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Reif

reif

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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

Words that Characterize Semantic Polarity between Fox News & CNN in 2020

Authors:
(1) Simon Kafader, University of Bern, Bern, Switzerland (simon.kafader@inf.unibe.ch);
(2) Mohammad Ghafari, University of Auckland, Auckland, New Zealand (m.ghafari@auckland.ac.nz). Table of Links Abstract and I. Introduction
II. Related Work
III. Background and Motivation
IV. FluentCrypto
V. Study Setup
VI. Study Result
VII. Threats to Validity
VIII. Conclusion and References VIII. CONCLUSION We developed FluentCrypto to relieve mainstream developers from dealing with low-level cryptography complexities. It is built on top of the standard Node.js API and provides a task-based solution i.e., developers only state “what” they need rather than being concerned about “how” to implement a cryptography task. We also developed a domain-specific language, called CryRule, that crypto experts can use to specify constraints on crypto objects. FluentCrypto relies on these constraints to determine a secure configuration of the API. Through an initial study, we found that FluentCrypto greatly helps developers to deliver secure solutions in a shorter time. It prevents common errors by novice developers, but at the same time it still allows experienced developers to access advanced settings. This work is the first step toward supporting mainstream developers with a crypto API that is secure by design, and further studies are essential to claim any generalization. We plan to conduct an extensive experiment with more subjects when COVID-19 restrictions relax and developers come back to their work offices. Particularly, we are interested to assess the usability of FluentCrypto in depth as well as its runtime overhead. In terms of extending FluentCrypto, we will investigate whether its current design would support other cryptography tasks as well. Besides, in this work we relied on a dynamic approach to enforce crypto constraints mainly due to the use of JavaScript, but it is worthwhile to investigate the possibility of adopting statical techniques to provide developers with JIT feedback especially in other settings. REFERENCES [1] T. IBM and the Ponemon Institute, “Cost of a data breach report,” Tech. Rep., 2020. [2] N. Patnaik, J. Hallett, and A. Rashid, “Usability smells: An analysis of developers’ struggle with crypto libraries,” in Fifteenth Symposium on Usable Privacy and Security (SOUPS 2019). Santa Clara, CA: USENIX Association, Aug. 2019. [Online]. Available: https://www.usenix.org/conference/soups2019/presentation/patnaik [3] M. Hazhirpasand, M. Ghafari, S. Krüger, E. Bodden, and O. Nierstrasz, “The impact of developer experience in using Java cryptography,” in 2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). IEEE, 2019, pp. 1–6. [4] M. Hazhirpasand, M. Ghafari, and O. Nierstrasz, “Java cryptography uses in the wild,” in Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), ser. ESEM ’20. New York, NY, USA: Association for Computing Machinery, 2020. [Online]. Available: https://doi.org/10.1145/3382494.3422166 [5] T. Zhang, G. Upadhyaya, A. Reinhardt, H. Rajan, and M. Kim, “Are code examples on an online q&a forum reliable?: a study of api misuse on stack overflow,” in 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE). IEEE, 2018, pp. 886–896. [6] M. Ghafari, P. Gadient, and O. Nierstrasz, “Security smells in android,” in 2017 IEEE 17th International Working Conference on Source Code Analysis and Manipulation (SCAM), 2017, pp. 121–130. [7] P. Gadient, M. Ghafari, P. Frischknecht, and O. Nierstrasz, “Security code smells in android icc,” Empirical Software Engineering, vol. 24, 2019. [8] S. Krüger, S. Nadi, M. Reif, K. Ali, M. Mezini, E. Bodden, F. Göpfert, F. Günther, C. Weinert, D. Demmler et al., “Cognicrypt: supporting developers in using cryptography,” in 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE, 2017, pp. 931–936. [9] S. Rahaman, Y. Xiao, S. Afrose, F. Shaon, K. Tian, M. Frantz, M. Kantarcioglu, and D. D. Yao, “Cryptoguard: High precision detection of cryptographic vulnerabilities in massive-sized java projects,” in Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, ser. CCS ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 2455–2472. [Online]. Available: https://doi.org/10.1145/3319535.3345659 [10] Y. Tymchuk, M. Ghafari, and O. Nierstrasz, “Jit feedback - what experienced developers like about static analysis,” in 2018 IEEE/ACM 26th International Conference on Program Comprehension (ICPC), 2018, pp. 64–6409. [11] J. Smith, L. N. Q. Do, and E. Murphy-Hill, “Why can’t johnny fix vulnerabilities: A usability evaluation of static analysis tools for security,” in Sixteenth Symposium on Usable Privacy and Security (SOUPS 2020). USENIX Association, Aug. 2020, pp. 221– 238. [Online]. Available: https://www.usenix.org/conference/soups2020/ presentation/smith [12] C. Corrodi, T. Spring, M. Ghafari, and O. Nierstrasz, “Idea: Benchmarking android data leak detection tools,” in Engineering Secure Software and Systems, M. Payer, A. Rashid, and J. M. Such, Eds. Cham: Springer International Publishing, 2018, pp. 116–123. [13] V.-P. Ranganath and J. Mitra, “Are free android app security analysis tools effective in detecting known vulnerabilities?” Empirical Software Engineering, vol. 25, 2020. [14] M. Hazhirpasand, M. Ghafari, and O. Nierstrasz, “Cryptoexplorer: An interactive web platform supporting secure use of cryptography APIs,” arXiv preprint arXiv:2001.00773, 2020. [15] S. Nadi, S. Krüger, M. Mezini, and E. Bodden, “Jumping through hoops: Why do Java developers struggle with cryptography APIs?” in Proceedings of the 38th International Conference on Software Engineering, 2016, pp. 935–946. [16] M. Green and M. Smith, “Developers are not the enemy!: The need for usable security APIs,” IEEE Security & Privacy, vol. 14, no. 5, pp. 40–46, 2016. [17] S. Das, V. Gopal, K. King, and A. Venkatraman, “IV= 0 security: Cryptographic misuse of libraries,” Massachusetts Institute of Technology, Final Rep, vol. 6, 2014. [18] S. Krüger, K. Ali, and E. Bodden, “CognicryptGEN : Generating code for the secure usage of crypto apis,” in Proceedings of the 18th ACM/IEEE International Symposium on Code Generation and Optimization, ser. CGO 2020. New York, NY, USA: Association for Computing Machinery, 2020, p. 185–198. [Online]. Available: https://doi.org/10.1145/3368826.3377905 [19] D. C. Nguyen, D. Wermke, Y. Acar, M. Backes, C. Weir, and S. Fahl, “A stitch in time: Supporting android developers in writingsecure code,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, ser. CCS ’17. New York, NY, USA: Association for Computing Machinery, 2017, p. 1065–1077. [Online]. Available: https://doi.org/10.1145/3133956.3133977 [20] L. Singleton, R. Zhao, M. Song, and H. Siy, “Cryptotutor: Teaching secure coding practices through misuse pattern detection,” in Proceedings of the 21st Annual Conference on Information Technology Education, ser. SIGITE ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 403–408. [Online]. Available: https://doi.org/10.1145/3368308.3415419 [21] L. Piccolboni, G. Di Guglielmo, L. P. Carloni, and S. Sethumadhavan, “Crylogger: Detecting crypto misuses dynamically,” in IEEE Symposium on Security & Privacy (SP) 2021, 2021. [22] P. L. Gorski, Y. Acar, L. Lo Iacono, and S. Fahl, “Listen to developers! a participatory design study on security warnings for cryptographic apis,” in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, ser. CHI ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 1–13. [Online]. Available: https://doi.org/10.1145/3313831.3376142 [23] M. Fowler. (2005) Fluentinterface. [Online]. Available: https://www. martinfowler.com/bliki/FluentInterface.html This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Simon Kafader, University of Bern, Bern, Switzerland (simon.kafader@inf.unibe.ch); (2) Mohammad Ghafari, University of Auckland, Auckland, New Zealand (m.ghafari@auckland.ac.nz). Authors: Authors: (1) Simon Kafader, University of Bern, Bern, Switzerland (simon.kafader@inf.unibe.ch); (2) Mohammad Ghafari, University of Auckland, Auckland, New Zealand (m.ghafari@auckland.ac.nz). Table of Links Abstract and I. Introduction II. Related Work III. Background and Motivation IV. FluentCrypto V. Study Setup VI. Study Result VII. Threats to Validity VIII. Conclusion and References Abstract and I. Introduction Abstract and I. Introduction II. Related Work II. Related Work III. Background and Motivation III. Background and Motivation IV. FluentCrypto IV. FluentCrypto V. Study Setup V. Study Setup VI. Study Result VI. Study Result VII. Threats to Validity VII. Threats to Validity VIII. Conclusion and References VIII. Conclusion and References VIII. CONCLUSION We developed FluentCrypto to relieve mainstream developers from dealing with low-level cryptography complexities. It is built on top of the standard Node.js API and provides a task-based solution i.e., developers only state “what” they need rather than being concerned about “how” to implement a cryptography task. We also developed a domain-specific language, called CryRule, that crypto experts can use to specify constraints on crypto objects. FluentCrypto relies on these constraints to determine a secure configuration of the API. Through an initial study, we found that FluentCrypto greatly helps developers to deliver secure solutions in a shorter time. It prevents common errors by novice developers, but at the same time it still allows experienced developers to access advanced settings. This work is the first step toward supporting mainstream developers with a crypto API that is secure by design, and further studies are essential to claim any generalization. We plan to conduct an extensive experiment with more subjects when COVID-19 restrictions relax and developers come back to their work offices. Particularly, we are interested to assess the usability of FluentCrypto in depth as well as its runtime overhead. In terms of extending FluentCrypto, we will investigate whether its current design would support other cryptography tasks as well. Besides, in this work we relied on a dynamic approach to enforce crypto constraints mainly due to the use of JavaScript, but it is worthwhile to investigate the possibility of adopting statical techniques to provide developers with JIT feedback especially in other settings. REFERENCES [1] T. IBM and the Ponemon Institute, “Cost of a data breach report,” Tech. Rep., 2020. [2] N. Patnaik, J. Hallett, and A. Rashid, “Usability smells: An analysis of developers’ struggle with crypto libraries,” in Fifteenth Symposium on Usable Privacy and Security (SOUPS 2019). Santa Clara, CA: USENIX Association, Aug. 2019. [Online]. Available: https://www.usenix.org/conference/soups2019/presentation/patnaik [3] M. Hazhirpasand, M. Ghafari, S. Krüger, E. Bodden, and O. Nierstrasz, “The impact of developer experience in using Java cryptography,” in 2019 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM). IEEE, 2019, pp. 1–6. [4] M. Hazhirpasand, M. Ghafari, and O. Nierstrasz, “Java cryptography uses in the wild,” in Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM), ser. ESEM ’20. New York, NY, USA: Association for Computing Machinery, 2020. [Online]. Available: https://doi.org/10.1145/3382494.3422166 [5] T. Zhang, G. Upadhyaya, A. Reinhardt, H. Rajan, and M. Kim, “Are code examples on an online q&a forum reliable?: a study of api misuse on stack overflow,” in 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE). IEEE, 2018, pp. 886–896. [6] M. Ghafari, P. Gadient, and O. Nierstrasz, “Security smells in android,” in 2017 IEEE 17th International Working Conference on Source Code Analysis and Manipulation (SCAM), 2017, pp. 121–130. [7] P. Gadient, M. Ghafari, P. Frischknecht, and O. Nierstrasz, “Security code smells in android icc,” Empirical Software Engineering, vol. 24, 2019. [8] S. Krüger, S. Nadi, M. Reif, K. Ali, M. Mezini, E. Bodden, F. Göpfert, F. Günther, C. Weinert, D. Demmler et al., “Cognicrypt: supporting developers in using cryptography,” in 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE). IEEE, 2017, pp. 931–936. [9] S. Rahaman, Y. Xiao, S. Afrose, F. Shaon, K. Tian, M. Frantz, M. Kantarcioglu, and D. D. Yao, “Cryptoguard: High precision detection of cryptographic vulnerabilities in massive-sized java projects,” in Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, ser. CCS ’19. New York, NY, USA: Association for Computing Machinery, 2019, p. 2455–2472. [Online]. Available: https://doi.org/10.1145/3319535.3345659 [10] Y. Tymchuk, M. Ghafari, and O. Nierstrasz, “Jit feedback - what experienced developers like about static analysis,” in 2018 IEEE/ACM 26th International Conference on Program Comprehension (ICPC), 2018, pp. 64–6409. [11] J. Smith, L. N. Q. Do, and E. Murphy-Hill, “Why can’t johnny fix vulnerabilities: A usability evaluation of static analysis tools for security,” in Sixteenth Symposium on Usable Privacy and Security (SOUPS 2020). USENIX Association, Aug. 2020, pp. 221– 238. [Online]. Available: https://www.usenix.org/conference/soups2020/ presentation/smith [12] C. Corrodi, T. Spring, M. Ghafari, and O. Nierstrasz, “Idea: Benchmarking android data leak detection tools,” in Engineering Secure Software and Systems, M. Payer, A. Rashid, and J. M. Such, Eds. Cham: Springer International Publishing, 2018, pp. 116–123. [13] V.-P. Ranganath and J. Mitra, “Are free android app security analysis tools effective in detecting known vulnerabilities?” Empirical Software Engineering, vol. 25, 2020. [14] M. Hazhirpasand, M. Ghafari, and O. Nierstrasz, “Cryptoexplorer: An interactive web platform supporting secure use of cryptography APIs,” arXiv preprint arXiv:2001.00773, 2020. [15] S. Nadi, S. Krüger, M. Mezini, and E. Bodden, “Jumping through hoops: Why do Java developers struggle with cryptography APIs?” in Proceedings of the 38th International Conference on Software Engineering, 2016, pp. 935–946. [16] M. Green and M. Smith, “Developers are not the enemy!: The need for usable security APIs,” IEEE Security & Privacy, vol. 14, no. 5, pp. 40–46, 2016. [17] S. Das, V. Gopal, K. King, and A. Venkatraman, “IV= 0 security: Cryptographic misuse of libraries,” Massachusetts Institute of Technology, Final Rep, vol. 6, 2014. [18] S. Krüger, K. Ali, and E. Bodden, “CognicryptGEN : Generating code for the secure usage of crypto apis,” in Proceedings of the 18th ACM/IEEE International Symposium on Code Generation and Optimization, ser. CGO 2020. New York, NY, USA: Association for Computing Machinery, 2020, p. 185–198. [Online]. Available: https://doi.org/10.1145/3368826.3377905 [19] D. C. Nguyen, D. Wermke, Y. Acar, M. Backes, C. Weir, and S. Fahl, “A stitch in time: Supporting android developers in writingsecure code,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, ser. CCS ’17. New York, NY, USA: Association for Computing Machinery, 2017, p. 1065–1077. [Online]. Available: https://doi.org/10.1145/3133956.3133977 [20] L. Singleton, R. Zhao, M. Song, and H. Siy, “Cryptotutor: Teaching secure coding practices through misuse pattern detection,” in Proceedings of the 21st Annual Conference on Information Technology Education, ser. SIGITE ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 403–408. [Online]. Available: https://doi.org/10.1145/3368308.3415419 [21] L. Piccolboni, G. Di Guglielmo, L. P. Carloni, and S. Sethumadhavan, “Crylogger: Detecting crypto misuses dynamically,” in IEEE Symposium on Security & Privacy (SP) 2021, 2021. [22] P. L. Gorski, Y. Acar, L. Lo Iacono, and S. Fahl, “Listen to developers! a participatory design study on security warnings for cryptographic apis,” in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, ser. CHI ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 1–13. [Online]. Available: https://doi.org/10.1145/3313831.3376142 [23] M. Fowler. (2005) Fluentinterface. [Online]. Available: https://www. martinfowler.com/bliki/FluentInterface.html This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

FluentCrypto: Cryptography in Easy Mode: Conclusion and References

A few days ago, someone launched a DDoS attack on the Internet Archive servers. https://twitter.com/internetarchive/status/1795117949499445554?embedable=true I know, I know, who the heck would stoop to such a despicable act?! This incident made me ponder whether this points to some systemic, conceptual flaw that made it possible in the first place. Ethereum Swarm is a decentralized storage solution. When I first read about the concept in the Book of Swarm, the incentive system seemed a bit strange to me, where we have to pay not only for storage but also for retrieval. The node serving the content receives compensation for the data traffic. It's like browsing the internet or scrolling through Facebook and constantly seeing a little counter in the top right corner of the screen showing how much we need to pay for the content. Wouldn't that be weird? Of course, anyone with a server at a cloud service provider knows very well that bandwidth does cost money. For example, on AWS, you currently have to pay $0.09 for every outgoing GB of data traffic. On the Internet, almost every service is "push" oriented, meaning the service provider has some interest in us consuming their content. This is true even in cases where we think it is in our interest to consume the content. An example of this is when we use Facebook. Users obviously scroll through the Facebook feed for the content, but from a business perspective, the key is the advertisements appearing between the posts, as this is what generates profit. If you're not paying for it, you become the product! This is why we typically receive content for free on the Internet, and companies not only cover the infrastructure costs but also spend a lot of money on marketing to ensure we consume content on their platforms. This, of course, leads to a vicious cycle because users have become accustomed to free content. If a new player enters the market, they too are forced to offer free content to be competitive, leaving the service provider no other option but to monetize the content. If you don’t want to be a product, pay for it! Since Ethereum Swarm is a decentralized system of peer nodes, there is no possibility here to "hide" such infrastructural costs. Ethereum Swarm is honest! In the Swarm network, if you need any content, you have to pay for it. Even if the content itself is free, you still have to pay for the data transfer. This solution, however, brings new opportunities. Ethereum Swarm uses forwarding Kademlia for content delivery. This means that if we request content from a node and that node doesn't have the content, it must fetch it from a neighboring node. In this case, the neighboring node must be paid for the given content. Due to this operation, it is beneficial for nodes to cache popular content. Such content can thus persist in the network even if no one is paying for its storage anymore. Ethereum Swarm is a perfect client-sponsored Content Delivery Network (CDN). Thanks to its decentralization, this system can automatically scale according to demand, while content providers are not forced to bear the infrastructural costs themselves covertly. At this point, we can return to the idea raised at the beginning of the article. For a nonprofit system like the Internet Archive, it would be an ideal solution to entrust content delivery to Ethereum Swarm. On one hand, there would be significant cost savings if data transfer costs were borne by the content consumers instead of the provider (for the consumer, it's just a few cents, while for the provider, with many consumers, it could be thousands of dollars). On the other hand, such a decentralized system cannot be subjected to a DoS attack (only the nodes can be targeted, but attacking the entire system is very difficult). Given the above, an Internet Archive system based on Ethereum Swarm would look like this: crawlers continuously scan the web and upload the content to Swarm. The storage cost would be minimal because the goal is not long-term storage but merely to get the content into the cache. (It's important to keep a minimal storage cost  to prevent spam the caches). If someone needs archived content, they can obtain it from Swarm. If the content is not in the cache, it can be requested from the Internet Archive. This request could also have a minimal fee, providing direct revenue for the Internet Archive. In such cases, the Internet Archive would retrieve the content from deep archive storage and re-upload it to Swarm, where it would become accessible again. In this architecture, the Internet Archive's cost would only be for deep archive storage, which, for example, is about $1 per TB on AWS Glacier. Since Ethereum Swarm would handle the actual content delivery, the users would bear this cost. The ideal solution for mapping content is to use Swarm feeds. Feeds allow mutable content to be stored under a given topic ID while making the history accessible as well. In this case, web URLs would serve as the topic IDs and older versions would be accessible through the feed history. Of course, the Internet Archive is just one of the potential use cases. The main point is: If we use Ethereum Swarm as a content delivery network, there is no longer a need to monetize content or hide the infrastructural costs. For example, we can build a social network without ads and without the need to sell user data (I covered this in a full article), stream multimedia content at minimal cost, or operate large data storage for training artificial intelligence algorithms at minimal cost. If you are more interested in how Ethereum Swarm works, read my article about the topic: What's the Difference Between IPFS and Ethereum Swarm? A few days ago, someone launched a DDoS attack on the Internet Archive servers. DDoS https://twitter.com/internetarchive/status/1795117949499445554?embedable=true https://twitter.com/internetarchive/status/1795117949499445554?embedable=true I know, I know, who the heck would stoop to such a despicable act?! This incident made me ponder whether this points to some systemic, conceptual flaw that made it possible in the first place. Ethereum Swarm is a decentralized storage solution. When I first read about the concept in the Book of Swarm , the incentive system seemed a bit strange to me, where we have to pay not only for storage but also for retrieval. The node serving the content receives compensation for the data traffic. It's like browsing the internet or scrolling through Facebook and constantly seeing a little counter in the top right corner of the screen showing how much we need to pay for the content. Wouldn't that be weird? Of course, anyone with a server at a cloud service provider knows very well that bandwidth does cost money. For example, on AWS, you currently have to pay $0.09 for every outgoing GB of data traffic. Ethereum Swarm Book of Swarm Facebook On the Internet, almost every service is "push" oriented, meaning the service provider has some interest in us consuming their content. This is true even in cases where we think it is in our interest to consume the content. An example of this is when we use Facebook. Users obviously scroll through the Facebook feed for the content, but from a business perspective, the key is the advertisements appearing between the posts, as this is what generates profit. If you're not paying for it, you become the product! If you're not paying for it, you become the product! This is why we typically receive content for free on the Internet, and companies not only cover the infrastructure costs but also spend a lot of money on marketing to ensure we consume content on their platforms. This, of course, leads to a vicious cycle because users have become accustomed to free content. If a new player enters the market, they too are forced to offer free content to be competitive, leaving the service provider no other option but to monetize the content. If you don’t want to be a product, pay for it! Since Ethereum Swarm is a decentralized system of peer nodes, there is no possibility here to "hide" such infrastructural costs. Ethereum Swarm is honest! Ethereum Swarm is honest! In the Swarm network, if you need any content, you have to pay for it. Even if the content itself is free, you still have to pay for the data transfer. This solution, however, brings new opportunities. Ethereum Swarm uses forwarding Kademlia for content delivery. This means that if we request content from a node and that node doesn't have the content, it must fetch it from a neighboring node. In this case, the neighboring node must be paid for the given content. Due to this operation, it is beneficial for nodes to cache popular content. Such content can thus persist in the network even if no one is paying for its storage anymore. Ethereum Swarm is a perfect client-sponsored Content Delivery Network (CDN). Ethereum Swarm is a perfect client-sponsored Content Delivery Network (CDN). Thanks to its decentralization, this system can automatically scale according to demand, while content providers are not forced to bear the infrastructural costs themselves covertly. At this point, we can return to the idea raised at the beginning of the article. For a nonprofit system like the Internet Archive, it would be an ideal solution to entrust content delivery to Ethereum Swarm. On one hand, there would be significant cost savings if data transfer costs were borne by the content consumers instead of the provider (for the consumer, it's just a few cents, while for the provider, with many consumers, it could be thousands of dollars). On the other hand, such a decentralized system cannot be subjected to a DoS attack (only the nodes can be targeted, but attacking the entire system is very difficult). Given the above, an Internet Archive system based on Ethereum Swarm would look like this: crawlers continuously scan the web and upload the content to Swarm. The storage cost would be minimal because the goal is not long-term storage but merely to get the content into the cache. (It's important to keep a minimal storage cost  to prevent spam the caches). If someone needs archived content, they can obtain it from Swarm. If the content is not in the cache, it can be requested from the Internet Archive. This request could also have a minimal fee, providing direct revenue for the Internet Archive. In such cases, the Internet Archive would retrieve the content from deep archive storage and re-upload it to Swarm, where it would become accessible again. In this architecture, the Internet Archive's cost would only be for deep archive storage, which, for example, is about $1 per TB on AWS Glacier. Since Ethereum Swarm would handle the actual content delivery, the users would bear this cost. The ideal solution for mapping content is to use Swarm feeds . Feeds allow mutable content to be stored under a given topic ID while making the history accessible as well. In this case, web URLs would serve as the topic IDs and older versions would be accessible through the feed history. Swarm feeds Of course, the Internet Archive is just one of the potential use cases. The main point is: If we use Ethereum Swarm as a content delivery network, there is no longer a need to monetize content or hide the infrastructural costs. If we use Ethereum Swarm as a content delivery network, there is no longer a need to monetize content or hide the infrastructural costs. For example, we can build a social network without ads and without the need to sell user data (I covered this in a full article ), stream multimedia content at minimal cost, or operate large data storage for training artificial intelligence algorithms at minimal cost. full article If you are more interested in how Ethereum Swarm works, read my article about the topic: What's the Difference Between IPFS and Ethereum Swarm? What's the Difference Between IPFS and Ethereum Swarm?

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