Introduction

Quora Statistics: Quora is on the verge of establishing itself as a prominent question-answer website in which questioners can seek knowledge from a worldwide community. Over the years, Quora has dramatically expanded in users and finances. The present paper is dedicated to the statistical landscape of Quora statistics and trends to define its current standing within society.

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.

Quora Question Answer Dataset (Quora-QuAD) contains 56,402 question-answer pairs scraped from Quora.

  Usage:

For instructions on fine-tuning a model (Flan-T5) with this dataset, please check out the article: https://www.toughdata.net/blog/post/finetune-flan-t5-question-answer-quora-dataset

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...

Traffic analytics, rankings, and competitive metrics for quora.com as of May 2025

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.

quora.com is ranked #39 in US with 459.35M Traffic. Categories: Online Services. Learn more about website traffic, market share, and more!

Historical Dataset of Quora Secondary School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2009-2023),Total Classroom Teachers Trends Over Years (2009-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2009-2023),American Indian Student Percentage Comparison Over Years (2013-2023),Asian Student Percentage Comparison Over Years (2013-2023),Hispanic Student Percentage Comparison Over Years (2009-2023),Black Student Percentage Comparison Over Years (2009-2023),White Student Percentage Comparison Over Years (2009-2023),Two or More Races Student Percentage Comparison Over Years (2012-2023),Diversity Score Comparison Over Years (2009-2023),Free Lunch Eligibility Comparison Over Years (2009-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2009-2023),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-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 Shilpa

Released under CC0: Public Domain

Contents

It contains the following files:

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

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.

Dataset

This dataset was created by Dànae Canillas

Contents

It contains the following files:

Dataset

This dataset was created by Hyndavi Ambati

Contents

# RP-commenting-conventions-multiple-sources
Replication Package for the paper "What do Developers Discuss about Code Comment Conventions?"

## Structure
```
Appendix.pdf

RQ1/
  LDA_input/
    stackoverfow_raw_dataset.csv

  LDA_output/
    Mallet/
      output_csv/
        docs-in-topics.csv
        topic-words.csv
        topics-in-docs.csv
        topics-metadata.csv
      output_html/
        all_topics.html
        Docs/
        Topics/

RQ2/
  datasource_rawdata/
    mailing_lists_selection_criteria.csv
    quora.csv
    stackoverflow.csv
  manual_analysis_output/
    stackoverflow_quora_taxonomy.xlsx
```

## Contents of the Replication Package
---
- **Appendix.pdf**- Appendix of the paper containing supplement tables

- **RQ1/** - contains the data used to answer RQ1
 - **LDA_input/** - input data used for LDA analysis
  - `stackoverfow_raw_dataset.csv` - stackoverflow questions used to perform LDA analysis
 - **LDA_output/**
  - **Mallet/** - contains the LDA output generated by MALLET tool
     - **output_csv/**
      - `docs-in-topics.csv` - documents per topic
      - `topic-words.csv` - most relevant topic words
      - `topics-in-docs.csv` - topic probability per document
      - `topics-metadata.csv` - metadata per document and topic probability
    - **output_html/** - Browsable results of mallet output
      - `all_topics.html`
      - `Docs/`
      - `Topics/`

- **RQ2/** - contains the data used to answer RQ2
 - **datasource_rawdata/** - contains the raw data for each source
  - `mailing_lists_selection_criteria.csv` - criteria used to select mailing_lists.
  - `quora.csv` - contains the processed dataset (like removing HTML tags). To know more about the preprocessing steps, please refer to the reproducibility section in the paper. The data is preprocessed using [Makar](https://github.com/maethub/makar) tool.
  - `stackoverflow.csv` - contains the processed Stack Overflow dataset. To know more about the preprocessing steps, please refer to the reproducibility section in the paper. The data is preprocessed using [Makar](https://github.com/maethub/makar) tool.
 - **manual_analysis_output/**
  - `stackoverflow_quora_taxonomy.xlsx` - contains the classified dataset of Stack Overflow and quora and a description of taxonomy.
    - `Taxonomy` - contains the description of the first dimension and second dimension categories. Second dimension categories are further divided into levels, separated by `|` symbol. 
    - `stackoverflow-posts` - the questions are labelled relevant or irrelevant and categorized into the first dimension and second dimension categories.
     - `quota-posts` - the questions are labelled relevant or irrelevant and categorized into the first dimension and second dimension categories.     
---

Context

Quora's first public dataset is related to the problem of identifying duplicate questions. At Quora, an important product principle is that there should be a single question page for each logically distinct question. For 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.

The dataset is based on actual data from Quora and will give anyone the opportunity to train and test models of semantic equivalence.

Content

There are 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.

Acknowledgements

For more information on this dataset, check out Quora's first dataset release page.

License

This data is subject to Quora's Terms of Service, allowing for non-commercial use.

RP-commenting-practices-multiple-sources

Replication package for the paper "What do Developers Discuss about Code Comments?"

Structure

Appendix.pdf
Tags-topics.md
Stack-exchange-query.md

RQ1/
  LDA_input/
    combined-so-quora-mallet-metadata.csv
    topic-input.mallet

  LDA_output/
    Mallet/
      output_csv/
        docs-in-topics.csv
        topic-words.csv
        topics-in-docs.csv
        topics-metadata.csv
      output_html/
        all_topics.html
        Docs/
        Topics/

RQ2/
  datasource_rawdata/
    quora.csv
    stackoverflow.csv
  manual_analysis_output/
    stackoverflow_quora_taxonomy.xlsx

Contents of the Replication Package

• Appendix.pdf- Appendix of the paper containing supplement tables

• Tags-topics.md tags selected from Stack overflow and topics selected from Quora for the study (RQ1 & RQ2)

• Stack-exchange-query.md the query interface used to extract the posts from stack exchnage explorer.

• RQ1/ - contains the data used to answer RQ1

  • LDA_input/ - input data used for LDA analysis
  • combined-so-quora-mallet-metadata.csv - Stack overflow and Quora questions used to perform LDA analysis
  • topic-input.mallet - input file to the mallet tool
  • LDA_output/
  • Mallet/ - contains the LDA output generated by MALLET tool
    • output_csv/
      • docs-in-topics.csv - documents per topic
      • topic-words.csv - most relevant topic words
      • topics-in-docs.csv - topic probability per document
      • topics-metadata.csv - metadata per document and topic probability
    • output_html/ - Browsable results of mallet output
      • all_topics.html
      • Docs/
      • Topics/

• RQ2/ - contains the data used to answer RQ2

  • datasource_rawdata/ - contains the raw data for each source
  • quora.csv - contains the processed dataset (like removing html tags). To know more about the preprocessing steps, please refer to the reproducibility section in the paper. The data is preprocessed using Makar tool.
  • stackoverflow.csv - contains the processed stackoverflow dataset. To know more about the preprocessing steps, please refer to the reproducibility section in the paper. The data is preprocessed using Makar tool.
  • manual_analysis_output/
  • stackoverflow_quora_taxonomy.xlsx - contains the classified dataset of stackoverflow and quora and description of taxonomy.
    • Taxonomy - contains the description of the first dimension and second dimension categories. Second dimension categories are further divided into levels, separated by | symbol.
    • stackoverflow-posts - the questions are labelled relevant or irrelevant and categorized into the first dimension and second dimension categories.

      - quota-posts - the questions are labelled relevant or irrelevant and categorized into the first dimension and second dimension categories.

In the forth quarter of 2024, Zhihu Inc. reported its average monthly active users (MAUs) having reached about **** million. Meanwhile, the Chinese Quora-like Q&A platform attracted an average of **** million monthly active paying users in that quarter.

GLUE, the General Language Understanding Evaluation benchmark (https://gluebenchmark.com/) is a collection of resources for training, evaluating, and analyzing natural language understanding systems.