Communities of Data: A Coral Project Hackathon


On Saturday, May 7th, we held a hackathon around data, communities, and journalism. You can see some of the ideas that came up here.

This is where participants are invited to share their ideas, and everyone is encouraged to discuss them.


Using tags/story meta data to allow for sorting of comments.

highlighted (via every sentence being tappable) text helps adds a new spin to the concept of up-voting

Quick Reply

Showing the comment that someone is replying to helps make sure user is responding to the correct intended comment.

Once reply is sent off, you see other replies.

Showing Context

Comment is then showed relative to where it appeared in the core thread.

Card Tap

replies to comment are shown

swiping up reveals comment’s parent comment.


This is so great, thanks Tim! Can you briefly outline what your group was thinking, and why there is a need for this?



Our team explored the emotional dimension of reader comments. We were interested in which articles tended to trigger joy, fear, anger, sadness, or disgust in readers; we were also curious whether we could build an “emotional profile” of users and articles. Harnessing emotional data from comments could start to answer useful questions: do certain types of stories tend to elicit a particular tone in the comments? Is an angry comment more likely to be abusive, or an emotionally balanced comment more insightful? Do readers adopt consistent emotional tones, and what does it mean if they veer away from their usual emotional character? Are readers reacting to the content of the story, the quality of the coverage, or a previous comment?

We started by gathering all the comments from two sample stories, and running them through AlchemyAPI’s Emotion Analysis service. With this emotionally-enhanced comment dataset, we created some visualizations and explorers, which are screenshotted and pasted below. We also began work on an exploratory tool for analyzing the relationship between keywords and emotions, which we’ll share here when finished.

These outputs were interesting and useful right away, but we also found that different emotions had different numeric baselines, so that for instance the “anger” meter was always significantly higher than “joy.” We’re not sure if this is a bias in AlchemyAPI in general or our dataset in particular; but it speaks to a need to normalize emotional data relative to a desired baseline (e.g. an emotionally-balanced, average news article). Future explorations would want to compare comments’ emotional tone to one another rather than analyzing the emotions in the absolute.


Tool for comparing most joyful and angriest comments
by EJ Fox, source

Static visualizations of comment data
by Sean Mussenden


This is so great, thanks Liam. So excited to see what happens with the tool.


We wrote about the event here. Thanks everyone again and do add your projects below if you haven’t already!