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

# 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.     
---

# 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.     
---

The online question and answer (Q&A) service market is experiencing robust growth, driven by the increasing demand for readily accessible information and expert opinions across personal and professional domains. The market, segmented by communication type (real-time and reward-response) and user type (personal, business, and others), shows significant potential. While precise market sizing data is unavailable, considering the presence of major players like Quora and Bytedance, and a global reach encompassing North America, Europe, and Asia-Pacific, we can estimate the 2025 market value at approximately $5 billion USD. This estimation considers the substantial user base of existing platforms and the ongoing expansion into new markets and applications. A Compound Annual Growth Rate (CAGR) of 15% is plausible given the continued digitalization and the growing need for quick, reliable information solutions across various industries. This growth is fueled by factors such as the rising penetration of smartphones and internet access, coupled with increasing user engagement in online communities. However, market growth faces some restraints. Competition is intense, with established platforms vying for market share and emerging players constantly entering the arena. Furthermore, maintaining the quality and accuracy of answers, combating misinformation, and ensuring user privacy and data security remain significant challenges. Future growth hinges on addressing these concerns through innovative features, robust moderation policies, and a focus on delivering personalized and reliable information. The successful integration of AI and machine learning for improved search functionality and personalized recommendations will be crucial in driving further expansion. Regional variations in market penetration will also play a significant role, with continued growth anticipated across Asia-Pacific and other developing regions where internet access is rapidly expanding. Focus on tailored solutions for business users, leveraging the Q&A platform for internal knowledge management and customer support, offers a substantial avenue for future growth.

The online question and answer (Q&A) service market, valued at $3012 million in 2025, is experiencing robust growth, projected to expand at a compound annual growth rate (CAGR) of 8.2% from 2025 to 2033. This growth is fueled by several key factors. The increasing penetration of the internet and smartphones globally, coupled with the rising demand for readily accessible information and expert opinions, is driving significant user adoption. Furthermore, advancements in artificial intelligence (AI) and natural language processing (NLP) are enhancing the efficiency and accuracy of Q&A platforms, leading to improved user experiences and increased engagement. The integration of Q&A functionalities into various platforms, from social media networks to e-commerce websites, further expands market reach and contributes to growth. Competition in the market is fierce, with established players like Quora and Stack Overflow vying for market share alongside newer entrants leveraging innovative features and targeted marketing strategies. This competitive landscape fosters innovation and pushes the industry forward, creating a dynamic and evolving market. The market segmentation, although not explicitly provided, is likely multifaceted. We can expect to see segments based on user type (individuals, businesses), platform type (web-based, mobile app), industry vertical (e.g., technology, healthcare, finance), and monetization strategy (advertising, subscriptions, premium services). The geographic distribution of the market is also expected to be diverse, with developed regions like North America and Europe holding significant shares initially, but with developing economies in Asia and Latin America exhibiting substantial growth potential driven by increasing internet penetration and smartphone adoption. While regulatory hurdles and data privacy concerns represent potential restraints, the overall market outlook for online Q&A services remains exceptionally positive, driven by consistent technological advancements and evolving user demands for quick, reliable information access.

The advent of deepfake technology has raised significant concerns regarding its impact on individuals’ cognitive processes and beliefs, considering the pervasive relationships between technology and human cognition. This study delves into the psychological literature surrounding deepfakes, focusing on people’s public representation of this emerging technology and highlighting prevailing themes, opinions, and emotions. Under the media framing, the theoretical framework is crucial in shaping individuals’ cognitive schemas regarding technology. A qualitative method has been applied to unveil patterns, correlations, and recurring themes of beliefs about the main topic, deepfake, discussed on the forum Quora. The final extracted text corpus consisted of 166 answers to 17 questions. Analysis results highlighted the 20 most prevalent critical lemmas, and deepfake was the main one. Moreover, co-occurrence analysis identified words frequently appearing with the lemma deepfake, including video, create, and artificial intelligence—finally, thematic analysis identified eight main themes within the deepfake corpus. Cognitive processes rely on critical thinking skills in detecting anomalies in fake videos or discerning between the negative and positive impacts of deepfakes from an ethical point of view. Moreover, people adapt their beliefs and mental schemas concerning the representation of technology. Future studies should explore the role of media literacy in helping individuals to identify deepfake content since people may not be familiar with the concept of deepfakes or may not fully understand the negative or positive implications. Increased awareness and understanding of technology can empower individuals to evaluate critically the media related to Artificial Intelligence.

The global R&D Spending Optimization market is projected to reach USD XXX million by 2033, expanding at a CAGR of XX% over the forecast period (2025-2033). Growing emphasis on maximizing return on investment (ROI) in research and development (R&D) activities, increasing technological advancements in data analytics and artificial intelligence (AI), and the need to optimize resource allocation in R&D are driving market growth. Key segments of the market include type (return on R&D investment, R&D spending allocation, R&D spending transparency) and application (SMEs, large enterprises). North America and Europe are expected to remain dominant markets, while the Asia Pacific region is anticipated to witness significant growth due to increasing government investments in R&D and rising demand for optimization solutions among businesses. Key players in the market include BCG, Gartner, LinearB, ITONICS GmbH, TRG Screen, RingStone, Quora, and Fujitsu.