Hacker News Comments With High User Engagement by Kaggle | Media and Entertainment

About this Dataset

Hacker News Comments With High User Engagement

Hacker News Comments with High User Engagement Levels

Exploring Dynamic Conversations and User Behavior

About this dataset

This dataset is perfect for analyzing highly engaged conversations and understanding user behavior. It contains over 10,000 comments by more than 10 authors on Hacker News stories with more than 10 comments. Each comment is tagged with its story_id, story_time, story_url, story_text, story_author and comment_id, providing an in-depth look at the relationships between these users and their interactions with the stories they are commenting on. Discover new insights about user behavior when discussing technology and innovation by examining this data - build profiles for each commenter and track patterns of conversation as it progresses over time. Find out which topics draw high engagement levels from multiple users to provide a clearer picture of how people contribute to conversations in the tech sector. With this dataset you can gain powerful insights into highly engaged conversations that provide valuable information to those working in technology today!

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How to use the dataset

Download the dataset from Kaggle and read through it to get a better understanding of what data is available in the dataset. Examine the various columns such as Story Time, Story URL, Story Text, Story Author, Comment Text, Comment Author, Comment Ranking, Author Comment Count and Story Comment Count

Take a look at all of these variables as well as any additional data that may be associated with them – for example if there are different story topics that are associated or have influence over engagement levels of users or comments by authors.

Gather several examples from each variable in order to familiarise yourself more with how they interact and relate to one another within this particular dataset; you can use Excel’s “Find and Replace” tool or manually sort through each field – whatever works best for you! Utilizing this method should help you gain an improved understanding of how these variables interact with one another throughout the entire dataset which can give you valuable insights into user behavior patterns within Hacker News comments specifically.

Create your own user clusters based on authorship & comment activity (this can either be done manually or using predefined algorithms like K-means clustering). This will enable us to identify trends in conversations depending on who is involved & active in the discussion; this could provide us with information regarding conversation dynamics & narrative structure which could hint at topics related to technology nd innovation that may interest viewers most heavily etc.. Additionally just like we mentioned before we can draw correlations between comment activity/ranking amongst other factors such as story time posted or type posted too!

Analyze whether or not any changes occurred over time due to various engagement levels among authors & their stories – perhaps there were changes made when it comes down to commenting habits upon starting threads/stories? These changes could also help inform decisions about content delivery/hosting strategies for future posts shared online via Hacker News (or other mediums). Taking into account things like sentiment towards certain topics within engaging conversations might even provide interesting insights when determining product development paths next steps pursue during product launch cycles etc... There is still so much left undiscovered here don’t let those opportunities go untapped - enjoy!

Research Ideas

Tracking user engagement on different stories, as well as analyzing how posts are interconnected through comment replies and topics.

Measuring the impact of lengthy conversations on a story's overall rating or comment count;

Creating user profiles based on the frequency of their comments, the topics they engage with, their reactions to other users' comments and their contribution to specific stories

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: hacker_news_comments.csv

Column name	Description
story_time	The time the story was posted. (DateTime)
story_url	The URL of the story. (String)
story_text	The text of the story. (String)
story_author	The author of the story. (String)
comment_text	The text of the comment. (String)
comment_author	The author of the comment. (String)
comment_ranking	The ranking of the comment. (Integer)
author_comment_count	The number of comments the author has made. (Integer)
story_comment_count	The number of comments the story has received. (Integer)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

Hacker News Comments

@kaggle.thedevastator_hacker_news_comments_with_high_user_engagement_l.hacker_news_comments

358.84 MB
1165439 rows
11 columns


CREATE TABLE hacker_news_comments (
  "story_id" BIGINT,
  "story_time" BIGINT,
  "story_url" VARCHAR,
  "story_text" VARCHAR,
  "story_author" VARCHAR,
  "comment_id" BIGINT,
  "comment_text" VARCHAR,
  "comment_author" VARCHAR,
  "comment_ranking" BIGINT,
  "author_comment_count" BIGINT,
  "story_comment_count" BIGINT
);