Unlock surprising user trends, traffic data, and ad insights in this deep dive into Quora Statistics. Essential reading for every marketer.

We'll tailor a Quora dataset to meet your unique needs, encompassing question topics, user engagement metrics, answer quality, demographic data of users, upvote counts, comment statistics, and other pertinent metrics. Leverage our Quora datasets for diverse applications to bolster strategic planning and market analysis. Scrutinizing these datasets enables organizations to grasp user preferences and community trends, facilitating nuanced content development and engagement initiatives. Customize your access to the entire dataset or specific subsets as per your business requisites. Popular use cases involve optimizing content strategy based on question popularity and user engagement, enhancing marketing strategies through targeted audience segmentation, and identifying and forecasting trends in community discussions to stay ahead in the knowledge-sharing landscape.

Comprehensive dataset of 1 web crawlers and user-agents operated by Quora.

Dataset

This dataset was created by Marshal Baskey

Contents

Dataset Card for Quora Duplicate Questions

This dataset contains the Quora Question Pairs dataset in four formats that are easily used with Sentence Transformers to train embedding models. The data was originally created by Quora for this Kaggle Competition.

  Dataset Subsets





  pair-class subset

Columns: "sentence1", "sentence2", "label" Column types: str, str, class with {"0": "different", "1": "duplicate"} Examples:{ 'sentence1': 'What is the step by step… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/quora-duplicates.

Dataset

This dataset was created by azhaku durai

Contents

Today, we are excited to announce the first in what we plan to be a series of public dataset releases. Our dataset releases will be oriented around various problems of relevance to Quora and will give researchers in diverse areas such as machine learning, natural language processing, network science, etc. the opportunity to try their hand at some of the challenges that arise in building a scalable online knowledge-sharing platform. Our first dataset is related to the problem of identifying duplicate questions.

An important product principle for Quora is that there should be a single question page for each logically distinct question. As a simple example, the queries “What is the most populous state in the USA?” and “Which state in the United States has the most people?” should not exist separately on Quora because the intent behind both is identical. Having a canonical page for each logically distinct query makes knowledge-sharing more efficient in many ways: for example, knowledge seekers can access all the answers to a question in a single location, and writers can reach a larger readership than if that audience was divided amongst several pages.

To mitigate the inefficiencies of having duplicate question pages at scale, we need an automated way of detecting if pairs of question text actually correspond to semantically equivalent queries. This is a challenging problem in natural language processing and machine learning, and it is a problem for which we are always searching for a better solution.

The dataset that we are releasing today will give anyone the opportunity to train and test models of semantic equivalence, based on actual Quora data. We are eager to see how diverse approaches fare on this problem.

Our dataset consists of over 400,000 lines of potential question duplicate pairs. Each line contains IDs for each question in the pair, the full text for each question, and a binary value that indicates whether the line truly contains a duplicate pair. Here are a few sample lines of the dataset:

Here are a few important things to keep in mind about this dataset:

Our original sampling method returned an imbalanced dataset with many more true examples of duplicate pairs than non-duplicates. Therefore, we supplemented the dataset with negative examples. One source of negative examples were pairs of “related questions” which, although pertaining to similar topics, are not truly semantically equivalent. The distribution of questions in the dataset should not be taken to be representative of the distribution of questions asked on Quora. This is, in part, because of the combination of sampling procedures and also due to some sanitization measures that have been applied to the final dataset (e.g., removal of questions with extremely long question details).

links for download data: http://qim.ec.quoracdn.net/quora_duplicate_questions.tsv

source: https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs

The historical dataset of Quora Secondary School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2008-2024),Total Classroom Teachers Trends Over Years (2008-2024),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2008-2024),American Indian Student Percentage Comparison Over Years (2012-2024),Asian Student Percentage Comparison Over Years (2014-2024),Hispanic Student Percentage Comparison Over Years (2008-2024),Black Student Percentage Comparison Over Years (2010-2024),White Student Percentage Comparison Over Years (2008-2024),Two or More Races Student Percentage Comparison Over Years (2012-2024),Diversity Score Comparison Over Years (2008-2024),Free Lunch Eligibility Comparison Over Years (2008-2024),Reduced-Price Lunch Eligibility Comparison Over Years (2008-2024),Reading and Language Arts Proficiency Comparison Over Years (2011-2022),Math Proficiency Comparison Over Years (2012-2023),Science Proficiency Comparison Over Years (2021-2022),Overall School Rank Trends Over Years (2012-2023),Graduation Rate Comparison Over Years (2013-2023)

During a December 2023 survey, ** percent of responding pay-per-click marketers worldwide said they planned to maintain their Quora Ads budgets. Slight increases were reported by *** percent, while *** percent planned significant increases. Additionally, ** percent and ** percent stated they intended to significantly or slightly decrease their budgets, respectively.

Quora Traders Limited recorded an import turnover of USD 0 and an export turnover of USD 92,715.72 million between November 2024 and October 2025. Explore detailed trade value insights, supply chain analytics, HS code-wise data, shipment history, partner countries, customs trade values, top import and export commodities with pricing, buyers, suppliers, ports, and key competitors in Kenya.

Quora Duplicate Questions Detection

Binary Classification of Potential Duplicate Questions on Quora

By Social Media Data [source]

About this dataset

Quora Question Pairs for Duplicate Detection: A Comprehensive Dataset for Semantic Equivalence Modelling

Embark on an enriched journey of semantic analysis with this comprehensive dataset sourced from Quora, a knowledge-sharing platform renowned globally. This dataset, named 'Question Pairs', throws light on semantic equivalence by providing potential duplicate questions present in the Quora community.

With an impressive collection of over 400,000 pairs of potentially duplicate questions, this dataset serves as an ideal platform for training and validating various natural language processing models. It provides a real-time opportunity to learn and experiment with machine learning algorithms designed to detect semantic similarity among pieces of text - a problem that holds significant importance in the digital era.

This extensive dataset is composed of several lines featuring potential question duplicates pairs. Each line within the file contains unique IDs assigned to each question within a pair, the whole text for each question, and finally, whether or not they are genuine duplicates through binary values (1 signifies duplicates; 0 otherwise).

The intriguing aspect about this voluminous data set is its imbalanced structure when it comes to labeling true examples of duplicate pairs versus non-duplicates. To overcome this disbalance and provide better versatility for learners and researchers alike, the original sampling method was fine-tuned to include 'negative' examples or non-duplicates.

These negative examples were derived from pairs that referred consistently to similar topics but were not semantically identical. However one must keep in mind that these negative additions don't disrupt proportional distribution since such related questions might indeed look like potential duplicates due to thematic similarities.

It's important also remember that while comprehensive and substantial,the provided dataset isn't fully representative of all types of queries posed on Quora regularly.This disparity arises partially due to our selective sanitization process which includes removal tactics for entries like extremely elongated question descriptions causing irregularities.

Furthermore,it should be noted,& appreciated,the ground-truth labels,whilst precise & accurate on many occasions,may still contain marginal levels of noise,hence aren't perfectly impeccable.The dataset is prepared under shared authorship by notables: Shankar Iyer, Nikhil Dandekar, and Kornél Csernai.

The utilisation of this enriching resource is subject to abiding by Quora's Terms of ServicesPlease find the original dataset on its dedicated page at[Quora's data section.](https://data.quora.com/First-

How to use the dataset

1. Understanding the Data:

Each row in this dataset contains a pair of questions that are potentially duplicates. There are three main columns you should focus on:

• question1: This represents the text contents of the first question.
• question2: This represents the text contents of the second question.
• is_duplicate: This binary indicator communicates whether or not these two questions are indeed duplicates.

2. Application:

Applications can range from natural language processing tasks such as semantic similarity detection, instance matching, and data deduplication. These can solve real-world problems like creating a better search engine, recommendation systems and enhancing chatbot performance.

3. Preprocessing:

As per any other textual data analysis tasks, it’s important to clean up and preprocess your data before using this dataset for modeling purposes.

A few steps could be: * Removing HTML tags (if any) * Converting all characters into lowercase * Removing punctuations * Tokenizing * Removing Stopwords

You might consider using libraries like NLTK or Spacy for some of these preprocessing operations.

4. Modeling & Evaluation:

After preprocessing your data based on your specific needs, you could choose among several types of models depending upon what task you want to handle - both rule-based (like Levenshtein distance) and machine learning approaches (such as linear regression or decision trees), along with more advanced deep learning methods – LSTM (Long Short Term Memory), Siamese Networks etc where embeddings are used eit...

Auto-generated structured data of Quora Ads Field Reference from table Fields

Quora-Italian-Fauno-Baize

This dataset is an Italian translation of the Quora dataset presented by Baize's authors.

  Languages

Italian

  Dataset Structure





  Data Instances

Sentences 54,456 average number of turns 3.9 response lengths of each turn 35.9

  Data Fields

topic, input

  Data Splits

Train

  Dataset Creation





  Source Data





  Initial Data Collection and Normalization… See the full description on the dataset page: https://huggingface.co/datasets/andreabac3/Quora-Italian-Fauno-Baize.

Dataset

This dataset was created by Arooba Zaman

Released under MIT

Contents

This dataset provides comprehensive real-time Google search in forum and discussion board data aggregated from across the web. The data is continuously updated to provide the most current discussions and conversations. Users can leverage this dataset for community research, social listening, market research, and trend analysis tools. Whether you're building a forum aggregator, conducting community research, or developing social listening tools, this dataset provides current and reliable forum data. The dataset is delivered in a JSON format via REST API.

README

Predicting if a Quora user, once followed, will follow back within a few months. These are some statistics found via experimentation. The training data currently consists of user data that I had collected from 2023-09-27 until 2026-05-28 inclusive. For the test data, I had collected it from 2026-05-29 until 2026-06-04 inclusive.

  Example

See https://bitbucket.org/zhudavi2/quorafollowback file test.py

Dataset

This dataset was created by Yash Gupta

Contents

This dataset contains 54,444 conversations between een AI assistant and a (fake) "Human" (generated) in Dutch. They are translations of Baize's machine-generated answers to the Quora dataset.

Data Instances

{ 'id': 10, 'input': 'Het gesprek tussen de mens en de AI-assistent. ' '[|Human|] Ik vroeg me af wanneer de Moto G3 de Android 7.0-update ' 'zal krijgen? ' '[|AI|] De Moto G3 (Moto G 3rd gen 2015) komt in aanmerking voor de ' 'Android 7.0 (Nougat) update. ' '[|Human|] Geweldig! Weet je wanneer de update beschikbaar zal ' 'zijn? ' '[|AI|] De update werd uitgebracht in december 2016 voor de Moto G4 ' 'en G4 Plus. Er is echter nog geen woord over wanneer de Moto G3 de ' 'update zal ontvangen. ' '[|Human|] Is er nieuws of het binnenkort de update zal ontvangen? ' '[|AI|] Het spijt me, ik heb momenteel geen informatie daarover. ' 'Het is het beste om uit te kijken naar updates van Motorola. ' '[|Human|] ', 'topic': 'Wanneer krijgt Moto G3 (Moto G 3e gen 2015) de Android 7.0 ' '(Nougat) update?' }

Data Fields

id: the ID of the item. The following IDs are not included because they could not be translated: [1352, 14599, 25985, 27167, 28254, 33731, 41550, 44364, 47293, 49060, 51383, 53740]

input: the machine-generated conversation between AI and "Human". Always starts with Het gesprek tussen de mens en de AI-assistent. and has at least one occurrence of both [|AI|] and [|Human|].

topic: the topic description

Dataset Creation

Both the translations and the topics were translated with OpenAI's API for gpt-3.5-turbo. max_tokens=1024, temperature=0 as parameters.

The prompt template to translate the input is:

CONVERSATION_TRANSLATION_PROMPT = """You are asked to translate a conversation between an AI assistant and a human from {src_lang} into {tgt_lang}.

Here are the requirements that you should adhere to: 1. maintain the format: the conversation consists of the AI (marked as [|AI|]) and the human ([|Human|]) talking in turns and responding to each other; 2. do not translate the speaker identifiers [|AI|] and [|Human|] but always copy them into the translation in appropriate places; 3. ensure accurate translation and keep the correctness of the conversation; 4. make sure that text is fluent to read and does not contain grammatical errors. Use standard {tgt_lang} without regional bias; 5. translate the human's text using informal, but standard, language; 6. make sure to avoid biases (such as gender bias, grammatical bias, social bias); 7. if the human asks to correct grammar mistakes or spelling mistakes then you have to generate a similar mistake in {tgt_lang}, and then also generate a corrected output version for the AI in {tgt_lang}; 8. if the human asks to translate text from one to another language, then you only translate the human's question to {tgt_lang} but you keep the translation that the AI provides in the language that the human requested; 9. do not translate code fragments but copy them as they are. If there are English examples, variable names or definitions in code fragments, keep them in English.

Now translate the following conversation with the requirements set out above. Do not provide an explanation and do not add anything else.

"""

The prompt to translate the topic is:

TOPIC_TRANSLATION_PROMPT = "Translate the following title of a conversation from {src_lang} to {tgt_lang} in a succinct,"
" summarizing manner. Translate accurately and formally. Do not provide any explanation"
" about the translation and do not include the original title.

"

The system message was:

You are a helpful assistant that translates English to Dutch to the requirements that are given to you.

Note that 12 items (0.02%) were not successfully translated. The translation was missing the AI identifier [|AI|] and/or the human one [|Human|]. The IDs for the missing items are [1352, 14599, 25985, 27167, 28254, 33731, 41550, 44364, 47293, 49060, 51383, 53740].

The translation quality has not been verified. Use at your own risk!

Licensing Information

Licensing info for quora is listed as unknown. Baize is listed as GPLv3. So this dataset is also listed as such. A specific restriction is in place: as per OpenAI's terms of use, this dataset cannot be used to build a commercial system that competes with OpenAI's services.

This text was generated (either in part or in full) with GPT-3 (gpt-3.5-turbo), OpenAI’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.

If you use this dataset, you must also follow the Sharing and Usage policies.

As clearly stated in their Terms of Use, specifically 2c.iii, "[you may not] use output from the Services to develop models that compete with OpenAI". That means that you cannot use this dataset to build models that are intended to commercially compete with OpenAI. As far as I am aware, that is a specific restriction that should serve as an addendum to the current license.

This dataset is also available on the Hugging Face hub which is the canonical repository for this dataset. See the link for more info.

View Quora Global Trade Co Llc import export trade data, including shipment records, HS codes, top buyers, suppliers, trade values, and global market insights.

Dataset

This dataset was created by Shilpa

Released under CC0: Public Domain

Contents