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. Where: μ(x) fake = Σ(μ. * * *;) Σμ - Hi the degree of membership for the i-th 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! Demonstrating the calculus is required. Article 1 Word Count: 600, Emotional Tone: 0.4, and Source Reliability: 0.6 Fuzzification Process: Participants demonstrate the calculation to infer the fuzzy value through the crisp one's existence in the problem formulation. Defuzzification Process: Participants demonstrate the calculation to infer the crisp value through the aggregation and application of the COG function. Calculations: The calculations do not have any mistakes (wrong calculus). The calculations are precise (the equations are correctly applied). Result: The Participants cites if the paper is fake or not! 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: 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 Emotional Tone Source Reliability Universe of discourse (U): Set of possible word counts (e.g., U = [0, 1000]) "Low(x)= 1 200-1000 if x=200 if 200x1000 200- 0 ifxz1000 Universe of discourse (U): Set of Universe of discourse (U): Set emotional tone scores (e.g., U= [-| of reliability ratings (e.g., U = 1,1]) [0, 1]) if x 0.2 ga(x)= 1 -0.5- -05-05 if x-0.5 if -0.5
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. Where: μ(x) fake = Σ(μ. * * *;) Σμ - Hi the degree of membership for the i-th 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! Demonstrating the calculus is required. Article 1 Word Count: 600, Emotional Tone: 0.4, and Source Reliability: 0.6 Fuzzification Process: Participants demonstrate the calculation to infer the fuzzy value through the crisp one's existence in the problem formulation. Defuzzification Process: Participants demonstrate the calculation to infer the crisp value through the aggregation and application of the COG function. Calculations: The calculations do not have any mistakes (wrong calculus). The calculations are precise (the equations are correctly applied). Result: The Participants cites if the paper is fake or not! 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: 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 Emotional Tone Source Reliability Universe of discourse (U): Set of possible word counts (e.g., U = [0, 1000]) "Low(x)= 1 200-1000 if x=200 if 200x1000 200- 0 ifxz1000 Universe of discourse (U): Set of Universe of discourse (U): Set emotional tone scores (e.g., U= [-| of reliability ratings (e.g., U = 1,1]) [0, 1]) if x 0.2 ga(x)= 1 -0.5- -05-05 if x-0.5 if -0.5
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Transcribed Image Text: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.
Where:
μ(x) fake =
Σ(μ. * * *;)
Σμ
-
Hi the degree of membership for the i-th 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!
Demonstrating the calculus is required.
Article 1
Word Count: 600, Emotional Tone: 0.4, and Source Reliability: 0.6
Fuzzification Process: Participants demonstrate the calculation to infer the fuzzy value through the
crisp one's existence in the problem formulation.
Defuzzification Process:
Participants demonstrate the calculation to infer the crisp value
through the aggregation and application of the COG function.
Calculations: The calculations do not have any mistakes (wrong calculus).
The calculations are precise (the equations are correctly applied).
Result: The Participants cites if the paper is fake or not!
![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:
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
Emotional Tone
Source Reliability
Universe of discourse (U): Set of
possible word counts (e.g., U =
[0, 1000])
"Low(x)=
1
200-1000
if x=200
if 200x1000
200-
0
ifxz1000
Universe of discourse (U): Set of Universe of discourse (U): Set
emotional tone scores (e.g., U= [-| of reliability ratings (e.g., U =
1,1])
[0, 1])
if x 0.2
ga(x)=
1
-0.5-
-05-05
if x-0.5
if -0.5<x<0.5
0.2-0.8
Plow (x) = if 0.2<x<0.8
0
if x 0.8
0
if x≥0.5
P(x)=
0
if x 200 orx≥ 800
3-200
0
ifx 0.2 or x 10.9
if 200x800
0
500
if x-0.5 or x≥0.5
x-0.2
1000-
if 800 1000
Metal(x)-
x+05
if -0.5<x<0.5
'stecum(x)=
if 0.2<x<0.9
0.7
200
1-%
0.1
if 0.9 x 1
Mean(x)=
0
x-808
200
1
if x 800
if 800 x 1000
if x 1000
"Pos(x)=
0
X-0.5
0.5
if x ≤0.5
if 0.5<x<1
0
if x 0.9
1
if x≥1
M(4)
X-09
if 0.9 x 1
0.1
1
if x 1
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
1](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fa2fae0d2-f8de-4d86-b278-3d843b434350%2F8ccfbfbd-d32a-4a73-9d91-e04d5f455a57%2Fc9h8vt_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:
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
Emotional Tone
Source Reliability
Universe of discourse (U): Set of
possible word counts (e.g., U =
[0, 1000])
"Low(x)=
1
200-1000
if x=200
if 200x1000
200-
0
ifxz1000
Universe of discourse (U): Set of Universe of discourse (U): Set
emotional tone scores (e.g., U= [-| of reliability ratings (e.g., U =
1,1])
[0, 1])
if x 0.2
ga(x)=
1
-0.5-
-05-05
if x-0.5
if -0.5<x<0.5
0.2-0.8
Plow (x) = if 0.2<x<0.8
0
if x 0.8
0
if x≥0.5
P(x)=
0
if x 200 orx≥ 800
3-200
0
ifx 0.2 or x 10.9
if 200x800
0
500
if x-0.5 or x≥0.5
x-0.2
1000-
if 800 1000
Metal(x)-
x+05
if -0.5<x<0.5
'stecum(x)=
if 0.2<x<0.9
0.7
200
1-%
0.1
if 0.9 x 1
Mean(x)=
0
x-808
200
1
if x 800
if 800 x 1000
if x 1000
"Pos(x)=
0
X-0.5
0.5
if x ≤0.5
if 0.5<x<1
0
if x 0.9
1
if x≥1
M(4)
X-09
if 0.9 x 1
0.1
1
if x 1
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
1
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