This dataset provides comprehensive real-time data from Amazon's global marketplaces. It includes detailed product information, reviews, seller profiles, best sellers, deals, influencers, and more across all Amazon domains worldwide. The data covers product attributes like pricing, availability, specifications, reviews and ratings, as well as seller information including profiles, contact details, and performance metrics. Users can leverage this dataset for price monitoring, competitive analysis, market research, and building e-commerce applications. The API enables real-time access to Amazon's vast product catalog and marketplace data, helping businesses make data-driven decisions about pricing, inventory, and market positioning. Whether you're conducting market analysis, tracking competitors, or building e-commerce tools, this dataset provides current and reliable Amazon marketplace data. The dataset is delivered in a JSON format via REST API.

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Real-time Amazon Data API with 600M+ products across 22 countries - get products by keyword or category, including product details, Amazon product reviews data, offers, best sellers, deals, Amazon sellers data, Amazon influencers data, and more.

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OpenWeb Ninja's Amazon Data API provides fast and reliable access to real-time Amazon data across all 22 Amazon domains. With over 600 million product listings and more than 40 data points per product, the API makes it simple to search products, query by category, and extract structured ecommerce product data at scale.

Key capabilities: - Product Search & Categories: search Amazon by keyword or retrieve products directly from categories. - Product Data: titles, descriptions, images, pricing, availability, attributes. - Amazon Reviews Data: full review content, ratings, timestamps, helpful counts. - Offers & Sellers Data: all current offers, with sellers data, and more. - Amazon Sellers Data: Amazon sellers profile, sold products, and seller reviews. - Best Sellers & Deals: Amazon Best Sellers by category, Today’s Deals, and promotions. - ASIN to GTIN: convert ASIN to GTIN/EAN/ISBN for external integrations.

Coverage & Scale: - 600M+ products across all major categories and industries. - 22 Amazon countries/domains supported. - 40+ structured data points per product. - Real-time updates, delivered via a fast and reliable REST API.

Use cases: - Pricing and product comparison tools. - Ecommerce and market research. - Seller and competitor monitoring. - Product discovery and trend analysis. - Sentiment analysis with customer product reviews data.

With OpenWeb Ninja's Amazon Data API, you get the most complete Amazon data - from product details and reviews to best sellers and deals - always delivered in real time through a fast and reliable REST API.

This dataset contains anonymized YouTube comment data associated with the 2019 online controversy known as Dramageddon, involving beauty influencers James Charles, Tati Westbrook, and Jeffree Star. The dataset was created for research on online hostility, cancel culture, and competitive communication dynamics among influencers.

The dataset includes public user comments collected from 14 YouTube videos posted during May–June 2019, including primary source videos from the influencers involved and reaction videos from commentary channels. A total of ~15,000 comments were collected using the YouTube Data API v3. All comments are anonymized and contain no personally identifiable information.

Each comment record is enriched with metadata and derived variables, including: - Sentiment score (range −1 to +1) - Toxicity score (probability 0–1) - Cancel behavior classification (cold, cool, hot) - Moral language category - Engagement metrics (likes, reply depth) - Time of posting - Video-level metadata (creator, phase of controversy)

This dataset supports research in computational social science, communication studies, digital sociology, and platform governance. It has been used in studies on cancel culture, moral contagion, algorithmic amplification, and influencer reputation dynamics. This dataset contains only publicly available YouTube comments retrieved in accordance with the YouTube Terms of Service. All usernames, channel IDs, and profile references were hashed or removed during preprocessing to ensure anonymization. No attempts were made to identify or contact any YouTube users. The dataset is provided strictly for research purposes. Users must agree to comply with ethical guidelines for internet research (AoIR 2019) and cite the dataset appropriately.

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About Ivana Alawi

https://www.youtube.com/watch?v=pLF0bhcY5l8" alt="">

From Youtube channel - Half Moroccan / Half Filipina - Actress in the Philippines

Channel details - www.youtube.com/@IvanaAlawi - 17.4M subscribers - 198 videos - 1,439,294,300 views - Joined Jun 1, 2018 - Philippines

From Wikipedia

Regarded as one of the biggest social media influencers of her time, Alawi is the most subscribed Filipino celebrity on YouTube, having been honored by Google as the "Top YouTube Content Creator" in the Philippines for two consecutive years. In 2019, she won "Best New Female TV Personality" at the PMPC Star Awards for Television. In 2021, Alawi was ranked fourth on the "100 Most Beautiful Faces in the World" list by TC Candler.

Cover Image

From Official Youtube Channel https://www.youtube.com/@IvanaAlawi

Important Note

There may be some missing videos esp if the channel has more than 600+ videos, this is because the API itself doesn't return all the videos as explained in this Stackoverlow post.

Authors, through Twitter API, collected this database over eight months. These data are tweets of over 50 experts regarding market analysis of 40 cryptocurrencies. These experts are known as influencers on social networks such as Twitter. The theory of Behavioral economics shows that the opinions of people, especially experts, can impact the stock market trend (here, cryptocurrencies). Existing databases often cover tweets related to one or more cryptocurrencies. Also, in these databases, no attention is paid to the user's expertise, and most of the data is extracted using hashtags. Failure to pay attention to the user's expertise causes the irrelevant volume to increase and the neutral polarity to increase considerably. This database has a main table named "Tweets1" with 11 columns and 40 tables to separate comments related to each cryptocurrency. The columns of the main table and the cryptocurrency tables are explained in the attached document. Researchers can use this dataset in various machine learning tasks, such as sentiment analysis and deep transfer learning with sentiment analysis. Also, this data can be used to check the impact of influencers' opinions on the cryptocurrency market trend. The use of this database is allowed by mentioning the source. Also, in this version, we have added the excel version of the database and Python code to extract the names of influencers and tweets. in Version(3): In the new version, three datasets related to historical prices and sentiments related to Bitcoin, Ethereum, and Binance have been added as Excel files from January 1, 2023, to June 12, 2023. Also, two datasets of 52 influential tweets in cryptocurrencies have been published, along with the score and polarity of sentiments regarding more than 300 cryptocurrencies from February 2021 to June 2023. Also, two Python codes related to the sentiment analysis algorithm of tweets with Python have been published. This algorithm combines RoBERTa pre-trained deep neural network and BiGRU deep neural network with an attention layer (see code Preprocessing_and_sentiment_analysis with python).

Analyzing user behavior on Instagram📱🤳🙎‍♂️ can provide valuable insights into user engagement, preferences, and overall user experience. As a social media platform, Instagram offers various opportunities to study user behavior. Here are some aspects to consider when analyzing user behavior on Instagram:

User Engagement Metrics:

Likes: Analyze the number of likes on posts to understand which content resonates well with users. Comments: Study the comments on posts to gauge the level of interaction and engagement. Shares: Examine the number of times users share posts to understand content virality. Content Analysis:

Content Types: Identify the types of content (photos, videos, stories, etc.) that receive the highest engagement. Hashtags: Study the effectiveness of different hashtags in driving user interactions. Captions: Analyze the impact of captions on engagement and user response. User Demographics:

Age, Gender, and Location: Understand the demographics of the user base and how it affects behavior. Active Hours: Determine when users are most active to optimize content posting times. Follower Growth and Churn:

Follower Growth: Track the growth rate of followers over time. Follower Churn: Investigate the reasons for follower losses or unfollows. Influencers and Brand Advocacy:

Identify influential users who drive engagement and contribute to brand advocacy. Measure the impact of influencer collaborations on engagement and brand reach. Story Engagement:

Analyze user interactions with Instagram Stories, such as taps, swipes, and exits. Assess the performance of Stories compared to regular posts. Explore Page and Discoverability:

Study the factors that influence content appearing on users' Explore pages. Analyze the impact of Instagram's algorithms on content discoverability. User Journey Analysis:

Track user interactions from initial discovery to conversion (e.g., website visits or product purchases). Understand the user journey and potential drop-off points. User Sentiment Analysis:

Conduct sentiment analysis on comments and captions to gauge user feelings towards content or products. Ad Engagement:

Analyze user interactions with sponsored posts and advertisements. Measure the effectiveness of ad campaigns in driving engagement and conversions. User Activity Patterns:

Study the frequency and duration of user interactions on the platform. Identify peak activity times and user behavior during different days of the week. Privacy and Data Ethics:

Ensure compliance with user privacy regulations and data protection policies. Respect user data and privacy while conducting the analysis. Remember, Instagram's API access and data usage policies might limit the depth of analysis. As such, ensure compliance with Instagram's terms of service and privacy guidelines while performing user behavior analysis on the platform.

Context

Microblog sites are environments where people follow people. With this feature, a microblog site is a convenient environment for spreading an opinion or introducing a new product. The key point is determination of individuals who maximize the spreading. This problem is known as Influence Maximization (IM) and has attracted attention of many researchers. Many studies in the literature have modeled IM problem on graphs for different propagation models such as Independent Cascade (IC) and Linear Threshold (LT). However, microblogs like Twitter have their own features. Many works on IM in Twitter derive new metrics from user and tweet features; apply a greedy approach for selecting influencers. We adopted different approach for IM problem, and we dealt it as a classification problem. Firstly, we collected data on International Women Day 2018; empirically we labeled the users as either influencer candidates or non-influencers; then we applied classification methods for classifying users into one class with using features of users. By this way, we obtained an influencer candidates set, which is very smaller than entire dataset.

Content

Twitter provides enhanced APIs (Application Programming Interface) in order to collect, query, and update its data programmatically. Since we need a daily topic for this study, we have decided to fetch the tweets related to “International Women’s Day” which is a global event and celebrated on March 8 every year. On this day, a service implemented based on Java programming language which utilizes twitter4j1, an open source Java library for Twitter APIs, is executed in order to collect tweets through the Twitter Streaming API. Three keywords which are directly related to “International Women’s Day” and amongst to the trending topics about this day namely “InternationalWomensDay”, “IWD2018”, and “WomensDay” are used to filter streaming tweets. The language of tweets which are fetched through the Twitter Streaming API is set to English. With considering the time differences amongst on various continents, the developed service has run for 48 hours. During this time, 219,076 tweets have collected from 168.168 unique Twitter users.

How to cite?

If you use these datasets please cite the following paper: Şimşek, M , Kabakuş, A . (2018). Finding Influencers on Twitter with Using Machine Learning Classification Algorithms. Gazi Mühendislik Bilimleri Dergisi (GMBD) , 4 (3) , 183-196 . Retrieved from https://dergipark.org.tr/tr/pub/gmbd/issue/41439/468269. DOI: 10.30855/GJES.2018.04.03.005

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Rhetorik is a premier global provider of high-quality, compliant B2B data, meticulously curated through a vast network of first-party, second-party, and third-party sources. Our proprietary AI-driven technology standardises, deduplicates, and verifies data, ensuring the most comprehensive single source of global business intelligence and actionable insights available.

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Beyond data licensing, our comprehensive enrichment solutions ensure your existing datasets are cleansed, validated, and enhanced, guaranteeing optimal accuracy and effectiveness.

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Harness the power of data-driven decision-making with Rhetorik. Contact us today to discover how our solutions can elevate your business to new heights.

Rhetorik: Your Global Leader in Premium B2B Data Solutions.

Rhetorik is a premier global provider of high-quality, compliant B2B data, meticulously curated through a vast network of first-party, second-party, and third-party sources. Our proprietary AI-driven technology standardises, deduplicates, and verifies data, ensuring the most comprehensive single source of global business intelligence and actionable insights available.

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Recognising that every business is unique, we offer flexible pricing options tailored to your specific needs, data use cases, and requirements.

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Harness the power of data-driven decision-making with Rhetorik. Contact us today to discover how our solutions can elevate your business to new heights.

Rhetorik, a Lightcast Company provides the largest known database of globally compliant profiles of professionals, companies, and contacts—continuously updated to ensure accuracy. Powered by AI, the platform supports a wide range of use cases, including sales, marketing, recruiting, and investment banking, with flexible delivery options through platforms, APIs, and services.

Rhetorik: Your Global Leader in Premium B2B Data Solutions.

Rhetorik is a premier global provider of high-quality, compliant B2B data, meticulously curated through a vast network of first-party, second-party, and third-party sources. Our proprietary AI-driven technology standardises, deduplicates, and verifies data, ensuring the most comprehensive single source of global business intelligence and actionable insights available.

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Recognising that every business is unique, we offer flexible pricing options tailored to your specific needs, data use cases, and requirements.

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