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Reif

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The HackerNoon Newsletter: Refactoring 019 - How to Reify Email Addresses (12/5/2024)

Say it once and only once TL;DR: Avoid duplicate email validations. Problems Addressed Repeated email validation logic in multiple places.
Risk of inconsistent validation rules.
Bijection violation
Primitive Obsession
Premature Optimization Related Code Smells https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-x-i7r34uj https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxv https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xiv https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxxvi Code Smell 20 - Premature Optimization Steps Identify where email validation logic is duplicated.


Create an Email Address class to encapsulate validation rules.


Refactor code to use the Email Address class instead of raw strings. Sample Code Before public class Person {
    private String emailAddress;
    // Primitive Obsession

    public void setEmailAddress(String emailAddress) {
        // Duplicated code
        if (!emailAddress.matches(
            "^[\\w.%+-]+@[\\w.-]+\\.[a-zA-Z]{2,}$")) {
            throw new IllegalArgumentException(
                "Invalid email address format");
        }
        this.emailAddress = emailAddress;
    }
}

public class JobApplication {
    private String applicantEmailAddress;

    public void setApplicantEmailAddress(String emailAddress) {
        // Duplicated code
        if (!emailAddress.matches(
            "^[\\w.%+-]+@[\\w.-]+\\.[a-zA-Z]{2,}$")) {
            throw new IllegalArgumentException(
                "Invalid email address format");
        }
        this.applicantEmailAddress = emailAddress;
    }
} After public class EmailAddress {
    // 2. Create an `EmailAddress` class to encapsulate validation rules.
    private final String value;

    public EmailAddress(String value) {
        // The rules are in a single place
        // And all objects are created valid
        if (!value.matches("^[\\w.%+-]+@[\\w.-]+\\.[a-zA-Z]{2,}$")) {
            throw new IllegalArgumentException(
                "Invalid email address format");
        }
        this.value = value;
    }
}

public class Person {
    private final EmailAddress emailAddress;

    public Person(EmailAddress emailAddress) {
        // 1. Identify where email validation logic is duplicated.
        // 3. Refactor code to use the `Email Address`
        // class instead of raw strings.
        // No validation is required
        this.emailAddress = emailAddress;
    } 
}

public class JobApplication {
    private EmailAddress applicantEmailAddress;

    public JobApplication(EmailAddress applicantEmailAddress) {
        this.applicantEmailAddress = applicantEmailAddress;
    }
} Type [x]Semi-Automatic Safety This refactoring is safe if you replace all occurrences of raw email strings with the 'EmailAddress' class and ensure all tests pass. Why Is the Code Better? You make email validation consistent across your application. Since validation rules are centralized in one place, the code becomes easier to maintain. You also reduce the risk of bugs caused by inconsistent logic. In the real world, Email Addresses are small objects that exist and are not strings. The refactored code is closer to the real-world MAPPER. Notice that bijection names are essential. It would help to create an EmailAddress, not an Email, since the Email should map to the actual message. Don't let Premature Optimizators tell you this solution has a performance penalty. They never do actual benchmarks with real-world data. Refactor With AI Without Proper Instructions

With Specific Instructions



ChatGPT

ChatGPT



Claude

Claude



Perplexity

Perplexity



Copilot

Copilot



Gemini

Gemini Tags Encapsulation Related Refactorings https://maximilianocontieri.com/refactoring-007-extract-class?embedable=true https://maximilianocontieri.com/refactoring-012-reify-associative-arrays?embedable=true https://hackernoon.com/improving-the-code-one-line-at-a-time?embedable=true Credits Image by Gerd Altmann on Pixabay This article is part of the Refactoring Series. How to Improve Your Code With Easy Refactorings Say it once and only once Say it once and only once TL;DR: Avoid duplicate email validations. TL;DR: Avoid duplicate email validations. Problems Addressed Repeated email validation logic in multiple places. Risk of inconsistent validation rules. Bijection violation Primitive Obsession Premature Optimization Repeated email validation logic in multiple places. Repeated email validation Risk of inconsistent validation rules. Bijection violation Bijection Primitive Obsession Primitive Obsession Premature Optimization Premature Optimization Related Code Smells https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-x-i7r34uj https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-x-i7r34uj https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxv https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxv https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xiv https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xiv https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxxvi https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-xxxvi Code Smell 20 - Premature Optimization Code Smell 20 - Premature Optimization Steps Identify where email validation logic is duplicated. Create an Email Address class to encapsulate validation rules. Refactor code to use the Email Address class instead of raw strings. Identify where email validation logic is duplicated. Identify where email validation logic is duplicated. Create an Email Address class to encapsulate validation rules. Create an Email Address class to encapsulate validation rules. Email Address Refactor code to use the Email Address class instead of raw strings. Refactor code to use the Email Address class instead of raw strings. Email Address Sample Code Before public class Person {
    private String emailAddress;
    // Primitive Obsession

    public void setEmailAddress(String emailAddress) {
        // Duplicated code
        if (!emailAddress.matches(
            "^[\\w.%+-]+@[\\w.-]+\\.[a-zA-Z]{2,}$")) {
            throw new IllegalArgumentException(
                "Invalid email address format");
        }
        this.emailAddress = emailAddress;
    }
}

public class JobApplication {
    private String applicantEmailAddress;

    public void setApplicantEmailAddress(String emailAddress) {
        // Duplicated code
        if (!emailAddress.matches(
            "^[\\w.%+-]+@[\\w.-]+\\.[a-zA-Z]{2,}$")) {
            throw new IllegalArgumentException(
                "Invalid email address format");
        }
        this.applicantEmailAddress = emailAddress;
    }
} public class Person {
    private String emailAddress;
    // Primitive Obsession

    public void setEmailAddress(String emailAddress) {
        // Duplicated code
        if (!emailAddress.matches(
            "^[\\w.%+-]+@[\\w.-]+\\.[a-zA-Z]{2,}$")) {
            throw new IllegalArgumentException(
                "Invalid email address format");
        }
        this.emailAddress = emailAddress;
    }
}

public class JobApplication {
    private String applicantEmailAddress;

    public void setApplicantEmailAddress(String emailAddress) {
        // Duplicated code
        if (!emailAddress.matches(
            "^[\\w.%+-]+@[\\w.-]+\\.[a-zA-Z]{2,}$")) {
            throw new IllegalArgumentException(
                "Invalid email address format");
        }
        this.applicantEmailAddress = emailAddress;
    }
} After public class EmailAddress {
    // 2. Create an `EmailAddress` class to encapsulate validation rules.
    private final String value;

    public EmailAddress(String value) {
        // The rules are in a single place
        // And all objects are created valid
        if (!value.matches("^[\\w.%+-]+@[\\w.-]+\\.[a-zA-Z]{2,}$")) {
            throw new IllegalArgumentException(
                "Invalid email address format");
        }
        this.value = value;
    }
}

public class Person {
    private final EmailAddress emailAddress;

    public Person(EmailAddress emailAddress) {
        // 1. Identify where email validation logic is duplicated.
        // 3. Refactor code to use the `Email Address`
        // class instead of raw strings.
        // No validation is required
        this.emailAddress = emailAddress;
    } 
}

public class JobApplication {
    private EmailAddress applicantEmailAddress;

    public JobApplication(EmailAddress applicantEmailAddress) {
        this.applicantEmailAddress = applicantEmailAddress;
    }
} public class EmailAddress {
    // 2. Create an `EmailAddress` class to encapsulate validation rules.
    private final String value;

    public EmailAddress(String value) {
        // The rules are in a single place
        // And all objects are created valid
        if (!value.matches("^[\\w.%+-]+@[\\w.-]+\\.[a-zA-Z]{2,}$")) {
            throw new IllegalArgumentException(
                "Invalid email address format");
        }
        this.value = value;
    }
}

public class Person {
    private final EmailAddress emailAddress;

    public Person(EmailAddress emailAddress) {
        // 1. Identify where email validation logic is duplicated.
        // 3. Refactor code to use the `Email Address`
        // class instead of raw strings.
        // No validation is required
        this.emailAddress = emailAddress;
    } 
}

public class JobApplication {
    private EmailAddress applicantEmailAddress;

    public JobApplication(EmailAddress applicantEmailAddress) {
        this.applicantEmailAddress = applicantEmailAddress;
    }
} Type [x]Semi-Automatic [x] Semi-Automatic [x] Safety This refactoring is safe if you replace all occurrences of raw email strings with the 'EmailAddress' class and ensure all tests pass. Why Is the Code Better? You make email validation consistent across your application. Since validation rules are centralized in one place, the code becomes easier to maintain. You also reduce the risk of bugs caused by inconsistent logic. In the real world, Email Addresses are small objects that exist and are not strings. Email Addresses small objects The refactored code is closer to the real-world MAPPER . MAPPER Notice that bijection names are essential. It would help to create an EmailAddress , not an Email , since the Email should map to the actual message. EmailAddress Email map Don't let Premature Optimizators tell you this solution has a performance penalty. Premature Optimizators They never do actual benchmarks with real-world data. Refactor With AI Without Proper Instructions

With Specific Instructions



ChatGPT

ChatGPT



Claude

Claude



Perplexity

Perplexity



Copilot

Copilot



Gemini

Gemini Without Proper Instructions

With Specific Instructions ChatGPT

ChatGPT Claude

Claude Perplexity

Perplexity Copilot

Copilot Gemini

Gemini Without Proper Instructions With Specific Instructions Without Proper Instructions Without Proper Instructions With Specific Instructions With Specific Instructions ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT Claude Claude Claude Claude Claude Claude Claude Claude Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Copilot Copilot Copilot Copilot Copilot Copilot Copilot Copilot Gemini Gemini Gemini Gemini Gemini Gemini Gemini Gemini Tags Encapsulation Encapsulation Related Refactorings https://maximilianocontieri.com/refactoring-007-extract-class?embedable=true https://maximilianocontieri.com/refactoring-007-extract-class?embedable=true https://maximilianocontieri.com/refactoring-012-reify-associative-arrays?embedable=true https://maximilianocontieri.com/refactoring-012-reify-associative-arrays?embedable=true https://hackernoon.com/improving-the-code-one-line-at-a-time?embedable=true https://hackernoon.com/improving-the-code-one-line-at-a-time?embedable=true Credits Image by Gerd Altmann on Pixabay Gerd Altmann Pixabay This article is part of the Refactoring Series. How to Improve Your Code With Easy Refactorings How to Improve Your Code With Easy Refactorings

Refactoring 019 - How to Reify Email Addresses

إعادة الهيكلة 019 - كيفية إعادة هيكلة عناوين البريد الإلكتروني

রিফ্যাক্টরিং 019 - ইমেল ঠিকানাগুলিকে কীভাবে রিফাই করা যায়

Refactoring 019 - Sådan bekræftes e-mail-adresser

Refactorización 019: Cómo reificar direcciones de correo electrónico

Refactoring 019 - כיצד לאמת כתובות דוא"ל

Refactoring 019 - Ինչպես վերականգնել էլփոստի հասցեները

リファクタリング 019 - メールアドレスを具体化する方法

Refactoring 019 - របៀបកែតម្រូវអាសយដ្ឋានអ៊ីមែល

Refactoring 019 - ວິທີການ Reify ທີ່ຢູ່ອີເມວ

Refactoring 019 - Tsela ya go Reify Diaterese tša Imeile

Refactoring 019 - Si të rivendosni adresat e postës elektronike

Refactoring 019 - Paano Reify ang Mga Email Address

DreamLLM: What We Can Conclude From This Comprehensive Framework?

Got A Startup With An App? You Need AppLovin

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একটি অ্যাপ দিয়ে একটি স্টার্টআপ পেয়েছেন? আপনার অ্যাপলোভিন দরকার

Imate startup sa aplikacijom? Potreban vam je AppLovin

¿Tienes una startup con una aplicación? Necesitas AppLovin

Tes un inicio cunha aplicación? Necesitas AppLovin

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Avoid Ambiguous Naming for Path Variables Problems Unclear variable purpose
Bijection Fault
Misleading context
Repetitive code
Harder maintenance
Reduced readability
Abbreviations
Increased cognitive load Solutions Honor the Bijection
Use precise names
Avoid ambiguity
Keep context clear
Extract reusable code
Follow naming conventions
Reify names with their rules Context When handling a single fileName or directoryPath, vague names like file or dirName create confusion. A file should represent a File
A fileName should represent the name of a file
fileContents should represent the content of a file
A fileHandler should represent a pointer to an external resource
dirName should be directoryPath Clear names like fileName for names and directoryPath for directories communicate each variable’s role. When you name a variable file, it can confuse others about its purpose. Does it store a file object or just the filename? When you name a variable dirName instead of directoryName it leads to ambiguity. Clear and descriptive variable names improve code readability and maintainability, especially in collaborative environments. Sample Code Wrong function importBoardGameScores(file) {
  if (file) {
    const data = fs.readFileSync(file, 'utf-8');
    // Process board game scores...
  }
}

function importDirectoryScores(dirName) {
  // 'dir' is an abbreviation
  const files = fs.readdirSync(dirName);
  files.forEach(file => {
    const data = fs.readFileSync(`${dirName}/${file}`, 'utf-8');
    // Process each file's board game scores...
  });
  }
} Right function importBoardGameScores(fileName) {
  if (fileName) {
    const data = fs.readFileSync(fileName, 'utf-8');
    // Process board game scores...
  }
}

function importDirectoryBoardGamesScores(directoryPath) {
    const fileNames = fs.readdirSync(directoryPath);
    // Note the variable holding filenames 
    // and not files
  
   fileNames.forEach(filename => {
        const fullPath = path.join(directoryPath, filename);
        const scores = importBoardGameScores(fullPath);
        allScores.push(scores);
   });
       
   return allScores.flat();
  
  
// You can also reify the concept of a filename
// And avoid repeating the rules everywhere
  
class Filename {
    value;
    
    constructor(value) {
        this.validateFilename(value);
        this.value = value;
    }

    validateFilename(value) {      
        const invalidCharacters = /[<>:"/\\|?*\x00-\x1F]/g;
        if (invalidCharacters.test(value)) {
            throw new Error
              ('Filename contains invalid characters');
        }

        if (/^[. ]+$/.test(value)) {
            throw new Error
              ('Filename cannot consist only of dots or spaces');
        }

        if (value.length > 255) {
            throw new Error
              ('Filename is too long');
        }
    }

    toString() {
        return this.value;
    }

    get value() {
        return this.value;
    }
} Detection [x]Semi-Automatic Look for generic names in code handling files or directory paths like file or dirName. Tags Naming Level [x]Beginner AI Generation AI models may default to ambiguous names like file or dirName without specific instructions. Adding descriptive naming and code extraction guidelines can improve the AI's output. AI Detection AI tools can fix this smell by using clear naming conventions and suggesting code extraction if prompted to avoid redundant code. Try Them! Remember: AI Assistants make lots of mistakes Without Proper Instructions

With Specific Instructions



ChatGPT

ChatGPT



Claude

Claude



Perplexity

Perplexity



Copilot

Copilot



Gemini

Gemini Conclusion By using precise names like fileName and directoryPath and extracting reusable methods, you improve code clarity and maintainability. These simple practices help reduce redundancy and keep your code understandable. Related Reading https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-vii-8dk31x0 Disclaimer: Code Smells are my opinion. Credit(s): Photo by Gabriel Heinzer on Unsplash Code should be written to be read by humans first, and machines second. Don Laabs https://hackernoon.com/400-thought-provoking-software-engineering-quotes?embedable=true This article is part of the CodeSmell Series: How to Find the Stinky Parts of your Code Avoid Ambiguous Naming for Path Variables Avoid Ambiguous Naming for Path Variables Avoid Ambiguous Naming for Path Variables Problems Unclear variable purpose Bijection Fault Misleading context Repetitive code Harder maintenance Reduced readability Abbreviations Increased cognitive load Unclear variable purpose Bijection Fault Bijection Misleading context Repetitive code Harder maintenance Reduced readability Abbreviations Abbreviations Increased cognitive load Solutions Honor the Bijection Use precise names Avoid ambiguity Keep context clear Extract reusable code Follow naming conventions Reify names with their rules Honor the Bijection Bijection Use precise names Avoid ambiguity Keep context clear Extract reusable code Follow naming conventions Reify names with their rules Context When handling a single fileName or directoryPath , vague names like file or dirName create confusion. fileName directoryPath file dirName A file should represent a File A fileName should represent the name of a file fileContents should represent the content of a file A fileHandler should represent a pointer to an external resource dirName should be directoryPath A file should represent a File file A fileName should represent the name of a file fileName fileContents should represent the content of a file fileContents A fileHandler should represent a pointer to an external resource fileHandler dirName should be directoryPath dirName directoryPath Clear names like fileName for names and directoryPath for directories communicate each variable’s role. fileName directoryPath When you name a variable file, it can confuse others about its purpose. Does it store a file object or just the filename? When you name a variable dirName instead of directoryName it leads to ambiguity. dirName Clear and descriptive variable names improve code readability and maintainability, especially in collaborative environments. Sample Code Wrong function importBoardGameScores(file) {
  if (file) {
    const data = fs.readFileSync(file, 'utf-8');
    // Process board game scores...
  }
}

function importDirectoryScores(dirName) {
  // 'dir' is an abbreviation
  const files = fs.readdirSync(dirName);
  files.forEach(file => {
    const data = fs.readFileSync(`${dirName}/${file}`, 'utf-8');
    // Process each file's board game scores...
  });
  }
} function importBoardGameScores(file) {
  if (file) {
    const data = fs.readFileSync(file, 'utf-8');
    // Process board game scores...
  }
}

function importDirectoryScores(dirName) {
  // 'dir' is an abbreviation
  const files = fs.readdirSync(dirName);
  files.forEach(file => {
    const data = fs.readFileSync(`${dirName}/${file}`, 'utf-8');
    // Process each file's board game scores...
  });
  }
} Right function importBoardGameScores(fileName) {
  if (fileName) {
    const data = fs.readFileSync(fileName, 'utf-8');
    // Process board game scores...
  }
}

function importDirectoryBoardGamesScores(directoryPath) {
    const fileNames = fs.readdirSync(directoryPath);
    // Note the variable holding filenames 
    // and not files
  
   fileNames.forEach(filename => {
        const fullPath = path.join(directoryPath, filename);
        const scores = importBoardGameScores(fullPath);
        allScores.push(scores);
   });
       
   return allScores.flat();
  
  
// You can also reify the concept of a filename
// And avoid repeating the rules everywhere
  
class Filename {
    value;
    
    constructor(value) {
        this.validateFilename(value);
        this.value = value;
    }

    validateFilename(value) {      
        const invalidCharacters = /[<>:"/\\|?*\x00-\x1F]/g;
        if (invalidCharacters.test(value)) {
            throw new Error
              ('Filename contains invalid characters');
        }

        if (/^[. ]+$/.test(value)) {
            throw new Error
              ('Filename cannot consist only of dots or spaces');
        }

        if (value.length > 255) {
            throw new Error
              ('Filename is too long');
        }
    }

    toString() {
        return this.value;
    }

    get value() {
        return this.value;
    }
} function importBoardGameScores(fileName) {
  if (fileName) {
    const data = fs.readFileSync(fileName, 'utf-8');
    // Process board game scores...
  }
}

function importDirectoryBoardGamesScores(directoryPath) {
    const fileNames = fs.readdirSync(directoryPath);
    // Note the variable holding filenames 
    // and not files
  
   fileNames.forEach(filename => {
        const fullPath = path.join(directoryPath, filename);
        const scores = importBoardGameScores(fullPath);
        allScores.push(scores);
   });
       
   return allScores.flat();
  
  
// You can also reify the concept of a filename
// And avoid repeating the rules everywhere
  
class Filename {
    value;
    
    constructor(value) {
        this.validateFilename(value);
        this.value = value;
    }

    validateFilename(value) {      
        const invalidCharacters = /[<>:"/\\|?*\x00-\x1F]/g;
        if (invalidCharacters.test(value)) {
            throw new Error
              ('Filename contains invalid characters');
        }

        if (/^[. ]+$/.test(value)) {
            throw new Error
              ('Filename cannot consist only of dots or spaces');
        }

        if (value.length > 255) {
            throw new Error
              ('Filename is too long');
        }
    }

    toString() {
        return this.value;
    }

    get value() {
        return this.value;
    }
} Detection [x]Semi-Automatic [x] Semi-Automatic [x] Look for generic names in code handling files or directory paths like file or dirName . file dirName Tags Naming Naming Level [x]Beginner [x] Beginner [x] AI Generation AI models may default to ambiguous names like file or dirName without specific instructions. file dirName Adding descriptive naming and code extraction guidelines can improve the AI's output. AI Detection AI tools can fix this smell by using clear naming conventions and suggesting code extraction if prompted to avoid redundant code. Try Them! Try Them! Remember: AI Assistants make lots of mistakes Remember: AI Assistants make lots of mistakes Without Proper Instructions

With Specific Instructions



ChatGPT

ChatGPT



Claude

Claude



Perplexity

Perplexity



Copilot

Copilot



Gemini

Gemini Without Proper Instructions

With Specific Instructions ChatGPT

ChatGPT Claude

Claude Perplexity

Perplexity Copilot

Copilot Gemini

Gemini Without Proper Instructions With Specific Instructions Without Proper Instructions Without Proper Instructions With Specific Instructions With Specific Instructions ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT ChatGPT Claude Claude Claude Claude Claude Claude Claude Claude Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Perplexity Copilot Copilot Copilot Copilot Copilot Copilot Copilot Copilot Gemini Gemini Gemini Gemini Gemini Gemini Gemini Gemini Conclusion By using precise names like fileName and directoryPath and extracting reusable methods, you improve code clarity and maintainability. fileName directoryPath These simple practices help reduce redundancy and keep your code understandable. Related Reading https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-vii-8dk31x0 https://hackernoon.com/how-to-find-the-stinky-parts-of-your-code-part-vii-8dk31x0 Disclaimer: Code Smells are my opinion. Disclaimer: Code Smells are my opinion . opinion Credit(s): Photo by Gabriel Heinzer on Unsplash Credit(s): Photo by Gabriel Heinzer on Unsplash Gabriel Heinzer Unsplash Code should be written to be read by humans first, and machines second. Code should be written to be read by humans first, and machines second. Don Laabs Don Laabs https://hackernoon.com/400-thought-provoking-software-engineering-quotes?embedable=true https://hackernoon.com/400-thought-provoking-software-engineering-quotes?embedable=true This article is part of the CodeSmell Series: How to Find the Stinky Parts of your Code This article is part of the CodeSmell Series: How to Find the Stinky Parts of your Code How to Find the Stinky Parts of your Code

Code Smell 278 - DirName and File

Specific Problems with UMA Requests on Android

Textbooks Are All You Need: Conclusion and References

AI and RAG in Knowledge Graph Enlightenment 

Authors:
(1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu);
(2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu);
(3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ). Table of Links Abstract and Introduction Related Work Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Discussion and Ethics Statement Appendix and References Discussion The rising levels of semantic polarity between the two major broadcast news organizations, as demonstrated in our work may render people’s ability to reach across partisan divides and to perceive and solve issues democratically more difficult. The way CNN and Fox News discuss identical keywords on their programs is remarkably distinct in 2020 where semantic polarity between the two stations reaches its peak over an 11-year period, corroborating the widening partisan media gap highlighted by recent scholarship. Framing Theory argues that even subtle changes in the wording around how an issue is described by the media can significantly influence how audiences interpret the issue (Scheufele 2000; Entman 2003). Yet, our findings show that the contextual language driving semantic polarization in broadcast media is not nuanced at all. The surrounding words that characterize how each station discusses topically important keywords are drastically different, to the extent that identical words seem to reflect different meanings altogether. This is a crucial point of consideration, as linguistic cues in the media can play a powerful role in selectively reifying certain aspects of the perceived reality of an issue while downplaying others. Our findings suggest that TV news language does not only shape how online audiences perceive issues, but also how they talk about them. In linguistics, collocation is the property of language where two or more words appear in each other’s company with greater than random chance (Hoey 2005), such as “illegal” and “immigrants” in Fox News and “climate change” and “science” from CNN, as shown in our findings. Repeated encounters with collocated words drive what linguists call lexical priming (Hoey 2005), in which a priming word (e.g., ”blacklivesmatter”) provokes a particular target word (“protests” for CNN and “violence” for Fox) more strongly and quickly than disparate words encountered more rarely. In this vein, online audiences who consume very different perceived realities from the media may be lexically primed through repeated exposure to collocated words that frame identical issues in acutely contrasting contexts. This may help theorize why TV news language can shape how online audiences engage in public discourse as demonstrated in this work. Semantic polarization in televised media not only forecasts semantic polarity trends on Twitter, but the words that characterize broadcast media polarization re-appear across Twitter discussions, separated by significant lag months. Our results demonstrate that the language characterizing opposing media narratives around topically similar news events on TV can be linguistically mimicked in how social media users are polarized in their discourse around important social issues. Limitations. Differences in cosine values between contextual word representations stem not only from semantic, but also from positional differences of words in sentences. Although word embeddings generally embody contextual nuances, we acknowledge that our work predominantly considers semantic rather than syntactic aspects, and that to some extent our calculation of semantic polarity could embody syntactic differences in how keywords are discussed. Furthermore, our findings pertain to American political contexts, which might not be generalizable to foreign public discourse and media language. For future work, we aim to apply our model to relevant textual corpora in other languages by incorporating cross-lingual transfer learning methods to better understand the generalizability of our model by comparing distributional differences with cross-lingual data. Ethics Statement We aim to strictly adhere to the AAAI Code of Ethics and Conduct by ensuring that no personal information of Twitter users was collected nor compromised throughout our project. All data in this work are securely stored on servers only accessible by the authors. Our semantic polarization framework is made publicly available on the authors’ GitHub page and can be applied on a variety of textual corpora with diachronic and contrastive properties. This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu); (2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu); (3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ). Authors: Authors: (1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu); (2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu); (3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ). Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Discussion and Ethics Statement Discussion and Ethics Statement Appendix and References Appendix and References Discussion The rising levels of semantic polarity between the two major broadcast news organizations, as demonstrated in our work may render people’s ability to reach across partisan divides and to perceive and solve issues democratically more difficult. The way CNN and Fox News discuss identical keywords on their programs is remarkably distinct in 2020 where semantic polarity between the two stations reaches its peak over an 11-year period, corroborating the widening partisan media gap highlighted by recent scholarship. Framing Theory argues that even subtle changes in the wording around how an issue is described by the media can significantly influence how audiences interpret the issue (Scheufele 2000; Entman 2003). Yet, our findings show that the contextual language driving semantic polarization in broadcast media is not nuanced at all. The surrounding words that characterize how each station discusses topically important keywords are drastically different, to the extent that identical words seem to reflect different meanings altogether. This is a crucial point of consideration, as linguistic cues in the media can play a powerful role in selectively reifying certain aspects of the perceived reality of an issue while downplaying others. Our findings suggest that TV news language does not only shape how online audiences perceive issues, but also how they talk about them. In linguistics, collocation is the property of language where two or more words appear in each other’s company with greater than random chance (Hoey 2005), such as “illegal” and “immigrants” in Fox News and “climate change” and “science” from CNN, as shown in our findings. Repeated encounters with collocated words drive what linguists call lexical priming (Hoey 2005), in which a priming word (e.g., ”blacklivesmatter”) provokes a particular target word (“protests” for CNN and “violence” for Fox) more strongly and quickly than disparate words encountered more rarely. In this vein, online audiences who consume very different perceived realities from the media may be lexically primed through repeated exposure to collocated words that frame identical issues in acutely contrasting contexts. This may help theorize why TV news language can shape how online audiences engage in public discourse as demonstrated in this work. Semantic polarization in televised media not only forecasts semantic polarity trends on Twitter, but the words that characterize broadcast media polarization re-appear across Twitter discussions, separated by significant lag months. Our results demonstrate that the language characterizing opposing media narratives around topically similar news events on TV can be linguistically mimicked in how social media users are polarized in their discourse around important social issues. Limitations . Differences in cosine values between contextual word representations stem not only from semantic, but also from positional differences of words in sentences. Although word embeddings generally embody contextual nuances, we acknowledge that our work predominantly considers semantic rather than syntactic aspects, and that to some extent our calculation of semantic polarity could embody syntactic differences in how keywords are discussed. Furthermore, our findings pertain to American political contexts, which might not be generalizable to foreign public discourse and media language. For future work, we aim to apply our model to relevant textual corpora in other languages by incorporating cross-lingual transfer learning methods to better understand the generalizability of our model by comparing distributional differences with cross-lingual data. Limitations Limitations Ethics Statement We aim to strictly adhere to the AAAI Code of Ethics and Conduct by ensuring that no personal information of Twitter users was collected nor compromised throughout our project. All data in this work are securely stored on servers only accessible by the authors. Our semantic polarization framework is made publicly available on the authors’ GitHub page and can be applied on a variety of textual corpora with diachronic and contrastive properties. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Bridging the Partisan Gap: Analyzing Broadcast Media's Role in Political Divide

Authors:
(1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu);
(2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu);
(3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ). Table of Links Abstract and Introduction Related Work Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Discussion and Ethics Statement Appendix and References Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Method and Analysis Semantic polarity between Fox and CNN is predominantly high in 2020, echoing the series of highly publicized events in the media (e.g., COVID-19, George Floyd, and BLM demonstrations) that reified a widening ideological gap among the American public on a number of issues. Hence, in Study 2, we aim to go beyond providing a mere quantification of polarization by examining how the language surrounding the use of contentious keywords by CNN and Fox contributes to the 2020 peak in semantic polarity. In other words, how does CNN and Fox News contextually differ in their use of topical keywords on their news programs in 2020? To answer this question, we identified attributive words or contextual tokens that are most predictive of whether a speaker turn containing a topical keyword was from CNN or Fox in 2020. This task entailed a two-step process. First, we trained a BERT-based classifier using the 2020 corpus of closed captions from both stations to predict whether a speaker turn was from CNN or Fox. Then, we used a model interpretation technique called Integrated Gradients (Sundararajan, Taly, and Yan 2017) to identify which words (except for the topical keyword) in each speaker turn contributed most to the classification decision. Classifying 2020 Speaker Turns. To identify whether a speaker turn was broadcast from Fox or CNN in 2020, we built six BERT-based classifiers pertaining to each topic. First, we merged the 2020 corpus of speaker turns, each containing one of the nine topical keywords by topic (e.g., speaker turns containing “racism” or “racist” were merged into one topical corpus). For each of the six topical corpora, we trained BERT (BERT-Base-Uncased) by masking 15% of the words in each speaker turn and trained the model to fill in the masked words using a standard cross-entropy loss function. We then fine-tuned the adapted model to predict whether a speaker turn was from CNN or Fox News. For this step, we split the data into 80% training, 10% validation, and 10% test sets. We set batch sizes to 16, training epochs to 6 with 3-fold cross-validation, and used AdamW for optimization with a learning rate of 2e−05. We used PyTorch for all model implementations. Model Interpretation. To identify the contextual language or the attributive words that contributed most to the 2020 semantic polarity between CNN and Fox News, we used Integrated Gradients (IG) to interpret the classification results from our BERT-classifier in the previous step. IG is an attribution method that allows us to understand how much an input feature contributes to the output result of a deep neural network (Sundararajan, Taly, and Yan 2017). This post-hoc interpretability technique combines the input’s gradients by interpolating in short increments along a straight line between a baseline x (usually a vector with all zeros) and the input x 0 . Formally, if F : Rn → [0, 1] represents BERT, then the integrated gradient (IG) of the i th dimension of x is: Large pre-trained transformer-based language models, such as BERT may have strong predictive powers, but their multiple nonlinearities prevent users from directly attributing model outputs to various input features. To overcome this lack of interpretive insight, we leverage IG to identify words that are most attributable to each predicted class. Thus, we identify which contextual words within a speaker turn (input features of a classifier) contribute most to, or are most predictive of the output decision, namely, whether the speaker turn is from CNN or Fox News in 2020. Results Our BERT-classifier distinguishes 2020 speaker turns from CNN and Fox News with a mean accuracy of 75.3% across all six topics (Table 3). Model performance is highest for speaker turns topically focused on the police, corresponding to Study 1 findings demonstrating highest SP scores for “police” across all keywords in 2020. In Table 4, we show the top 10 words most predictive of how CNN and Fox News discuss the six topics on their news programs in 2020. To avoid noise caused by low-frequency words, we limited our token selection to words with a frequency above the 95th percentile. We categorized the tokens with the highest and lowest attribution scores ranging from positive (attributed to CNN) to negative (attributed to Fox News) values for each topic. As shown, across all topics, none of the top tokens overlap between the two stations, indicating the drastically distinct contexts in which the two channels discuss key topics in 2020. For example, words like “illegal”, “enforcement”, and “order” contextualize immigration discussions in a rather legalistic manner on Fox News whereas, “parents”, “family”, “children”, “daughter”, and “communities” - humanizing words connotative of family relations best predict how CNN discusses the same topic. Both stations may be discussing the same topic with identical keywords, but the contextual language surrounding their discussion is strikingly different. Such divergence sheds light on the underlying differences in the linguistic signature of televised media language that help explain the 2020 peak in semantic polarity between the two stations. This paper is available on arxiv under CC 4.0 license. Authors: (1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu); (2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu); (3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ). Authors: Authors: (1) Xiaohan Ding, Department of Computer Science, Virginia Tech, (e-mail: xiaohan@vt.edu); (2) Mike Horning, Department of Communication, Virginia Tech, (e-mail: mhorning@vt.edu); (3) Eugenia H. Rho, Department of Computer Science, Virginia Tech, (e-mail: eugenia@vt.edu ). Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) Study 1: Evolution of Semantic Polarity in Broadcast Media Language (2010-2020) Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Study 3: How Semantic Polarization in Broadcast Media Language Forecasts Semantic Polarity in Social Media Discourse Discussion and Ethics Statement Discussion and Ethics Statement Appendix and References Appendix and References Study 2: Words that Characterize Semantic Polarity between Fox News & CNN in 2020 Method and Analysis Semantic polarity between Fox and CNN is predominantly high in 2020, echoing the series of highly publicized events in the media (e.g., COVID-19, George Floyd, and BLM demonstrations) that reified a widening ideological gap among the American public on a number of issues. Hence, in Study 2, we aim to go beyond providing a mere quantification of polarization by examining how the language surrounding the use of contentious keywords by CNN and Fox contributes to the 2020 peak in semantic polarity. In other words, how does CNN and Fox News contextually differ in their use of topical keywords on their news programs in 2020? To answer this question, we identified attributive words or contextual tokens that are most predictive of whether a speaker turn containing a topical keyword was from CNN or Fox in 2020. This task entailed a two-step process. First, we trained a BERT-based classifier using the 2020 corpus of closed captions from both stations to predict whether a speaker turn was from CNN or Fox. Then, we used a model interpretation technique called Integrated Gradients (Sundararajan, Taly, and Yan 2017) to identify which words (except for the topical keyword) in each speaker turn contributed most to the classification decision. Classifying 2020 Speaker Turns. To identify whether a speaker turn was broadcast from Fox or CNN in 2020, we built six BERT-based classifiers pertaining to each topic. First, we merged the 2020 corpus of speaker turns, each containing one of the nine topical keywords by topic (e.g., speaker turns containing “racism” or “racist” were merged into one topical corpus). Classifying 2020 Speaker Turns. For each of the six topical corpora, we trained BERT (BERT-Base-Uncased) by masking 15% of the words in each speaker turn and trained the model to fill in the masked words using a standard cross-entropy loss function. We then fine-tuned the adapted model to predict whether a speaker turn was from CNN or Fox News. For this step, we split the data into 80% training, 10% validation, and 10% test sets. We set batch sizes to 16, training epochs to 6 with 3-fold cross-validation, and used AdamW for optimization with a learning rate of 2e−05. We used PyTorch for all model implementations. Model Interpretation. To identify the contextual language or the attributive words that contributed most to the 2020 semantic polarity between CNN and Fox News, we used Integrated Gradients (IG) to interpret the classification results from our BERT-classifier in the previous step. IG is an attribution method that allows us to understand how much an input feature contributes to the output result of a deep neural network (Sundararajan, Taly, and Yan 2017). This post-hoc interpretability technique combines the input’s gradients by interpolating in short increments along a straight line between a baseline x (usually a vector with all zeros) and the input x 0 . Formally, if F : Rn → [0, 1] represents BERT, then the integrated gradient (IG) of the i th dimension of x is: Model Interpretation. Large pre-trained transformer-based language models, such as BERT may have strong predictive powers, but their multiple nonlinearities prevent users from directly attributing model outputs to various input features. To overcome this lack of interpretive insight, we leverage IG to identify words that are most attributable to each predicted class. Thus, we identify which contextual words within a speaker turn (input features of a classifier) contribute most to, or are most predictive of the output decision, namely, whether the speaker turn is from CNN or Fox News in 2020. Results Our BERT-classifier distinguishes 2020 speaker turns from CNN and Fox News with a mean accuracy of 75.3% across all six topics (Table 3). Model performance is highest for speaker turns topically focused on the police, corresponding to Study 1 findings demonstrating highest SP scores for “police” across all keywords in 2020. In Table 4, we show the top 10 words most predictive of how CNN and Fox News discuss the six topics on their news programs in 2020. To avoid noise caused by low-frequency words, we limited our token selection to words with a frequency above the 95th percentile. We categorized the tokens with the highest and lowest attribution scores ranging from positive (attributed to CNN) to negative (attributed to Fox News) values for each topic. As shown, across all topics, none of the top tokens overlap between the two stations, indicating the drastically distinct contexts in which the two channels discuss key topics in 2020. For example, words like “illegal”, “enforcement”, and “order” contextualize immigration discussions in a rather legalistic manner on Fox News whereas, “parents”, “family”, “children”, “daughter”, and “communities” - humanizing words connotative of family relations best predict how CNN discusses the same topic. Both stations may be discussing the same topic with identical keywords, but the contextual language surrounding their discussion is strikingly different. Such divergence sheds light on the underlying differences in the linguistic signature of televised media language that help explain the 2020 peak in semantic polarity between the two stations. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

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