\section{Related Work}
\label{sec:related}

\subsection{Misinformation and Deception Detection}

Misinformation detection is a task that has arisen in order to combat the influence of false or misleading information.
\citet{oshikawa-etal-2020-survey} note that, even though it is often framed as a binary veracity classification, it has also been framed using scales of truth \citep{rashkin-etal-2017-truth, wang-2017-liar} or through political bias \citep{potthast-etal-2018-stylometric}.

Research from psychology has shown that liars attempt to relieve the cognitive burden of deception by distancing themselves from their false statements \citep{newman_2003}.
Similar effects have been reported when looking at ``trolls'' on social media \citep{Addawood_2019}.
However, it is important to note that veracity can be complicated to establish, which can lead to issues such as sampling biases \citep{zhou-etal-2021-hidden}.

\citet{ruffo_studying_2023} note that a lot of terminology in this area tends to have fuzzy or ambiguous definitions.
They argue that terms such as ``fake news'' are often ill-defined, even in an academic setting.
They mention that this blurs the lines between misinformation detection and similar tasks, such as automated fact-checking, propaganda, and hyper-partisan bias detection.


%fake info'
Although automated fact-checking is a distinct task that has been applied to various types of media,\footnote{See \citealp{thorne-vlachos-2018-automated} for an overview of the task up to 2018 or the yearly FEVER Workshop, organized since 2018: \url{https://fever.ai/}} it is also used in knowledge-based approaches to detect misinformation.
An example of this is \citet{kumar-etal-2025-kgfakenet}, who used factual statements to form knowledge graphs to provide models with updated contextually relevant information for fact-checking.

Several other approaches have used features within the text, such as syntactic \cite{huang_conquering_2020} or discourse features \cite{karimi-tang-2019-learning}
One such approach by \citet{ghanem-etal-2021-fakeflow} modelled emotional shifts throughout an article and employed the information as features for fake news detection.
\citet{oshikawa-etal-2020-survey} note that the most commonly used content features tend to be bag-of-words features, frequency of punctuation, and psycholinguistic features from LIWC.\footnote{Linguistic Inquiry Word Count, originally introduced by \citet{pennebaker_linguistic_1999}.} 

Another common way to tackle misinformation detection uses metadata, such as social media interaction or web traffic.
An example of this is a study by \citet{baly-etal-2018-predicting}, which establishes a link between news article reliability and publisher credibility by checking for the existence of a Wikipedia page or Twitter account.

%bias
Credibility, partisanship, and misinformation have also been investigated in prediction and detection tasks.
Rather than explicit fact-checking, \citet{potthast-etal-2018-stylometric} argue that stylistic differences in partisan news are sufficient to detect disinformation.
\citet{potthast-etal-2018-stylometric} and \citet{baly-etal-2019-multi} noted that hyper-partisan news articles across the political spectrum are more similar to each other in terms of style than to more balanced news.

Furthermore, the political orientation of a reader can affect how believable or factual a piece of information is perceived to be.
\citet{Landreville_2019} note that if the political orientation of a news outlet aligns with that of its reader, it is considered to be more reliable.
This is the case even if said statements are opinions instead of facts.
On the other hand, \citet{Morris_2020} point out that news readers in the United States tend to consider a news outlet more trustworthy if it is critical of the opposing political party.
Even though this effect is present across the whole political spectrum, they note that it is stronger in conservative readers.
Both of these studies point out that these effects increase the likelihood of believing disinformation as long as it aligns with our political values or is critical to those perceived to be opposing.


\subsection{Argumentation Mining of News}

Argumentation mining is a subfield of NLP that studies argumentation,
ranging from identifying argumentative passages to analyzing argumentative structures and reasoning
\cite{stede_argumentation_2019,lawrence-reed-2019-argument}.
Argumentation mining of news media has generally focused on annotation of editorials and opinion pieces.
\citet{rocha-etal-2022-annotating} created a dataset of opinion articles in Portuguese annotated with argumentative discourse units,\footnote{Argumentative units are categorized according to the role they play in argumentation.} argumentative components, and relations.
Another corpus created by \citet{habernal-gurevych-2017-argumentation} annotated user comments on news articles, discussion forums, and blog posts related to controversial issues in education. 
Similarly, \citet{goudas_argument_2014} collected documents in Greek from social media (including news articles) and annotated them to identify sentences containing argumentation and whether they are claims or premises. 

Several studies have bridged misinformation and argument mining.
Rhetorical structure theory (RST) has been used to detect deceptive content \citep{vargas-etal-2022-rhetorical}, while stance detection has close ties with argumentation mining \citep{weinzierl-harabagiu-2024-discovering,saha-etal-2024-stance} and has often been studied alongside news credibility (e.g.\citealp{kotonya-toni-2019-gradual} and the Fake News Challenge\footnote{\url{http://www.fakenewschallenge.org/}} shared task).


In this study, we use the Webis-16 dataset \citep{al-khatib-etal-2016-news}.
It consists of news editorials annotated with argumentation types and information for the argumentative role they play.
The paper that introduced the dataset used it to investigate patterns in argumentation strategies across various news topics.
It has also been used by \citet{ajjour-etal-2017-unit} to identify argumentative segments in written news media.


\subsection{Arguments and Persuasion in News and Politics}

In a study of news editorials, \citet{el-baff-etal-2018-challenge} classified articles as challenging or reinforcing.
Challenging editorials make the reader rethink their prior stance, while reinforcing editorials strengthen their prior stance.
They show in a later paper \citep{el-baff-etal-2020-analyzing} that persuasive reinforcing editorials often start and end with negative tone.
They also observe that persuasive articles often start with an engaging hook and fortify arguments with a `punchy' closing.
On the other hand, they note that ineffective articles tend to feel inauthentic and have positive tone in the article body.

\citet{yu-etal-2021-interpretable} focuses on the emotional aspect of news articles.
They show that persuasive articles leverage the reader's emotions by using loaded language and logical fallacies, such as straw-man arguments and ad-hominem attacks.

Political speech in online news media often takes the form of advertisement, mimicking the style and format of the platform on which it appears \citep{Amazeen_2019}.
\citet{Nelson_2021} show that readers are not very successful at identifying this type of advertising.
They also note that, unlike commercial ads, regulations guiding truth in advertising are typically not applied to political content \citep{Nelson_2021}.
Given the impact that news media has on society, this makes for a powerful political tool \citep{konieczny_ignorance_2023}.





