Fuzzy Logic-Based Fake News Detection Example Suppose we have a news article with the following features that you use to determine whether the article is likely to be fake news: This Task involves working on several problems, and you will use fuzzy set theory to solve them. Word Count: The total number of words in the article. Emotional Tone: A score indicating the article's emotional tone (positive, negative, or neutral). Source Reliability: A rating (on a scale of 0 to 1) representing how reliable the news source is. To describe the qualitative characteristics of the above features and represent its inherently vague or imprecise, we use these linguistic variables: - Word Count: Low, Medium, High Emotional Tone: Negative, Neutral, Positive Source Reliability: Low, Medium, High These membership functions represent each feature. Word Count Universe of discourse (U): Set of possible word counts (e.g., U = [0, 1000]) Emotional Tone Universe of discourse (U): Set of emotional tone scores (e.g., U = [-1, 1]) 1, x5-0.5 -0.5-x 1. x200 (x)= x-200 200-1000° 200
Fuzzy Logic-Based Fake News Detection Example Suppose we have a news article with the following features that you use to determine whether the article is likely to be fake news: This Task involves working on several problems, and you will use fuzzy set theory to solve them. Word Count: The total number of words in the article. Emotional Tone: A score indicating the article's emotional tone (positive, negative, or neutral). Source Reliability: A rating (on a scale of 0 to 1) representing how reliable the news source is. To describe the qualitative characteristics of the above features and represent its inherently vague or imprecise, we use these linguistic variables: - Word Count: Low, Medium, High Emotional Tone: Negative, Neutral, Positive Source Reliability: Low, Medium, High These membership functions represent each feature. Word Count Universe of discourse (U): Set of possible word counts (e.g., U = [0, 1000]) Emotional Tone Universe of discourse (U): Set of emotional tone scores (e.g., U = [-1, 1]) 1, x5-0.5 -0.5-x 1. x200 (x)= x-200 200-1000° 200
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![Fuzzy Logic-Based Fake News Detection Example
Suppose we have a news article with the following features that you use to determine whether the article
is likely to be fake news: This Task involves working on several problems, and you will use fuzzy set theory
to solve them.
Word Count: The total number of words in the article.
Emotional Tone: A score indicating the article's emotional tone (positive, negative, or neutral).
Source Reliability: A rating (on a scale of 0 to 1) representing how reliable the news source is.
To describe the qualitative characteristics of the above features and represent its inherently vague or
imprecise, we use these linguistic variables:
-
Word Count: Low, Medium, High
Emotional Tone: Negative, Neutral, Positive
Source Reliability: Low, Medium, High
These membership functions represent each feature.
Word Count
Universe of discourse (U): Set of possible
word counts (e.g., U = [0, 1000])
Emotional Tone
Universe of discourse (U): Set of emotional
tone scores (e.g., U = [-1, 1])
1, x5-0.5
-0.5-x
1.
x200
(x)=
x-200
200-1000°
200 <x<1000
"Negative(x)=
.-05<x<05
-1
a.
*≥1000
0,
x≥5
200
Mattium(x)=
x-200
500
1000-x
200
0,
x-0.5 or ≥0.5
200 < x < 800
P(x)=
+05
-0.5<x<0.5
800 ≤ x ≤ 1000
0.
x505
a.
x800
x-0.5
Apan(x)=
05<x<1
800
Amar(x)=-
800x1000
1,
*21
200
1,
x1000](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fa2fae0d2-f8de-4d86-b278-3d843b434350%2F473bda8c-30ea-477f-97a0-449a65376a3b%2F72gc5iv_processed.png&w=3840&q=75)
Transcribed Image Text:Fuzzy Logic-Based Fake News Detection Example
Suppose we have a news article with the following features that you use to determine whether the article
is likely to be fake news: This Task involves working on several problems, and you will use fuzzy set theory
to solve them.
Word Count: The total number of words in the article.
Emotional Tone: A score indicating the article's emotional tone (positive, negative, or neutral).
Source Reliability: A rating (on a scale of 0 to 1) representing how reliable the news source is.
To describe the qualitative characteristics of the above features and represent its inherently vague or
imprecise, we use these linguistic variables:
-
Word Count: Low, Medium, High
Emotional Tone: Negative, Neutral, Positive
Source Reliability: Low, Medium, High
These membership functions represent each feature.
Word Count
Universe of discourse (U): Set of possible
word counts (e.g., U = [0, 1000])
Emotional Tone
Universe of discourse (U): Set of emotional
tone scores (e.g., U = [-1, 1])
1, x5-0.5
-0.5-x
1.
x200
(x)=
x-200
200-1000°
200 <x<1000
"Negative(x)=
.-05<x<05
-1
a.
*≥1000
0,
x≥5
200
Mattium(x)=
x-200
500
1000-x
200
0,
x-0.5 or ≥0.5
200 < x < 800
P(x)=
+05
-0.5<x<0.5
800 ≤ x ≤ 1000
0.
x505
a.
x800
x-0.5
Apan(x)=
05<x<1
800
Amar(x)=-
800x1000
1,
*21
200
1,
x1000
![Source Reliability
Universe of discourse (U): Set of reliability ratings (e.g.,
U = [0, 1])
1, x502
0.2-x
Min(x)=
0.2<x<0.8
0.2-0.8"
0,
x20.8
0,
x≤0.2
x-02
Mattium(x)=
0.2<x<0.9
0.7
1-x
0.9≤x≤1
0.1
0,
x≤0.9
Maar(x)=
x-0.9
0.1
1.
0.9<x<1
x21
Consulting a group of experts, they suggest that to classify an article as fake news, we must follow two
basic rules:
If the Word Count is High AND the Emotional Tone is Negative, the article is likely fake.
If Source Reliability is Low, then the article is likely fake.
The final step is the defuzzification process. It is based on a threshold defined for the problem. In the
current process, the crips value resulting from the defuzzification process is more significant than 0.7, and
then the article will be considered fake news.
We use a MAX aggregation function and a centroid method (COG) to find this crisp value. The defuzzied
value (COG) can be calculated using this formula.
μ(x)fake =
Σ(μπε πι)
Σμι
Where:
the degree of membership for the ith fuzzy set.
x; is the representative value (often the centroid) of the iii-th fuzzy set.
Using the previous model, calculate whether the following articles could be considered fake. Answer if
the article is fake or not! Screenshots and explanations, and result.
Demonstrating the calculus is required.
Article 1
Word Count: 600, Emotional Tone: 0.4, and Source Reliability: 0.6](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fa2fae0d2-f8de-4d86-b278-3d843b434350%2F473bda8c-30ea-477f-97a0-449a65376a3b%2Fhi3ez7n_processed.png&w=3840&q=75)
Transcribed Image Text:Source Reliability
Universe of discourse (U): Set of reliability ratings (e.g.,
U = [0, 1])
1, x502
0.2-x
Min(x)=
0.2<x<0.8
0.2-0.8"
0,
x20.8
0,
x≤0.2
x-02
Mattium(x)=
0.2<x<0.9
0.7
1-x
0.9≤x≤1
0.1
0,
x≤0.9
Maar(x)=
x-0.9
0.1
1.
0.9<x<1
x21
Consulting a group of experts, they suggest that to classify an article as fake news, we must follow two
basic rules:
If the Word Count is High AND the Emotional Tone is Negative, the article is likely fake.
If Source Reliability is Low, then the article is likely fake.
The final step is the defuzzification process. It is based on a threshold defined for the problem. In the
current process, the crips value resulting from the defuzzification process is more significant than 0.7, and
then the article will be considered fake news.
We use a MAX aggregation function and a centroid method (COG) to find this crisp value. The defuzzied
value (COG) can be calculated using this formula.
μ(x)fake =
Σ(μπε πι)
Σμι
Where:
the degree of membership for the ith fuzzy set.
x; is the representative value (often the centroid) of the iii-th fuzzy set.
Using the previous model, calculate whether the following articles could be considered fake. Answer if
the article is fake or not! Screenshots and explanations, and result.
Demonstrating the calculus is required.
Article 1
Word Count: 600, Emotional Tone: 0.4, and Source Reliability: 0.6
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