This research proposal aims to analyze the linguistic patterns in selected controversial news articles and social media posts to determine if they contain factual information or fake news. The study will use content analysis to examine 50 sentences from 10 online articles and social media posts published from 2020 to 2023. The sentences will be classified as stating facts or opinions, and their semantic structure and linguistic patterns will be analyzed. The results intend to help improve media literacy and identify cues that can point to fake news.

1. DEBUNKING FAKE NEWS: A CONTENT ANALYSIS OF COMMON LINGUISTIC
PATTERNS IN SELECTED CONTROVERSIAL ARTICLES
MILLI CANE AZUCENA
A Research Proposal presented to
the faculty of Arts and Sciences
La Consolacion College Bacolod
In partial fulfillment of the requirements in
Bachelor of Arts in English Language Studies
2022-2023
February 2023

2. Chapter 1
Background of the Study
The proliferation of “fake news” in the Philippines has become increasingly concerning in
recent years. In fact, a Pulse Asia survey conducted on September 2022 shows that 86% or nearly
9 out of 10 Filipinos believe that fake news is a problem in the country. It also added that an
overwhelming majority of the country’s adult population (90%) have read, heard, and/or watched
fake news.
One perplexing aspect of the viral spread of fake news is why people believe and spread it.
As it turns out, the distinction is difficult to point out because facts are not communicated using
specialized language. There is no linguistic checkmark or authenticity test that alerts us when we
are presented with false information. The language of facts can be very similar to the language of
lies. However, there may be differences acting as subtle cues that can point us to the writer’s
familiarity with the language of journalism (Taboada, 1).
Using insights from linguistics, it has been found that, while fake and fact-based news
stories can be easily confused, large-scale text analyses reveal interesting differences. This led to
a text classification approach that has been successfully applied to situations like social media
monitoring. To learn the characteristics of the data, it generally employs supervised machine
learning on large, labeled datasets. The problem is, those datasets need to be larger and more
accurate to solve the simple problem of determining whether or not an article contains incorrect
information (Taboada, 1). While we have yet to develop a system that can better detect fake news,
we are left with education and common sense as our only tools.

3. Objectives
This study will analyze the linguistic patterns in the semantic structure of articles and
posts found online and on social media which will answer the following:
1. How were sentences classified in terms of:
a. being factual
b. being opinionated
2. What structure do these sentences reveal in terms of:
a. function
b. notion
Framework
This linguistic study asserts that fact-based and fake news stories can be easily confused,
however, content analysis can reveal differences between the two. Taboada used this in her
research on the authenticity of fake news which will be validated in this study. The concept is
supported by Gottlob Frege’s Principle of Compositionality which states that the meaning of
speech is the sum of the meanings of the individual words plus the way in which they are arranged
into a structure. (“What does semantics study?”)
The schematic diagram of the framework is illustrated below:
INPUT
Selected
Controversial
Articles found in
News Stories
and Social
Media Posts
The Principle of
Compositionalit
y
Linguistic
Patterns in
Semantic
Structure:
Classification of
Controversial
News Stories
and Social
Media Posts:
BASIS
ANALYSIS OUTPUT

4. Figure 1 shows the schematic diagram which follows the sequence; input, basis, analysis,
and output of the linguistic patterns found in the semantic structure of selected controversial news
stories and social media posts. This process will be used to determine whether the materials
presented are either fact-based or fake news.
Scope and Limitations
This study will use 10 controversial articles in the Philippines containing fake news specific
to disinformation only. Samples may also include social media posts on Facebook and Twitter.
From these materials, the researcher will choose 50 sentences to be analyzed.
The analysis will focus on the linguistic patterns in the semantic structure (function and
notion) of the sentences which will be used to determine whether the material can be classified as
fact-based or fake news.
Significance of the Study
With the widespread use of the Internet where content is not immediately verifiable, the
proliferation of fake news has become a growing problem in the Philippines. 90% of the adult
population in the Philippines have read, heard, and/or watched fake news according to a recent
survey conducted by Pulse Asia Research Inc. on September 2022. The respondents also identified
the Internet or Social Media to be the leading source of these articles and posts.
Ultimately, this study will help improve the media literacy of Internet and Social Media
users by educating them on the linguistic patterns to watch out for to avoid being a victim of fake
news.

5. Definition of Terms
Linguistics is the study of human speech including the units, nature, structure, and modification
of language. (“Linguistics”)
Linguistic patterns are predictable formations in a language, whether it is read or heard, that
construct its words and sounds and give them meaning. (“What are linguistic patterns?”)
Content analysis is a research method used to identify patterns in recorded communication where
data is systematically collected from a set of texts. (Lou, 1)
Semantics is the field of linguistics that is concerned with the study of meaning in language.
(Nordquist, 1)
Deep structure or semantic structure focuses on the meaning of a text that serves as the base for
translation into another language. (“Semantic Structure”)
Function or speech act is an utterance defined in terms of a speaker's intention and its effect on a
listener. It is the action that the speaker hopes to provoke in his or her audience. (Nordquist, 1)
Notion is the concept or idea that the text generates for the listener. (Pesirla, 14)
Fake News articles that deliberately spread hoaxes, propaganda, and disinformation to profit from
gullible readers. (“Fake News”)
Disinformation is content that is intentionally false and designed to cause harm. It is motivated
by three factors: to make money; to have political influence, either foreign or domestic; or to cause
trouble for the sake of it. (Wardle, 1)
Machine learning is a form of artificial intelligence that performs automatic classification and
can help complement the efforts of fact-checking sites. (Taboada, 1)

6. Chapter 2
Methodology
This chapter presents the study's research design, the data sources, the samples used, the
Coding, the data-gathering procedure, and the adherence to ethical procedures.
Research Method
Because this study aims to identify patterns in recorded communication, it will employ
Content Analysis as the research method. Data will be systematically collected from a set of written
texts from web content and social media posts. It will be both quantitative (focused on counting
and measuring) and qualitative (focused on interpreting and understanding). In both types, the
study will categorize or “code” words, themes, and concepts within the texts and then analyze the
results.
Source of Data
The sources of written materials will be web content or social media posts (Facebook and
Twitter) that were considered controversial from January 2020 - January 2023. From the 10 articles
that the researcher will choose for this study, 50 sentences will be analyzed.
Data gathering procedure
Phase 1 Developing inclusion criteria for the written materials to be used
Phase 2 Data gathering
Phase 3 Identification of Linguistic Patterns in the Semantic Structure of Sentences

7. Coding
Table 1
Sentence Source Sentences
Stating
Facts
Sentences
Stating
Opinion
Common Linguistic
Patterns Observed in
Articles stating
Disinformation
Distinction of
Articles stating
Disinformation
1.
2.
3.
4.
5.
Table 2
Sentences Stating Opinion Function Notion
1.
2.
3.
4.
5.
Validity and Reliability
The data gathered from the different sources of data will be analyzed through a process
known as content analysis. This method is most effective for studies aiming to find correlations

8. and patterns in how concepts are communicated, understand the intentions of a speaker, identify
bias in communication, or reveal differences in communication in different contexts (Luo, 1).
Non-probability sampling method will be utilized to determine the 10 articles to be used in
this study. This type of sampling involves random selection making it possible to produce strong
statistical inferences about the data (Nikolopoulou, 1). The researcher will first gather the
controversial articles or posts for each year from January 2022-January 2023 and use a randomizer
that will choose the materials to be analyzed.
Ethical Procedure
The researcher will follow ethical standards in acquiring the data for this study. Only
articles and posts that are publicly available on the Internet and Social Media will be used and the
names and any other personal information of the individuals involved in the samples chosen will
not be revealed.

9. References and Works Cited
Taboada, Maite. “Authentic Language in Fake News.” Items, 23 Sept. 2021,
items.ssrc.org/beyond-disinformation/authentic-language-in-fake-news.
Luo, Amy. “Content Analysis | Guide, Methods and Examples.” Scribbr, 5 Dec. 2022,
www.scribbr.com/methodology/content-analysis.
Wardle, Claire. “Understanding Information Disorder.” First Draft, 22 Sept. 2022,
firstdraftnews.org/long-form-article/understanding-information-disorder.
Asr, Fatemeh Torabi. “The Language Gives It Away: How an Algorithm Can Help Us Detect
Fake News.” The Conversation, 14 Aug. 2019, theconversation.com/the-language-gives-it-away-
how-an-algorithm-can-help-us-detect-fake-news-120199.
“September 2022 Nationwide Survey on Fake News.” Pulse Asia Research Inc.,
pulseasia.ph/updates/september-2022-nationwide-survey-on-fake-news/?portfolioCats=249.
Nikolopoulou, Kassiani. “What Is Probability Sampling? | Types and Examples.” Scribbr, 1 Dec.
2022, www.scribbr.com/methodology/probability-sampling.

10. Wang, Chih-Chien. “Fake News and Related Concepts: Definitions and Recent Research
Development.” Contemporary Management Research, vol. 16, no. 3, Academy of Taiwan
Information Systems Research, Sept. 2020, pp. 145–74. https://doi.org/10.7903/cmr.20677.
What Does Semantics Study? – All About Linguistics. all-about-
linguistics.group.shef.ac.uk/branches-of-linguistics/semantics/what-does-semantics-study.
Guides: Fake News and Misinformation: Introduction. cerrocoso.libguides.com/fakenews/intro.
Nordquist, Richard. "An Introduction to Semantics." ThoughtCo, Aug. 27, 2020,
thoughtco.com/semantics-linguistics-1692080.
Compositionality (Stanford Encyclopedia of Philosophy). 17 Aug. 2020,
plato.stanford.edu/entries/compositionality/#1.
Pesirla, Angel. “Generative Grammar Analysis of J.G. Villa’s “Divine Poem 59” Print.
Nordquist, Richard. "Speech Acts in Linguistics." ThoughtCo, Aug. 27, 2020,
thoughtco.com/speech-act-linguistics-1692119.