness once it has been identified and supporting or refuting evidence has been retrieved. The simplest approach is binary classification, labeling claims as true or false [15]. When evidence is considered, a more nuanced approach involves using supported/refuted labels based on the retrieved evidence. Currently, LLMs achieve state-of-the-art performance in verdict prediction, ranging from prompt-based methods [17] to fine-tuned models tailored [2].

**Justification Production** focuses on generating natural language explanations for veracity labels based on the claim and evidence. The output is typically a coherent and meaningful textual explanation that provides insights into the model's decision-making process. Recent studies have explored techniques for enhancing justification production in fact verification. The FOLK framework is proposed, which leverages LLMs' explanation capabilities to summarize the decision process behind veracity labels [22]. A knowledge graph (KG)-based approach for fact verification and evidence attribution is introduced, where an LLM is fine-tuned to associate retrieved knowledge graph triplets with claim predictions, generating detailed explanations for fact-checking decisions [3].

**Research Question:** As presented above, fact-checking can have multiple phases, each of which can apply different techniques. For example, the Evidence Retrieval phase can choose from techniques such as [1] [25] [13], while the Verdict Prediction phase can apply techniques like [15] [17] [2]. Therefore, when performing automated fact-checking, combinations such as [1] [15], [1] [17], etc., can be used. According to our current understanding, there is no comprehensive comparison indicating which combination is the most optimal. Given the continuously growing trend of techniques, selecting the most effective combination of techniques at each phase requires a framework that simultaneously compares all possible combinations to choose the optimal one. Is there any framework that has the ability to solve this problem?

**The concept of CCS** enables the simultaneous execution of multiple experiments [20]. Typically, concurrent experiments result from running the same program multiple times with different parameters or executing different instructions
tions within a single program. This leads to two classifications of CCS: different data paths and different data values.

**DEVS formalism** offers a modular framework for discrete event systems, enhancing complex system analysis and design with features like completeness and extensibility. It distinguishes between atomic models, which detail operational dynamics, and coupled models, which integrate multiple models for complex systems [24].

**DEVS atomic model** defines the dynamic behavior of a system using input/output ports, state variables, transition functions, and an output function. Atomic models handle external events (triggered by other models) and internal events (triggered when the state expires). When triggered, the state transitions and outputs are updated. The atomic model structure is:

$$AM = \langle X, Y, S, \delta_{\text{ext}}, \delta_{\text{int}}, \lambda, ta \rangle \qquad (1)$$

where **X** denotes the input ports and values, **Y** represents the output ports and values, **S** is the set of state variables, $\delta_{\text{ext}}$ is the external transition function (reacts to external events), $\delta_{\text{int}}$ is the internal transition function (triggers when the state lifetime expires), $\lambda$ is the output function, and **ta** is the lifetime of the current state.

This model allows dynamic and event-driven system behavior.

**DEVS-based CCS** introduces a new concurrent behavior to any DEVS atomic model. To enable CCS simulation, a new transition function is introduced. A concurrent behavioral function $f_{\text{conc}}$ is added to the atomic model to represent its concurrent behavior. This concurrent behavioral function will be the only one responsible for handling the experiment signatures. During a concurrent execution, each atomic model can modify a portion of the experiment's signature. This portion is referred to as a sub-signature. A sub-signature represents the part of the experiment's signature that is private to an atomic model [19].

A concurrent atomic model introduces a new complementary structure, which is described as follows:

$$AM' = \langle X, Y, S, \{H_n\}, \delta_{\text{int}}, \delta_{\text{ext}}, f_{\text{conc}}, \lambda, ta \rangle \qquad (2)$$

Where $X = \{(p, v) | p \in \text{in\_ports}, v \in X_p\}$ is the set of input ports and their corresponding values. Similarly, $Y = \{(p, v) | p \in \text{out\_ports}, v \in Y_p\}$ denotes the set of output ports and values. Let $S$ be the set of state variables.

The set of sub-signatures is denoted as $H_n = \{(X_n, Y_n, v_n)\}$, where $X_n$ and $Y_n$ are the input and output ports (and their values) specific to experiment $n$, and $v_n$ is the set of private variables for that experiment. Here, $n \in \mathbb{N}$ such that $0 \le n \le N$, where $N$ is the total number of experiments in the simulation.

The internal transition function is defined as $\delta_{\text{int}}: S \to S$. The external transition function is $\delta_{\text{ext}}: Q \times X \to S$, where $Q = \{(s, e) | s \in S, 0 \le e \le t_a(s)\}$ represents the set of states paired with elapsed times, and $e$ is the time since the last transition.
The concurrent behavioral function is denoted as $f_{conc}: H_n \rightarrow H_n$. The output function is given by $\lambda: S \rightarrow Y$. Finally, the lifetime function is $t_a: S \rightarrow \mathbb{R}_{0,+\infty}$, where $t_a \in [0, \infty[$ indicates the duration a state remains active.

Each atomic model has one private sub-signature per experiment. Therefore, for *N* experiments, an atomic model must have *N* sub-signatures.

**Our Contribution:** To answer Research Question, we can apply the concept of concurrent simulation (CCS) implemented by DEVS, which allows for concurrent simulation across multiple experiences. Typically, concurrent experiments arise from executing the same program multiple times with different parameters or from executing different instructions within the same program. Previous research has applied DEVS-based CCS in wind turbine diagnostics with various ANN configurations, yielding promising results [19].

## 2 Hypothesis

DEVS-based CCS can be used to compare all possible combinations of techniques at each stage in automated fact-checking to select the combination with the highest performance. The fact-checking pipeline will be modeled as a concurrent atomic model, and this model will then be used to simulate all combinations within the same timeframe. Afterward, the results will be compared, and the optimal combination will be selected. A draft of this experimental setup can be illustrated in Table 1 below. In this setup, ER (evidence retrieval) and VP (veracity prediction) are the two phases that have multiple technical configurations to be considered for combination.

<table><caption>Table 1: List of further experimental combinations for the two processes: ER (evidence retrieval) and VP (veracity prediction).</caption><thead><tr><th>Experiment ID</th><th>ER</th><th>VP</th><th>Performance score</th></tr></thead><tbody><tr><td>0</td><td>[1]</td><td>[15]</td><td>To do</td></tr><tr><td>1</td><td>[1]</td><td>[17]</td><td>To do</td></tr><tr><td>2</td><td>[1]</td><td>[2]</td><td>To do</td></tr><tr><td>3</td><td>[25]</td><td>[15]</td><td>To do</td></tr><tr><td>4</td><td>[25]</td><td>[17]</td><td>To do</td></tr><tr><td>5</td><td>[25]</td><td>[2]</td><td>To do</td></tr><tr><td>6</td><td>[13]</td><td>[15]</td><td>To do</td></tr><tr><td>7</td><td>[13]</td><td>[17]</td><td>To do</td></tr><tr><td>8</td><td>[13]</td><td>[2]</td><td>To do</td></tr></tbody></table>

## Experiment Protocol

**Step 1:** The various phases in fact-checking will be transformed into an concurrent atomic model. This model will have states corresponding to each phase. Depending on the configuration techniques in CCS, these states will change to accommodate multiple experiments running concurrently.
**Step 2:** The combinations of techniques for each step will be mapped, allowing them to be executed in concurrent experiments based on the CCS concept.

**Step 3:** The simulation will be run, and results from parallel experiments will be compared to select the most suitable combined technique.

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## Author Biographies

**QUY THANH LE** is a PhD candidate at Aix-Marseille University, France. His research interests include the application of large language models (LLMs) in the modeling and simulation of fake news detection across multiple aspects. His email address is *quy-thanh.le@lis-lab.fr*.

**MAAMAR EL-AMINE HAMRI** is an Associate Professor at Aix-Marseille University, France. He is also a member of Laboratoire d'Informatique et des Systemes (LIS), Marseille, France. He has been active for many years in Modeling and Simulation research area. His email address is *amine.hamri@lis-lab.fr*.

**AZNAM YACOUB** is an Assistant Professor at University of Windsor. His research interests include software engineering, artificial intelligence, cognitive science and multimedia. His email address is *aznam.yacoub@uwindsor.ca*.

**ISMAIL BADACHE** is an Associate Professor at Aix-Marseille University, France. He is also a member of Laboratoire d'Informatique et des Systemes (LIS), Marseille, France. His research interests include digital education revolution, information retrieval, social network analysis and natural language processing. His email address is *ismail.badache@lis-lab.fr*.