Showing 553-560 of 573 results
Social circles: Facebook

Social circles: Facebook

Stefan
Open Dataset
Creators: McAuley, Julian; Leskovec, Jure
Publication Date: 2012
Creators: McAuley, Julian; Leskovec, Jure

This dataset consists of ‘circles’ (or ‘friends lists’) from Facebook. Facebook data was collected from survey participants using this Facebook app. The dataset includes node features (profiles), circles, and ego networks, offering valuable insights into the structure and characteristics of social connections. The dataset includes 4,039 nodes and 88,234 edges. Facebook data has been anonymized by replacing the Facebook-internal ids for each user with a new value. The dataset is approximately 0.01 GB in size.

The dataset is structured into:​

  • Node features (profiles): Anonymized user profile information, where specific attributes (e.g., political affiliation) are replaced with generic labels (e.g., ‘anonymized feature 1’).

  • Circles: Lists of friends grouped by users, representing their social circles.​

  • Ego networks: Subgraphs centered around individual users (egos), including their direct friends and the connections among those friends.​

AmazonQA

AmazonQA

Stefan
Open Dataset
Creators: Gupta, Mansi; Kulkarni, Nitish; Chanda, Raghuveer; Rayasam, Anirudha; Lipton, Zachary C.
Publication Date: 2019
Creators: Gupta, Mansi; Kulkarni, Nitish; Chanda, Raghuveer; Rayasam, Anirudha; Lipton, Zachary C.

We introduce a new dataset and propose a method that combines information retrieval techniques for selecting relevant reviews (given a question) and “reading comprehension” models for synthesizing an answer (given a question and review). Our dataset consists of 923k questions, 3.6M answers and 14M reviews across 156k products. Building on the well-known Amazon dataset, we collect additional annotations, marking each question as either answerable or unanswerable based on the available reviews. This dataset is particularly valuable for developing models that integrate information retrieval techniques to select relevant reviews and “reading comprehension” models to synthesize answers based on those reviews. The dataset is approximately 4 GB in size and is available in JSON format.

The dataset uses the following variables:​

  • questionText: The text of the question posed by the consumer.​

  • questionType: Indicates whether the question is ‘yes/no’ for boolean questions or ‘descriptive’ for open-ended questions.​

  • review_snippets: A list of extracted review snippets relevant to the question (up to ten).​

  • answerText: The text of the answer provided.​

  • answerType: Specifies the type of answer.​

  • helpful: A list containing two integers; the first indicates the number of users who found the answer helpful, and the second indicates the total number of responses.​

  • asin: The unique Amazon Standard Identification Number (ASIN) for the product the question pertains to.​

  • qid: A unique question identifier within the dataset.​

  • category: The product category.​

  • top_review_wilson: The review with the highest Wilson score.​

  • top_review_helpful: The review voted as most helpful by users.​

  • is_answerable: A boolean indicating whether the question is answerable using the review snippets, based on an answerability classifier.​

  • top_sentences_IR: A list of top sentences (up to ten) based on Information Retrieval (IR) score with the question.

Amazon question/answer data

Amazon question/answer data

Stefan
Open Dataset
Creators: McAuley, Julian; Yang, Alex
Publication Date: 2016
Creators: McAuley, Julian; Yang, Alex
This dataset contains Question and Answer data from Amazon, totaling around 1.4 million answered questions and around 4 million answers. This dataset offers valuable insights into consumer inquiries and the corresponding responses, facilitating research in natural language processing, question-answering systems, and e-commerce analytics. It can be combined with Amazon product review data (available here) by matching ASINs in the Q/A dataset with ASINs in the review data. The dataset is approximately 766 kB in size and is available in JSON format.

Structurally, each entry in the dataset includes the following variables:​

  • asin: The Amazon Standard Identification Number (ASIN) of the product, e.g., “B000050B6Z”.​

  • questionType: The type of question, either ‘yes/no’ or ‘open-ended’.​

  • answerType: For yes/no questions, this indicates the type of answer: ‘Y’ for yes, ‘N’ for no, or ‘?’ if the polarity of the answer could not be determined.​

  • answerTime: The raw timestamp of when the answer was provided.

  • unixTime: The answer timestamp converted to Unix time.​

  • question: The text of the question asked by the consumer.​

  • answer: The text of the answer provided.

Amazon Reviews: Unlocked Mobile Phones

Amazon Reviews: Unlocked Mobile Phones

Stefan
Open Dataset
Creators: PromptCloud, Inc.
Publication Date: 2019
Creators: PromptCloud, Inc.
We analyzed more than 400,000 reviews of close to 4,400 unlocked mobile phones sold on Amazon.com to find out insights with respect to reviews, ratings, price and their relationships, making it a rich resource for analyzing customer sentiment and product performance. The dataset is approximately 0,13 GB in size and is available in CSV format. The author found that on Amazon’s product review platform most of the reviewers have given 4-star and 3-star ratings. The average length of the reviews comes close to 230 characters. They also uncovered that lengthier reviews tend to be more helpful and there is a positive correlation between price & rating. 

Structurally, each entry in the dataset includes the following variables:​

  • Product Name: The name of the product (e.g., “Sprint EPIC 4G Galaxy SPH-D7”).​
  • Brand Name: The manufacturer or parent company (e.g., “Samsung”).​
  • Price: The listed price of the product, with values ranging from a minimum of $1.73 to a maximum of $2,598, and an average price of $226.86.​
  • Rating: The user-assigned rating, ranging between 1 and 5 stars.​
  • Reviews: The textual content of the user’s review, detailing their experience and opinions.​
  • Review Votes: The number of helpfulness votes each review received from other users, with a minimum of 0, a maximum of 645, and an average of 1.50 votes.​
YouTube-8M Dataset

YouTube-8M Dataset

Stefan
Open Dataset
Creators: Abu-El-Haija, Sami; Kothari, Nisarg; Lee, Joonseok; Natsev, Paul; Toderici, George; Varadarajan, Balakrishnan; Vijayanarasimhan, Sudheendra
Publication Date: 2016
Creators: Abu-El-Haija, Sami; Kothari, Nisarg; Lee, Joonseok; Natsev, Paul; Toderici, George; Varadarajan, Balakrishnan; Vijayanarasimhan, Sudheendra

YouTube-8M is a large-scale labeled video dataset that consists of millions of YouTube video IDs and with high-quality machine-generated & partially human-verified annotations from a diverse vocabulary of 3,800+ visual entities.

It comprises two subsets:

8M Segments Dataset: 230K human-verified segment labels, 1000 classes, 5 segments/video
8M Dataset: May 2018 version (current): 6.1M videos, 3862 classes, 3.0 labels/video, 2.6B audio-visual features

Thus, it comes with precomputed audio-visual features from billions of frames and audio segments, designed to fit on a single hard disk. This makes it possible to train a strong baseline model on this dataset in less than a day on a single GPU! At the same time, the dataset’s scale and diversity can enable deep exploration of complex audio-visual models that can take weeks to train even in a distributed fashion.

YouTube offers the YouTube8M dataset for download as TensorFlow Record files on their website. Starter code for the dataset can be found on their GitHubpage.

Consumer Complaint Database

Consumer Complaint Database

Stefan
Open Dataset
Creators: Consumer Financial Protection Bureau (CFPB)
Publication Date: 2011
Creators: Consumer Financial Protection Bureau (CFPB)
The database encompasses a wide array of complaints related to various financial sectors, including debt collection, credit reporting, mortgages, credit cards, and more. Each week we send thousands of consumers’ complaints about financial products and services to companies for response. Those complaints are published here after the company responds, confirming a commercial relationship with the consumer, or after 15 days, whichever comes first. Complaint narratives are consumers’ descriptions of their experiences in their own words. By adding their voice, consumers help improve the financial marketplace. The database generally updates daily.  Each complaint entry includes details such as the date received, product type, issue, company involved, consumer’s narrative (if consented for publication), company response, and the complaint’s current status. This dataset serves as a valuable resource for identifying trends, assessing company practices, and informing policy decisions. 
As of February 22, 2025, the database contains a total of 7,867,198 complaints. The dataset is 1,36 GB in size and available for download in CSV format. The dataset spans from December 1, 2011, to the present, with regular updates to include new complaints.
Instagram Influencer Marketing Dataset

Instagram Influencer Marketing Dataset

Stefan
Open Dataset
Creators: Kim, Seungbae; Jiang, Jyun-Yu; Nakada, Masaki; Han, Jinyoung; Wang, Wie
Publication Date: 2020
Creators: Kim, Seungbae; Jiang, Jyun-Yu; Nakada, Masaki; Han, Jinyoung; Wang, Wie

This dataset contains 33,935 Instagram influencers who are classified into the following nine categories including beauty, family, fashion, fitness, food, interior, pet, travel, and other. The dataset is 262 GB in size, including both metadata in JSON format and images in JPEG format. We collect 300 posts per influencer so that there are 10,180,500 Instagram posts in the dataset. The dataset includes two types of files, post metadata and image files. Post metadata files are in JSON format and contain the following information: caption, usertags, hashtags, timestamp, sponsorship, likes, comments, etc. Image files are in JPEG format and the dataset contains 12,933,406 image files since a post can have more than one image file. If a post has only one image file then the JSON file and the corresponding image files have the same name. However, if a post has more than one image then the JSON file and corresponding image files have different names. Therefore, we also provide a JSON-Image_mapping file that shows a list of image files that corresponds to post metadata.

If you want to use this dataset, please cite it accordingly. The data can be accessed on the respective website link below.

“Multimodal Post Attentive Profiling for Influencer Marketing,” Seungbae Kim, Jyun-Yu Jiang, Masaki Nakada, Jinyoung Han and Wei Wang. In Proceedings of The Web Conference (WWW ’20), ACM, 2020.

Social Interaction QA; Social IQA

Social Interaction QA; Social IQA

Stefan
Open Dataset
Creators: Sap, Maarten; Rashkin, Hannah; Chen, Derek; Le Bras, Ronan; Choi, Yejin
Publication Date: 2019
Creators: Sap, Maarten; Rashkin, Hannah; Chen, Derek; Le Bras, Ronan; Choi, Yejin

Social Interaction QA, a new question-answering benchmark for testing social commonsense intelligence. Contrary to many prior benchmarks that focus on physical or taxonomic knowledge, Social IQa focuses on reasoning about people’s actions and their social implications. For example, given an action like “Jesse saw a concert” and a question like “Why did Jesse do this?”, humans can easily infer that Jesse wanted “to see their favorite performer” or “to enjoy the music”, and not “to see what’s happening inside” or “to see if it works”. The actions in Social IQa span a wide variety of social situations, and answer candidates contain both human-curated answers and adversarially-filtered machine-generated candidates. Social IQa contains over 37,000 QA pairs for evaluating models’ abilities to reason about the social implications of everyday events and situations. The dataset is relatively small, with a size of about 0.01 GB, and is available in JSON format.

The structure of the dataset consists of a set of question-answer pairs, where each entry contains:

  • A context describing a social situation.
  • A question that requires reasoning about the context.
  • Three answer choices (one correct, two incorrect).
  • A label indicating the correct answer.

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