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Howdy Hackers, The wait is over—we’re thrilled to announce the finalists and winners of the AI Writing Contest, presented by Bright Data and HackerNoon. This contest, which kicked off on September 2, 2024, put the spotlight on a key player in the world of Artificial Intelligence—data. Writers explored how AI training data is collected, how it shapes performance, and the best tools for sourcing high-quality datasets. With AI being one of the hottest topics in tech, it was no surprise that nearly 300 stories were published under the #ai tag for the duration of the contest, racking up 290K+ page views and over 450 hours of reading time. After a two-week extension, the contest officially wrapped up as 2024 came to a close. Our editors carefully reviewed all submissions and narrowed them down to 10 finalists—all in the running for a share of the $2,500 prize pool as follows: And now, it’s time for the big reveal… 🏆 New to HackerNoon writing contests?
Find out about active contests, participation guidelines, and more at contests.hackernoon.com Let’s meet our finalists! AI Writing Contest: Finalists How to Utilize Bright Data for Sentiment Analysis by @adnanaleeza Beyond Text Embeddings: Addressing the Gaps in RAG Applications for Structured Data Queries by @neo4j How I Scraped YouTube Comments with Bright Data to Understand Customer Sentiment by @ayinketh Can We Train AI to Become More Human-Like? by @ssukhpinder How Bright Data Simplifies Web Scraping/Data Collection for AI Training by @diamondolmd Data Centric AI? Yes, but… by @docligot Let's Build a Free Web Scraping Tool That Combines Proxies and AI for Data Analysis by @aviatorscode2 Is AI Progress Slowing? The Scaling Debate OpenAI Doesn’t Want to Have by @dosseyrichards Weather Image Recognition with AI: Dataset Labeling, Model Training, and Real-world Usecases by @induction The One-Shot Generalization Paradox: Why Generative AI Struggles With New Information by @pawarashishanil From making AI more human-like to scraping YouTube comments for real insights, these finalists are tackling some of the most exciting challenges in AI today. Whether you're interested in data collection techniques, sentiment analysis, or the future of AI scaling, there's a story here for you. Jump in, explore their work, and follow their journey on HackerNoon! After collating votes from our editorial team, we're thrilled to announce the winners of the AI Writing Contest! The Winners of the AI Writing Contest Are… Best Bright Data Story https://hackernoon.com/how-i-scraped-youtube-comments-with-bright-data-to-understand-customer-sentiment?embedable=true TLDR: Overcome the challenges with traditional web scraping process, with the use of Bright Data as an all-in-one efficient tool designed to tackle CAPTCHAs, Honeypot, Rate limiting, Block request, and IP blocks faced during data retrieval. Along with Python libraries like Playwright, to scrap data from YouTube. Congratulations @ayinketh, you’ve won $1000. Best AI Story https://hackernoon.com/the-one-shot-generalization-paradox-why-generative-ai-struggles-with-new-information?embedable=true Generative AI, like GPT-4, is extraordinary at generating text based on vast amounts of data but fails when confronted with new, unfamiliar information. This “One-Shot Generalization Paradox” demonstrates that despite their power, current AI models rely on pre-existing patterns and struggle with novel tasks. We explore the reasons behind this (from transformer architecture limitations to dense vector representations) and look at promising solutions like meta-learning and neuro-symbolic architectures to enable true generalization in AI. Congratulations @pawarashishanil, you’ve won $1000 Best Web Scraping Story https://hackernoon.com/how-bright-data-ai-made-web-data-scrapingcollection-effortless-the-challenges-before-bright-data-ai-solutions?embedable=true As a business owner, student, drop shipper, or corporate worker, you must have faced the risk of going out of business if you lack access to some (if not the most) crucial piece of data. But fear not; there is a solution that helps you unlock the full potential of web data scraping/collection, which I will show you in this article. Congratulations @diamondolmd, you’ve won $500 Congratulations once again to all our winners. Thank you for your hard work! How to Claim Your HackerNoon Writing Contest Prize Contact yes-reply@hackernoon.com and sidra@hackernoon.com and share the email ID/and or wallet address attached to the winning hackernoon account.
We will validate your claim and share a form requesting your details for reward distribution.
You will receive your winnings in 2-4 weeks after completing the form. Please note that you must contact us within 60 days after the winners’ announcement date. Ready to be a winner? Discover active writing contests on HackerNoon and learn how you can earn a spot on our next winners' list at: contests.hackernoon.com Howdy Hackers, The wait is over—we’re thrilled to announce the finalists and winners of the AI Writing Contest , presented by Bright Data and HackerNoon. AI Writing Contest Bright Data This contest, which kicked off on September 2, 2024, put the spotlight on a key player in the world of Artificial Intelligence— data . Writers explored how AI training data is collected, how it shapes performance, and the best tools for sourcing high-quality datasets. data With AI being one of the hottest topics in tech, it was no surprise that nearly 300 stories were published under the #ai tag for the duration of the contest, racking up 290K+ page views and over 450 hours of reading time . After a two-week extension, the contest officially wrapped up as 2024 came to a close. 290K+ page views 450 hours of reading time Our editors carefully reviewed all submissions and narrowed them down to 10 finalists —all in the running for a share of the $2,500 prize pool as follows: finalists $2,500 prize pool And now, it’s time for the big reveal… 🏆 And now, it’s time for the big reveal… 🏆 New to HackerNoon writing contests? Find out about active contests, participation guidelines, and more at contests.hackernoon.com New to HackerNoon writing contests? New to HackerNoon writing contests? New to HackerNoon writing contests? Find out about active contests, participation guidelines, and more at contests.hackernoon.com Find out about active contests, participation guidelines, and more at contests.hackernoon.com contests.hackernoon.com contests.hackernoon.com contests.hackernoon.com Let’s meet our finalists! AI Writing Contest: Finalists How to Utilize Bright Data for Sentiment Analysis by @adnanaleeza How to Utilize Bright Data for Sentiment Analysis by @adnanaleeza How to Utilize Bright Data for Sentiment Analysis @adnanaleeza Beyond Text Embeddings: Addressing the Gaps in RAG Applications for Structured Data Queries by @neo4j Beyond Text Embeddings: Addressing the Gaps in RAG Applications for Structured Data Queries by @neo4j Beyond Text Embeddings: Addressing the Gaps in RAG Applications for Structured Data Queries @neo4j How I Scraped YouTube Comments with Bright Data to Understand Customer Sentiment by @ayinketh How I Scraped YouTube Comments with Bright Data to Understand Customer Sentiment by @ayinketh How I Scraped YouTube Comments with Bright Data to Understand Customer Sentiment @ayinketh Can We Train AI to Become More Human-Like? by @ssukhpinder Can We Train AI to Become More Human-Like? by @ssukhpinder Can We Train AI to Become More Human-Like? @ssukhpinder How Bright Data Simplifies Web Scraping/Data Collection for AI Training by @diamondolmd How Bright Data Simplifies Web Scraping/Data Collection for AI Training by @diamondolmd How Bright Data Simplifies Web Scraping/Data Collection for AI Training @diamondolmd Data Centric AI? Yes, but… by @docligot Data Centric AI? Yes, but… by @docligot Data Centric AI? Yes, but… @docligot Let's Build a Free Web Scraping Tool That Combines Proxies and AI for Data Analysis by @aviatorscode2 Let's Build a Free Web Scraping Tool That Combines Proxies and AI for Data Analysis by @aviatorscode2 Let's Build a Free Web Scraping Tool That Combines Proxies and AI for Data Analysis @aviatorscode2 Is AI Progress Slowing? The Scaling Debate OpenAI Doesn’t Want to Have by @dosseyrichards Is AI Progress Slowing? The Scaling Debate OpenAI Doesn’t Want to Have by @dosseyrichards Is AI Progress Slowing? The Scaling Debate OpenAI Doesn’t Want to Have @dosseyrichards Weather Image Recognition with AI: Dataset Labeling, Model Training, and Real-world Usecases by @induction Weather Image Recognition with AI: Dataset Labeling, Model Training, and Real-world Usecases by @induction Weather Image Recognition with AI: Dataset Labeling, Model Training, and Real-world Usecases @induction The One-Shot Generalization Paradox: Why Generative AI Struggles With New Information by @pawarashishanil The One-Shot Generalization Paradox: Why Generative AI Struggles With New Information by @pawarashishanil The One-Shot Generalization Paradox: Why Generative AI Struggles With New Information @pawarashishanil From making AI more human-like to scraping YouTube comments for real insights, these finalists are tackling some of the most exciting challenges in AI today. Whether you're interested in data collection techniques, sentiment analysis, or the future of AI scaling, there's a story here for you. Jump in, explore their work, and follow their journey on HackerNoon! From making AI more human-like to scraping YouTube comments for real insights, these finalists are tackling some of the most exciting challenges in AI today. Whether you're interested in data collection techniques, sentiment analysis, or the future of AI scaling, there's a story here for you. Jump in, explore their work, and follow their journey on HackerNoon! After collating votes from our editorial team, we're thrilled to announce the winners of the AI Writing Contest! After collating votes from our editorial team, we're thrilled to announce the winners of the AI Writing Contest! After collating votes from our editorial team, we're thrilled to announce the winners of the AI Writing Contest! The Winners of the AI Writing Contest Are… Best Bright Data Story https://hackernoon.com/how-i-scraped-youtube-comments-with-bright-data-to-understand-customer-sentiment?embedable=true https://hackernoon.com/how-i-scraped-youtube-comments-with-bright-data-to-understand-customer-sentiment?embedable=true TLDR: Overcome the challenges with traditional web scraping process, with the use of Bright Data as an all-in-one efficient tool designed to tackle CAPTCHAs, Honeypot, Rate limiting, Block request, and IP blocks faced during data retrieval. Along with Python libraries like Playwright, to scrap data from YouTube. TLDR: Overcome the challenges with traditional web scraping process, with the use of Bright Data as an all-in-one efficient tool designed to tackle CAPTCHAs, Honeypot, Rate limiting, Block request, and IP blocks faced during data retrieval. Along with Python libraries like Playwright, to scrap data from YouTube. Congratulations @ayinketh, you’ve won $1000. Congratulations @ayinketh, you’ve won $1000. Congratulations @ayinketh , you’ve won $1000. @ayinketh Best AI Story https://hackernoon.com/the-one-shot-generalization-paradox-why-generative-ai-struggles-with-new-information?embedable=true https://hackernoon.com/the-one-shot-generalization-paradox-why-generative-ai-struggles-with-new-information?embedable=true Generative AI, like GPT-4, is extraordinary at generating text based on vast amounts of data but fails when confronted with new, unfamiliar information. This “One-Shot Generalization Paradox” demonstrates that despite their power, current AI models rely on pre-existing patterns and struggle with novel tasks. We explore the reasons behind this (from transformer architecture limitations to dense vector representations) and look at promising solutions like meta-learning and neuro-symbolic architectures to enable true generalization in AI. Generative AI, like GPT-4, is extraordinary at generating text based on vast amounts of data but fails when confronted with new, unfamiliar information. This “One-Shot Generalization Paradox” demonstrates that despite their power, current AI models rely on pre-existing patterns and struggle with novel tasks. We explore the reasons behind this (from transformer architecture limitations to dense vector representations) and look at promising solutions like meta-learning and neuro-symbolic architectures to enable true generalization in AI. Congratulations @pawarashishanil, you’ve won $1000 Congratulations @pawarashishanil, you’ve won $1000 Congratulations @pawarashishanil , you’ve won $1000 @pawarashishanil Best Web Scraping Story https://hackernoon.com/how-bright-data-ai-made-web-data-scrapingcollection-effortless-the-challenges-before-bright-data-ai-solutions?embedable=true https://hackernoon.com/how-bright-data-ai-made-web-data-scrapingcollection-effortless-the-challenges-before-bright-data-ai-solutions?embedable=true As a business owner, student, drop shipper, or corporate worker, you must have faced the risk of going out of business if you lack access to some (if not the most) crucial piece of data. But fear not; there is a solution that helps you unlock the full potential of web data scraping/collection, which I will show you in this article. As a business owner, student, drop shipper, or corporate worker, you must have faced the risk of going out of business if you lack access to some (if not the most) crucial piece of data. But fear not; there is a solution that helps you unlock the full potential of web data scraping/collection, which I will show you in this article. Congratulations @diamondolmd, you’ve won $500 Congratulations @diamondolmd, you’ve won $500 Congratulations @diamondolmd , you’ve won $500 @diamondolmd Congratulations once again to all our winners. Thank you for your hard work! Congratulations once again to all our winners. Thank you for your hard work! How to Claim Your HackerNoon Writing Contest Prize Contact yes-reply@hackernoon.com and sidra@hackernoon.com and share the email ID/and or wallet address attached to the winning hackernoon account. We will validate your claim and share a form requesting your details for reward distribution. You will receive your winnings in 2-4 weeks after completing the form. Contact yes-reply@hackernoon.com and sidra@hackernoon.com and share the email ID/and or wallet address attached to the winning hackernoon account. yes-reply@hackernoon.com sidra@hackernoon.com We will validate your claim and share a form requesting your details for reward distribution. You will receive your winnings in 2-4 weeks after completing the form. Please note that you must contact us within 60 days after the winners’ announcement date. Please note that you must contact us within 60 days after the winners’ announcement date. Ready to be a winner? Ready to be a winner? Discover active writing contests on HackerNoon and learn how you can earn a spot on our next winners' list at: contests.hackernoon.com Discover active writing contests on HackerNoon and learn how you can earn a spot on our next winners' list at: contests.hackernoon.com Discover active writing contests on HackerNoon and learn how you can earn a spot on our next winners' list at: contests.hackernoon.com contests.hackernoon.com contests.hackernoon.com

The AI Writing Contest by Bright Data & HackerNoon: Results Announcement 🎉

How I Scraped YouTube Comments with Bright Data to Understand Customer Sentiment

The Role of Data in Machine Learning In today's AI-driven landscape, machine learning, AI, and chatbots are transforming industries at an unprecedented pace. More businesses are looking to integrate AI into their operations, but how these systems are built often gets overlooked. The main ingredient? Data. A machine learning model is only as good as the data it’s trained on. But where does all this data come from? A significant portion of the data we rely on is publicly available, often found in social media posts, user reviews, and other online content. For sentiment analysis, one of the richest sources of data is Twitter, which continuously streams real-time user-generated content. The challenge, however, lies in how to collect this data effectively and cleanly. The Problem with Traditional Twitter Data Collection When I began working on a sentiment analysis project using Twitter data, I initially considered using the Twitter API. However, I quickly encountered a significant limitation: the free-tier API only provides access to tweets from the previous seven days. For comprehensive sentiment analysis, especially for trending topics or political events spanning weeks or months, this restriction severely limits the scope and quality of the data available. While upgrading to a higher API tier was an option, the cost was prohibitive for a small-scale project like mine. This left me with two paths: attempt to collect the data manually through traditional scraping (which came with its own set of challenges like CAPTCHAs, redundancies, and incomplete records) or find an alternative solution. That's where Bright Data comes into play, solving these issues seamlessly by offering clean, scalable, and historical Twitter datasets. Instead of being confined by API limitations, I could access diverse, high-quality data for my project without the usual hassles. Step-by-Step Guide to Using Bright Data’s Prebuilt Twitter Dataset In this article, I’ll walk you through how you can leverage Bright Data’s Marketplace to acquire high-quality Twitter data for sentiment analysis and share my experience. Step 1: Sign Up and Access the Bright Data Platform Head to the Bright Data website and create a free account to start. Once logged in, you'll be directed to the Data Marketplace, where various prebuilt datasets can be accessed, including those related to Twitter. Step 2: Search for the Relevant Dataset Once you select your dataset, you can download it directly to your local machine or access it via API. The quick process allows you to start working with high-quality data almost immediately. I used the Twitter-posts data set Step 3: Load and Clean the Data The raw Twitter data typically contains a lot of metadata that may not be relevant for sentiment analysis, such as user information, image URLs, and other non-textual data. We'll focus on the text of the tweets, as that is where the sentiment analysis will be applied, along with relevant engagement metrics like reposts and likes. import pandas as pd
df = pd.read_csv("path_to_your_dataset.csv")
df.head() The next step is to clean the data by handling missing values and removing irrelevant columns. df_clean = df[['user_posted', 'description', 'date_posted', 'reposts', 'likes']]
df_clean = df_clean.dropna(subset=['description'])
df_clean = df_clean.drop_duplicates(subset=['description'])
df_clean.head() Step 4: Sentiment Analysis — Extracting Meaning from Tweets Now that we have cleaned the data and have the relevant text, it’s time to dive into the sentiment analysis. Sentiment analysis means determining the emotional tone behind words—whether the tweet is positive, negative, or neutral. I’ve used a popular library called VADER (Valence Aware Dictionary and Sentiment Reasoner), specifically designed for social media text. It’s great at handling informal language like slang and emojis. The code snippet below performs sentiment analysis using VADER from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer

analyzer = SentimentIntensityAnalyzer()

def get_sentiment(text):
    sentiment = analyzer.polarity_scores(text)
    return sentiment['compound']  

df_clean['sentiment'] = df_clean['description'].apply(get_sentiment)

df_clean.head() Step 5: Analyzing Sentiment Distribution Once sentiment scores are calculated, we can analyze the distribution to see how positive or negative the tweets are on average. This can provide insights into the overall public mood about a particular topic or event. We can also visualize the sentiment distribution using a histogram or wordcloud to identify key emotions. This plot will give you a good sense of the sentiment spread, whether it's leaning toward positive, negative, or neutral. In this case, There’s a high frequency of neutral tweets. Step 6: Correlating Sentiment with Engagement Now that we have the sentiment scores for each tweet, the next step is to explore whether there's any correlation between sentiment and engagement metrics like likes and reposts. In social media analytics, one might expect that more positive sentiment could lead to higher engagement (more likes, more reposts). However, is that truly the case? To find out, we can calculate the correlation between the sentiment scores and engagement metrics, the results are: Correlation between sentiment and likes: 0.022806738610786123 Correlation between sentiment and reposts: 0.008885789875330416 Given the weak correlation values from my data analysis, we see that there isn't a strong connection between sentiment and engagement. The correlation between sentiment and likes was 0.02, and for sentiment and reposts, it was only 0.008. These values suggest that engagement metrics like likes and reposts are not significantly influenced by the sentiment of tweets. Rethinking the Role of Sentiment in Engagement The weak correlation between sentiment and engagement shows that there are other factors at play. While sentiment analysis helps us understand public opinion, engagement is likely driven by additional factors, such as: Content relevance: Trending topics tend to generate more engagement regardless of sentiment.
User influence: Popular accounts often receive higher engagement, irrespective of their tweet’s sentiment.
Timing: Tweets posted during live events or peak times are more likely to get attention. While sentiment analysis is a valuable tool for understanding how people feel about a topic, it’s not always a reliable predictor of engagement. To gain deeper insights into user behavior, it’s essential to consider other factors such as the content’s timing, relevance, and the user’s influence. The Role of Data in Machine Learning In today's AI-driven landscape, machine learning, AI, and chatbots are transforming industries at an unprecedented pace. More businesses are looking to integrate AI into their operations, but how these systems are built often gets overlooked. The main ingredient? Data. A machine learning model is only as good as the data it’s trained on. But where does all this data come from? A significant portion of the data we rely on is publicly available, often found in social media posts, user reviews, and other online content. For sentiment analysis , one of the richest sources of data is Twitter , which continuously streams real-time user-generated content. The challenge, however, lies in how to collect this data effectively and cleanly. sentiment analysis Twitter The Problem with Traditional Twitter Data Collection When I began working on a sentiment analysis project using Twitter data, I initially considered using the Twitter API. However, I quickly encountered a significant limitation: the free-tier API only provides access to tweets from the previous seven days. For comprehensive sentiment analysis, especially for trending topics or political events spanning weeks or months, this restriction severely limits the scope and quality of the data available. While upgrading to a higher API tier was an option, the cost was prohibitive for a small-scale project like mine. This left me with two paths: attempt to collect the data manually through traditional scraping (which came with its own set of challenges like CAPTCHAs, redundancies, and incomplete records) or find an alternative solution. That's where Bright Data comes into play, solving these issues seamlessly by offering clean, scalable, and historical Twitter datasets. Instead of being confined by API limitations, I could access diverse, high-quality data for my project without the usual hassles. Step-by-Step Guide to Using Bright Data’s Prebuilt Twitter Dataset In this article, I’ll walk you through how you can leverage Bright Data’s Marketplace to acquire high-quality Twitter data for sentiment analysis and share my experience. Step 1: Sign Up and Access the Bright Data Platform Head to the Bright Data website and create a free account to start. Once logged in, you'll be directed to the Data Marketplace, where various prebuilt datasets can be accessed, including those related to Twitter. Step 2: Search for the Relevant Dataset Once you select your dataset, you can download it directly to your local machine or access it via API. The quick process allows you to start working with high-quality data almost immediately. I used the Twitter-posts data set Step 3: Load and Clean the Data The raw Twitter data typically contains a lot of metadata that may not be relevant for sentiment analysis, such as user information, image URLs, and other non-textual data. We'll focus on the text of the tweets, as that is where the sentiment analysis will be applied, along with relevant engagement metrics like reposts and likes. import pandas as pd
df = pd.read_csv("path_to_your_dataset.csv")
df.head() import pandas as pd
df = pd.read_csv("path_to_your_dataset.csv")
df.head() The next step is to clean the data by handling missing values and removing irrelevant columns. df_clean = df[['user_posted', 'description', 'date_posted', 'reposts', 'likes']]
df_clean = df_clean.dropna(subset=['description'])
df_clean = df_clean.drop_duplicates(subset=['description'])
df_clean.head() df_clean = df[['user_posted', 'description', 'date_posted', 'reposts', 'likes']]
df_clean = df_clean.dropna(subset=['description'])
df_clean = df_clean.drop_duplicates(subset=['description'])
df_clean.head() Step 4: Sentiment Analysis — Extracting Meaning from Tweets Now that we have cleaned the data and have the relevant text, it’s time to dive into the sentiment analysis . Sentiment analysis means determining the emotional tone behind words—whether the tweet is positive, negative, or neutral. sentiment analysis I’ve used a popular library called VADER (Valence Aware Dictionary and Sentiment Reasoner), specifically designed for social media text. It’s great at handling informal language like slang and emojis. VADER The code snippet below performs sentiment analysis using VADER from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer

analyzer = SentimentIntensityAnalyzer()

def get_sentiment(text):
    sentiment = analyzer.polarity_scores(text)
    return sentiment['compound']  

df_clean['sentiment'] = df_clean['description'].apply(get_sentiment)

df_clean.head() from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer

analyzer = SentimentIntensityAnalyzer()

def get_sentiment(text):
    sentiment = analyzer.polarity_scores(text)
    return sentiment['compound']  

df_clean['sentiment'] = df_clean['description'].apply(get_sentiment)

df_clean.head() Step 5: Analyzing Sentiment Distribution Once sentiment scores are calculated, we can analyze the distribution to see how positive or negative the tweets are on average. This can provide insights into the overall public mood about a particular topic or event. We can also visualize the sentiment distribution using a histogram or wordcloud to identify key emotions. histogram wordcloud This plot will give you a good sense of the sentiment spread, whether it's leaning toward positive, negative, or neutral. In this case, There’s a high frequency of neutral tweets. Step 6: Correlating Sentiment with Engagement Now that we have the sentiment scores for each tweet, the next step is to explore whether there's any correlation between sentiment and engagement metrics like likes and reposts. In social media analytics, one might expect that more positive sentiment could lead to higher engagement (more likes, more reposts). However, is that truly the case? To find out, we can calculate the correlation between the sentiment scores and engagement metrics, the results are: Correlation between sentiment and likes: 0.022806738610786123 Correlation between sentiment and likes: 0.022806738610786123 Correlation between sentiment and reposts: 0.008885789875330416 Correlation between sentiment and reposts: 0.008885789875330416 Given the weak correlation values from my data analysis, we see that there isn't a strong connection between sentiment and engagement. The correlation between sentiment and likes was 0.02, and for sentiment and reposts, it was only 0.008. These values suggest that engagement metrics like likes and reposts are not significantly influenced by the sentiment of tweets . engagement metrics like likes and reposts are not significantly influenced by the sentiment of tweets Rethinking the Role of Sentiment in Engagement The weak correlation between sentiment and engagement shows that there are other factors at play. While sentiment analysis helps us understand public opinion, engagement is likely driven by additional factors, such as: Content relevance: Trending topics tend to generate more engagement regardless of sentiment. User influence: Popular accounts often receive higher engagement, irrespective of their tweet’s sentiment. Timing: Tweets posted during live events or peak times are more likely to get attention. Content relevance : Trending topics tend to generate more engagement regardless of sentiment. Content relevance User influence : Popular accounts often receive higher engagement, irrespective of their tweet’s sentiment. User influence Timing : Tweets posted during live events or peak times are more likely to get attention. Timing While sentiment analysis is a valuable tool for understanding how people feel about a topic, it’s not always a reliable predictor of engagement. To gain deeper insights into user behavior, it’s essential to consider other factors such as the content’s timing, relevance, and the user’s influence.

How to Utilize Bright Data for Sentiment Analysis 

كيفية الاستفادة من البيانات الذكية لتحليل المشاعر

Com utilitzar dades brillants per a l'anàlisi de sentiments 

Sådan bruges lyse data til sentimentanalyse 

Cómo utilizar datos brillantes para el análisis de sentimientos

نحوه استفاده از داده های روشن برای تحلیل احساسات 

Ki jan yo itilize done ki klere pou analiz santiman 

感情分析にブライトデータを活用する方法

Мэдрэмжийн шинжилгээнд тод өгөгдлийг хэрхэн ашиглах вэ 

د احساس تحلیل لپاره روښانه ډیټا څنګه وکاروئ 

Как использовать яркие данные для анализа настроений

Sida Loo Isticmaalo Xogta Iftiiminaysa ee Falanqaynta Dareenka 

Ndlela Yo Tirhisa Data Yo Vonakala Eka Nxopaxopo Wa Mintlhaveko 

There is a popular saying that: Data is the new gold, and the web (internet) is the biggest gold mine. As a business owner, student, drop shipper, or corporate worker, you must have faced the risk of going out of business if you lack access to some (if not the most) crucial piece of data. But fear not; there is a solution that helps you unlock the full potential of web data scraping/collection, which I will show you in this article. Why Do We Collect Data: The Essence of Data Scraping/Collection Before I dive into the challenges faced with data scraping/collection and the wonders of Bright Data solutions, it is important to understand the essence of data scraping in this digital age that we are in. Data scraping is a powerful technique that efficiently navigates the internet to extract specific valuable informations, acting like a personal assistant for data retrieval. Whether you seek to analyze market trends and performance, track competitor prices, or simply satisfy your thirst for knowledge, data scraping is the key to making your life easier. Challenges With Data Scraping: The Limitations Before Bright Data Solutions Like an adventure, data scraping comes with its share of challenges and obstacles. One significant hurdle is the risk of websites feeling under attack when excessive data is scraped too quickly. To counter such attempts, websites deploy defensive measures, such as CAPTCHAs, to determine whether the user is a human or a computer. If the volume of requests persists, the website may resort to blocking users, hindering or halting data scraping altogether. To avoid such unfavorable situations, a more versatile and intelligent solution is required. Bright Data: The Ultimate Solution for Web Data Collection Bright Data comes as the ultimate solution for web data collection tailored specifically for businesses. With its automated online data collection platform, Bright Data offers unlimited access to the vast wisdom of the web without exposing you to any risks. How Bright Data Works The magic behind Bright Data lies in its web unlocker: a unique P2P network composed of millions of genuine residential and mobile IPs from across the globe. These IPs serve as secret identities, making your data requests appear as if they come from real people, making it highly unlikely for websites to block them. Why I Am Using Bright Data: The Power of Using Bright Data 1. Versatility and Ease of Use Bright Data shines with its user-friendly approach. To start scraping vast amounts of data, all you need is a scraping browser — an automated browser that allows you to interact with websites just like a human user. It efficiently handles challenges such as CAPTCHAs, fingerprints, and retries while mimicking the behavior of a regular user. Compatible with popular automation tools like Puppeteer and Playwright and programming languages like Python and Node.js, the scraping browser makes data collection seamless and efficient. 2. Ready-to-Use Data Sets If you ever need ready-to-use data, Bright Data’s data sets tool provides pre-collected and structured data from various domains, including popular platforms like LinkedIn, Amazon, Crunchbase, Indeed, and Twitter. Regularly updated, these comprehensive data sets offer you a treasure trove of information at your fingertips. Practical Examples of What I Have Used Bright Data for 1. E-Commerce: Monitoring Competitor Prices and Enriching Product Catalogs In the world of e-commerce, web data plays a crucial role in monitoring competitor prices, ensuring product availability, and understanding customer reviews. For instance, a global Beauty and Personal Care Marketplace efficiently uses web data to check competitor prices in real-time, enabling them to adjust pricing strategies and optimize promotions on the fly, enhancing their market competitiveness. 2. Social Media: Analyzing Consumer Sentiment and Trends Social media platforms offer a gold mine of user-generated content, providing valuable insights into consumer behavior, trends, and sentiment. A global digital marketing agency effectively analyzes consumer sentiment towards their client’s brands using web data from various social media platforms. This deeper understanding empowers them to refine marketing strategies and achieve better results. In Conclusion: The Bright Future of Data Collection As I conclude with Bright Data's powerful web data collection solutions, one thing becomes very clear; data scraping is a game-changer in today’s digital landscape. Whether you are a business owner, entrepreneur, or data professional, harnessing the power of web data can unlock countless opportunities for growth and success. Traditional manual data collection methods are slow, labor-intensive, and error-prone, rendering them ineffective and inefficient in the face of today’s data-driven world. By automating the process with Bright Data, you can extract specific and relevant information from web pages effortlessly, propelling your business to new heights. So, if you find yourself in need of vast amounts of data, fast and easy, look no further than Bright Data. Embrace the power of web data, and let it guide you to new horizons of success in this ever-evolving digital landscape. There is a popular saying that: Data is the new gold, and the web (internet) is the biggest gold mine. Data is the new gold, and the web (internet) is the biggest gold mine. As a business owner, student, drop shipper, or corporate worker, you must have faced the risk of going out of business if you lack access to some (if not the most) crucial piece of data. But fear not; there is a solution that helps you unlock the full potential of web data scraping/collection, which I will show you in this article. (if not the most) Why Do We Collect Data: The Essence of Data Scraping/Collection Before I dive into the challenges faced with data scraping/collection and the wonders of Bright Data solutions, it is important to understand the essence of data scraping in this digital age that we are in. Bright Data Data scraping is a powerful technique that efficiently navigates the internet to extract specific valuable informations, acting like a personal assistant for data retrieval. Whether you seek to analyze market trends and performance, track competitor prices, or simply satisfy your thirst for knowledge, data scraping is the key to making your life easier. Challenges With Data Scraping: The Limitations Before Bright Data Solutions Bright Data Like an adventure, data scraping comes with its share of challenges and obstacles. One significant hurdle is the risk of websites feeling under attack when excessive data is scraped too quickly. To counter such attempts, websites deploy defensive measures, such as CAPTCHAs, to determine whether the user is a human or a computer. If the volume of requests persists, the website may resort to blocking users, hindering or halting data scraping altogether. To avoid such unfavorable situations, a more versatile and intelligent solution is required. Bright Data: The Ultimate Solution for Web Data Collection Bright Data comes as the ultimate solution for web data collection tailored specifically for businesses. With its automated online data collection platform, Bright Data offers unlimited access to the vast wisdom of the web without exposing you to any risks. Bright Data How Bright Data Works How Bright Data Works The magic behind Bright Data lies in its web unlocker: a unique P2P network composed of millions of genuine residential and mobile IPs from across the globe. These IPs serve as secret identities, making your data requests appear as if they come from real people, making it highly unlikely for websites to block them. Why I Am Using Bright Data: The Power of Using Bright Data 1. Versatility and Ease of Use 1. Versatility and Ease of Use Bright Data shines with its user-friendly approach. To start scraping vast amounts of data, all you need is a scraping browser — an automated browser that allows you to interact with websites just like a human user. It efficiently handles challenges such as CAPTCHAs, fingerprints, and retries while mimicking the behavior of a regular user. Compatible with popular automation tools like Puppeteer and Playwright and programming languages like Python and Node.js, the scraping browser makes data collection seamless and efficient. 2. Ready-to-Use Data Sets 2. Ready-to-Use Data Sets If you ever need ready-to-use data, Bright Data’s data sets tool provides pre-collected and structured data from various domains, including popular platforms like LinkedIn, Amazon, Crunchbase, Indeed, and Twitter. Regularly updated, these comprehensive data sets offer you a treasure trove of information at your fingertips. Practical Examples of What I Have Used Bright Data for 1. E-Commerce: Monitoring Competitor Prices and Enriching Product Catalogs 1. E-Commerce: Monitoring Competitor Prices and Enriching Product Catalogs In the world of e-commerce, web data plays a crucial role in monitoring competitor prices, ensuring product availability, and understanding customer reviews. For instance, a global Beauty and Personal Care Marketplace efficiently uses web data to check competitor prices in real-time, enabling them to adjust pricing strategies and optimize promotions on the fly, enhancing their market competitiveness. 2. Social Media: Analyzing Consumer Sentiment and Trends 2. Social Media: Analyzing Consumer Sentiment and Trends Social media platforms offer a gold mine of user-generated content, providing valuable insights into consumer behavior, trends, and sentiment. A global digital marketing agency effectively analyzes consumer sentiment towards their client’s brands using web data from various social media platforms. This deeper understanding empowers them to refine marketing strategies and achieve better results. In Conclusion: The Bright Future of Data Collection As I conclude with Bright Data's powerful web data collection solutions, one thing becomes very clear; data scraping is a game-changer in today’s digital landscape. Whether you are a business owner, entrepreneur, or data professional, harnessing the power of web data can unlock countless opportunities for growth and success. Traditional manual data collection methods are slow, labor-intensive, and error-prone, rendering them ineffective and inefficient in the face of today’s data-driven world. By automating the process with Bright Data, you can extract specific and relevant information from web pages effortlessly, propelling your business to new heights. So, if you find yourself in need of vast amounts of data, fast and easy, look no further than Bright Data. Embrace the power of web data, and let it guide you to new horizons of success in this ever-evolving digital landscape.

How Bright Data Simplifies Web Scraping/Data Collection for AI Training

Як AI Bright Data зрабіў скрабаванне/збор вэб-даных простым: праблемы перад рашэннямі AI Bright Data

Com Bright Data AI va fer que el raspat/recollida de dades web sigui sense esforç: els reptes abans de les solucions Bright Data AI

Cómo Bright Data AI hizo que la recopilación y el raspado de datos web fueran sencillos: los desafíos que enfrentaban las soluciones de Bright Data AI

Kako je Bright Data AI skrapiranje/prikupljanje web podataka učinio jednostavnim: Izazovi pred Bright Data AI rješenjima

Bright Data AI が Web データのスクレイピング/収集をいかに簡単にしたか: Bright Data AI ソリューションの課題

Cara AI Data Cerah Menjadikan Pengikisan/Pengumpulan Data Web Mudah: Cabaran Sebelum Penyelesaian AI Data Cerah

څنګه روښانه ډیټا AI د ویب ډیټا سکریپینګ/راټولول بې ځایه کړي: د روښانه ډیټا AI حلونو دمخه ننګونې

Bright Data AI විසින් Web Data Scraping/එකතු කිරීම වෙහෙසකින් තොරව සිදු කළේ කෙසේද: Bright Data AI විසඳුම් ඉදිරියේ ඇති අභියෝග

Jinsi Data Angavu AI Ilivyofanya Uchakachuaji/Ukusanyaji wa Data ya Wavuti Bila Jitihada: Changamoto Kabla ya Suluhisho Mkali za Data AI

எப்படி பிரைட் டேட்டா AI இணையத் தரவை ஸ்கிராப்பிங்/சேகரிப்பதை சிரமமின்றி உருவாக்கியது: பிரைட் டேட்டா AI தீர்வுகளுக்கு முன் உள்ள சவால்கள்

کس طرح برائٹ ڈیٹا اے آئی نے ویب ڈیٹا کو اسکریپنگ/جمع کرنے کو آسان بنایا: برائٹ ڈیٹا اے آئی کے حل کے سامنے چیلنجز

Bright Data AI đã giúp việc thu thập/sao chép dữ liệu web trở nên dễ dàng như thế nào: Những thách thức trước khi có giải pháp Bright Data AI

The rapid growth of AI is transforming many areas of life, including education, with ChatGPT at the forefront of this change. Recently, OpenAI, the company behind ChatGPT, introduced ChatGPT Edu—a tailored version of its flagship AI tool built for universities to “responsibly” deploy AI to students, faculty, researchers, and campus operations. Riding on the success of its ChatGPT Edu in Universities like the University of Oxford, Wharton School of the University of Pennsylvania(opens in a new window), University of Texas at Austin, Arizona State University(opens in a new window), and Columbia University in the City of New York, ChatGPT edu comes with a lot of promise— one that can help universities integrate AI into every aspect concerning students and faculties. But as always, with every ease comes hardship, and ChatGPT Edu too can be a double-edged sword. But we’ll discuss the downsides a little later. Let’s lead with how ChatGPT Edu can be constructive for higher educational institutions, their students, and faculties. Beginning with the fact that ChatGPT leverages AI, it shouldn’t be a debate that its Edu version can provide faculty members and students with a satisfying level of personalization. By tailoring content and study material as per every student’s need, it can be the silver bullet that universities desperately need. With faculties and mentors grappling with the grueling task of managing the rising number of students in a class, personalization through AI can bring tremendous results for them. The fact that it can identify knowledge gaps and adapt to different learning styles symbolizes hope for the future of academia. AI has always attracted keen minds and ChatGPT Edu, given its inherent character to integrate AI with interactive activities, can make learning more engaging and interactive. Students who are disengaged from routine lectures and other pedagogical processes may find AI’s use in education to be an attractive prospect. This can help keep the students more engaged– a challenge that is commonly observed among students. They are highly disoriented, given the fact that a vast majority of higher educational institutions are still sticking to rote learning practices. Enter ChatGPT Edu, by engaging students in simulated discussions, role-playing scenarios, and critical thinking skills, it can help them stay focused and better oriented, and help suitably streamline their academic priorities. For students with learning disabilities, ChatGPT Edu can be the holy grail. It can offer personalized feedback in various formats, including audio or visual aids, catering to different learning preferences. ChatGPT Edu can also help universities stay clear of the language barriers that often arise when students and faculties of one country inhabit the other. Offering real-time language translation and support can allow students to participate more actively in classroom discussions and access course materials in their preferred language. What’s beyond this is it can also assist in personalized pronunciation practice and real-time feedback, fostering greater confidence and fluency for non-native speakers. The faculty members, moreover, can use the translation feature to ensure all students understand the course material the way they want them to. They can translate lectures, presentations, and handouts, allowing students to follow along and engage more effectively by harnessing the tool properly. We’ll have to see how ChatGPT’s Edu version performs once it is widely circulated in the public domain. For students from diverse backgrounds who face difficulty understanding concepts and things, it can be a big breather as it can translate complex concepts and methods into simpler language. Being able to offer text-to-speech conversion, and the capability to generate visual aids and summaries will surely be a cherry on the cake for both students and faculties alike. Lastly, what seems to be interesting from the faculty’s perspective is that it can offer continued assistance to faculty research. It can certainly help analyze vast amounts of data, generate research hypotheses, and streamline literature reviews, freeing up valuable time for more focused research endeavors. AI in education isn’t a capricious idea anymore. The downsides One of the biggest concerns arising out of the use of AI in educational institutions is there is a chance of students getting too reliant on it for everything. This can have serious repercussions. They can lose touch with realistic teaching pedagogy and become more AI-dependent thereby stepping into that alarming “virtual” zone of learning, where anything without the aid of AI is less understood or best, misunderstood. An equally concerning aspect is the fact it can severely hinder critical thinking and problem-solving skills for a lot of the students. Secondly, it’s believed AI is only as good as the data it's trained on. If that data is biased or fed with wrong inputs, ChatGPT Edu could perpetuate stereotypes or deliver inaccurate information—something which can lead the learner astray, thereby inhibiting the whole learning experience and marring the learning curve. Lastly, overuse of this technology could blur the lines between original and duplicate content (read plagiarism). Rightly so! Plagiarism has been a serious concern for academic institutions, as cases of AI-blended learning have risen over the years. And these concerns of AI and plagiarism are legitly myriad. We’ll have to see how this tailor-made ChatGPT tool can minimize incidents of students and learners plagiarizing content in universities. Summing Up Education offers incredible potential for the application of AI technologies. With the introduction of ChatGPT Edu and its subsequent integration into educational institutions across all research and learning touchpoints, it seems the educational landscape may be in for some unanticipated and enormous change. Nevertheless, learning the tricks of AI-based learning practices and dupery that could be possibly built around it by mala fide learners, can certainly be a solution and shouldn’t be taken for granted. While on one hand, adopting regular audits and data curation practices can help minimize bias and misinformation, on the other, universities can design courses that blend ChatGPT Edu for personalized learning while prioritizing interactive activities and discussions that are likely to produce the maximum output while remaining efficient. With the integration of this innovative tool, learning practices bear a likelihood of showing improved outcomes but it also brings with itself a multitude of vulnerabilities and risks. It just remains to be seen if post its integration with universities on a massive scale, whether it will endanger institutions and recede learning to a dangerous low or will it push towards an academic environment that appears technologically and educationally utopian. The rapid growth of AI is transforming many areas of life, including education, with ChatGPT at the forefront of this change. Recently, OpenAI, the company behind ChatGPT, introduced ChatGPT Edu—a tailored version of its flagship AI tool built for universities to “responsibly” deploy AI to students, faculty, researchers, and campus operations. Riding on the success of its ChatGPT Edu in Universities like the University of Oxford, Wharton School of the University of Pennsylvania(opens in a new window), University of Texas at Austin, Arizona State University(opens in a new window), and Columbia University in the City of New York, ChatGPT edu comes with a lot of promise— one that can help universities integrate AI into every aspect concerning students and faculties. But as always, with every ease comes hardship, and ChatGPT Edu too can be a double-edged sword. But we’ll discuss the downsides a little later. the success of its ChatGPT Edu in Universities Let’s lead with how ChatGPT Edu can be constructive for higher educational institutions, their students, and faculties. Beginning with the fact that ChatGPT leverages AI , it shouldn’t be a debate that its Edu version can provide faculty members and students with a satisfying level of personalization. By tailoring content and study material as per every student’s need, it can be the silver bullet that universities desperately need. With faculties and mentors grappling with the grueling task of managing the rising number of students in a class, personalization through AI can bring tremendous results for them. The fact that it can identify knowledge gaps and adapt to different learning styles symbolizes hope for the future of academia. ChatGPT leverages AI ChatGPT leverages AI personalization through AI personalization through AI AI has always attracted keen minds and ChatGPT Edu, given its inherent character to integrate AI with interactive activities, can make learning more engaging and interactive. Students who are disengaged from routine lectures and other pedagogical processes may find AI’s use in education to be an attractive prospect. This can help keep the students more engaged– a challenge that is commonly observed among students. They are highly disoriented, given the fact that a vast majority of higher educational institutions are still sticking to rote learning practices. Enter ChatGPT Edu, by engaging students in simulated discussions, role-playing scenarios, and critical thinking skills, it can help them stay focused and better oriented, and help suitably streamline their academic priorities. For students with learning disabilities, ChatGPT Edu can be the holy grail. It can offer personalized feedback in various formats, including audio or visual aids, catering to different learning preferences. ChatGPT Edu can also help universities stay clear of the language barriers that often arise when students and faculties of one country inhabit the other. Offering real-time language translation and support can allow students to participate more actively in classroom discussions and access course materials in their preferred language. What’s beyond this is it can also assist in personalized pronunciation practice and real-time feedback, fostering greater confidence and fluency for non-native speakers. The faculty members, moreover, can use the translation feature to ensure all students understand the course material the way they want them to. They can translate lectures, presentations, and handouts, allowing students to follow along and engage more effectively by harnessing the tool properly. We’ll have to see how ChatGPT’s Edu version performs once it is widely circulated in the public domain. translation feature translation feature For students from diverse backgrounds who face difficulty understanding concepts and things, it can be a big breather as it can translate complex concepts and methods into simpler language. Being able to offer text-to-speech conversion, and the capability to generate visual aids and summaries will surely be a cherry on the cake for both students and faculties alike. Lastly, what seems to be interesting from the faculty’s perspective is that it can offer continued assistance to faculty research. It can certainly help analyze vast amounts of data, generate research hypotheses, and streamline literature reviews, freeing up valuable time for more focused research endeavors. AI in education isn’t a capricious idea anymore. The downsides One of the biggest concerns arising out of the use of AI in educational institutions is there is a chance of students getting too reliant on it for everything. This can have serious repercussions. They can lose touch with realistic teaching pedagogy and become more AI-dependent thereby stepping into that alarming “virtual” zone of learning, where anything without the aid of AI is less understood or best, misunderstood. An equally concerning aspect is the fact it can severely hinder critical thinking and problem-solving skills for a lot of the students. Secondly, it’s believed AI is only as good as the data it's trained on. If that data is biased or fed with wrong inputs, ChatGPT Edu could perpetuate stereotypes or deliver inaccurate information—something which can lead the learner astray, thereby inhibiting the whole learning experience and marring the learning curve. Lastly, overuse of this technology could blur the lines between original and duplicate content (read plagiarism). Rightly so! Plagiarism has been a serious concern for academic institutions, as cases of AI-blended learning have risen over the years. And these concerns of AI and plagiarism are legitly myriad. We’ll have to see how this tailor-made ChatGPT tool can minimize incidents of students and learners plagiarizing content in universities. AI and plagiarism AI and plagiarism Summing Up Education offers incredible potential for the application of AI technologies. With the introduction of ChatGPT Edu and its subsequent integration into educational institutions across all research and learning touchpoints, it seems the educational landscape may be in for some unanticipated and enormous change. incredible potential for the application incredible potential for the application Nevertheless, learning the tricks of AI-based learning practices and dupery that could be possibly built around it by mala fide learners, can certainly be a solution and shouldn’t be taken for granted. While on one hand, adopting regular audits and data curation practices can help minimize bias and misinformation, on the other, universities can design courses that blend ChatGPT Edu for personalized learning while prioritizing interactive activities and discussions that are likely to produce the maximum output while remaining efficient. With the integration of this innovative tool, learning practices bear a likelihood of showing improved outcomes but it also brings with itself a multitude of vulnerabilities and risks. It just remains to be seen if post its integration with universities on a massive scale, whether it will endanger institutions and recede learning to a dangerous low or will it push towards an academic environment that appears technologically and educationally utopian.

ChatGPT Edu: Dawn of a Brighter Academic Future, or a Slippery Slope Towards Diminished Learning?

Data is the lifeblood of AI. And as the world embraces the potential of intelligent systems, the value of the data that fuels them becomes even clearer. To foster meaningful conversations around data collection for AI and LLMs, Bright Data and HackerNoon are delighted to launch the AI Writing Contest. Share your thoughts, research, and/or projects on HackerNoon using the #ai tag for a chance to win from a $2500 prize pool. What to Write About Demonstrate how you've used Bright Data to collect web data for AI, and provide a detailed analysis of the tasks performed and their results.
Discuss new trends in AI and LLM data collection and their potential impact on current practices.
Highlight web scraping tools that could transform AI training data.
Demonstrate how public web data can be labeled for AI training, with examples of how labeled data improved AI performance in specific applications.
Explore how Bright Data's tools can be leveraged for improved AI and LLM training.
Provide examples of real-world applications where data collected at scale through web scraping has been used to train successful AI models.
Examine techniques for optimizing data pipelines to enhance the efficiency and effectiveness of training large language models.
Discuss how Bright Data's APIs and Marketplace provide sources for text, video, images, audio, and other dataset types needed for AI model training, and how they can be used for various AI tasks. See the full list of writing prompts here. Get Started with Bright Data: To help you prepare for the contest, Bright Data is offering $15 in credit for you to test out its tools. Use this credit to explore:


Web Data for AI


Dataset Marketplace


Participants will have access to free datasets, highlighted on the platform. For specific dataset requests, contact hackernoon@brightdata.com, and the data will be provided to your account within 24 hours. Prize Breakdown for the AI Writing Contest

$$$



Best Bright Data Story

$1000



Best AI Story

$1000



Best Web Scraping Story

$500 The AI Writing Contest Rules & Guidelines Must be 18+ to enter (no location restrictions)
Create a HackerNoon account
You can enter the contest by adding the #ai tag to your story.
No AI-generated entries. (Read: the acceptable way to use AI on HackerNoon) FAQs Can I Write Under a Pen Name? Yes!You can use your real name, or a pseudonym when setting up your HackerNoon profile. How Long Will the Contest Run? The Contest will run for 3 months, starting on September 1, 2024, and ending on December 1, 2024. How are the winners selected? After 3 months, we’ll share a shortlist of the stories that receive the most eyeballs (real humans, not bots!).
Next, the shortlisted stories will be voted on by HackerNoon staff.
Then, the best in each prize category; AI, Bright Data, and Web Scraping, will be selected and announced Ready to give it a shot? Start a draft now, or use this template to enter the AI writing contest. Good luck and may the force be with you! Data is the lifeblood of AI. And as the world embraces the potential of intelligent systems, the value of the data that fuels them becomes even clearer. Data is the lifeblood of AI. To foster meaningful conversations around data collection for AI and LLMs, Bright Data and HackerNoon are delighted to launch the AI Writing Contest . AI Writing Contest Share your thoughts, research, and/or projects on HackerNoon using the #ai tag for a chance to win from a $2500 prize pool. Share your thoughts, research, and/or projects on HackerNoon using the #ai tag for a chance to win from a $2500 prize pool. Share your thoughts, research, and/or projects on HackerNoon using the #ai tag for a chance to win from a $2500 prize pool. #ai What to Write About Demonstrate how you've used Bright Data to collect web data for AI, and provide a detailed analysis of the tasks performed and their results. Discuss new trends in AI and LLM data collection and their potential impact on current practices. Highlight web scraping tools that could transform AI training data. Demonstrate how public web data can be labeled for AI training, with examples of how labeled data improved AI performance in specific applications. Explore how Bright Data's tools can be leveraged for improved AI and LLM training. Provide examples of real-world applications where data collected at scale through web scraping has been used to train successful AI models. Examine techniques for optimizing data pipelines to enhance the efficiency and effectiveness of training large language models. Discuss how Bright Data's APIs and Marketplace provide sources for text, video, images, audio, and other dataset types needed for AI model training, and how they can be used for various AI tasks. Demonstrate how you've used Bright Data to collect web data for AI, and provide a detailed analysis of the tasks performed and their results. Discuss new trends in AI and LLM data collection and their potential impact on current practices. Highlight web scraping tools that could transform AI training data. Demonstrate how public web data can be labeled for AI training, with examples of how labeled data improved AI performance in specific applications. Explore how Bright Data's tools can be leveraged for improved AI and LLM training. Provide examples of real-world applications where data collected at scale through web scraping has been used to train successful AI models. Examine techniques for optimizing data pipelines to enhance the efficiency and effectiveness of training large language models. Discuss how Bright Data's APIs and Marketplace provide sources for text, video, images, audio, and other dataset types needed for AI model training, and how they can be used for various AI tasks. See the full list of writing prompts here. See the full list of writing prompts here. See the full list of writing prompts here. here. Get Started with Bright Data: To help you prepare for the contest, Bright Data is offering $15 in credit for you to test out its tools. Use this credit to explore: Web Data for AI


Dataset Marketplace Participants will have access to free datasets, highlighted on the platform. For specific dataset requests, contact hackernoon@brightdata.com, and the data will be provided to your account within 24 hours. Get Started with Bright Data: To help you prepare for the contest, Bright Data is offering $15 in credit for you to test out its tools. Use this credit to explore: Get Started with Bright Data: Use this credit to explore: Web Data for AI Dataset Marketplace Web Data for AI Web Data for AI Web Data for AI Dataset Marketplace Dataset Marketplace Dataset Marketplace Participants will have access to free datasets, highlighted on the platform. For specific dataset requests, contact hackernoon@brightdata.com, and the data will be provided to your account within 24 hours. Participants will have access to free datasets, highlighted on the platform. For specific dataset requests, contact hackernoon@brightdata.com , and the data will be provided to your account within 24 hours. hackernoon@brightdata.com hackernoon@brightdata.com Prize Breakdown for the AI Writing Contest

$$$



Best Bright Data Story

$1000



Best AI Story

$1000



Best Web Scraping Story

$500 Prize Breakdown for the AI Writing Contest

$$$



Best Bright Data Story

$1000



Best AI Story

$1000



Best Web Scraping Story

$500 Prize Breakdown for the AI Writing Contest

$$$ Best Bright Data Story

$1000 Best AI Story

$1000 Best Web Scraping Story

$500 Prize Breakdown for the AI Writing Contest $$$ Prize Breakdown for the AI Writing Contest Prize Breakdown for the AI Writing Contest Prize Breakdown for the AI Writing Contest $$$ $$$ $$$ Best Bright Data Story $1000 Best Bright Data Story Best Bright Data Story $1000 $1000 Best AI Story $1000 Best AI Story Best AI Story $1000 $1000 Best Web Scraping Story $500 Best Web Scraping Story Best Web Scraping Story $500 $500 The AI Writing Contest Rules & Guidelines Must be 18+ to enter (no location restrictions) Create a HackerNoon account You can enter the contest by adding the #ai tag to your story. No AI-generated entries. (Read: the acceptable way to use AI on HackerNoon) Must be 18+ to enter (no location restrictions) Create a HackerNoon account Create a HackerNoon account Create a HackerNoon account You can enter the contest by adding the #ai tag to your story. #ai #ai No AI-generated entries. (Read: the acceptable way to use AI on HackerNoon ) the acceptable way to use AI on HackerNoon the acceptable way to use AI on HackerNoon FAQs Can I Write Under a Pen Name? Yes! You can use your real name, or a pseudonym when setting up your HackerNoon profile. How Long Will the Contest Run? The Contest will run for 3 months, starting on September 1, 2024, and ending on December 1, 2024. How are the winners selected? After 3 months, we’ll share a shortlist of the stories that receive the most eyeballs (real humans, not bots!). Next, the shortlisted stories will be voted on by HackerNoon staff. Then, the best in each prize category; AI, Bright Data, and Web Scraping, will be selected and announced After 3 months, we’ll share a shortlist of the stories that receive the most eyeballs (real humans, not bots!). Next, the shortlisted stories will be voted on by HackerNoon staff. Then, the best in each prize category; AI, Bright Data, and Web Scraping, will be selected and announced Ready to give it a shot? Ready to give it a shot? Start a draft now, or use this template to enter the AI writing contest. Start a draft now , or use this template to enter the AI writing contest . Start a draft now this template AI writing contest Good luck and may the force be with you! Good luck and may the force be with you!

Win Up to $2500 in the AI Writing Contest by Bright Data and HackerNoon 

ব্রাইট ডেটা এবং হ্যাকারনুন দ্বারা এআই লেখার প্রতিযোগিতায় $2500 পর্যন্ত জিতে নিন 

Gewinnen Sie bis zu 2500 $ im AI Writing Contest von Bright Data und HackerNoon

Gane hasta $2500 en el concurso de redacción sobre inteligencia artificial de Bright Data y HackerNoon

Gagnez jusqu'à 2 500 $ dans le concours d'écriture sur l'IA organisé par Bright Data et HackerNoon

ब्राइट डेटा और हैकरनून द्वारा आयोजित एआई लेखन प्रतियोगिता में $2500 तक जीतें

Bright Data と HackerNoon による AI ライティング コンテストで最大 2,500 ドルを獲得

Bright Data와 HackerNoon의 AI Writing Contest에서 최대 $2500을 획득하세요

Ganhe até $ 2.500 no concurso de redação de IA da Bright Data e HackerNoon

Выиграйте до 2500 долларов в конкурсе по написанию текстов на тему ИИ от Bright Data и HackerNoon

Bright Data ve HackerNoon'un AI Yazma Yarışmasında 2500$'a Kadar Kazanın

Thắng giải thưởng lên đến 2500 đô la trong Cuộc thi viết về AI do Bright Data và HackerNoon tổ chức

在 Bright Data 和 HackerNoon 举办的 AI 写作比赛中赢取高达 2500 美元的奖金

Bright Data infrastructure is championed around collecting only publicly available data, backed by an industry-leading know-your-customer (KYC) process and a transparent, acceptable use policy. Rated number one by customers on G2, Bright Data provides two scraping solutions, the Web Scraper API and the Scraping Browser, that simplify the data collection process. This guide covers the following: Advantages of using Bright Data’s scraping tools over traditional methods
Practical demonstration of building a dataset with a scraping browser
Overview of Bright Data’s dataset marketplace for ease of use Let’s get started. Advantages of using Bright Data’s scraping tools The Scraping Browser is one of the industry’s most cutting-edge scraping tools. With this tool, you can run your scripts on fully hosted browsers equipped with a CAPTCHA auto-solver, unlimited scalability, and residential IPs to enhance data collection. According to Bright Data CEO Or Lenchner, the Internet is the world’s largest database; the only issue is organizing its data. Using the Scraping Browser, Bright Data helps you manage your data with its in-built unblocking and hosting properties. Some of the advantages of using Bright Data’s scraping tool includes: Reason #1: Efficiency Bright Data comes embedded with a developer-first dynamic scraping ability with pre-written scripts with different scraping technologies, making it possible to accelerate the data collection process compared to traditional methods. Reason #2: Reliability Data companies trust Bright Data tools for their robustness and stability in handling large-scale data collection across their vast residential IP pool and network, ensuring high-quality and accurate data. Another critical point to note is that Bright Data offers support and resolves issues quickly for its customers around the clock. Reason #3: Global adoption Bright Data powers many global brands, which is interesting to note with Bright Insights. Bright Insights leverages deep technology infrastructure to transform public data into actionable insights that serve more than 20,000+ businesses with crucial public web data. Some notable companies that use Bright Data are Microsoft, Epson, Mozilla, and so on. One widespread use case where companies use Bright Data for their data collection needs is using data for AI. In such a scenario, Bright Data assures its customers and users (data scientists) that they always have AI training data for their machine learning models, providing everything you need from discovering, curating, and collecting web data at scale. The four essential components of the data from Bright Data are: Continuously refreshed


Clean and validated


Compliant and ethical


Scalable and performant Check out the different data types used in AI models to learn more about using datasets to train AI models and LLMs. Practical demonstration of building a dataset with a Scraping Browser According to Bright Data documentation, the Scraping Browser is one of their proxy-unlocking solutions designed to help you focus on your multi-step data collection from browsers while taking care of your full proxy and unblocking infrastructure. Some of the benefits of the Scraping Browser are: Boost developer productivity
Cut infrastructure overheads
Increase success rates
Ease of use and integration with libraries like Puppeteer, Playwright, and Selenium To get started, follow the instructions in the documentation that describes how to use the Scraping Browser. Sign up for free and receive a $5 credit. For a more profound practice on integrating and using Playwright with Python, this guide takes you through scraping public web data. Overview of Bright Data’s dataset marketplace Get fresh datasets from the Bright Data dataset marketplace, a repository of datasets from popular public websites. Browse the dataset category and select the one you want to use. The dataset pricing model is calculated based on the number of records used, whether one-time, biannual, quarterly, or monthly. Advantages of using the marketplace No-code web scraping
Strict validation methods
API for on-demand data Conclusion In this article, we discussed the importance of the Scraping Browser and the readily available pre-built datasets in the marketplace. Before scraping or understanding data from a company dataset, you need not be technical. Streamlining data serves as a means to support companies needing extensive training data and capitalizing on building efficient models. Finally, according to Forbes, the future of web scraping is intricately tied to ML and AI technologies. In 2024, scraping tools will become more intelligent, and the need for manual intervention will diminish. The implications of this can raise concerns about data privacy and ethical use if it scrapes sensitive information from the target site without consent. Are you a developer who wants to try Bright Data and its integration? It supports JavaScript, Node, Python, and programming languages like C#, Java, Go, and Ruby. Learn more Web Scraper APIs Bright Data infrastructure is championed around collecting only publicly available data, backed by an industry-leading know-your-customer (KYC) process and a transparent, acceptable use policy. Rated number one by customers on G2 , Bright Data provides two scraping solutions, the Web Scraper API and the Scraping Browser , that simplify the data collection process. by customers on G2 by customers on G2 Web Scraper API Web Scraper API Scraping Browser Scraping Browser This guide covers the following: Advantages of using Bright Data’s scraping tools over traditional methods Practical demonstration of building a dataset with a scraping browser Overview of Bright Data’s dataset marketplace for ease of use Advantages of using Bright Data’s scraping tools over traditional methods Practical demonstration of building a dataset with a scraping browser Overview of Bright Data’s dataset marketplace for ease of use Let’s get started. Advantages of using Bright Data’s scraping tools Advantages of using Bright Data’s scraping tools The Scraping Browser is one of the industry’s most cutting-edge scraping tools. With this tool, you can run your scripts on fully hosted browsers equipped with a CAPTCHA auto-solver, unlimited scalability, and residential IPs to enhance data collection. According to Bright Data CEO Or Lenchner, the Internet is the world’s largest database; the only issue is organizing its data. Using the Scraping Browser, Bright Data helps you manage your data with its in-built unblocking and hosting properties. Some of the advantages of using Bright Data’s scraping tool includes: Reason #1: Efficiency Reason #1: Efficiency Bright Data comes embedded with a developer-first dynamic scraping ability with pre-written scripts with different scraping technologies, making it possible to accelerate the data collection process compared to traditional methods. Reason #2: Reliability Reason #2: Reliability Data companies trust Bright Data tools for their robustness and stability in handling large-scale data collection across their vast residential IP pool and network, ensuring high-quality and accurate data. Another critical point to note is that Bright Data offers support and resolves issues quickly for its customers around the clock. Reason #3: Global adoption Reason #3: Global adoption Bright Data powers many global brands, which is interesting to note with Bright Insights. Bright Insights leverages deep technology infrastructure to transform public data into actionable insights that serve more than 20,000+ businesses with crucial public web data. Some notable companies that use Bright Data are Microsoft, Epson, Mozilla, and so on. One widespread use case where companies use Bright Data for their data collection needs is using data for AI. In such a scenario, Bright Data assures its customers and users (data scientists) that they always have AI training data for their machine learning models, providing everything you need from discovering, curating, and collecting web data at scale. The four essential components of the data from Bright Data are: Continuously refreshed Clean and validated Compliant and ethical Scalable and performant Continuously refreshed Continuously refreshed Clean and validated Clean and validated Compliant and ethical Compliant and ethical Scalable and performant Scalable and performant Check out the different data types used in AI models to learn more about using datasets to train AI models and LLMs. data types used in AI models data types used in AI models Practical demonstration of building a dataset with a Scraping Browser Practical demonstration of building a dataset with a Scraping Browser According to Bright Data documentation, the Scraping Browser is one of their proxy-unlocking solutions designed to help you focus on your multi-step data collection from browsers while taking care of your full proxy and unblocking infrastructure. Some of the benefits of the Scraping Browser are: Boost developer productivity Cut infrastructure overheads Increase success rates Ease of use and integration with libraries like Puppeteer, Playwright, and Selenium Boost developer productivity Cut infrastructure overheads Increase success rates Ease of use and integration with libraries like Puppeteer, Playwright, and Selenium To get started, follow the instructions in the documentation that describes how to use the Scraping Browser. Sign up for free and receive a $5 credit. documentation documentation Sign up for free Sign up for free $5 For a more profound practice on integrating and using Playwright with Python, this guide takes you through scraping public web data. this guide this guide Overview of Bright Data’s dataset marketplace Overview of Bright Data’s dataset marketplace Get fresh datasets from the Bright Data dataset marketplace , a repository of datasets from popular public websites. Browse the dataset category and select the one you want to use. Bright Data dataset marketplace Bright Data dataset marketplace The dataset pricing model is calculated based on the number of records used, whether one-time, biannual, quarterly, or monthly. Advantages of using the marketplace Advantages of using the marketplace No-code web scraping Strict validation methods API for on-demand data No-code web scraping Strict validation methods API for on-demand data Conclusion Conclusion In this article, we discussed the importance of the Scraping Browser and the readily available pre-built datasets in the marketplace. Before scraping or understanding data from a company dataset, you need not be technical. Streamlining data serves as a means to support companies needing extensive training data and capitalizing on building efficient models. Finally, according to Forbes , the future of web scraping is intricately tied to ML and AI technologies. In 2024, scraping tools will become more intelligent, and the need for manual intervention will diminish. The implications of this can raise concerns about data privacy and ethical use if it scrapes sensitive information from the target site without consent. according to Forbes according to Forbes Are you a developer who wants to try Bright Data and its integration ? It supports JavaScript, Node, Python, and programming languages like C#, Java, Go, and Ruby. try Bright Data and its integration try Bright Data and its integration Learn more Web Scraper APIs Web Scraper APIs Web Scraper APIs Web Scraper APIs

Streamlining AI Data Collection with Bright Data’s Scraping Browser

The Noonification: Meet Bright Data: HackerNoon Company of the Week (7/22/2024)

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BRIGHT

choosebright.com

-- employees

Since n.d.

COMPANY RANKING

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EVERGREEN INDEX #4165

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